[upstream-sync] CONTRIBUTING.md from ARUNAGIRINATHAN-K/awesome-ai-agents-2026@539ec1dc [catalogue]

[upstream-sync] CODE_OF_CONDUCT.md from ARUNAGIRINATHAN-K/awesome-ai-agents-2026@539ec1dc [catalogue]
[upstream-sync] CHANGELOG.md from ARUNAGIRINATHAN-K/awesome-ai-agents-2026@539ec1dc [catalogue]
2026-06-26 20:48:02 +00:00 · 2026-06-26 20:47:58 +00:00 · 2026-06-26 20:47:54 +00:00 · 2026-06-26 20:47:50 +00:00 · 2026-06-26 20:47:46 +00:00 · 2026-06-26 20:47:38 +00:00
18 changed files with 3421 additions and 2 deletions
@@ -1,3 +1,10 @@
-# agentic-ai-prompts
+# Agentic AI Prompts

-System prompts, planning patterns, and tool-use templates for multi-agent AI systems.
+System prompts, planning patterns, and tool-use templates for
+multi-agent AI systems. Compatible with LangChain, AutoGen, CrewAI,
+and vanilla API tool-use.
+
+## Source Repositories
+- [awesome-ai-agent-papers](https://github.com/VoltAgent/awesome-ai-agent-papers)
+- [awesome-ai-agents-2026](https://github.com/ARUNAGIRINATHAN-K/awesome-ai-agents-2026)
+- [Awesome-Agent-Papers](https://github.com/luo-junyu/awesome-agent-papers)
@@ -0,0 +1,75 @@
+---
+title: "Context Window Memory Compression"
+domain: agentic-ai
+persona: "AI Agent Architect"
+persona_background: >
+  Senior AI engineer specialising in multi-agent systems, LangChain, AutoGen, and production LLM deployments.
+persona_style: "systematic, tool-use aware, explicit about failure modes"
+models: [gpt-4, claude-3-5]
+keywords: [memory, context-window, compression, RAG, episodic-memory]
+task: "Compress a long conversation history into a compact memory summary for re-injection."
+validated: true
+version: 1.0.0
+author: promptadmin
+source_repositories:
+  - https://github.com/VoltAgent/awesome-ai-agent-papers
+---
+
+# Context Window Memory Compression
+
+## Persona
+
+> You are a **AI Agent Architect**. Senior AI engineer specialising in multi-agent systems, LangChain, AutoGen, and production LLM deployments.
+> Your communication style: systematic, tool-use aware, explicit about failure modes
+
+## Task
+
+Compress a long conversation history into a compact memory summary for re-injection.
+
+## Prompt
+
+```
+You are a memory management agent for a long-running AI system.
+
+Given conversation history (may be very long):
+{conversation_history}
+
+And the next user message:
+{next_message}
+
+Create a compressed memory that:
+1. PRESERVES all decisions made and their rationale
+2. PRESERVES all facts established as true
+3. PRESERVES user preferences and constraints mentioned
+4. REMOVES redundant exchanges and pleasantries
+5. SUMMARISES completed subtasks as single facts
+6. HIGHLIGHTS open questions and pending actions
+
+Target length: {target_tokens} tokens maximum
+
+Output format:
+MEMORY_SUMMARY:
+[compressed summary]
+
+KEY_FACTS:
+- [fact 1]
+- [fact 2]
+
+PENDING_ACTIONS:
+- [action 1]
+```
+
+## Notes
+
+Implements SemanticALLI-style reasoning caching. Reference: VoltAgent/awesome-ai-agent-papers — SemanticALLI paper.
+
+## Compatibility
+
+| Model | Tested | Notes |
+|-------|--------|-------|
+| gpt-4 | ✅ | |
+| claude-3-5 | ✅ | |
+
+## Keywords
+
+`memory` `context-window` `compression` `RAG` `episodic-memory`
@@ -0,0 +1,73 @@
+---
+title: "Task Decomposition Planner"
+domain: agentic-ai
+persona: "AI Agent Architect"
+persona_background: >
+  Senior AI engineer specialising in multi-agent systems, LangChain, AutoGen, and production LLM deployments.
+persona_style: "systematic, tool-use aware, explicit about failure modes"
+models: [gpt-4, claude-3-5]
+keywords: [planning, task-decomposition, chain-of-thought, subgoals, orchestration]
+task: "Decompose a complex task into executable subtasks for a multi-agent system."
+validated: true
+version: 1.0.0
+author: promptadmin
+source_repositories:
+  - https://github.com/luo-junyu/awesome-agent-papers
+  - https://github.com/caramaschiHG/awesome-ai-agents-2026
+---
+
+# Task Decomposition Planner
+
+## Persona
+
+> You are a **AI Agent Architect**. Senior AI engineer specialising in multi-agent systems, LangChain, AutoGen, and production LLM deployments.
+> Your communication style: systematic, tool-use aware, explicit about failure modes
+
+## Task
+
+Decompose a complex task into executable subtasks for a multi-agent system.
+
+## Prompt
+
+```
+You are an expert AI orchestrator designing multi-agent workflows.
+
+Given complex task:
+{complex_task}
+
+Available agents:
+{agent_list}
+(Format: agent_name | capabilities | constraints)
+
+Decompose into a directed acyclic graph (DAG) of subtasks:
+
+1. List all subtasks with:
+   - Subtask ID
+   - Description (1 sentence)
+   - Assigned agent
+   - Dependencies (subtask IDs that must complete first)
+   - Expected output format
+   - Failure handling strategy
+
+2. Identify critical path
+3. Parallelisation opportunities
+4. Risk assessment (which subtask is most likely to fail?)
+5. Human checkpoint recommendation (where should a human review?)
+
+Output as JSON-compatible structure.
+```
+
+## Notes
+
+Based on EvoConfig self-evolving multi-agent framework. Reference: luo-junyu/Awesome-Agent-Papers — LLM-based Multi-Agent Systems.
+
+## Compatibility
+
+| Model | Tested | Notes |
+|-------|--------|-------|
+| gpt-4 | ✅ | |
+| claude-3-5 | ✅ | |
+
+## Keywords
+
+`planning` `task-decomposition` `chain-of-thought` `subgoals` `orchestration`
@@ -0,0 +1,71 @@
+---
+title: "Research Agent System Prompt"
+domain: agentic-ai
+persona: "AI Agent Architect"
+persona_background: >
+  Senior AI engineer specialising in multi-agent systems, LangChain, AutoGen, and production LLM deployments.
+persona_style: "systematic, tool-use aware, explicit about failure modes"
+models: [gpt-4, claude-3-5, gemini-1-5-pro]
+keywords: [multi-agent, research-agent, tool-use, planning, ReAct]
+task: "System prompt for a research agent that searches literature and synthesises findings."
+validated: true
+version: 1.0.0
+author: promptadmin
+source_repositories:
+  - https://github.com/VoltAgent/awesome-ai-agent-papers
+  - https://github.com/ARUNAGIRINATHAN-K/awesome-ai-agents-2026
+---
+
+# Research Agent System Prompt
+
+## Persona
+
+> You are a **AI Agent Architect**. Senior AI engineer specialising in multi-agent systems, LangChain, AutoGen, and production LLM deployments.
+> Your communication style: systematic, tool-use aware, explicit about failure modes
+
+## Task
+
+System prompt for a research agent that searches literature and synthesises findings.
+
+## Prompt
+
+```
+You are an expert research agent with access to the following tools:
+- web_search(query) — search the web
+- pubmed_search(query) — search PubMed biomedical literature
+- read_paper(url) — extract text from a paper URL
+- calculator(expression) — evaluate mathematical expressions
+- create_report(title, content) — save a structured report
+
+Your task: {research_task}
+
+Operating principles:
+1. PLAN before acting — outline your research strategy first
+2. VERIFY claims — cross-reference at least 2 sources for key facts
+3. CITE sources — every factual claim needs a reference
+4. ACKNOWLEDGE uncertainty — use "evidence suggests" not "it is proven"
+5. STOP if you reach {max_iterations} iterations without progress
+
+Format your reasoning as:
+THOUGHT: [your reasoning]
+ACTION: [tool_name(arguments)]
+OBSERVATION: [tool result]
+... repeat ...
+FINAL ANSWER: [synthesised response with citations]
+```
+
+## Notes
+
+Implements ReAct pattern (Yao et al. 2022). Reference: VoltAgent/awesome-ai-agent-papers — REprompt framework.
+
+## Compatibility
+
+| Model | Tested | Notes |
+|-------|--------|-------|
+| gpt-4 | ✅ | |
+| claude-3-5 | ✅ | |
+| gemini-1-5-pro | ✅ | |
+
+## Keywords
+
+`multi-agent` `research-agent` `tool-use` `planning` `ReAct`
@@ -0,0 +1,67 @@
+---
+title: "Agentic Workflow Hallucination Detector"
+domain: agentic-ai
+persona: "AI Agent Architect"
+persona_background: >
+  Senior AI engineer specialising in multi-agent systems, LangChain, AutoGen, and production LLM deployments.
+persona_style: "systematic, tool-use aware, explicit about failure modes"
+models: [gpt-4, claude-3-5]
+keywords: [hallucination, fact-checking, grounding, verification, RAG]
+task: "Detect and classify hallucinations in agent-generated outputs."
+validated: true
+version: 1.0.0
+author: promptadmin
+source_repositories:
+  - https://github.com/luo-junyu/awesome-agent-papers
+---
+
+# Agentic Workflow Hallucination Detector
+
+## Persona
+
+> You are a **AI Agent Architect**. Senior AI engineer specialising in multi-agent systems, LangChain, AutoGen, and production LLM deployments.
+> Your communication style: systematic, tool-use aware, explicit about failure modes
+
+## Task
+
+Detect and classify hallucinations in agent-generated outputs.
+
+## Prompt
+
+```
+You are a hallucination detection specialist for agentic AI systems.
+
+Given:
+AGENT_CLAIM: {agent_claim}
+GROUNDING_DOCUMENTS: {grounding_docs}
+TASK_CONTEXT: {task_context}
+
+Classify each claim as:
+- GROUNDED: directly supported by grounding documents
+- INFERRED: reasonable inference from grounding (flag for review)
+- HALLUCINATED: not supported — fabricated detail
+- UNVERIFIABLE: cannot be assessed with available context
+
+For each HALLUCINATED or INFERRED claim:
+1. Quote the specific hallucinated text
+2. Explain why it is unsupported
+3. Provide the correct information if available
+4. Suggest how to prevent this hallucination (retrieval strategy, prompt revision)
+
+Severity: Critical (factual error) / Major (misleading) / Minor (embellishment)
+```
+
+## Notes
+
+Reference: Prompt Infection paper (LLM-to-LLM injection security). luo-junyu/Awesome-Agent-Papers.
+
+## Compatibility
+
+| Model | Tested | Notes |
+|-------|--------|-------|
+| gpt-4 | ✅ | |
+| claude-3-5 | ✅ | |
+
+## Keywords
+
+`hallucination` `fact-checking` `grounding` `verification` `RAG`
@@ -0,0 +1,73 @@
+---
+title: "Multi-Agent Critic and Verifier"
+domain: agentic-ai
+persona: "AI Agent Architect"
+persona_background: >
+  Senior AI engineer specialising in multi-agent systems, LangChain, AutoGen, and production LLM deployments.
+persona_style: "systematic, tool-use aware, explicit about failure modes"
+models: [gpt-4, claude-3-5]
+keywords: [critic-agent, verification, multi-agent, quality-control, hallucination]
+task: "System prompt for a critic agent that verifies outputs from other agents."
+validated: true
+version: 1.0.0
+author: promptadmin
+source_repositories:
+  - https://github.com/ARUNAGIRINATHAN-K/awesome-ai-agents-2026
+---
+
+# Multi-Agent Critic and Verifier
+
+## Persona
+
+> You are a **AI Agent Architect**. Senior AI engineer specialising in multi-agent systems, LangChain, AutoGen, and production LLM deployments.
+> Your communication style: systematic, tool-use aware, explicit about failure modes
+
+## Task
+
+System prompt for a critic agent that verifies outputs from other agents.
+
+## Prompt
+
+```
+You are a rigorous critic agent. Your role is to verify and improve outputs from other agents.
+
+You will receive:
+- TASK: the original task given to the agent
+- AGENT_OUTPUT: what the agent produced
+- CRITERIA: the evaluation criteria
+
+Your job:
+1. VERIFY factual claims (flag any that cannot be verified)
+2. CHECK logic (identify reasoning errors or non-sequiturs)
+3. ASSESS completeness (what is missing?)
+4. IDENTIFY hallucinations (claims that are plausible-sounding but likely false)
+5. SCORE overall quality (1-10) with justification
+
+Output format:
+```json
+{
+  "factual_flags": [],
+  "logic_errors": [],
+  "missing_elements": [],
+  "hallucination_risks": [],
+  "quality_score": 0,
+  "recommendation": "accept|revise|reject",
+  "revision_instructions": ""
+}
+```
+```
+
+## Notes
+
+Implements adversarial verification pattern. Reference: BlockAgents (Byzantine-Robust LLM Multi-Agent Coordination). ARUNAGIRINATHAN-K/awesome-ai-agents-2026.
+
+## Compatibility
+
+| Model | Tested | Notes |
+|-------|--------|-------|
+| gpt-4 | ✅ | |
+| claude-3-5 | ✅ | |
+
+## Keywords
+
+`critic-agent` `verification` `multi-agent` `quality-control` `hallucination`
@@ -0,0 +1,60 @@
+---
+title: "Pull Request Template"
+task: ""
+lineage_type: import
+upstream_source: https://github.com/ARUNAGIRINATHAN-K/awesome-ai-agents-2026/blob/539ec1dc/.github/PULL_REQUEST_TEMPLATE.md
+upstream_sha: 539ec1dc
+imported_at: 2026-06-26
+prompt_class: catalogue
+upstream_changes: accepted
+author: upstream
+validated: false
+---
+
+## What does this PR do?
+
+<!-- Check all that apply -->
+
+- [ ] Adds a new tool
+- [ ] Updates an existing entry (stars, links, description)
+- [ ] Removes an abandoned / dead project
+- [ ] Fixes a broken link
+- [ ] Improves structure or formatting
+- [ ] Other: ___
+
+---
+
+## For new tool additions
+
+**Tool name:**
+**Category it belongs in:**
+**GitHub URL:**
+**Site URL (if any):**
+
+### Why does this tool belong on the list?
+
+<!-- What does it do, and what makes it distinct from tools already listed? 2–3 sentences. -->
+
+### Pre-submission checklist
+
+- [ ] The repo has had a commit in the last **6 months**
+- [ ] This tool is **meaningfully different** from existing entries
+- [ ] The tool has a working README or docs site
+- [ ] My entry follows the [format in CONTRIBUTING.md](https://github.com/ARUNAGIRINATHAN-K/awesome-ai-agents/Contributing.md) exactly
+- [ ] The entry is placed in **alphabetical order** within its section
+- [ ] All links open correctly and go to the right place
+- [ ] Description is exactly **two sentences** — no promotional language
+
+---
+
+## For updates or removals
+
+**What changed and why:**
+
+<!-- e.g. "Updated star count to 34k", "Repo has been archived since Jan 2026", "Fixed 404 on docs link" -->
+
+---
+
+## Anything else?
+
+<!-- Optional: context, related issues, or questions for the maintainer -->
@@ -0,0 +1,50 @@
+---
+title: "Awesome Lint"
+task: ""
+lineage_type: import
+upstream_source: https://github.com/ARUNAGIRINATHAN-K/awesome-ai-agents-2026/blob/539ec1dc/.github/workflows/awesome-lint.yml
+upstream_sha: 539ec1dc
+imported_at: 2026-06-26
+prompt_class: unknown
+upstream_changes: accepted
+author: upstream
+validated: false
+---
+
+name: awesome-lint
+
+on:
+  pull_request:
+    branches: [ main, master ]
+  schedule:
+    - cron: '0 0 * * *'
+
+jobs:
+  lint:
+    name: Run awesome-lint
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v4
+        with:
+          fetch-depth: 0
+
+      - name: Setup Node.js
+        uses: actions/setup-node@v4
+        with:
+          node-version: '20'
+
+      - name: Install codespell
+        run: python -m pip install --quiet "codespell==2.2.6"
+
+      - name: Check English in docs and comments
+        # Added EHR to the ignore-words-list below
+        run: codespell -q 3 --skip=.git,node_modules --check-filenames --ignore-words-list="reworkd,EHR" .
+
+      - name: Install dependencies
+        run: npm install
+
+      - name: Run awesome-lint
+        run: npx awesome-lint
+        env:
+          CI: true
@@ -0,0 +1,60 @@
+---
+title: "Link Check"
+task: ""
+lineage_type: import
+upstream_source: https://github.com/ARUNAGIRINATHAN-K/awesome-ai-agents-2026/blob/539ec1dc/.github/workflows/link-check.yml
+upstream_sha: 539ec1dc
+imported_at: 2026-06-26
+prompt_class: unknown
+upstream_changes: accepted
+author: upstream
+validated: false
+---
+
+name: Check Links
+
+on:
+  schedule:
+    - cron: '0 0 * * 0'  # Weekly on Sunday
+  pull_request:
+    branches: [main, master]
+  workflow_dispatch:
+
+permissions:
+  contents: read
+  issues: write
+
+jobs:
+  link-check:
+    name: Verify all links are alive
+    runs-on: ubuntu-latest
+    permissions:
+      issues: write
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v4
+
+      - name: Check links with Lychee
+        uses: lycheeverse/lychee-action@v2
+        with:
+          args: >-
+            --verbose
+            --no-progress
+            --accept 200,204,301,302,403,429
+            --exclude-path node_modules
+            --exclude "star-history.com"
+            --exclude "shields.io"
+            --exclude "stableaudio.com"
+            --exclude "mckinsey.com"
+            --exclude "servicenow.com"
+            --timeout 30
+            README.md
+          fail: true
+
+      - name: Create issue on failure
+        if: failure() && github.event_name != 'pull_request'
+        uses: peter-evans/create-issue-from-file@v5
+        with:
+          title: "🔗 Broken links detected"
+          content-filepath: ./lychee/out.md
+          labels: bug, maintenance
@@ -0,0 +1,16 @@
+---
+title: ".Markdownlint"
+task: ""
+lineage_type: import
+upstream_source: https://github.com/ARUNAGIRINATHAN-K/awesome-ai-agents-2026/blob/539ec1dc/.markdownlint.json
+upstream_sha: 539ec1dc
+imported_at: 2026-06-26
+prompt_class: catalogue
+upstream_changes: accepted
+author: upstream
+validated: false
+---
+
+{
+  "MD013": false
+}
@@ -0,0 +1,53 @@
+---
+title: "Changelog"
+task: ""
+lineage_type: import
+upstream_source: https://github.com/ARUNAGIRINATHAN-K/awesome-ai-agents-2026/blob/539ec1dc/CHANGELOG.md
+upstream_sha: 539ec1dc
+imported_at: 2026-06-26
+prompt_class: catalogue
+upstream_changes: accepted
+author: upstream
+validated: false
+---
+
+# Changelog
+
+All notable changes to this list will be documented here.
+
+> Each row is a dated update. The most recent changes are always at the top.
+
+---
+
+## 2026
+
+| Date | Change |
+|---|---|
+| June 26 2026 | **Style reformatting** — Converted all 407 tool entries to the new compact format: `` `TIER` `[Language]` `[Type]` `` replacing the legacy `(🏷️ ...)` tag style. Tier badges (`🚀` Production-Ready, `🌱` Growing, `🔬` Emerging) applied consistently across all sections. Descriptions trimmed to one clear sentence, promotional language removed. Zero duplicate links. Full awesome-lint structural compliance maintained. |
+| June 07 2026 | Performed link audit: fixed redirected URLs (including Bland AI, Podman, Strands Agents, Google ADK, Mistral, Railway, Sistava, Windsurf, Nuance, ADK loop-agents), updated Project Glasswing to correct official link, and removed dead TinyTools link. |
+| May 25 2026 | Added Sistava to Multi-Agent Consumer Platforms. |
+| May 11 2026 | Added 15 new May 2026 tools (LangSmith Fleet, AWS Bedrock Agents, Vertex AI Agents, Wispr Flow, ElevenAgents, Nuance AI, Patronus AI LYNX, Perplexity Personal Computer, and 7 more). Updated 8 descriptions (Cursor, Devin, Claude Code, Pipecat, LangSmith, CrewAI, Pinecone, Skyvern) with 2026 metrics and features. Added enterprise compliance tags ([Enterprise], [HIPAA], [SOC2], [GDPR]) to 20+ tools. Highlighted trending projects: Browser Use (50K⭐), Playwright MCP (10K⭐), DeerFlow (25K⭐). Updated tool count 426→450+. |
+| May 11 2026 | Added new categories `Agent Testing & Debugging` and `Industry-Specific Agents` with 20+ industry entries (Finance, Healthcare, Legal, Manufacturing, Government). Added conceptual testing tools (pytest-Agents, Agent Mutation Testing, Cortex Debugger, Agent Coverage Tools) and a testing comparison table. Standardized enterprise metadata across verticals. Updated README badge to 470+. |
+| May 10 2026 | Added CAJAL to Deep Research Agents as a local scientific paper generation agent for publication-ready IMRaD papers with verified arXiv citations and AI tribunal scoring. |
+| May 04 2026 | Massive expansion to 426+ tools across 33 categories. Added 13 new sections (Creative AI, Customer Support/CRM, Voice Platforms, Deep Research, App Builders, Multi-Agent Platforms, Open-Source Models, Market Stats, Local/Self-Hosted AI, AI Governance, Cybersecurity, Healthcare/Therapy, Newsletters). Backfilled 60+ missing entries across existing categories. Added GNAP, OpenClaw, Bernstein, MagiC, Miyabi, n8n, Temporal, OWASP, and more. Full awesome-lint compliance. |
+| May 03 2026 | Added agenttrace to Safety Guardrails and Observability as a local-first TUI for AI coding agent session observability, covering tokens, cost, latency, tool failures, anomalies, reports, diffs, and CI health gates. |
+| April 28 2026 | Major content merge: added 3 new categories (Agent Communication, Data Pipeline and Workflow, Secure Execution Environments) and 50+ new tools including Crawl4AI, Docling, Chroma, Qdrant, Weaviate, Milvus, LanceDB, Zep, Motorhead, DSPy, Novu, Slack Bolt, Twilio, Apprise, GPT Researcher, STORM, Hamilton, Prefect, Dagster, Burr, Playwright, Firecracker, Docker, Podman, gVisor, Kata Containers, Sysbox, AWS Lambda, Google Cloud Run, Cloudflare Workers, and more. Tool count now at 173+. |
+| April 28 2026 | Added new category "Agent Interfaces and UIs" and 5 missing top-tier tools. Implemented comprehensive metadata tagging (`Language` `Framework` `Environment`) for all 70+ tools while maintaining awesome-lint compliance. |
+| April 27 2026 | Updated Agent Tooling & Infrastructure - KubeStellar Console to Agent Tooling and Infrastructure section. |
+| April 25 2026 | Quality upgrade - fixed 6 broken images, added 4 comparison tables (Coding Agents, Memory, Browser, Protocols), added decision guides for all categories, added A2A and Agent Communication Protocols section, rewrote Contributing.md, fixed all redirected and broken links, added tool count badge |
+| April 24 2026 | Added 21 new tools - Cursor, Windsurf, Devin, Codex CLI, Augment Code to Coding Agents; Acontext to Memory; Docker MCP, Cloudflare MCP, Microsoft Fabric MCP to MCP; Operator to Browser Agents; Langfuse, Arize Phoenix, Laminar, Logfire, AgentDoG, DriftGuard to Safety & Observability; Terminal-Bench to Evaluation |
+| April 2026 | Major overhaul — added comparison tables, star counts, language tags, 3 new categories (MCP & Tool Protocols, Browser & Computer Use Agents, Agent Evaluation & Benchmarks), link-checker CI, badges, and "Why This List?" section |
+| April 2026 | Add new memory-related resources and Coding Agents |
+| April 2026 | Added new frameworks and resources for orchestration |
+| March 2026 | Added Smolagents, PydanticAI, and Letta to Orchestration Frameworks |
+| March 2026 | Added Semantic Kernel to Orchestration Frameworks. Added Agent Deployment and Hosting category with 5 tools. |
+| March 2026 | Initial release — 80+ tools across 9 categories |
+
+---
+
+
+Minor fixes (typos, broken links, small star count corrections) are not logged here individually — those are tracked in [commit history](https://github.com/ARUNAGIRINATHAN-K/awesome-ai-agents/commits/main).
+
+---
+
+*For the full history of changes, see the [commit log](https://github.com/ARUNAGIRINATHAN-K/awesome-ai-agents/commits/main).*
@@ -0,0 +1,79 @@
+---
+title: "Code of Conduct"
+task: ""
+lineage_type: import
+upstream_source: https://github.com/ARUNAGIRINATHAN-K/awesome-ai-agents-2026/blob/539ec1dc/CODE_OF_CONDUCT.md
+upstream_sha: 539ec1dc
+imported_at: 2026-06-26
+prompt_class: catalogue
+upstream_changes: accepted
+author: upstream
+validated: false
+---
+
+# Code of Conduct
+
+## Our Pledge
+
+This is a community list. Everyone who contributes whether opening a PR, filing an issue, or leaving a comment is part of what makes it useful.
+
+We pledge to make participation in this project a harassment-free experience for everyone, regardless of age, body size, visible or invisible disability, ethnicity, sex characteristics, gender identity and expression, level of experience, education, socioeconomic status, nationality, personal appearance, race, caste, color, religion, or sexual identity and orientation.
+
+We pledge to act and interact in ways that contribute to an open, welcoming, diverse, inclusive, and healthy community.
+
+---
+
+## Our Standards
+
+**Behavior that contributes to a positive environment:**
+
+- Being respectful of differing opinions, viewpoints, and experiences
+- Giving and gracefully accepting constructive feedback
+- Taking responsibility and apologizing when we make mistakes, and learning from the experience
+- Focusing on what is best for the community, not just individual preferences
+- Assuming good intent — most people are here to help, not to cause problems
+
+**Behavior that is not acceptable:**
+
+- Harassment of any kind, public or private
+- Trolling, insulting, or derogatory comments
+- Personal or political attacks
+- Publishing others' private information without explicit permission
+- Sustained disruption of discussions
+- Spam, self-promotion beyond what's relevant, or aggressive advocacy for a specific tool
+- Any conduct that would reasonably be considered inappropriate in a professional setting
+
+---
+
+## Scope
+
+This Code of Conduct applies within all project spaces — GitHub issues, pull requests, discussions, and any other community channels associated with this project.
+
+It also applies when an individual is officially representing the project in public spaces, such as posting on behalf of the project on social media.
+
+---
+
+## Enforcement
+
+Instances of unacceptable behavior may be reported by opening a GitHub issue at [https://github.com/ARUNAGIRINATHAN-K/awesome-ai-agents/issues](https://github.com/ARUNAGIRINATHAN-K/awesome-ai-agents/issues) and requesting confidentiality, or by contacting the maintainer directly.
+
+All complaints will be reviewed and investigated promptly, fairly, and with respect for the reporter's privacy. The maintainer will do their best to keep sensitive details confidential while resolving the issue.
+
+**Enforcement actions, in order of severity:**
+
+| Action | When it applies |
+|---|---|
+| **Correction** | A private written warning with clarity on why the behavior was inappropriate |
+| **Warning** | A formal warning with consequences for continued behavior |
+| **Temporary ban** | Temporary removal from all community interaction |
+| **Permanent ban** | Permanent removal for severe, repeated, or sustained violations |
+
+---
+
+## Attribution
+
+This Code of Conduct is adapted from the [Contributor Covenant](https://www.contributor-covenant.org), version 2.1, which is the standard used across thousands of open-source projects including those listed in this repo.
+
+---
+
+*A good community is what turns a list into a resource. Thanks for helping keep this one worth contributing to.*
@@ -0,0 +1,189 @@
+---
+title: "Contributing to Awesome AI Agents 2026"
+task: ""
+lineage_type: import
+upstream_source: https://github.com/ARUNAGIRINATHAN-K/awesome-ai-agents-2026/blob/539ec1dc/CONTRIBUTING.md
+upstream_sha: 539ec1dc
+imported_at: 2026-06-26
+prompt_class: catalogue
+upstream_changes: accepted
+author: upstream
+validated: false
+---
+
+# Contributing to Awesome AI Agents 2026
+
+First off, thank you. This list only stays useful because people like you take the time to keep it accurate and current.
+
+There are four ways to contribute:
+
+- **Add a new tool** - something missing that belongs here
+- **Update an existing entry** - description is wrong or links are broken
+- **Remove a dead project** - unmaintained repos that no longer belong
+- **Improve the structure** - better categories, clearer descriptions, fixed typos
+
+All contributions go through a pull request. It takes about 5 minutes.
+
+---
+
+## Entry Format
+
+Every entry follows this exact compact format:
+
+```markdown
+- [Tool Name](https://github.com/org/repo) `TIER` `[Language]` `[Type]` - One sentence describing what the tool does.
+```
+
+### Tier Badge (required — pick exactly one)
+
+| Badge | Meaning | Criteria |
+|---|---|---|
+| `🚀` | Production-Ready | 10K+ GitHub stars, enterprise adoption, major company (OpenAI, Google, Microsoft, Meta, AWS, Anthropic), or 100K+ users |
+| `🌱` | Growing | Active community, 500–5K stars, regular commits, gaining momentum |
+| `🔬` | Emerging | Novel idea, <500 stars, experimental, recently launched, or research-only |
+
+When in doubt, use `🌱`. Maintainers will adjust tiers during review.
+
+### Language Tag (required — pick exactly one)
+
+Use the **primary implementation language or runtime**:
+
+`Python` · `TypeScript` · `Go` · `Rust` · `Java` · `C#` · `C++` · `Kotlin` · `Swift` · `Ruby` · `WebAssembly` · `Cloud` · `Browser` · `Desktop` · `Mobile` · `No-Code`
+
+- Use `Cloud` for fully managed SaaS products with no local runtime
+- Use `Browser` for browser extensions and purely web-based tools
+- Use `Desktop` for Electron or native desktop apps
+- Use `No-Code` for visual/drag-and-drop builders with no coding required
+
+### Type / Framework Tag (required — pick exactly one)
+
+Use the **most specific framework or architectural type** that applies:
+
+`LangChain` · `OpenAI` · `Anthropic` · `Microsoft` · `Pydantic` · `FastAPI` · `Multi-Agent` · `Graph-Based` · `Stateful` · `Event-Driven` · `MCP` · `RAG` · `Voice` · `Vision` · `Multimodal` · `Memory` · `Observability` · `Security` · `Compliance` · `Testing` · `Evaluation` · `Benchmark` · `CLI` · `IDE` · `VS Code` · `JetBrains` · `GitHub` · `Docker` · `Kubernetes` · `AWS` · `GCP` · `Azure` · `Self-Hosted` · `Local` · `Serverless` · `No-Code` · `Visual Builder` · `Vector DB` · `Database` · `Pipeline` · `Streaming` · `STT` · `TTS` · `Research` · `Paper` · `Guide` · `Newsletter`
+
+Priority order when multiple tags could apply: `MCP` > `RAG` > framework (LangChain, OpenAI…) > architectural style (Multi-Agent, Graph-Based…) > environment (CLI, IDE, Cloud…)
+
+### Description (required)
+
+- **One sentence only**, ending with a period
+- **12–18 words** recommended — specific enough to be useful, short enough to scan
+- No promotional language: no "best", "revolutionary", "game-changing", "powerful", "seamlessly", "amazing"
+- Start with an action verb or noun phrase that describes what the tool **does**, not what it **is**
+
+---
+
+## Complete Examples
+
+**Good entries:**
+
+```markdown
+- [CrewAI](https://github.com/crewAIInc/crewAI) `🚀` `[Python]` `[Multi-Agent]` - Production multi-agent framework with role-based collaboration and 1500+ company adoptions.
+- [Mastra](https://github.com/mastra-ai/mastra) `🌱` `[TypeScript]` `[MCP]` - Opinionated TypeScript framework with RAG, observability, and MCP support built in.
+- [Modus](https://github.com/hypermodeinc/modus) `🔬` `[WebAssembly]` `[Serverless]` - Serverless framework for high-throughput agent workloads with minimal cold starts.
+```
+
+**Bad entries:**
+
+```markdown
+# Wrong: missing tier, language, and type tags (old format)
+- [CrewAI](https://github.com/crewAIInc/crewAI) - Production multi-agent framework (🏷️ `Python` `LangChain` `SDK`).
+
+# Wrong: em-dash instead of hyphen-space after closing tag
+- [Mastra](https://github.com/mastra-ai/mastra) `🌱` `[TypeScript]` `[MCP]` — Opinionated TypeScript framework.
+
+# Wrong: multiple sentences
+- [Modus](https://github.com/hypermodeinc/modus) `🔬` `[WebAssembly]` `[Serverless]` - Serverless framework. It has minimal cold starts.
+
+# Wrong: promotional language
+- [SomeTool](https://example.com) `🚀` `[Python]` `[Multi-Agent]` - The most powerful and revolutionary AI framework ever built.
+
+# Wrong: tier badge missing brackets around language/type
+- [SomeTool](https://example.com) `🌱` `Python` `Multi-Agent` - Does something useful.
+```
+
+---
+
+## Inclusion Criteria
+
+Your tool should meet **all** of the following:
+
+1. **Directly related to AI agents** — not a general LLM tool, not a prompt library, not a generic API wrapper
+2. **Actively maintained** — last commit within the past 6 months (or a live hosted product with recent activity)
+3. **Publicly available** — open-source with a GitHub/GitLab repo, or a live hosted product with a public URL
+4. **Not a duplicate** — check the list first; the URL must not already appear elsewhere in the README
+5. **Functional** — the tool must actually work, not just be a README with no code
+
+Tools that are **experimental, early-stage, or have few stars** are welcome as long as they meet all five criteria. Use `🔬` for those. We value breadth of coverage.
+
+---
+
+## Category Placement
+
+Place your entry in the **most specific category** that fits. If it spans multiple categories, pick the primary one. Maintainers may move it during review.
+
+Current categories:
+
+- **Orchestration Frameworks** — core agent building frameworks
+- **Coding Agents** — tools that write, edit, and debug code
+- **Memory and Context** — persistent memory and knowledge graphs
+- **Multi-Agent Systems** — multi-agent coordination frameworks
+- **Agent Communication Protocols** — MCP, A2A, and tool protocol implementations
+- **Browser and Computer Use Agents** — web navigation and UI automation
+- **Agent Tooling and Infrastructure** — sandboxes, scrapers, and networking
+- **Low and No-Code Builders** — visual and browser-based agent builders
+- **Voice and Multimodal Agents** — audio, video, and cross-modal agents
+- **Safety Guardrails and Observability** — monitoring, security, and governance
+- **Agent Interfaces and UIs** — frontend workspaces and chat interfaces
+- **Agent Deployment and Hosting** — platforms for running agents in production
+- **Agent Evaluation and Benchmarks** — benchmarks and evaluation frameworks
+- **Learning Resources** — courses, papers, and guides
+- **Agent Communication** — notifications, messaging, and channel integrations
+- **Data Pipeline and Workflow** — data orchestration and long-running workflows
+- **Secure Execution Environments** — sandboxes, VMs, and serverless runtimes
+- **Creative AI Agents** — image, video, music, audio, and 3D generation
+- **Customer Support and CRM Agents** — support automation, CRM, and sales outreach
+- **Voice Agent Platforms** — voice AI platforms for calls and conversations
+- **Deep Research Agents** — autonomous multi-step research and report generation
+- **Prompt-to-App Builders** — natural language to full-stack application generation
+- **Multi-Agent Consumer Platforms** — all-in-one consumer AI platforms
+- **Open-Source Models for Agents** — open-weight LLMs optimized for agentic tasks
+- **Market Stats and Benchmarks 2026** — market data, valuations, and growth metrics
+- **Local and Self-Hosted AI** — local LLM runners and self-hosted agent platforms
+- **AI Governance and Compliance** — regulatory frameworks and governance tools
+- **Cybersecurity Agents** — pentesting, threat detection, and security analysis
+- **Healthcare and Therapy Agents** — mental health, cognitive training, and therapy AI
+- **Newsletters and Communities** — curated newsletters, podcasts, and forums
+- **Experience Intent Libraries** — prompt templates and vibe libraries for interaction design
+
+---
+
+## Pull Request Process
+
+1. **Fork** this repo
+2. **Add** your entry in the correct category, in alphabetical order
+3. **Verify** the link works, your entry follows the format above, and the URL does not already appear elsewhere in the list
+4. **Submit** a pull request with a clear title: `Add [Tool Name] to [Category]`
+
+The maintainers will review your PR within a few days. We may suggest edits to the description, tier, tags, or category.
+
+---
+
+## Quality Standards
+
+This list passes `awesome-lint` and automated link checking on every push. Your PR must:
+
+- Pass the awesome-lint check (no em-dashes, no duplicate links, correct formatting)
+- Have no broken links
+- Not reuse a URL already present elsewhere in the README
+- Follow alphabetical ordering within its category
+- Use the new compact format (tier badge + language + type + one-sentence description)
+
+---
+
+## Code of Conduct
+
+By contributing, you agree to abide by the [Code of Conduct](CODE_OF_CONDUCT.md).
+
+---
+
+Thank you for helping make this the most useful AI agent resource on GitHub.
@@ -0,0 +1,70 @@
+---
+title: "Contributing to Awesome AI Agent Papers"
+task: ""
+lineage_type: import
+upstream_source: https://github.com/VoltAgent/awesome-ai-agent-papers/blob/d467d641/CONTRIBUTING.md
+upstream_sha: d467d641
+imported_at: 2026-06-26
+prompt_class: catalogue
+upstream_changes: accepted
+author: upstream
+validated: false
+---
+
+# Contributing to Awesome AI Agent Papers
+
+A curated weekly list of AI agent research papers from arXiv for AI/LLM agent developers.
+
+## Adding a Paper
+
+### Entry Format
+
+Add your paper to the relevant category in `README.md`:
+
+```markdown
+| **[Paper Title](https://arxiv.org/pdf/XXXX.XXXXvX)** - 1–2 sentence description. |
+```
+
+### Where to Add
+
+Add to the matching category:
+
+- **Tooling** — frameworks, training, optimization, planning, context engineering, architecture
+- **Memory & RAG** — memory systems, knowledge retrieval, RAG pipelines, procedural memory
+- **Multi-Agent** — coordination, communication, orchestration, collaboration, negotiation
+- **Eval & Observability** — benchmarks, testing, monitoring, debugging, tracing, explainability
+- **Security** — attacks, defenses, alignment, trust, prompt injection, safety
+
+### Requirements
+
+- Paper must be published on arXiv
+- Paper must be relevant to AI/LLM agent developers
+- Description must be 1–2 sentences, plain English, no academic phrasing
+- Assign exactly one category per paper
+- Link must point to the PDF URL
+
+### Description Guidelines
+
+- Write like you're explaining to a developer friend — no academic language
+- Explain what the research is about, not why it's important
+- No "helping developers" product-pitch tone
+- No editorial commentary or personal opinions
+- Numbers need context (e.g., "93% precision on function-level TTP recognition")
+
+### PR Title
+
+`Add paper: Paper Title`
+
+## Adding a Weekly List
+
+Weekly lists go under `lists/` with the naming convention `awesome-agents-YYYY-MM-DD-to-YYYY-MM-DD.md`.
+
+## Important
+
+- Verify your links work before submitting
+- We review all submissions and may decline papers that don't fit the AI agent ecosystem
+
+## Help
+
+- Check existing issues and PRs first
+- Open a new issue for questions
@@ -0,0 +1,515 @@
+---
+title: "Awesome AI Agent Papers"
+task: ""
+lineage_type: import
+upstream_source: https://github.com/VoltAgent/awesome-ai-agent-papers/blob/d467d641/README.md
+upstream_sha: d467d641
+imported_at: 2026-06-26
+prompt_class: catalogue
+upstream_changes: accepted
+author: upstream
+validated: false
+---
+
+
+
+<div align="center">
+
+<a href="https://github.com/VoltAgent/voltagent">
+<img width="1500" height="500" alt="cover-image" src="https://github.com/user-attachments/assets/23718e60-9ad3-4105-999c-8372713c3fbb" />
+</a>
+
+<br/>
+<br/>
+
+<div align="center">
+    <strong>Hand-picked research papers on the AI agent ecosystem, published in 2026.
+    </strong>
+    <br />
+    <br />
+
+</div>
+
+[![Awesome](https://awesome.re/badge.svg)](https://awesome.re)
+![Papers Count](https://img.shields.io/badge/Research%20Papers-363+-b31b1b)
+![Last Update](https://img.shields.io/github/last-commit/VoltAgent/awesome-ai-agent-papers?label=Last%20update)
+<a href="https://github.com/VoltAgent/voltagent">
+  <img alt="VoltAgent" src="https://cdn.voltagent.dev/website/logo/logo-2-svg.svg" height="20" />
+</a>
+[![Discord](https://img.shields.io/discord/1361559153780195478.svg?label=&logo=discord&logoColor=ffffff&color=7389D8&labelColor=6A7EC2)](https://s.voltagent.dev/discord)
+
+</div>
+
+<div align="center">
+    <strong>More awesome collections for developers</strong>
+    <br />
+    <br />
+</div>
+
+
+
+# Awesome AI Agent Papers
+
+A curated collection of research papers **published in 2026** and sourced from arXiv, covering core topics from the AI agent ecosystem like **multi-agent coordination**, **memory & RAG**, **tooling**, **evaluation & observability**, and **security**.
+
+Whether you're an AI engineer building agent systems, a researcher exploring new architectures, or a developer integrating LLM agents into products, these papers help you stay on top of what's actually working, what's breaking, and where the field is heading. Updated weekly from arXiv.
+
+### Why this list exists
+
+Hundreds of papers are published on arXiv every week, and a growing number of them touch on AI agents. We go through them all, filter the ones that are directly relevant to the AI agent ecosystem, and categorize them so you don't have to. This list only includes papers published from January 2026 onward.
+
+### Table of Contents
+
+- [Multi-Agent](#multi-agent) (53)
+- [Memory & RAG](#memory--rag) (57)
+- [Eval & Observability](#eval--observability) (80)
+- [Agent Tooling](#agent-tooling) (95)
+- [AI Agent Security](#ai-agent-security) (82)
+
+<br>
+
+<details open id="multi-agent">
+<summary><h3 style="display:inline">Multi-Agent (53)</h3></summary>
+
+<br>
+
+| Paper | arXiv ID |
+|---|:---:|
+| **[AutoNumerics: An Autonomous, PDE-Agnostic Multi-Agent Pipeline for Scientific Computing](https://arxiv.org/pdf/2602.17607)** - A multi-agent pipeline that reads a PDE problem description in plain text and writes, debugs, and validates a classical numerical solver end-to-end. Generates spectral and finite-difference code (no neural networks), scoring ~6 orders of magnitude below FNO and CodePDE baselines. | <a href="https://arxiv.org/abs/2602.17607"><img src="https://img.shields.io/badge/arXiv-2602.17607-b31b1b.svg" alt="arXiv" /></a> |
+| **[Beyond Offline A/B Testing: Context-Aware Agent Simulation for Recommender System Evaluation](https://arxiv.org/abs/2604.09549)** - Evaluates recommender systems via agent-RS interactions. | <a href="https://arxiv.org/abs/2604.09549"><img src="https://img.shields.io/badge/arXiv-2602.06039-b31b1b.svg" alt="arXiv" /></a> |
+| **[CORAL: Towards Autonomous Multi-Agent Evolution for Open-Ended Discovery](https://arxiv.org/pdf/2604.01658)** - Introduces long-running multi-agent systems that self-evolve via shared persistent memory, asynchronous execution, and heartbeat-based interventions; 3–10× higher improvement rates than fixed evolutionary-search baselines on 10 math/algorithmic/systems tasks. | <a href="https://arxiv.org/abs/2604.01658"><img src="https://img.shields.io/badge/arXiv-2604.01658-b31b1b.svg" alt="arXiv" /></a> |
+| **[DyTopo: Dynamic Topology Routing for Multi-Agent Reasoning via Semantic Matching](https://arxiv.org/pdf/2602.06039v1)** - Investigates dynamically rewiring agent-to-agent connections at each reasoning round via semantic matching instead of fixed communication topologies. | <a href="https://arxiv.org/abs/2602.06039v1"><img src="https://img.shields.io/badge/arXiv-2602.06039-b31b1b.svg" alt="arXiv" /></a> |
+| **[RuleSmith: Multi-Agent LLMs for Automated Game Balancing](https://arxiv.org/pdf/2602.06232v1)** - Explores automated game balancing by combining multi-agent LLM self-play with Bayesian optimization on a civ-style game. | <a href="https://arxiv.org/abs/2602.06232v1"><img src="https://img.shields.io/badge/arXiv-2602.06232-b31b1b.svg" alt="arXiv" /></a> |
+| **[CommCP: Efficient Multi-Agent Coordination via LLM-Based Communication with Conformal Prediction](https://arxiv.org/pdf/2602.06038v1)** - Examines how conformal prediction can filter noisy inter-agent messages to improve multi-robot coordination. | <a href="https://arxiv.org/abs/2602.06038v1"><img src="https://img.shields.io/badge/arXiv-2602.06038-b31b1b.svg" alt="arXiv" /></a> |
+| **[AgenticPay: A Multi-Agent LLM Negotiation System for Buyer-Seller Transactions](https://arxiv.org/pdf/2602.06008v1)** - Introduces a 110+ task benchmark to evaluate how well multi-agent LLM systems handle buyer-seller negotiation through natural language. | <a href="https://arxiv.org/abs/2602.06008v1"><img src="https://img.shields.io/badge/arXiv-2602.06008-b31b1b.svg" alt="arXiv" /></a> |
+| **[Gender Dynamics and Homophily in a Social Network of LLM Agents](https://arxiv.org/pdf/2602.02606v1)** - Analyzes social network formation among 70K+ autonomous LLM agents on Chirper.ai to study emergent group behavior and bias. | <a href="https://arxiv.org/abs/2602.02606v1"><img src="https://img.shields.io/badge/arXiv-2602.02606-b31b1b.svg" alt="arXiv" /></a> |
+| **[ROMA: Recursive Open Meta-Agent Framework for Long-Horizon Multi-Agent Systems](https://arxiv.org/pdf/2602.01848v1)** - Proposes breaking large tasks into subtask trees that run in parallel across multiple agents to handle long-horizon workflows without exceeding context windows. | <a href="https://arxiv.org/abs/2602.01848v1"><img src="https://img.shields.io/badge/arXiv-2602.01848-b31b1b.svg" alt="arXiv" /></a> |
+| **[ORCH: many analyses, one merge — a deterministic multi-agent orchestrator](https://arxiv.org/pdf/2602.01797v1)** - Proposes a deterministic multi-agent orchestrator where multiple LLMs analyze a problem independently and a merge agent selects the best answer without any training. | <a href="https://arxiv.org/abs/2602.01797v1"><img src="https://img.shields.io/badge/arXiv-2602.01797-b31b1b.svg" alt="arXiv" /></a> |
+| **[H-AdminSim: A Multi-Agent Simulator for Realistic Hospital Administrative Workflows](https://arxiv.org/pdf/2602.05407v1)** - Simulates end-to-end hospital administrative workflows with multi-agent LLMs and FHIR integration to test LLM-driven automation in healthcare settings. | <a href="https://arxiv.org/abs/2602.05407v1"><img src="https://img.shields.io/badge/arXiv-2602.05407-b31b1b.svg" alt="arXiv" /></a> |
+| **[Agyn: A Multi-Agent System for Team-Based Autonomous Software Engineering](https://arxiv.org/pdf/2602.01465v2)** - Proposes a multi-agent system for autonomous software engineering that assigns specialized agents to roles like coordination, research, implementation, and review. | <a href="http://arxiv.org/abs/2602.01465v2"><img src="https://img.shields.io/badge/arXiv-2602.01465-b31b1b.svg" alt="arXiv" /></a> |
+| **[Multi-Agent Teams Hold Experts Back](https://arxiv.org/pdf/2602.01011v3)** - Examines whether self-organizing LLM agent teams can match or beat their best member's performance across collaborative benchmarks. | <a href="http://arxiv.org/abs/2602.01011v3"><img src="https://img.shields.io/badge/arXiv-2602.01011-b31b1b.svg" alt="arXiv" /></a> |
+| **[Evolving Interpretable Constitutions for Multi-Agent Coordination](https://arxiv.org/pdf/2602.00755v1)** - Explores using LLM-driven genetic programming to automatically discover behavioral norms for multi-agent coordination in a survival-pressure grid-world simulation. | <a href="http://arxiv.org/abs/2602.00755v1"><img src="https://img.shields.io/badge/arXiv-2602.00755-b31b1b.svg" alt="arXiv" /></a> |
+| **[Scaling Multiagent Systems with Process Rewards](https://arxiv.org/pdf/2601.23228v2)** - Proposes per-action process rewards from AI feedback to improve credit assignment and sample efficiency when finetuning multi-agent LLM systems. | <a href="http://arxiv.org/abs/2601.23228v2"><img src="https://img.shields.io/badge/arXiv-2601.23228-b31b1b.svg" alt="arXiv" /></a> |
+| **[MonoScale: Scaling Multi-Agent System with Monotonic Improvement](https://arxiv.org/pdf/2601.23219v1)** - Proposes a framework for safely growing multi-agent pools by generating familiarization tasks and building routing memory, with a guaranteed non-decreasing performance across onboarding rounds. | <a href="http://arxiv.org/abs/2601.23219v1"><img src="https://img.shields.io/badge/arXiv-2601.23219-b31b1b.svg" alt="arXiv" /></a> |
+| **[Task-Aware LLM Council with Adaptive Decision Pathways for Decision Support](https://arxiv.org/pdf/2601.22662v1)** - Proposes a task-adaptive multi-agent framework that routes control to the most suitable LLM at each decision step using semantic matching against each model's success history. | <a href="http://arxiv.org/abs/2601.22662v1"><img src="https://img.shields.io/badge/arXiv-2601.22662-b31b1b.svg" alt="arXiv" /></a> |
+| **[SYMPHONY: Synergistic Multi-agent Planning with Heterogeneous Language Model Assembly](https://arxiv.org/pdf/2601.22623v1)** - Explores using a pool of different LLM agents within MCTS planning to increase rollout diversity and improve multi-step reasoning. | <a href="http://arxiv.org/abs/2601.22623v1"><img src="https://img.shields.io/badge/arXiv-2601.22623-b31b1b.svg" alt="arXiv" /></a> |
+| **[Learning to Recommend Multi-Agent Subgraphs from Calling Trees](https://arxiv.org/pdf/2601.22209v1)** - Proposes a recommendation framework that uses historical calling trees to select the best agents or agent teams for each subtask in multi-agent orchestration. | <a href="http://arxiv.org/abs/2601.22209v1"><img src="https://img.shields.io/badge/arXiv-2601.22209-b31b1b.svg" alt="arXiv" /></a> |
+| **[Learning Decentralized LLM Collaboration with Multi-Agent Actor Critic](https://arxiv.org/pdf/2601.21972v2)** - Investigates actor-critic reinforcement learning methods for training decentralized LLM agent collaboration across writing, coding, and game-playing tasks. | <a href="http://arxiv.org/abs/2601.21972v2"><img src="https://img.shields.io/badge/arXiv-2601.21972-b31b1b.svg" alt="arXiv" /></a> |
+| **[AgenticSimLaw: A Juvenile Courtroom Multi-Agent Debate Simulation for Explainable High-Stakes Tabular Decision Making](https://arxiv.org/pdf/2601.21936v1)** - Proposes a role-structured multi-agent courtroom debate framework with defined agent roles, interaction protocols, and private reasoning strategies for auditable high-stakes decision-making. | <a href="http://arxiv.org/abs/2601.21936v1"><img src="https://img.shields.io/badge/arXiv-2601.21936-b31b1b.svg" alt="arXiv" /></a> |
+| **[Epistemic Context Learning: Building Trust the Right Way in LLM-Based Multi-Agent Systems](https://arxiv.org/pdf/2601.21742v1)** - Introduces a reasoning framework that builds peer reliability profiles from interaction history so agents in multi-agent systems learn which peers to trust when uncertain. | <a href="http://arxiv.org/abs/2601.21742v1"><img src="https://img.shields.io/badge/arXiv-2601.21742-b31b1b.svg" alt="arXiv" /></a> |
+| **[Adaptive Confidence Gating in Multi-Agent Collaboration for Efficient and Optimized Code Generation](https://arxiv.org/pdf/2601.21469v1)** - Explores structured multi-agent debate with three role-based agents and adaptive confidence gating to improve small language model code generation. | <a href="http://arxiv.org/abs/2601.21469v1"><img src="https://img.shields.io/badge/arXiv-2601.21469-b31b1b.svg" alt="arXiv" /></a> |
+| **[CASTER: Context-Aware Strategy for Task Efficient Routing in Multi-Agent Systems](https://arxiv.org/pdf/2601.19793v1)** - Proposes a lightweight router for dynamic model selection in graph-based multi-agent systems that combines semantic embeddings with structural meta-features and self-optimizes through on-policy negative feedback. | <a href="http://arxiv.org/abs/2601.19793v1"><img src="https://img.shields.io/badge/arXiv-2601.19793-b31b1b.svg" alt="arXiv" /></a> |
+| **[Phase Transition for Budgeted Multi-Agent Synergy](https://arxiv.org/pdf/2601.17311v1)** - Develops a theory for predicting when budgeted multi-agent LLM systems improve, saturate, or collapse based on context windows, communication fidelity, and shared-error correlation. | <a href="http://arxiv.org/abs/2601.17311v1"><img src="https://img.shields.io/badge/arXiv-2601.17311-b31b1b.svg" alt="arXiv" /></a> |
+| **[Dynamic Role Assignment for Multi-Agent Debate](https://arxiv.org/pdf/2601.17152v1)** - Proposes a meta-debate framework that dynamically assigns roles in multi-agent systems by matching model capabilities to positions through proposal and peer review stages. | <a href="http://arxiv.org/abs/2601.17152v1"><img src="https://img.shields.io/badge/arXiv-2601.17152-b31b1b.svg" alt="arXiv" /></a> |
+| **[Learning to Collaborate: An Orchestrated-Decentralized Framework for Peer-to-Peer LLM Federation](https://arxiv.org/pdf/2601.17133v1)** - Introduces orchestrated decentralized peer-to-peer LLM collaboration that uses contextual bandits to learn optimal matchmaking between heterogeneous agents via secure distillation. | <a href="http://arxiv.org/abs/2601.17133v1"><img src="https://img.shields.io/badge/arXiv-2601.17133-b31b1b.svg" alt="arXiv" /></a> |
+| **[Mixture-of-Models: Unifying Heterogeneous Agents via N-Way Self-Evaluating Deliberation](https://arxiv.org/pdf/2601.16863v1)** - Explores a runtime Mixture-of-Models architecture with a dynamic expertise broker and quadratic voting consensus that enables small model ensembles to match frontier performance. | <a href="http://arxiv.org/abs/2601.16863v1"><img src="https://img.shields.io/badge/arXiv-2601.16863-b31b1b.svg" alt="arXiv" /></a> |
+| **[Multi-Agent Constraint Factorization Reveals Latent Invariant Solution Structure](https://arxiv.org/pdf/2601.15077v1)** - Formalizes through operator theory why multi-agent LLM systems access invariant solutions that a single agent applying all constraints simultaneously cannot reach. | <a href="http://arxiv.org/abs/2601.15077v1"><img src="https://img.shields.io/badge/arXiv-2601.15077-b31b1b.svg" alt="arXiv" /></a> |
+| **[MAS-Orchestra: Understanding and Improving Multi-Agent Reasoning Through Holistic Orchestration and Controlled Benchmarks](https://arxiv.org/pdf/2601.14652v2)** - Proposes a training-time framework that formulates multi-agent orchestration as function-calling reinforcement learning with holistic system-level reasoning and introduces MASBENCH for controlled evaluation. | <a href="http://arxiv.org/abs/2601.14652v2"><img src="https://img.shields.io/badge/arXiv-2601.14652-b31b1b.svg" alt="arXiv" /></a> |
+| **[MASCOT: Towards Multi-Agent Socio-Collaborative Companion Systems](https://arxiv.org/pdf/2601.14230v1)** - Proposes a bi-level optimization framework for multi-agent companions that aligns individual personas via RLAIF and optimizes collaborative dialogue through group-level meta-policy rewards. | <a href="http://arxiv.org/abs/2601.14230v1"><img src="https://img.shields.io/badge/arXiv-2601.14230-b31b1b.svg" alt="arXiv" /></a> |
+| **[If You Want Coherence, Orchestrate a Team of Rivals: Multi-Agent Models of Organizational Intelligence](https://arxiv.org/pdf/2601.14351v1)** - Explores a team-of-rivals multi-agent architecture with specialized roles and a remote code executor that separates reasoning from data execution to maintain clean context windows. | <a href="http://arxiv.org/abs/2601.14351v1"><img src="https://img.shields.io/badge/arXiv-2601.14351-b31b1b.svg" alt="arXiv" /></a> |
+| **[The Orchestration of Multi-Agent Systems: Architectures, Protocols, and Enterprise Adoption](https://arxiv.org/pdf/2601.13671v1)** - Formalizes a unified architectural framework for orchestrated multi-agent systems integrating MCP for tool access and Agent2Agent protocol for peer coordination, delegation, and policy enforcement. | <a href="http://arxiv.org/abs/2601.13671v1"><img src="https://img.shields.io/badge/arXiv-2601.13671-b31b1b.svg" alt="arXiv" /></a> |
+| **[MARO: Learning Stronger Reasoning from Social Interaction](https://arxiv.org/pdf/2601.12323v2)** - Proposes Multi-Agent Reward Optimization, a method that decomposes multi-agent social interaction outcomes into per-behavior learning signals to improve LLM reasoning through simulated social environments. | <a href="http://arxiv.org/abs/2601.12323v2"><img src="https://img.shields.io/badge/arXiv-2601.12323-b31b1b.svg" alt="arXiv" /></a> |
+| **[LSTM-MAS: A Long Short-Term Memory Inspired Multi-Agent System for Long-Context Understanding](https://arxiv.org/pdf/2601.11913v1)** - Introduces an LSTM-inspired multi-agent architecture with worker, filter, judge, and manager agents that emulate gated memory mechanisms to control information flow for long-context understanding. | <a href="http://arxiv.org/abs/2601.11913v1"><img src="https://img.shields.io/badge/arXiv-2601.11913-b31b1b.svg" alt="arXiv" /></a> |
+| **[Do We Always Need Query-Level Workflows? Rethinking Agentic Workflow Generation for Multi-Agent Systems](https://arxiv.org/pdf/2601.11147v1)** - Examines whether query-level workflow generation is always necessary in multi-agent systems and proposes a low-cost task-level framework that uses self-prediction with few-shot calibration instead of full execution. | <a href="http://arxiv.org/abs/2601.11147v1"><img src="https://img.shields.io/badge/arXiv-2601.11147-b31b1b.svg" alt="arXiv" /></a> |
+| **[Learning Latency-Aware Orchestration for Parallel Multi-Agent Systems](https://arxiv.org/pdf/2601.10560v1)** - Proposes a latency-aware multi-agent orchestration framework that explicitly optimizes the critical execution path under parallel execution to reduce end-to-end latency while maintaining task performance. | <a href="http://arxiv.org/abs/2601.10560v1"><img src="https://img.shields.io/badge/arXiv-2601.10560-b31b1b.svg" alt="arXiv" /></a> |
+| **[TopoDIM: One-shot Topology Generation of Diverse Interaction Modes for Multi-Agent Systems](https://arxiv.org/pdf/2601.10120v1)** - Proposes a one-shot topology generation framework with diverse interaction modes that enables decentralized agents to autonomously construct heterogeneous communication topologies without iterative coordination. | <a href="http://arxiv.org/abs/2601.10120v1"><img src="https://img.shields.io/badge/arXiv-2601.10120-b31b1b.svg" alt="arXiv" /></a> |
+| **[Beyond Rule-Based Workflows: An Information-Flow-Orchestrated Multi-Agents Paradigm via A2A Communication from CORAL](https://arxiv.org/pdf/2601.09883v1)** - Replaces predefined multi-agent workflows with a dynamic information-flow orchestrator that coordinates agents through natural-language A2A communication. | <a href="https://arxiv.org/abs/2601.09883v1"><img src="https://img.shields.io/badge/arXiv-2601.09883-b31b1b.svg" alt="arXiv" /></a> |
+| **[LLM-Based Agentic Systems for Software Engineering: Challenges and Opportunities](https://arxiv.org/pdf/2601.09822v2)** - Reviews LLM-based multi-agent systems across the software development lifecycle, covering frameworks, communication protocols, and orchestration challenges from requirements to debugging. | <a href="https://arxiv.org/abs/2601.09822v2"><img src="https://img.shields.io/badge/arXiv-2601.09822-b31b1b.svg" alt="arXiv" /></a> |
+| **[Collaborative Multi-Agent Test-Time Reinforcement Learning for Reasoning](https://arxiv.org/pdf/2601.09667v2)** - Explores injecting structured textual experience into multi-agent deliberation at test time to improve reasoning accuracy without any model tuning. | <a href="https://arxiv.org/abs/2601.09667v2"><img src="https://img.shields.io/badge/arXiv-2601.09667-b31b1b.svg" alt="arXiv" /></a> |
+| **[The End of Reward Engineering: How LLMs Are Redefining Multi-Agent Coordination](https://arxiv.org/pdf/2601.08237v1)** - Argues that LLMs can replace hand-crafted numerical reward functions with language-based objective specifications for multi-agent coordination, drawing on EUREKA and RLVR as evidence. | <a href="https://arxiv.org/abs/2601.08237v1"><img src="https://img.shields.io/badge/arXiv-2601.08237-b31b1b.svg" alt="arXiv" /></a> |
+| **[A Large-Scale Study on the Development and Issues of Multi-Agent AI Systems](https://arxiv.org/pdf/2601.07136v1)** - Analyzes over 42K commits and 4.7K resolved issues across eight leading multi-agent AI systems (LangChain, CrewAI, AutoGen, etc.) to study development patterns, maintenance practices, and ecosystem maturity. | <a href="https://arxiv.org/abs/2601.07136v1"><img src="https://img.shields.io/badge/arXiv-2601.07136-b31b1b.svg" alt="arXiv" /></a> |
+| **[StackPlanner: A Centralized Hierarchical Multi-Agent System with Task-Experience Memory Management](https://arxiv.org/pdf/2601.05890v1)** - Proposes a hierarchical multi-agent framework that decouples high-level coordination from subtask execution with active task-level memory control and reinforcement-learning-driven experience reuse. | <a href="https://arxiv.org/abs/2601.05890v1"><img src="https://img.shields.io/badge/arXiv-2601.05890-b31b1b.svg" alt="arXiv" /></a> |
+| **[CTHA: Constrained Temporal Hierarchical Architecture for Stable Multi-Agent LLM Systems](https://arxiv.org/pdf/2601.10738v1)** - Proposes a constrained temporal hierarchical architecture for multi-agent LLM systems that projects inter-layer communication onto structured manifolds with typed message contracts and authority bounds. | <a href="https://arxiv.org/abs/2601.10738v1"><img src="https://img.shields.io/badge/arXiv-2601.10738-b31b1b.svg" alt="arXiv" /></a> |
+| **[DynaDebate: Breaking Homogeneity in Multi-Agent Debate with Dynamic Path Generation](https://arxiv.org/pdf/2601.05746v1)** - Introduces dynamic path generation for multi-agent debate that allocates diverse solution paths to agents, shifts focus to step-by-step logic critique, and uses a trigger-based verification agent to resolve deadlocks. | <a href="https://arxiv.org/abs/2601.05746v1"><img src="https://img.shields.io/badge/arXiv-2601.05746-b31b1b.svg" alt="arXiv" /></a> |
+| **[Demystifying Multi-Agent Debate: The Role of Confidence and Diversity](https://arxiv.org/pdf/2601.19921v1)** - Investigates how diversity-aware initialization and confidence-modulated updates improve multi-agent debate, connecting findings from human deliberation research to LLM-based debate protocols. | <a href="https://arxiv.org/abs/2601.19921v1"><img src="https://img.shields.io/badge/arXiv-2601.19921-b31b1b.svg" alt="arXiv" /></a> |
+| **[Orchestrating Intelligence: Confidence-Aware Routing for Multi-Agent Collaboration](https://arxiv.org/pdf/2601.04861v2)** - Proposes a multi-agent framework with confidence-aware routing that dynamically selects agent roles and model scales across heterogeneous LLMs based on task complexity. | <a href="https://arxiv.org/abs/2601.04861v2"><img src="https://img.shields.io/badge/arXiv-2601.04861-b31b1b.svg" alt="arXiv" /></a> |
+| **[Belief in Authority: Impact of Authority in Multi-Agent Evaluation Framework](https://arxiv.org/pdf/2601.04790v1)** - Analyzes role-based authority bias in multi-agent evaluation frameworks using French and Raven's power-based theory across legitimate, referent, and expert power types. | <a href="https://arxiv.org/abs/2601.04790v1"><img src="https://img.shields.io/badge/arXiv-2601.04790-b31b1b.svg" alt="arXiv" /></a> |
+| **[When Single-Agent with Skills Replace Multi-Agent Systems and When They Fail](https://arxiv.org/pdf/2601.04748v2)** - Investigates when a single agent with a skill library can replace multi-agent systems, studying scaling limits and phase transitions in skill selection as libraries grow. | <a href="https://arxiv.org/abs/2601.04748v2"><img src="https://img.shields.io/badge/arXiv-2601.04748-b31b1b.svg" alt="arXiv" /></a> |
+| **[ResMAS: Resilience Optimization in LLM-based Multi-Agent Systems](https://arxiv.org/pdf/2601.04694v1)** - Proposes a two-stage framework for enhancing multi-agent system resilience through RL-based topology generation and topology-aware prompt optimization under perturbations. | <a href="https://arxiv.org/abs/2601.04694v1"><img src="https://img.shields.io/badge/arXiv-2601.04694-b31b1b.svg" alt="arXiv" /></a> |
+| **[TCAndon-Router: Adaptive Reasoning Router for Multi-Agent Collaboration](https://arxiv.org/pdf/2601.04544v1)** - Proposes an adaptive reasoning router for multi-agent systems that generates natural-language reasoning chains before predicting candidate agents, with a collaborative execution pipeline. | <a href="https://arxiv.org/abs/2601.04544v1"><img src="https://img.shields.io/badge/arXiv-2601.04544-b31b1b.svg" alt="arXiv" /></a> |
+| **[When Numbers Start Talking: Implicit Numerical Coordination Among LLM-Based Agents](https://arxiv.org/pdf/2601.03846v1)** - Investigates covert communication in LLM multi-agent systems through game-theoretic analysis of implicit coordination signals across different communication regimes. | <a href="https://arxiv.org/abs/2601.03846v1"><img src="https://img.shields.io/badge/arXiv-2601.03846-b31b1b.svg" alt="arXiv" /></a> |
+| **[Bayesian Orchestration of Multi-LLM Agents for Cost-Aware Sequential Decision-Making](https://arxiv.org/pdf/2601.01522v1)** - Proposes a Bayesian, cost-aware multi-LLM orchestration framework that treats LLMs as approximate likelihood models and aggregates across diverse models for sequential decision-making. | <a href="https://arxiv.org/abs/2601.01522v1"><img src="https://img.shields.io/badge/arXiv-2601.01522-b31b1b.svg" alt="arXiv" /></a> |
+| **[OptimAI: Optimization from Natural Language Using LLM-Powered AI Agents](https://arxiv.org/pdf/2504.16918)** - Turns natural-language optimization problems into working solver code with a four-agent pipeline (Formulator, Planner, Coder, Critic) and UCB bandit scheduling over candidate formulations. Mixing Gemma 27B and Llama 70B beats any single model; gets 88% on NLP4LP vs. 72% prior best. | <a href="https://arxiv.org/abs/2504.16918"><img src="https://img.shields.io/badge/arXiv-2504.16918-b31b1b.svg" alt="arXiv" /></a> |
+
+</details>
+
+<br>
+
+<details open id="memory--rag">
+<summary><h3 style="display:inline">Memory & RAG (56)</h3></summary>
+
+<br>
+
+| Paper | arXiv ID |
+|---|:---:|
+| **[Corpus2Skill: Don't Retrieve, Navigate — Distilling Enterprise Knowledge into Navigable Agent Skills for QA and RAG](https://arxiv.org/pdf/2604.14572)** - Compiles a corpus offline into a hierarchical tree of Agent Skills that the LLM agent navigates at query time, replacing retrieval with skill-tree traversal. | <a href="https://arxiv.org/abs/2604.14572"><img src="https://img.shields.io/badge/arXiv-2604.14572-b31b1b.svg" alt="arXiv" /></a> |
+| **[BudgetMem: Learning Query-Aware Budget-Tier Routing for Runtime Agent Memory](https://arxiv.org/pdf/2602.06025v1)** - Investigates routing agent memory queries to different processing tiers based on query difficulty to control the cost-accuracy trade-off at runtime. | <a href="https://arxiv.org/abs/2602.06025v1"><img src="https://img.shields.io/badge/arXiv-2602.06025-b31b1b.svg" alt="arXiv" /></a> |
+| **[Learning to Share: Selective Memory for Efficient Parallel Agentic Systems](https://arxiv.org/pdf/2602.05965v1)** - Proposes a shared memory bank with a learned controller that decides what information is worth passing between parallel agent teams to reduce redundant work. | <a href="https://arxiv.org/abs/2602.05965v1"><img src="https://img.shields.io/badge/arXiv-2602.05965-b31b1b.svg" alt="arXiv" /></a> |
+| **[CompactRAG: Reducing LLM Calls and Token Overhead in Multi-Hop Question Answering](https://arxiv.org/pdf/2602.05728v1)** - Explores converting a corpus into atomic QA pairs offline to resolve multi-hop questions with just two LLM calls regardless of hop count. | <a href="https://arxiv.org/abs/2602.05728v1"><img src="https://img.shields.io/badge/arXiv-2602.05728-b31b1b.svg" alt="arXiv" /></a> |
+| **[Mitigating Hallucination in Financial Retrieval-Augmented Generation via Fine-Grained Knowledge Verification](https://arxiv.org/pdf/2602.05723v1)** - Examines breaking financial RAG answers into atomic facts and verifying each against retrieved documents using reinforcement learning rewards. | <a href="https://arxiv.org/abs/2602.05723v1"><img src="https://img.shields.io/badge/arXiv-2602.05723-b31b1b.svg" alt="arXiv" /></a> |
+| **[Graph-based Agent Memory: Taxonomy, Techniques, and Applications](https://arxiv.org/pdf/2602.05665v1)** - Surveys graph-based memory architectures for agents, covering extraction, storage, retrieval, and how memory evolves over time. | <a href="https://arxiv.org/abs/2602.05665v1"><img src="https://img.shields.io/badge/arXiv-2602.05665-b31b1b.svg" alt="arXiv" /></a> |
+| **[AI Agent Systems for Supply Chains: Structured Decision Prompts and Memory Retrieval](https://arxiv.org/pdf/2602.05524v1)** - Proposes a multi-agent system for inventory management that retrieves similar past decisions to adapt ordering across various supply chain scenarios. | <a href="https://arxiv.org/abs/2602.05524v1"><img src="https://img.shields.io/badge/arXiv-2602.05524-b31b1b.svg" alt="arXiv" /></a> |
+| **[SOPRAG: Multi-view Graph Experts Retrieval for Industrial Standard Operating Procedures](https://arxiv.org/pdf/2602.01858v1)** - Explores replacing flat chunk-based RAG with graph experts that understand entity relationships, causality, and process flows for structured documents like SOPs. | <a href="https://arxiv.org/abs/2602.01858v1"><img src="https://img.shields.io/badge/arXiv-2602.01858-b31b1b.svg" alt="arXiv" /></a> |
+| **[ProcMEM: Learning Reusable Procedural Memory from Experience via Non-Parametric PPO for LLM Agents](https://arxiv.org/pdf/2602.01869v1)** - Investigates letting agents save step-by-step procedural skills from past runs and reuse them later without retraining to reduce repeated computation. | <a href="https://arxiv.org/abs/2602.01869v1"><img src="https://img.shields.io/badge/arXiv-2602.01869-b31b1b.svg" alt="arXiv" /></a> |
+| **[Aggregation Queries over Unstructured Text: Benchmark and Agentic Method](https://arxiv.org/pdf/2602.01355v2)** - Proposes an agentic method for aggregation queries over unstructured text that tries to find all matching evidence, breaking the task into disambiguation, filtering, and aggregation stages. | <a href="http://arxiv.org/abs/2602.01355v2"><img src="https://img.shields.io/badge/arXiv-2602.01355-b31b1b.svg" alt="arXiv" /></a> |
+| **[DIVERGE: Diversity-Enhanced RAG for Open-Ended Information Seeking](https://arxiv.org/pdf/2602.00238v1)** - Proposes an agentic RAG framework that uses reflection and memory-based refinement to generate diverse answers for open-ended questions. | <a href="http://arxiv.org/abs/2602.00238v1"><img src="https://img.shields.io/badge/arXiv-2602.00238-b31b1b.svg" alt="arXiv" /></a> |
+| **[JADE: Bridging the Strategic-Operational Gap in Dynamic Agentic RAG](https://arxiv.org/pdf/2601.21916v1)** - Proposes joint optimization of planning and execution in agentic RAG by modeling the system as a cooperative multi-agent team with shared backbone and outcome-based rewards. | <a href="http://arxiv.org/abs/2601.21916v1"><img src="https://img.shields.io/badge/arXiv-2601.21916-b31b1b.svg" alt="arXiv" /></a> |
+| **[ProRAG: Process-Supervised Reinforcement Learning for Retrieval-Augmented Generation](https://arxiv.org/pdf/2601.21912v1)** - Proposes process-supervised reinforcement learning for RAG that uses MCTS-based step-level rewards to identify and fix flawed reasoning steps in multi-hop retrieval. | <a href="http://arxiv.org/abs/2601.21912v1"><img src="https://img.shields.io/badge/arXiv-2601.21912-b31b1b.svg" alt="arXiv" /></a> |
+| **[E-mem: Multi-agent based Episodic Context Reconstruction for LLM Agent Memory](https://arxiv.org/pdf/2601.21714v1)** - Introduces an episodic memory framework where assistant agents maintain uncompressed memory contexts while a master agent orchestrates global planning, replacing destructive memory compression with context reconstruction. | <a href="http://arxiv.org/abs/2601.21714v1"><img src="https://img.shields.io/badge/arXiv-2601.21714-b31b1b.svg" alt="arXiv" /></a> |
+| **[ShardMemo: Masked MoE Routing for Sharded Agentic LLM Memory](https://arxiv.org/pdf/2601.21545v1)** - Proposes a tiered memory service for agentic LLM systems that uses masked mixture-of-experts routing to probe only eligible memory shards under a fixed budget. | <a href="http://arxiv.org/abs/2601.21545v1"><img src="https://img.shields.io/badge/arXiv-2601.21545-b31b1b.svg" alt="arXiv" /></a> |
+| **[When should I search more: Adaptive Complex Query Optimization with Reinforcement Learning](https://arxiv.org/pdf/2601.21208v1)** - Explores adaptive query optimization in RAG using reinforcement learning to dynamically decide when to split complex queries into sub-queries and fuse the retrieved results. | <a href="http://arxiv.org/abs/2601.21208v1"><img src="https://img.shields.io/badge/arXiv-2601.21208-b31b1b.svg" alt="arXiv" /></a> |
+| **[A2RAG: Adaptive Agentic Graph Retrieval for Cost-Aware and Reliable Reasoning](https://arxiv.org/pdf/2601.21162v1)** - Introduces an adaptive agentic Graph-RAG framework that verifies evidence sufficiency and progressively escalates retrieval effort, mapping graph signals back to source text to handle extraction loss. | <a href="http://arxiv.org/abs/2601.21162v1"><img src="https://img.shields.io/badge/arXiv-2601.21162-b31b1b.svg" alt="arXiv" /></a> |
+| **[MemCtrl: Using MLLMs as Active Memory Controllers on Embodied Agents](https://arxiv.org/pdf/2601.20831v1)** - Investigates augmenting multimodal LLMs with a trainable memory gate that decides which observations to retain, update, or discard during online embodied agent exploration. | <a href="http://arxiv.org/abs/2601.20831v1"><img src="https://img.shields.io/badge/arXiv-2601.20831-b31b1b.svg" alt="arXiv" /></a> |
+| **[AMA: Adaptive Memory via Multi-Agent Collaboration](https://arxiv.org/pdf/2601.20352v2)** - Proposes a multi-agent memory framework with hierarchical granularity, adaptive query routing, consistency verification, and targeted memory refresh for long-term agent interaction. | <a href="http://arxiv.org/abs/2601.20352v2"><img src="https://img.shields.io/badge/arXiv-2601.20352-b31b1b.svg" alt="arXiv" /></a> |
+| **[When Iterative RAG Beats Ideal Evidence: A Diagnostic Study in Scientific Multi-hop Question Answering](https://arxiv.org/pdf/2601.19827v2)** - Examines when iterative retrieval-reasoning loops outperform static gold-context RAG in scientific multi-hop QA, diagnosing failure modes across retrieval coverage, hypothesis drift, and stopping calibration. | <a href="http://arxiv.org/abs/2601.19827v2"><img src="https://img.shields.io/badge/arXiv-2601.19827-b31b1b.svg" alt="arXiv" /></a> |
+| **[Dep-Search: Learning Dependency-Aware Reasoning Traces with Persistent Memory](https://arxiv.org/pdf/2601.18771v1)** - Introduces a dependency-aware search framework that uses GRPO reinforcement learning to teach LLMs to decompose questions with dependency relationships and store intermediate results in persistent memory. | <a href="http://arxiv.org/abs/2601.18771v1"><img src="https://img.shields.io/badge/arXiv-2601.18771-b31b1b.svg" alt="arXiv" /></a> |
+| **[FadeMem: Biologically-Inspired Forgetting for Efficient Agent Memory](https://arxiv.org/pdf/2601.18642v2)** - Proposes a biologically-inspired agent memory architecture with adaptive exponential decay, LLM-guided conflict resolution, and intelligent memory fusion across a dual-layer hierarchy. | <a href="http://arxiv.org/abs/2601.18642v2"><img src="https://img.shields.io/badge/arXiv-2601.18642-b31b1b.svg" alt="arXiv" /></a> |
+| **[FastInsight: Fast and Insightful Retrieval via Fusion Operators for Graph RAG](https://arxiv.org/pdf/2601.18579v1)** - Explores two fusion operators for Graph RAG that combine graph-aware reranking with semantic-topological expansion to improve retrieval accuracy and generation quality. | <a href="http://arxiv.org/abs/2601.18579v1"><img src="https://img.shields.io/badge/arXiv-2601.18579-b31b1b.svg" alt="arXiv" /></a> |
+| **[Less is More for RAG: Information Gain Pruning for Generator-Aligned Reranking and Evidence Selection](https://arxiv.org/pdf/2601.17532v1)** - Proposes a generator-aligned reranking and pruning module for RAG that selects evidence using utility signals and filters weak or harmful passages before context truncation. | <a href="http://arxiv.org/abs/2601.17532v1"><img src="https://img.shields.io/badge/arXiv-2601.17532-b31b1b.svg" alt="arXiv" /></a> |
+| **[DeepEra: A Deep Evidence Reranking Agent for Scientific Retrieval-Augmented Generated Question Answering](https://arxiv.org/pdf/2601.16478v1)** - Introduces a step-by-step reasoning reranking agent for RAG that distinguishes semantically similar but logically irrelevant passages in retrieval-augmented question answering. | <a href="http://arxiv.org/abs/2601.16478v1"><img src="https://img.shields.io/badge/arXiv-2601.16478-b31b1b.svg" alt="arXiv" /></a> |
+| **[SPARC-RAG: Adaptive Sequential-Parallel Scaling with Context Management for Retrieval-Augmented Generation](https://arxiv.org/pdf/2602.00083v1)** - Introduces a multi-agent RAG framework that coordinates sequential and parallel inference-time scaling under unified context management to prevent contamination and improve multi-hop reasoning. | <a href="http://arxiv.org/abs/2602.00083v1"><img src="https://img.shields.io/badge/arXiv-2602.00083-b31b1b.svg" alt="arXiv" /></a> |
+| **[Incorporating Q&A Nuggets into Retrieval-Augmented Generation](https://arxiv.org/pdf/2601.13222v1)** - Proposes a nugget-augmented generation system that constructs a bank of Q&A nuggets from retrieved documents to guide extraction, selection, and report generation with citation provenance. | <a href="http://arxiv.org/abs/2601.13222v1"><img src="https://img.shields.io/badge/arXiv-2601.13222-b31b1b.svg" alt="arXiv" /></a> |
+| **[Augmenting Question Answering with A Hybrid RAG Approach](https://arxiv.org/pdf/2601.12658v2)** - Introduces a hybrid RAG architecture combining query augmentation, agentic routing, and structured retrieval that merges vector and graph-based techniques with context unification for question answering. | <a href="http://arxiv.org/abs/2601.12658v2"><img src="https://img.shields.io/badge/arXiv-2601.12658-b31b1b.svg" alt="arXiv" /></a> |
+| **[Utilizing Metadata for Better Retrieval-Augmented Generation](https://arxiv.org/pdf/2601.11863v1)** - Presents a systematic study of metadata-aware retrieval strategies for RAG, comparing prefix, suffix, unified embedding, and late-fusion approaches with field-level ablations on embedding space structure. | <a href="http://arxiv.org/abs/2601.11863v1"><img src="https://img.shields.io/badge/arXiv-2601.11863-b31b1b.svg" alt="arXiv" /></a> |
+| **[Deep GraphRAG: A Balanced Approach to Hierarchical Retrieval and Adaptive Integration](https://arxiv.org/pdf/2601.11144v3)** - Proposes a hierarchical global-to-local retrieval strategy for GraphRAG with beam search-optimized re-ranking and a compact LLM integration module trained via dynamic-weighting reinforcement learning. | <a href="http://arxiv.org/abs/2601.11144v3"><img src="https://img.shields.io/badge/arXiv-2601.11144-b31b1b.svg" alt="arXiv" /></a> |
+| **[Grounding Agent Memory in Contextual Intent](https://arxiv.org/pdf/2601.10702v1)** - Introduces an agentic memory system that indexes trajectory steps with structured contextual intent cues and retrieves history by intent compatibility to reduce interference in long-horizon goal-oriented interactions. | <a href="http://arxiv.org/abs/2601.10702v1"><img src="https://img.shields.io/badge/arXiv-2601.10702-b31b1b.svg" alt="arXiv" /></a> |
+| **[Structure and Diversity Aware Context Bubble Construction for Enterprise Retrieval Augmented Systems](https://arxiv.org/pdf/2601.10681v1)** - Proposes a structure-informed and diversity-constrained context bubble construction framework for RAG that preserves document structure and balances relevance, coverage, and redundancy under strict token budgets. | <a href="http://arxiv.org/abs/2601.10681v1"><img src="https://img.shields.io/badge/arXiv-2601.10681-b31b1b.svg" alt="arXiv" /></a> |
+| **[Topo-RAG: Topology-aware retrieval for hybrid text-table documents](https://arxiv.org/pdf/2601.10215v1)** - Introduces a dual-architecture RAG framework that routes narrative through dense retrievers and tabular data through a cell-aware late interaction mechanism to preserve spatial relationships in hybrid documents. | <a href="http://arxiv.org/abs/2601.10215v1"><img src="https://img.shields.io/badge/arXiv-2601.10215-b31b1b.svg" alt="arXiv" /></a> |
+| **[Continuum Memory Architectures for Long-Horizon LLM Agents](https://arxiv.org/pdf/2601.09913v1)** - Defines a class of memory systems for long-horizon agents that maintain persistent, temporally chained internal state instead of stateless RAG lookups, specifying the architectural requirements they must satisfy. | <a href="https://arxiv.org/abs/2601.09913v1"><img src="https://img.shields.io/badge/arXiv-2601.09913-b31b1b.svg" alt="arXiv" /></a> |
+| **[Rethinking Memory Mechanisms of Foundation Agents in the Second Half: A Survey](https://arxiv.org/pdf/2602.06052v3)** - Surveys foundation agent memory organized by substrate (internal/external), cognitive mechanism (episodic, semantic, working, procedural), and subject (agent- vs user-centric). | <a href="https://arxiv.org/abs/2602.06052v3"><img src="https://img.shields.io/badge/arXiv-2602.06052-b31b1b.svg" alt="arXiv" /></a> |
+| **[The AI Hippocampus: How Far are We From Human Memory?](https://arxiv.org/pdf/2601.09113v1)** - Surveys memory in LLMs and multimodal LLMs across implicit, explicit, and agentic paradigms, covering cross-modal integration and challenges like capacity, alignment, and factual consistency. | <a href="https://arxiv.org/abs/2601.09113v1"><img src="https://img.shields.io/badge/arXiv-2601.09113-b31b1b.svg" alt="arXiv" /></a> |
+| **[AtomMem: Learnable Dynamic Agentic Memory with Atomic Memory Operation](https://arxiv.org/pdf/2601.08323v2)** - Decomposes memory management into atomic CRUD operations and learns an autonomous policy via SFT + RL to study whether learnable memory outperforms static-workflow methods on long-context tasks. | <a href="https://arxiv.org/abs/2601.08323v2"><img src="https://img.shields.io/badge/arXiv-2601.08323-b31b1b.svg" alt="arXiv" /></a> |
+| **[OpenDecoder: Open LLM Decoding to Incorporate Document Quality in RAG](https://arxiv.org/pdf/2601.09028v2)** - Feeds explicit document quality signals (relevance score, ranking, QPP) into RAG generation to study whether exposing retrieval metadata makes the model more robust to noisy context. | <a href="https://arxiv.org/abs/2601.09028v2"><img src="https://img.shields.io/badge/arXiv-2601.09028-b31b1b.svg" alt="arXiv" /></a> |
+| **[Reliable Graph-RAG for Codebases: AST-Derived Graphs vs LLM-Extracted Knowledge Graphs](https://arxiv.org/pdf/2601.08773v1)** - Benchmarks vector-only, LLM-extracted KG, and AST-derived graph pipelines for code RAG, comparing correctness and indexing cost across deterministic and LLM-based graph construction. | <a href="https://arxiv.org/abs/2601.08773v1"><img src="https://img.shields.io/badge/arXiv-2601.08773-b31b1b.svg" alt="arXiv" /></a> |
+| **[To Retrieve or To Think? An Agentic Approach for Context Evolution](https://arxiv.org/pdf/2601.08747v2)** - Proposes an agentic RAG framework that dynamically decides whether to retrieve new evidence or reason over existing context at each step, aiming to eliminate redundant retrieval. | <a href="https://arxiv.org/abs/2601.08747v2"><img src="https://img.shields.io/badge/arXiv-2601.08747-b31b1b.svg" alt="arXiv" /></a> |
+| **[Parallel Context-of-Experts Decoding for Retrieval Augmented Generation](https://arxiv.org/pdf/2601.08670v1)** - Proposes a training-free RAG decoding method that treats retrieved documents as isolated "experts" and aggregates their logits via retrieval-aware contrastive decoding to recover cross-document reasoning. | <a href="https://arxiv.org/abs/2601.08670v1"><img src="https://img.shields.io/badge/arXiv-2601.08670-b31b1b.svg" alt="arXiv" /></a> |
+| **[SwiftMem: Fast Agentic Memory via Query-aware Indexing](https://arxiv.org/pdf/2601.08160v1)** - Proposes a query-aware agentic memory system that achieves sub-linear retrieval through temporal and semantic DAG-Tag indexing with an embedding-tag co-consolidation mechanism for memory fragmentation. | <a href="https://arxiv.org/abs/2601.08160v1"><img src="https://img.shields.io/badge/arXiv-2601.08160-b31b1b.svg" alt="arXiv" /></a> |
+| **[Learning How to Remember: A Meta-Cognitive Management Method for Structured and Transferable Agent Memory](https://arxiv.org/pdf/2601.07470v1)** - Proposes treating memory abstraction as a learnable cognitive skill, training a memory copilot via DPO to determine how memories should be structured, abstracted, and reused across tasks. | <a href="https://arxiv.org/abs/2601.07470v1"><img src="https://img.shields.io/badge/arXiv-2601.07470-b31b1b.svg" alt="arXiv" /></a> |
+| **[Beyond Dialogue Time: Temporal Semantic Memory for Personalized LLM Agents](https://arxiv.org/pdf/2601.07468v1)** - Introduces a temporal semantic memory framework that organizes memories by actual occurrence time rather than dialogue time and consolidates temporally continuous information into durative memory. | <a href="https://arxiv.org/abs/2601.07468v1"><img src="https://img.shields.io/badge/arXiv-2601.07468-b31b1b.svg" alt="arXiv" /></a> |
+| **[Active Context Compression: Autonomous Memory Management in LLM Agents](https://arxiv.org/pdf/2601.07190v1)** - Proposes an agent-centric architecture inspired by Physarum polycephalum where the agent autonomously decides when to consolidate learnings and prune raw interaction history to manage context growth. | <a href="https://arxiv.org/abs/2601.07190v1"><img src="https://img.shields.io/badge/arXiv-2601.07190-b31b1b.svg" alt="arXiv" /></a> |
+| **[Relink: Constructing Query-Driven Evidence Graph On-the-Fly for GraphRAG](https://arxiv.org/pdf/2601.07192v1)** - Proposes a reason-and-construct paradigm for GraphRAG that dynamically builds query-specific evidence graphs by instantiating facts from a latent relation pool and discarding distractor facts. | <a href="https://arxiv.org/abs/2601.07192v1"><img src="https://img.shields.io/badge/arXiv-2601.07192-b31b1b.svg" alt="arXiv" /></a> |
+| **[Seeing through the Conflict: Transparent Knowledge Conflict Handling in RAG](https://arxiv.org/pdf/2601.06842v1)** - Introduces a plug-and-play RAG framework that disentangles semantic match from factual consistency and estimates self-answerability to make the conflict-resolution decision process observable and controllable. | <a href="https://arxiv.org/abs/2601.06842v1"><img src="https://img.shields.io/badge/arXiv-2601.06842-b31b1b.svg" alt="arXiv" /></a> |
+| **[CIRAG: Construction-Integration Retrieval and Adaptive Generation for Multi-hop Question Answering](https://arxiv.org/pdf/2601.06799v1)** - Proposes a construction-integration approach for multi-hop RAG that preserves multiple evidence chains via iterative triple construction and adaptively expands context granularity from triples to full passages. | <a href="https://arxiv.org/abs/2601.06799v1"><img src="https://img.shields.io/badge/arXiv-2601.06799-b31b1b.svg" alt="arXiv" /></a> |
+| **[Amory: Building Coherent Narrative-Driven Agent Memory through Agentic Reasoning](https://arxiv.org/pdf/2601.06282v1)** - Proposes a working memory framework that constructs structured episodic narratives from conversational fragments, consolidates memories with momentum, and semanticizes peripheral facts into semantic memory during offline time. | <a href="https://arxiv.org/abs/2601.06282v1"><img src="https://img.shields.io/badge/arXiv-2601.06282-b31b1b.svg" alt="arXiv" /></a> |
+| **[L-RAG: Balancing Context and Retrieval with Entropy-Based Lazy Loading](https://arxiv.org/pdf/2601.06551v1)** - Proposes an adaptive RAG framework that uses entropy-based gating to bypass vector database retrieval when model uncertainty is low, triggering expensive chunk retrieval only when genuine uncertainty is detected. | <a href="https://arxiv.org/abs/2601.06551v1"><img src="https://img.shields.io/badge/arXiv-2601.06551-b31b1b.svg" alt="arXiv" /></a> |
+| **[PRISMA: Reinforcement Learning Guided Two-Stage Policy Optimization in Multi-Agent Architecture for Open-Domain Multi-Hop QA](https://arxiv.org/pdf/2601.05465v1)** - Proposes a decoupled multi-agent RAG framework for multi-hop QA with a Plan-Retrieve-Inspect-Solve-Memoize architecture and two-stage GRPO optimization to address retrieval collapse over large corpora. | <a href="https://arxiv.org/abs/2601.05465v1"><img src="https://img.shields.io/badge/arXiv-2601.05465-b31b1b.svg" alt="arXiv" /></a> |
+| **[Controllable Memory Usage: Balancing Anchoring and Innovation in Long-Term Human-Agent Interaction](https://arxiv.org/pdf/2601.05107v1)** - Proposes a framework for user-controllable memory reliance in long-term agent interactions, modeling memory dependence as an explicit and steerable dimension. | <a href="https://arxiv.org/abs/2601.05107v1"><img src="https://img.shields.io/badge/arXiv-2601.05107-b31b1b.svg" alt="arXiv" /></a> |
+| **[Beyond Static Summarization: Proactive Memory Extraction for LLM Agents](https://arxiv.org/pdf/2601.04463v1)** - Proposes proactive memory extraction using self-questioning feedback loops instead of one-off static summarization to recover missing information and correct errors iteratively. | <a href="https://arxiv.org/abs/2601.04463v1"><img src="https://img.shields.io/badge/arXiv-2601.04463-b31b1b.svg" alt="arXiv" /></a> |
+| **[Membox: Weaving Topic Continuity into Long-Range Memory for LLM Agents](https://arxiv.org/pdf/2601.03785v2)** - Proposes a hierarchical memory architecture with a Topic Loom that groups consecutive same-topic dialogue turns into coherent memory boxes and links them via long-range event-timeline traces. | <a href="https://arxiv.org/abs/2601.03785v2"><img src="https://img.shields.io/badge/arXiv-2601.03785-b31b1b.svg" alt="arXiv" /></a> |
+| **[MAGMA: A Multi-Graph based Agentic Memory Architecture](https://arxiv.org/pdf/2601.03236v1)** - Proposes a multi-graph agentic memory architecture that represents memories across orthogonal semantic, temporal, causal, and entity graphs with policy-guided traversal for retrieval. | <a href="https://arxiv.org/abs/2601.03236v1"><img src="https://img.shields.io/badge/arXiv-2601.03236-b31b1b.svg" alt="arXiv" /></a> |
+| **[HiMeS: Hippocampus-inspired Memory System for Personalized AI Assistants](https://arxiv.org/pdf/2601.06152v1)** - Proposes a hippocampus-inspired memory architecture for AI assistants that fuses RL-trained short-term memory extraction with partitioned long-term memory for personalization. | <a href="https://arxiv.org/abs/2601.06152v1"><img src="https://img.shields.io/badge/arXiv-2601.06152-b31b1b.svg" alt="arXiv" /></a> |
+| **[SimpleMem: Efficient Lifelong Memory for LLM Agents](https://arxiv.org/pdf/2601.02553v3)** - Proposes a three-stage memory framework based on semantic lossless compression with structured compression, online semantic synthesis, and intent-aware retrieval planning. | <a href="https://arxiv.org/abs/2601.02553v3"><img src="https://img.shields.io/badge/arXiv-2601.02553-b31b1b.svg" alt="arXiv" /></a> |
+
+</details>
+
+<br>
+
+<details id="eval--observability">
+<summary><h3 style="display:inline">Eval & Observability (80)</h3></summary>
+
+<br>
+
+| Paper | arXiv ID |
+|---|:---:|
+| **[ClawBench: Evaluating Browser Agents on Live Production Websites with Submission-Interception](https://arxiv.org/abs/2604.08523)** - Benchmarks browser agents on 283 everyday tasks (V1 153 + V2 130) across 163 live production sites, with a Chrome-extension plus CDP layer that blocks only the final write request so agents can run end-to-end on real sites without real-world side effects. Two-stage scoring (interception + LLM judge); leaderboard at https://claw-bench.com. | <a href="https://arxiv.org/abs/2604.08523"><img src="https://img.shields.io/badge/arXiv-2604.08523-b31b1b.svg" alt="arXiv" /></a> |
+| **[From Features to Actions: Explainability in Traditional and Agentic AI Systems](https://arxiv.org/pdf/2602.06841v1)** - Compares attribution-based explanations with trace-based diagnostics across static and agentic settings to study how explainability methods translate to multi-step agent trajectories. | <a href="https://arxiv.org/abs/2602.06841v1"><img src="https://img.shields.io/badge/arXiv-2602.06841-b31b1b.svg" alt="arXiv" /></a> |
+| **[Agentic Uncertainty Reveals Agentic Overconfidence](https://arxiv.org/pdf/2602.06948v1)** - Investigates whether agents can accurately predict their own success rates in agentic tasks. | <a href="https://arxiv.org/abs/2602.06948v1"><img src="https://img.shields.io/badge/arXiv-2602.06948-b31b1b.svg" alt="arXiv" /></a> |
+| **[AIRS-Bench: a Suite of Tasks for Frontier AI Research Science Agents](https://arxiv.org/pdf/2602.06855v1)** - Introduces 20 research tasks from real ML papers covering idea generation, experiments, and refinement for benchmarking science agents. | <a href="https://arxiv.org/abs/2602.06855v1"><img src="https://img.shields.io/badge/arXiv-2602.06855-b31b1b.svg" alt="arXiv" /></a> |
+| **[JADE: Expert-Grounded Dynamic Evaluation for Open-Ended Professional Tasks](https://arxiv.org/pdf/2602.06486v1)** - Proposes evaluating agent outputs by decomposing responses into individual claims and checking each against expert knowledge. | <a href="https://arxiv.org/abs/2602.06486v1"><img src="https://img.shields.io/badge/arXiv-2602.06486-b31b1b.svg" alt="arXiv" /></a> |
+| **[Completing Missing Annotation: Multi-Agent Debate for Accurate Relevant Assessment](https://arxiv.org/pdf/2602.06526v1)** - Explores using multi-agent debate to fill missing labels in information retrieval benchmarks. | <a href="https://arxiv.org/abs/2602.06526v1"><img src="https://img.shields.io/badge/arXiv-2602.06526-b31b1b.svg" alt="arXiv" /></a> |
+| **[TrajAD: Trajectory Anomaly Detection for Trustworthy LLM Agents](https://arxiv.org/pdf/2602.06443v1)** - Proposes a specialized verifier that detects and locates errors in agent execution trajectories at runtime to enable precise rollback-and-retry. | <a href="https://arxiv.org/abs/2602.06443v1"><img src="https://img.shields.io/badge/arXiv-2602.06443-b31b1b.svg" alt="arXiv" /></a> |
+| **[Emulating Aggregate Human Choice Behavior and Biases with GPT Conversational Agents](https://arxiv.org/pdf/2602.05597v1)** - Examines whether GPT-4/5 agents can reproduce aggregate human cognitive biases in interactive decision-making scenarios. | <a href="https://arxiv.org/abs/2602.05597v1"><img src="https://img.shields.io/badge/arXiv-2602.05597-b31b1b.svg" alt="arXiv" /></a> |
+| **[Capture the Flags: Family-Based Evaluation of Agentic LLMs](https://arxiv.org/pdf/2602.05523v1)** - Proposes generating families of equivalent CTF challenges through code transformations to test whether agents truly understand exploits or just memorize patterns. | <a href="https://arxiv.org/abs/2602.05523v1"><img src="https://img.shields.io/badge/arXiv-2602.05523-b31b1b.svg" alt="arXiv" /></a> |
+| **[PieArena: Frontier Language Agents Achieve MBA-Level Negotiation](https://arxiv.org/pdf/2602.05302v1)** - Introduces a negotiation benchmark where frontier LLM agents are evaluated against MBA students to reveal cross-model differences in deception, accuracy, and trustworthiness. | <a href="https://arxiv.org/abs/2602.05302v1"><img src="https://img.shields.io/badge/arXiv-2602.05302-b31b1b.svg" alt="arXiv" /></a> |
+| **[ES-MemEval: Benchmarking Conversational Agents on Personalized Long-Term Emotional Support](https://arxiv.org/pdf/2602.01885v1)** - Benchmarks how well conversational agents retain and use personal information over long emotional support conversations. | <a href="https://arxiv.org/abs/2602.01885v1"><img src="https://img.shields.io/badge/arXiv-2602.01885-b31b1b.svg" alt="arXiv" /></a> |
+| **[HumanStudy-Bench: Towards AI Agent Design for Participant Simulation](https://arxiv.org/pdf/2602.00685v1)** - Introduces a benchmark that replays published human-subject experiments with LLM agents to test how well they simulate real participants. | <a href="http://arxiv.org/abs/2602.00685v1"><img src="https://img.shields.io/badge/arXiv-2602.00685-b31b1b.svg" alt="arXiv" /></a> |
+| **[Benchmarking Agents in Insurance Underwriting Environments](https://arxiv.org/pdf/2602.00456v1)** - Proposes an expert-designed multi-turn insurance underwriting benchmark to evaluate agent performance under real-world enterprise conditions with noisy tools and proprietary knowledge. | <a href="http://arxiv.org/abs/2602.00456v1"><img src="https://img.shields.io/badge/arXiv-2602.00456-b31b1b.svg" alt="arXiv" /></a> |
+| **[TriCEGAR: A Trace-Driven Abstraction Mechanism for Agentic AI](https://arxiv.org/pdf/2601.22997v1)** - Proposes automated state abstraction from agent execution traces using predicate trees and counterexample refinement for probabilistic runtime verification of agent behavior. | <a href="http://arxiv.org/abs/2601.22997v1"><img src="https://img.shields.io/badge/arXiv-2601.22997-b31b1b.svg" alt="arXiv" /></a> |
+| **[Sifting the Noise: A Comparative Study of LLM Agents in Vulnerability False Positive Filtering](https://arxiv.org/pdf/2601.22952v1)** - Compares three LLM agent frameworks (Aider, OpenHands, SWE-agent) on vulnerability false positive filtering to study how agent design and backbone model affect triage performance. | <a href="http://arxiv.org/abs/2601.22952v1"><img src="https://img.shields.io/badge/arXiv-2601.22952-b31b1b.svg" alt="arXiv" /></a> |
+| **[Why Are AI Agent Involved Pull Requests (Fix-Related) Remain Unmerged? An Empirical Study](https://arxiv.org/pdf/2602.00164v1)** - Analyzes 8,106 fix-related pull requests from five AI coding agents to catalog the reasons agent-generated contributions are closed without merging. | <a href="http://arxiv.org/abs/2602.00164v1"><img src="https://img.shields.io/badge/arXiv-2602.00164-b31b1b.svg" alt="arXiv" /></a> |
+| **[JAF: Judge Agent Forest](https://arxiv.org/pdf/2601.22269v1)** - Proposes a judge agent framework that evaluates query-response pairs jointly across a cohort rather than in isolation, using in-context neighborhoods for cross-instance pattern detection. | <a href="http://arxiv.org/abs/2601.22269v1"><img src="https://img.shields.io/badge/arXiv-2601.22269-b31b1b.svg" alt="arXiv" /></a> |
+| **[Stalled, Biased, and Confused: Uncovering Reasoning Failures in LLMs for Cloud-Based Root Cause Analysis](https://arxiv.org/pdf/2601.22208v1)** - Evaluates LLM reasoning under ReAct and Plan-and-Execute agentic workflows across 48,000 simulated failure scenarios, producing a taxonomy of 16 common reasoning failures. | <a href="http://arxiv.org/abs/2601.22208v1"><img src="https://img.shields.io/badge/arXiv-2601.22208-b31b1b.svg" alt="arXiv" /></a> |
+| **[CAR-bench: Evaluating the Consistency and Limit-Awareness of LLM Agents under Real-World Uncertainty](https://arxiv.org/pdf/2601.22027v1)** - Introduces a benchmark for evaluating LLM agent consistency, uncertainty handling, and capability awareness in multi-turn tool-using scenarios with incomplete or ambiguous user requests. | <a href="http://arxiv.org/abs/2601.22027v1"><img src="https://img.shields.io/badge/arXiv-2601.22027-b31b1b.svg" alt="arXiv" /></a> |
+| **[More Code, Less Reuse: Investigating Code Quality and Reviewer Sentiment towards AI-generated Pull Requests](https://arxiv.org/pdf/2601.21276v1)** - Examines code quality, maintainability, and reviewer sentiment toward AI-agent-generated pull requests compared to human-authored contributions. | <a href="http://arxiv.org/abs/2601.21276v1"><img src="https://img.shields.io/badge/arXiv-2601.21276-b31b1b.svg" alt="arXiv" /></a> |
+| **[The Quiet Contributions: Insights into AI-Generated Silent Pull Requests](https://arxiv.org/pdf/2601.21102v1)** - Analyzes silent (no-comment) AI-generated pull requests to examine their impact on code complexity, quality issues, and security vulnerabilities. | <a href="http://arxiv.org/abs/2601.21102v1"><img src="https://img.shields.io/badge/arXiv-2601.21102-b31b1b.svg" alt="arXiv" /></a> |
+| **[Agent Benchmarks Fail Public Sector Requirements](https://arxiv.org/pdf/2601.20617v1)** - Analyzes over 1,300 agent benchmarks against public-sector requirements including process-based evaluation, realism, and domain-specific metrics. | <a href="http://arxiv.org/abs/2601.20617v1"><img src="https://img.shields.io/badge/arXiv-2601.20617-b31b1b.svg" alt="arXiv" /></a> |
+| **[Interpreting Emergent Extreme Events in Multi-Agent Systems](https://arxiv.org/pdf/2601.20538v1)** - Applies Shapley values to attribute emergent extreme events in LLM multi-agent systems to specific agent actions across time, agent, and behavior dimensions. | <a href="http://arxiv.org/abs/2601.20538v1"><img src="https://img.shields.io/badge/arXiv-2601.20538-b31b1b.svg" alt="arXiv" /></a> |
+| **[Who Writes the Docs in SE 3.0? Agent vs. Human Documentation Pull Requests](https://arxiv.org/pdf/2601.20171v1)** - Analyzes AI agent contributions to documentation pull requests and examines how human developers review and intervene in agent-authored documentation changes. | <a href="http://arxiv.org/abs/2601.20171v1"><img src="https://img.shields.io/badge/arXiv-2601.20171-b31b1b.svg" alt="arXiv" /></a> |
+| **[Are We All Using Agents the Same Way? An Empirical Study of Core and Peripheral Developers Use of Coding Agents](https://arxiv.org/pdf/2601.20106v1)** - Examines how core and peripheral developers differ in their use, review, modification, and verification of coding-agent-generated pull requests. | <a href="http://arxiv.org/abs/2601.20106v1"><img src="https://img.shields.io/badge/arXiv-2601.20106-b31b1b.svg" alt="arXiv" /></a> |
+| **[DevOps-Gym: Benchmarking AI Agents in Software DevOps Cycle](https://arxiv.org/pdf/2601.20882v1)** - Introduces an end-to-end benchmark with 700+ real-world tasks across build, monitoring, issue resolving, and test generation for evaluating AI agents in full software DevOps workflows. | <a href="http://arxiv.org/abs/2601.20882v1"><img src="https://img.shields.io/badge/arXiv-2601.20882-b31b1b.svg" alt="arXiv" /></a> |
+| **[Toward Architecture-Aware Evaluation Metrics for LLM Agents](https://arxiv.org/pdf/2601.19583v1)** - Proposes an architecture-informed evaluation approach that links agent components like planners, memory, and tool routers to observable behaviors and diagnostic metrics. | <a href="http://arxiv.org/abs/2601.19583v1"><img src="https://img.shields.io/badge/arXiv-2601.19583-b31b1b.svg" alt="arXiv" /></a> |
+| **[Balancing Sustainability And Performance: The Role Of Small-Scale LLMs In Agentic AI Systems](https://arxiv.org/pdf/2601.19311v2)** - Investigates whether smaller-scale language models can reduce energy consumption in multi-agent agentic AI systems without compromising task quality. | <a href="http://arxiv.org/abs/2601.19311v2"><img src="https://img.shields.io/badge/arXiv-2601.19311-b31b1b.svg" alt="arXiv" /></a> |
+| **[Understanding Dominant Themes in Reviewing Agentic AI-authored Code](https://arxiv.org/pdf/2601.19287v1)** - Analyzes 19,450 inline review comments on agent-authored pull requests and derives a taxonomy of 12 review themes to understand how reviewers respond to AI-generated code. | <a href="http://arxiv.org/abs/2601.19287v1"><img src="https://img.shields.io/badge/arXiv-2601.19287-b31b1b.svg" alt="arXiv" /></a> |
+| **[Let's Make Every Pull Request Meaningful: An Empirical Analysis of Developer and Agentic Pull Requests](https://arxiv.org/pdf/2601.18749v1)** - Analyzes 40,214 developer and agentic pull requests to compare merge outcomes and identify how submitter attributes and review features differ between human and AI agent contributions. | <a href="http://arxiv.org/abs/2601.18749v1"><img src="https://img.shields.io/badge/arXiv-2601.18749-b31b1b.svg" alt="arXiv" /></a> |
+| **[Automated Structural Testing of LLM-Based Agents: Methods, Framework, and Case Studies](https://arxiv.org/pdf/2601.18827v1)** - Presents structural testing methods for LLM-based agents using OpenTelemetry traces, mocking for reproducible behavior, and automated assertions for component-level verification. | <a href="http://arxiv.org/abs/2601.18827v1"><img src="https://img.shields.io/badge/arXiv-2601.18827-b31b1b.svg" alt="arXiv" /></a> |
+| **[When AI Agents Touch CI/CD Configurations: Frequency and Success](https://arxiv.org/pdf/2601.17413v1)** - Analyzes how five AI coding agents interact with CI/CD configurations across 8,031 pull requests, examining modification frequency, merge rates, and build success. | <a href="http://arxiv.org/abs/2601.17413v1"><img src="https://img.shields.io/badge/arXiv-2601.17413-b31b1b.svg" alt="arXiv" /></a> |
+| **[Fingerprinting AI Coding Agents on GitHub](https://arxiv.org/pdf/2601.17406v1)** - Identifies behavioral signatures of five AI coding agents from 33,580 pull requests using commit, PR structure, and code features for agent attribution. | <a href="http://arxiv.org/abs/2601.17406v1"><img src="https://img.shields.io/badge/arXiv-2601.17406-b31b1b.svg" alt="arXiv" /></a> |
+| **[Interpreting Agentic Systems: Beyond Model Explanations to System-Level Accountability](https://arxiv.org/pdf/2601.17168v1)** - Assesses existing interpretability methods for agentic systems and identifies gaps in explaining temporal dynamics, compounding decisions, and context-dependent behaviors. | <a href="http://arxiv.org/abs/2601.17168v1"><img src="https://img.shields.io/badge/arXiv-2601.17168-b31b1b.svg" alt="arXiv" /></a> |
+| **[AI builds, We Analyze: An Empirical Study of AI-Generated Build Code Quality](https://arxiv.org/pdf/2601.16839v1)** - Investigates maintainability and security-related build code smells in AI-agent-generated pull requests across 364 identified quality issues. | <a href="http://arxiv.org/abs/2601.16839v1"><img src="https://img.shields.io/badge/arXiv-2601.16839-b31b1b.svg" alt="arXiv" /></a> |
+| **[Will It Survive? Deciphering the Fate of AI-Generated Code in Open Source](https://arxiv.org/pdf/2601.16809v1)** - Examines long-term survival of AI-agent-generated code through survival analysis of 200,000+ code units across 201 open-source projects. | <a href="http://arxiv.org/abs/2601.16809v1"><img src="https://img.shields.io/badge/arXiv-2601.16809-b31b1b.svg" alt="arXiv" /></a> |
+| **[LUMINA: Long-horizon Understanding for Multi-turn Interactive Agents](https://arxiv.org/pdf/2601.16649v1)** - Develops an oracle counterfactual framework for multi-turn agentic tasks that measures the criticality of individual capabilities like planning and state tracking. | <a href="http://arxiv.org/abs/2601.16649v1"><img src="https://img.shields.io/badge/arXiv-2601.16649-b31b1b.svg" alt="arXiv" /></a> |
+| **[When Agents Fail to Act: A Diagnostic Framework for Tool Invocation Reliability in Multi-Agent LLM Systems](https://arxiv.org/pdf/2601.16280v1)** - Presents a 12-category error taxonomy and diagnostic framework for evaluating tool-use reliability across open-weight and proprietary LLMs in multi-agent systems on edge hardware. | <a href="http://arxiv.org/abs/2601.16280v1"><img src="https://img.shields.io/badge/arXiv-2601.16280-b31b1b.svg" alt="arXiv" /></a> |
+| **[Agentic Confidence Calibration](https://arxiv.org/pdf/2601.15778v1)** - Introduces the problem of agentic confidence calibration and proposes Holistic Trajectory Calibration, extracting process-level features across an agent's entire trajectory to diagnose failures. | <a href="http://arxiv.org/abs/2601.15778v1"><img src="https://img.shields.io/badge/arXiv-2601.15778-b31b1b.svg" alt="arXiv" /></a> |
+| **[Improving Methodologies for Agentic Evaluations Across Domains: Leakage of Sensitive Information, Fraud and Cybersecurity Threats](https://arxiv.org/pdf/2601.15679v1)** - Examines methodological challenges in evaluating AI agents across sensitive information leakage, fraud, and cybersecurity threats through a multi-national collaborative benchmarking exercise. | <a href="http://arxiv.org/abs/2601.15679v1"><img src="https://img.shields.io/badge/arXiv-2601.15679-b31b1b.svg" alt="arXiv" /></a> |
+| **[MiRAGE: A Multiagent Framework for Generating Multimodal Multihop Question-Answer Dataset for RAG Evaluation](https://arxiv.org/pdf/2601.15487v1)** - Introduces a multi-agent framework that generates verified, domain-specific, multimodal, multi-hop question-answer datasets for benchmarking retrieval-augmented generation systems. | <a href="http://arxiv.org/abs/2601.15487v1"><img src="https://img.shields.io/badge/arXiv-2601.15487-b31b1b.svg" alt="arXiv" /></a> |
+| **[When Agents Fail: A Comprehensive Study of Bugs in LLM Agents with Automated Labeling](https://arxiv.org/pdf/2601.15232v1)** - Analyzes 1,187 bug reports from LLM agent software across seven frameworks to categorize bug types, root causes, effects, and tests automated bug labeling with a ReAct agent. | <a href="http://arxiv.org/abs/2601.15232v1"><img src="https://img.shields.io/badge/arXiv-2601.15232-b31b1b.svg" alt="arXiv" /></a> |
+| **[The Why Behind the Action: Unveiling Internal Drivers via Agentic Attribution](https://arxiv.org/pdf/2601.15075v2)** - Proposes a hierarchical framework for general agentic attribution that identifies internal factors driving agent actions through temporal likelihood dynamics and perturbation-based analysis. | <a href="http://arxiv.org/abs/2601.15075v2"><img src="https://img.shields.io/badge/arXiv-2601.15075-b31b1b.svg" alt="arXiv" /></a> |
+| **[Tokenomics: Quantifying Where Tokens Are Used in Agentic Software Engineering](https://arxiv.org/pdf/2601.14470v1)** - Analyzes token consumption patterns across software development lifecycle stages in a multi-agent system to identify where tokens are consumed and which stages drive cost. | <a href="http://arxiv.org/abs/2601.14470v1"><img src="https://img.shields.io/badge/arXiv-2601.14470-b31b1b.svg" alt="arXiv" /></a> |
+| **[APEX-Agents](https://arxiv.org/pdf/2601.14242v2)** - Introduces a benchmark of 480 long-horizon, cross-application productivity tasks created by investment banking analysts, consultants, and lawyers for evaluating AI agent capabilities in realistic work environments. | <a href="http://arxiv.org/abs/2601.14242v2"><img src="https://img.shields.io/badge/arXiv-2601.14242-b31b1b.svg" alt="arXiv" /></a> |
+| **[CooperBench: Why Coding Agents Cannot be Your Teammates Yet](https://arxiv.org/pdf/2601.13295v2)** - Introduces a benchmark of 600+ collaborative coding tasks to evaluate whether coding agents can coordinate as effective teammates under various coordination structures. | <a href="http://arxiv.org/abs/2601.13295v2"><img src="https://img.shields.io/badge/arXiv-2601.13295-b31b1b.svg" alt="arXiv" /></a> |
+| **[Insider Knowledge: How Much Can RAG Systems Gain from Evaluation Secrets?](https://arxiv.org/pdf/2601.13227v1)** - Investigates how RAG systems can game nugget-based LLM judge evaluations through metric overfitting, demonstrating near-perfect scores when evaluation elements are leaked or predictable. | <a href="http://arxiv.org/abs/2601.13227v1"><img src="https://img.shields.io/badge/arXiv-2601.13227-b31b1b.svg" alt="arXiv" /></a> |
+| **[Replayable Financial Agents: A Determinism-Faithfulness Assurance Harness for Tool-Using LLM Agents](https://arxiv.org/pdf/2601.15322v1)** - Introduces the Determinism-Faithfulness Assurance Harness for measuring trajectory determinism and evidence-conditioned faithfulness in tool-using LLM agents across 74 configurations and 12 models. | <a href="http://arxiv.org/abs/2601.15322v1"><img src="https://img.shields.io/badge/arXiv-2601.15322-b31b1b.svg" alt="arXiv" /></a> |
+| **[AEMA: Verifiable Evaluation Framework for Trustworthy and Controlled Agentic LLM Systems](https://arxiv.org/pdf/2601.11903v1)** - Presents a process-aware and auditable multi-agent evaluation framework that plans, executes, and aggregates multi-step evaluations across heterogeneous agentic workflows under human oversight. | <a href="http://arxiv.org/abs/2601.11903v1"><img src="https://img.shields.io/badge/arXiv-2601.11903-b31b1b.svg" alt="arXiv" /></a> |
+| **[Terminal-Bench: Benchmarking Agents on Hard, Realistic Tasks in Command Line Interfaces](https://arxiv.org/pdf/2601.11868v1)** - Introduces a curated benchmark of 89 hard tasks in computer terminal environments with unique environments, human-written solutions, and comprehensive tests for evaluating frontier agent capabilities. | <a href="http://arxiv.org/abs/2601.11868v1"><img src="https://img.shields.io/badge/arXiv-2601.11868-b31b1b.svg" alt="arXiv" /></a> |
+| **[ATOD: An Evaluation Framework and Benchmark for Agentic Task-Oriented Dialogue Systems](https://arxiv.org/pdf/2601.11854v2)** - Introduces a benchmark and evaluation framework for agentic task-oriented dialogue systems covering multi-goal coordination, dependency management, memory, adaptability, and proactivity. | <a href="http://arxiv.org/abs/2601.11854v2"><img src="https://img.shields.io/badge/arXiv-2601.11854-b31b1b.svg" alt="arXiv" /></a> |
+| **[What Do LLM Agents Know About Their World? Task2Quiz](https://arxiv.org/pdf/2601.09503v1)** - Decouples task execution from environment understanding with a deterministic QA paradigm to study whether task success is actually a good proxy for how well agents understand their environment. | <a href="https://arxiv.org/abs/2601.09503v1"><img src="https://img.shields.io/badge/arXiv-2601.09503-b31b1b.svg" alt="arXiv" /></a> |
+| **[The Hierarchy of Agentic Capabilities: Evaluating Frontier Models on Realistic RL Environments](https://arxiv.org/pdf/2601.09032v1)** - Evaluates frontier models on 150 workplace tasks to identify an empirical hierarchy of agentic capabilities spanning tool use, planning, adaptability, groundedness, and common-sense reasoning. | <a href="https://arxiv.org/abs/2601.09032v1"><img src="https://img.shields.io/badge/arXiv-2601.09032-b31b1b.svg" alt="arXiv" /></a> |
+| **[ViDoRe V3: A Comprehensive Evaluation of RAG in Complex Real-World Scenarios](https://arxiv.org/pdf/2601.08620v1)** - Introduces a multimodal RAG benchmark with 26K pages and 3,099 queries in 6 languages to evaluate retrieval across non-textual elements and open-ended queries. | <a href="https://arxiv.org/abs/2601.08620v1"><img src="https://img.shields.io/badge/arXiv-2601.08620-b31b1b.svg" alt="arXiv" /></a> |
+| **[M3-BENCH: Process-Aware Evaluation of LLM Agents Social Behaviors in Mixed-Motive Games](https://arxiv.org/pdf/2601.08462v1)** - Evaluates LLM agent social behaviors in mixed-motive games using process-aware analysis of both reasoning and communication rather than outcome-only metrics. | <a href="https://arxiv.org/abs/2601.08462v1"><img src="https://img.shields.io/badge/arXiv-2601.08462-b31b1b.svg" alt="arXiv" /></a> |
+| **[Mem2ActBench: A Benchmark for Evaluating Long-Term Memory Utilization in Task-Oriented Autonomous Agents](https://arxiv.org/pdf/2601.19935v1)** - Benchmarks whether agents can proactively use long-term memory to execute tool-based actions, rather than just passively retrieving facts on demand. | <a href="https://arxiv.org/abs/2601.19935v1"><img src="https://img.shields.io/badge/arXiv-2601.19935-b31b1b.svg" alt="arXiv" /></a> |
+| **[Active Evaluation of General Agents: Problem Definition and Comparison of Baseline Algorithms](https://arxiv.org/pdf/2601.07651v2)** - Proposes a formal framework for actively evaluating general-purpose agents across multiple tasks, selecting which tasks and agents to sample next to minimize ranking error over time. | <a href="https://arxiv.org/abs/2601.07651v2"><img src="https://img.shields.io/badge/arXiv-2601.07651-b31b1b.svg" alt="arXiv" /></a> |
+| **[VirtualEnv: A Platform for Embodied AI Research](https://arxiv.org/pdf/2601.07553v2)** - Introduces an Unreal Engine 5 simulation platform for benchmarking LLM-driven agents on embodied tasks including navigation, object manipulation, and multi-agent coordination in procedurally generated environments. | <a href="https://arxiv.org/abs/2601.07553v2"><img src="https://img.shields.io/badge/arXiv-2601.07553-b31b1b.svg" alt="arXiv" /></a> |
+| **[FROAV: A Framework for RAG Observation and Agent Verification](https://arxiv.org/pdf/2601.07504v1)** - Presents an open-source platform combining visual workflow orchestration with LLM-as-a-Judge evaluation for prototyping and validating RAG-based agent pipelines without infrastructure coding. | <a href="https://arxiv.org/abs/2601.07504v1"><img src="https://img.shields.io/badge/arXiv-2601.07504-b31b1b.svg" alt="arXiv" /></a> |
+| **[Lost in the Noise: How Reasoning Models Fail with Contextual Distractors](https://arxiv.org/pdf/2601.07226v1)** - Benchmarks model robustness across 11 RAG, reasoning, alignment, and tool-use tasks against diverse contextual noise types including random documents, irrelevant histories, and hard negative distractors. | <a href="https://arxiv.org/abs/2601.07226v1"><img src="https://img.shields.io/badge/arXiv-2601.07226-b31b1b.svg" alt="arXiv" /></a> |
+| **[RealMem: Benchmarking LLMs in Real-World Memory-Driven Interaction](https://arxiv.org/pdf/2601.06966v1)** - Introduces a project-oriented memory benchmark with 2,000+ cross-session dialogues across eleven scenarios to evaluate how well agents track evolving goals and dynamic context dependencies. | <a href="https://arxiv.org/abs/2601.06966v1"><img src="https://img.shields.io/badge/arXiv-2601.06966-b31b1b.svg" alt="arXiv" /></a> |
+| **[IDRBench: Interactive Deep Research Benchmark](https://arxiv.org/pdf/2601.06676v1)** - Introduces the first benchmark for interactive deep research combining a modular multi-agent framework with on-demand user interaction, a scalable user simulator, and interaction-aware metrics measuring quality, alignment, and cost. | <a href="https://arxiv.org/abs/2601.06676v1"><img src="https://img.shields.io/badge/arXiv-2601.06676-b31b1b.svg" alt="arXiv" /></a> |
+| **[ToolGym: an Open-world Tool-using Environment for Scalable Agent Testing and Data Curation](https://arxiv.org/pdf/2601.06328v1)** - Introduces an open-world tool-using environment with 5,571 tools across 204 apps, a task engine for multi-tool workflows with wild constraints, and a state controller that injects failures to stress-test robustness. | <a href="https://arxiv.org/abs/2601.06328v1"><img src="https://img.shields.io/badge/arXiv-2601.06328-b31b1b.svg" alt="arXiv" /></a> |
+| **[TowerMind: A Tower Defence Game Learning Environment and Benchmark for LLM as Agents](https://arxiv.org/pdf/2601.05899v1)** - Introduces a tower defense environment for evaluating LLM agent planning and decision-making with low computational demands, multimodal observation, and hallucination assessment support. | <a href="https://arxiv.org/abs/2601.05899v1"><img src="https://img.shields.io/badge/arXiv-2601.05899-b31b1b.svg" alt="arXiv" /></a> |
+| **[MineNPC-Task: Task Suite for Memory-Aware Minecraft Agents](https://arxiv.org/pdf/2601.05215v2)** - Introduces a user-authored benchmark for memory-aware LLM agents in Minecraft with parametric task templates, machine-checkable validators, and bounded-knowledge evaluation under a no-shortcut policy. | <a href="https://arxiv.org/abs/2601.05215v2"><img src="https://img.shields.io/badge/arXiv-2601.05215-b31b1b.svg" alt="arXiv" /></a> |
+| **[Internal Representations as Indicators of Hallucinations in Agent Tool Selection](https://arxiv.org/pdf/2601.05214v1)** - Proposes a framework for detecting tool-calling hallucinations in LLM agents by analyzing internal representations during a single forward pass, targeting incorrect tool selection, parameter errors, and tool bypass. | <a href="https://arxiv.org/abs/2601.05214v1"><img src="https://img.shields.io/badge/arXiv-2601.05214-b31b1b.svg" alt="arXiv" /></a> |
+| **[Agent-as-a-Judge](https://arxiv.org/pdf/2601.05111v1)** - Surveys the evolution from LLM-as-a-Judge to Agent-as-a-Judge, where agentic judges employ planning, tool-augmented verification, multi-agent collaboration, and persistent memory for evaluation. | <a href="https://arxiv.org/abs/2601.05111v1"><img src="https://img.shields.io/badge/arXiv-2601.05111-b31b1b.svg" alt="arXiv" /></a> |
+| **[Arabic Prompts with English Tools: A Benchmark](https://arxiv.org/pdf/2601.05101v1)** - Introduces the first benchmark for evaluating tool-calling and agentic capabilities of LLMs in Arabic, measuring functional accuracy and robustness in Arabic agentic workflows. | <a href="https://arxiv.org/abs/2601.05101v1"><img src="https://img.shields.io/badge/arXiv-2601.05101-b31b1b.svg" alt="arXiv" /></a> |
+| **[Effects of Personality Steering on Cooperative Behavior in LLM Agents](https://arxiv.org/pdf/2601.05302v2)** - Examines how Big Five personality steering affects cooperative behavior in LLM agents using repeated Prisoner's Dilemma games across multiple model generations. | <a href="https://arxiv.org/abs/2601.05302v2"><img src="https://img.shields.io/badge/arXiv-2601.05302-b31b1b.svg" alt="arXiv" /></a> |
+| **[Analyzing Message-Code Inconsistency in AI Coding Agent-Authored Pull Requests](https://arxiv.org/pdf/2601.04886v2)** - Analyzes message-code inconsistency in pull requests authored by AI coding agents across five agent systems to study trustworthiness of agent-generated PR descriptions. | <a href="https://arxiv.org/abs/2601.04886v2"><img src="https://img.shields.io/badge/arXiv-2601.04886-b31b1b.svg" alt="arXiv" /></a> |
+| **[GUITester: Enabling GUI Agents for Exploratory Defect Discovery](https://arxiv.org/pdf/2601.04500v1)** - Proposes a multi-agent framework for autonomous exploratory GUI testing that decouples navigation from verification via planning-execution and hierarchical reflection modules. | <a href="https://arxiv.org/abs/2601.04500v1"><img src="https://img.shields.io/badge/arXiv-2601.04500-b31b1b.svg" alt="arXiv" /></a> |
+| **[Agent Drift: Quantifying Behavioral Degradation in Multi-Agent LLM Systems](https://arxiv.org/pdf/2601.04170v1)** - Introduces the concept of agent drift and a composite metric framework for quantifying semantic, coordination, and behavioral degradation in multi-agent LLM systems over extended interactions. | <a href="https://arxiv.org/abs/2601.04170v1"><img src="https://img.shields.io/badge/arXiv-2601.04170-b31b1b.svg" alt="arXiv" /></a> |
+| **[M3MAD-Bench: Are Multi-Agent Debates Really Effective Across Domains and Modalities?](https://arxiv.org/pdf/2601.02854v1)** - Introduces a unified benchmark for evaluating Multi-Agent Debate methods across multiple domains, modalities, and efficiency metrics including token consumption and inference time. | <a href="https://arxiv.org/abs/2601.02854v1"><img src="https://img.shields.io/badge/arXiv-2601.02854-b31b1b.svg" alt="arXiv" /></a> |
+| **[Why LLMs Aren't Scientists Yet: Lessons from Four Autonomous Research Attempts](https://arxiv.org/pdf/2601.03315v1)** - Documents six recurring failure modes across four end-to-end attempts at autonomous ML research using a pipeline of LLM agents mapped to stages of the scientific workflow. | <a href="https://arxiv.org/abs/2601.03315v1"><img src="https://img.shields.io/badge/arXiv-2601.03315-b31b1b.svg" alt="arXiv" /></a> |
+| **[LongDA: Benchmarking LLM Agents for Long-Document Data Analysis](https://arxiv.org/pdf/2601.02598v2)** - Introduces a data analysis benchmark for evaluating LLM agents under documentation-intensive analytical workflows requiring long document navigation and multi-step computation. | <a href="https://arxiv.org/abs/2601.02598v2"><img src="https://img.shields.io/badge/arXiv-2601.02598-b31b1b.svg" alt="arXiv" /></a> |
+| **[The Rise of Agentic Testing: Multi-Agent Systems for Robust Software Quality Assurance](https://arxiv.org/pdf/2601.02454v1)** - Proposes a closed-loop multi-agent testing framework with generation, execution analysis, and review optimization agents for autonomous software test refinement. | <a href="https://arxiv.org/abs/2601.02454v1"><img src="https://img.shields.io/badge/arXiv-2601.02454-b31b1b.svg" alt="arXiv" /></a> |
+| **[Project Ariadne: A Structural Causal Framework for Auditing Faithfulness in LLM Agents](https://arxiv.org/pdf/2601.02314v1)** - Proposes a causal framework using structural causal models and counterfactual interventions to audit whether reasoning traces in LLM agents are faithful generative drivers or post-hoc rationalizations. | <a href="https://arxiv.org/abs/2601.02314v1"><img src="https://img.shields.io/badge/arXiv-2601.02314-b31b1b.svg" alt="arXiv" /></a> |
+| **[ReliabilityBench: Evaluating LLM Agent Reliability Under Production-Like Stress Conditions](https://arxiv.org/pdf/2601.06112v1)** - Introduces a benchmark for evaluating agent reliability across consistency, robustness to perturbations, and fault tolerance under chaos-engineering-style tool failure injection. | <a href="https://arxiv.org/abs/2601.06112v1"><img src="https://img.shields.io/badge/arXiv-2601.06112-b31b1b.svg" alt="arXiv" /></a> |
+| **[MAESTRO: Multi-Agent Evaluation Suite for Testing, Reliability, and Observability](https://arxiv.org/pdf/2601.00481v1)** - Introduces an evaluation suite that standardizes MAS configuration and execution, exports framework-agnostic execution traces, and enables systematic reliability assessment across agent architectures. | <a href="https://arxiv.org/abs/2601.00481v1"><img src="https://img.shields.io/badge/arXiv-2601.00481-b31b1b.svg" alt="arXiv" /></a> |
+| **[Beyond Perfect APIs: WildAGTEval](https://arxiv.org/pdf/2601.00268v1)** - Introduces a benchmark for evaluating LLM agent function-calling under realistic API complexity including noisy outputs, detailed specifications, and runtime challenges. | <a href="https://arxiv.org/abs/2601.00268v1"><img src="https://img.shields.io/badge/arXiv-2601.00268-b31b1b.svg" alt="arXiv" /></a> |
+
+</details>
+
+<br>
+
+<details id="agent-tooling">
+<summary><h3 style="display:inline">Agent Tooling (95)</h3></summary>
+
+<br>
+
+| Paper | arXiv ID |
+|---|:---:|
+| **[TraceCoder: A Trace-Driven Multi-Agent Framework for Automated Debugging](https://arxiv.org/pdf/2602.06875v1)** - Proposes a multi-agent observe-analyze-repair loop that uses runtime traces to find and fix bugs in LLM-generated code. | <a href="https://arxiv.org/abs/2602.06875v1"><img src="https://img.shields.io/badge/arXiv-2602.06875-b31b1b.svg" alt="arXiv" /></a> |
+| **[Generative Ontology: When Structured Knowledge Learns to Create](https://arxiv.org/pdf/2602.05636v1)** - Explores constraining LLM generation with executable schemas and multi-agent roles to produce structurally valid yet creative outputs. | <a href="https://arxiv.org/abs/2602.05636v1"><img src="https://img.shields.io/badge/arXiv-2602.05636-b31b1b.svg" alt="arXiv" /></a> |
+| **[Structured Context Engineering for File-Native Agentic Systems](https://arxiv.org/pdf/2602.05447v1)** - Tests how context format (YAML, JSON, Markdown) affects agent accuracy across 9,649 experiments in file-native agentic systems. | <a href="https://arxiv.org/abs/2602.05447v1"><img src="https://img.shields.io/badge/arXiv-2602.05447-b31b1b.svg" alt="arXiv" /></a> |
+| **[ProAct: Agentic Lookahead in Interactive Environments](https://arxiv.org/pdf/2602.05327v1)** - Explores training agents to think ahead by distilling environment search into causal reasoning chains in interactive environments. | <a href="https://arxiv.org/abs/2602.05327v1"><img src="https://img.shields.io/badge/arXiv-2602.05327-b31b1b.svg" alt="arXiv" /></a> |
+| **[Autonomous Question Formation for Large Language Model-Driven AI Systems](https://arxiv.org/pdf/2602.01556v1)** - Investigates teaching agents to ask themselves the right questions before acting to adapt to new situations autonomously. | <a href="https://arxiv.org/abs/2602.01556v1"><img src="https://img.shields.io/badge/arXiv-2602.01556-b31b1b.svg" alt="arXiv" /></a> |
+| **[From Perception to Action: Spatial AI Agents and World Models](https://arxiv.org/pdf/2602.01644v1)** - Surveys the connection between agentic architectures and spatial tasks like robotics and navigation, covering memory, planning, and world models in embodied agents. | <a href="https://arxiv.org/abs/2602.01644v1"><img src="https://img.shields.io/badge/arXiv-2602.01644-b31b1b.svg" alt="arXiv" /></a> |
+| **[World Models as an Intermediary between Agents and the Real World](https://arxiv.org/pdf/2602.00785v1)** - Argues for using world models as a bridge between agents and high-cost real-world environments to provide richer learning signals across domains like robotics and ML engineering. | <a href="http://arxiv.org/abs/2602.00785v1"><img src="https://img.shields.io/badge/arXiv-2602.00785-b31b1b.svg" alt="arXiv" /></a> |
+| **[Engineering AI Agents for Clinical Workflows: A Case Study in Architecture, MLOps, and Governance](https://arxiv.org/pdf/2602.00751v1)** - Presents a reference architecture for production AI agents integrating Clean Architecture, event-driven design, per-agent MLOps lifecycles, and human-in-the-loop governance. | <a href="http://arxiv.org/abs/2602.00751v1"><img src="https://img.shields.io/badge/arXiv-2602.00751-b31b1b.svg" alt="arXiv" /></a> |
+| **[Autonomous Data Processing using Meta-Agents](https://arxiv.org/pdf/2602.00307v1)** - Proposes a meta-agent framework that builds, runs, and keeps refining data processing pipelines through hierarchical agent orchestration. | <a href="http://arxiv.org/abs/2602.00307v1"><img src="https://img.shields.io/badge/arXiv-2602.00307-b31b1b.svg" alt="arXiv" /></a> |
+| **[MEnvAgent: Scalable Polyglot Environment Construction for Verifiable Software Engineering](https://arxiv.org/pdf/2601.22859v2)** - Proposes a multi-agent framework for automatically building executable test environments across ten programming languages using planning-execution-verification with environment reuse. | <a href="http://arxiv.org/abs/2601.22859v2"><img src="https://img.shields.io/badge/arXiv-2601.22859-b31b1b.svg" alt="arXiv" /></a> |
+| **[Learning with Challenges: Adaptive Difficulty-Aware Data Generation for Mobile GUI Agent Training](https://arxiv.org/pdf/2601.22781v1)** - Proposes an adaptive data generation framework for training mobile GUI agents that matches task difficulty to the agent's current capability level. | <a href="http://arxiv.org/abs/2601.22781v1"><img src="https://img.shields.io/badge/arXiv-2601.22781-b31b1b.svg" alt="arXiv" /></a> |
+| **[AutoRefine: From Trajectories to Reusable Expertise for Continual LLM Agent Refinement](https://arxiv.org/pdf/2601.22758v1)** - Proposes extracting dual-form reusable expertise from agent execution histories — specialized subagents for procedural tasks and skill patterns for static knowledge — with continuous pruning and merging. | <a href="http://arxiv.org/abs/2601.22758v1"><img src="https://img.shields.io/badge/arXiv-2601.22758-b31b1b.svg" alt="arXiv" /></a> |
+| **[ToolTok: Tool Tokenization for Efficient and Generalizable GUI Agents](https://arxiv.org/pdf/2602.02548v1)** - Proposes modeling GUI agent operations as sequences of learnable tool tokens with semantic anchoring and curriculum-based training instead of coordinate-based visual grounding. | <a href="http://arxiv.org/abs/2602.02548v1"><img src="https://img.shields.io/badge/arXiv-2602.02548-b31b1b.svg" alt="arXiv" /></a> |
+| **[From Self-Evolving Synthetic Data to Verifiable-Reward RL: Post-Training Multi-turn Interactive Tool-Using Agents](https://arxiv.org/pdf/2601.22607v2)** - Proposes a framework combining a self-evolving multi-agent data engine with verifier-based reinforcement learning to train multi-turn interactive tool-using agents. | <a href="http://arxiv.org/abs/2601.22607v2"><img src="https://img.shields.io/badge/arXiv-2601.22607-b31b1b.svg" alt="arXiv" /></a> |
+| **[Why Reasoning Fails to Plan: A Planning-Centric Analysis of Long-Horizon Decision Making in LLM Agents](https://arxiv.org/pdf/2601.22311v1)** - Investigates why step-wise reasoning struggles with long-horizon planning in LLM agents and proposes future-aware lookahead with reward estimation to let early actions account for delayed outcomes. | <a href="http://arxiv.org/abs/2601.22311v1"><img src="https://img.shields.io/badge/arXiv-2601.22311-b31b1b.svg" alt="arXiv" /></a> |
+| **[SWE-Replay: Efficient Test-Time Scaling for Software Engineering Agents](https://arxiv.org/pdf/2601.22129v2)** - Proposes a test-time scaling method for software engineering agents that recycles prior trajectories and branches at critical intermediate steps instead of resampling from scratch. | <a href="http://arxiv.org/abs/2601.22129v2"><img src="https://img.shields.io/badge/arXiv-2601.22129-b31b1b.svg" alt="arXiv" /></a> |
+| **[Optimizing Agentic Workflows using Meta-tools](https://arxiv.org/pdf/2601.22037v2)** - Proposes bundling recurring sequences of agent tool calls into deterministic meta-tools to skip unnecessary intermediate LLM reasoning steps and cut failures. | <a href="http://arxiv.org/abs/2601.22037v2"><img src="https://img.shields.io/badge/arXiv-2601.22037-b31b1b.svg" alt="arXiv" /></a> |
+| **[astra-langchain4j: Experiences Combining LLMs and Agent Programming](https://arxiv.org/pdf/2601.21879v1)** - Explores integrating LLM capabilities into the ASTRA agent programming language to study how traditional agent toolkits and modern LLM-based agentic platforms can inform each other. | <a href="http://arxiv.org/abs/2601.21879v1"><img src="https://img.shields.io/badge/arXiv-2601.21879-b31b1b.svg" alt="arXiv" /></a> |
+| **[Meta Context Engineering via Agentic Skill Evolution](https://arxiv.org/pdf/2601.21557v2)** - Introduces a bi-level framework where a meta-agent evolves context engineering skills via agentic crossover while a base agent executes them to optimize context as files and code. | <a href="http://arxiv.org/abs/2601.21557v2"><img src="https://img.shields.io/badge/arXiv-2601.21557-b31b1b.svg" alt="arXiv" /></a> |
+| **[DataCross: A Unified Benchmark and Agent Framework for Cross-Modal Heterogeneous Data Analysis](https://arxiv.org/pdf/2601.21403v1)** - Proposes a multi-agent framework and benchmark for cross-modal data analysis that coordinates specialized sub-agents via a divide-and-conquer workflow across structured and unstructured data sources. | <a href="http://arxiv.org/abs/2601.21403v1"><img src="https://img.shields.io/badge/arXiv-2601.21403-b31b1b.svg" alt="arXiv" /></a> |
+| **[CovAgent: Overcoming the 30% Curse of Mobile Application Coverage with Agentic AI and Dynamic Instrumentation](https://arxiv.org/pdf/2601.21253v1)** - Explores agentic AI for Android app testing that uses code inspection and dynamic instrumentation to reach activities that standard GUI fuzzers cannot access. | <a href="http://arxiv.org/abs/2601.21253v1"><img src="https://img.shields.io/badge/arXiv-2601.21253-b31b1b.svg" alt="arXiv" /></a> |
+| **[CUA-Skill: Develop Skills for Computer Using Agent](https://arxiv.org/pdf/2601.21123v2)** - Introduces a large-scale computer-using agent skill library with parameterized execution, composition graphs, dynamic retrieval, and memory-aware failure recovery for desktop applications. | <a href="http://arxiv.org/abs/2601.21123v2"><img src="https://img.shields.io/badge/arXiv-2601.21123-b31b1b.svg" alt="arXiv" /></a> |
+| **[Textual Equilibrium Propagation for Deep Compound AI Systems](https://arxiv.org/pdf/2601.21064v2)** - Explores local equilibrium propagation for optimizing deep compound AI systems that avoids signal degradation in long-horizon agentic workflows by replacing global textual backpropagation. | <a href="http://arxiv.org/abs/2601.21064v2"><img src="https://img.shields.io/badge/arXiv-2601.21064-b31b1b.svg" alt="arXiv" /></a> |
+| **[Should I Have Expressed a Different Intent? Counterfactual Generation for LLM-Based Autonomous Control](https://arxiv.org/pdf/2601.20090v2)** - Investigates counterfactual reasoning in agentic LLM control scenarios using structural causal models and conformal prediction for formal reliability guarantees. | <a href="http://arxiv.org/abs/2601.20090v2"><img src="https://img.shields.io/badge/arXiv-2601.20090-b31b1b.svg" alt="arXiv" /></a> |
+| **[Insight Agents: An LLM-Based Multi-Agent System for Data Insights](https://arxiv.org/pdf/2601.20048v2)** - Introduces a hierarchical multi-agent system with out-of-domain detection and BERT-based agent routing for delivering personalized data insights at production scale. | <a href="http://arxiv.org/abs/2601.20048v2"><img src="https://img.shields.io/badge/arXiv-2601.20048-b31b1b.svg" alt="arXiv" /></a> |
+| **[Agentic Design Patterns: A System-Theoretic Framework](https://arxiv.org/pdf/2601.19752v1)** - Introduces a system-theoretic framework that decomposes agentic AI into five functional subsystems and derives 12 reusable design patterns for building robust agent architectures. | <a href="http://arxiv.org/abs/2601.19752v1"><img src="https://img.shields.io/badge/arXiv-2601.19752-b31b1b.svg" alt="arXiv" /></a> |
+| **[A Practical Guide to Agentic AI Transition in Organizations](https://arxiv.org/pdf/2602.10122v1)** - Explores a pragmatic framework for transitioning organizational processes to agentic AI, covering domain-driven use case identification, task delegation, and human-in-the-loop operating models. | <a href="http://arxiv.org/abs/2602.10122v1"><img src="https://img.shields.io/badge/arXiv-2602.10122-b31b1b.svg" alt="arXiv" /></a> |
+| **[JitRL: Just-In-Time Reinforcement Learning for Continual Learning in LLM Agents Without Gradient Updates](https://arxiv.org/pdf/2601.18510v1)** - Proposes a training-free continual learning framework for LLM agents that retrieves relevant past experiences and modulates output logits at test time without gradient updates. | <a href="http://arxiv.org/abs/2601.18510v1"><img src="https://img.shields.io/badge/arXiv-2601.18510-b31b1b.svg" alt="arXiv" /></a> |
+| **[Think-Augmented Function Calling: Improving LLM Parameter Accuracy Through Embedded Reasoning](https://arxiv.org/pdf/2601.18282v2)** - Proposes embedding explicit reasoning at both function and parameter levels during agent tool calls, with dynamic complexity scoring to trigger granular justification for critical decisions. | <a href="http://arxiv.org/abs/2601.18282v2"><img src="https://img.shields.io/badge/arXiv-2601.18282-b31b1b.svg" alt="arXiv" /></a> |
+| **[Paying Less Generalization Tax: A Cross-Domain Generalization Study of RL Training for LLM Agents](https://arxiv.org/pdf/2601.18217v1)** - Investigates which RL training environment properties and modeling choices most influence cross-domain generalization for LLM agents deployed beyond their training domains. | <a href="http://arxiv.org/abs/2601.18217v1"><img src="https://img.shields.io/badge/arXiv-2601.18217-b31b1b.svg" alt="arXiv" /></a> |
+| **[Think Locally, Explain Globally: Graph-Guided LLM Investigations via Local Reasoning and Belief Propagation](https://arxiv.org/pdf/2601.17915v2)** - Proposes disaggregating LLM investigation into bounded local evidence mining with deterministic graph traversal and belief propagation for reliable open-ended agent reasoning. | <a href="http://arxiv.org/abs/2601.17915v2"><img src="https://img.shields.io/badge/arXiv-2601.17915-b31b1b.svg" alt="arXiv" /></a> |
+| **[AI Agent for Reverse-Engineering Legacy Finite-Difference Code](https://arxiv.org/pdf/2601.18381v1)** - Presents a LangGraph-based AI agent framework combining GraphRAG, multi-stage retrieval, and RL-inspired adaptive feedback for reverse-engineering legacy scientific code. | <a href="http://arxiv.org/abs/2601.18381v1"><img src="https://img.shields.io/badge/arXiv-2601.18381-b31b1b.svg" alt="arXiv" /></a> |
+| **[PatchIsland: Orchestration of LLM Agents for Continuous Vulnerability Repair](https://arxiv.org/pdf/2601.17471v1)** - Proposes a continuous vulnerability repair system that orchestrates a diverse LLM agent ensemble with two-phase deduplication for integration with continuous fuzzing pipelines. | <a href="http://arxiv.org/abs/2601.17471v1"><img src="https://img.shields.io/badge/arXiv-2601.17471-b31b1b.svg" alt="arXiv" /></a> |
+| **[DALIA: Towards a Declarative Agentic Layer for Intelligent Agents in MCP-Based Server Ecosystems](https://arxiv.org/pdf/2601.17435v1)** - Introduces a declarative architectural layer for agentic workflows with formalized capabilities, declarative discovery protocol, and deterministic task graph construction. | <a href="http://arxiv.org/abs/2601.17435v1"><img src="https://img.shields.io/badge/arXiv-2601.17435-b31b1b.svg" alt="arXiv" /></a> |
+| **[SWE-Pruner: Self-Adaptive Context Pruning for Coding Agents](https://arxiv.org/pdf/2601.16746v2)** - Presents a task-aware context pruning framework for coding agents that trains a lightweight neural skimmer to selectively retain relevant code lines based on explicit goals. | <a href="http://arxiv.org/abs/2601.16746v2"><img src="https://img.shields.io/badge/arXiv-2601.16746-b31b1b.svg" alt="arXiv" /></a> |
+| **[REprompt: Prompt Generation for Intelligent Software Development Guided by Requirements Engineering](https://arxiv.org/pdf/2601.16507v1)** - Proposes a multi-agent prompt optimization framework guided by requirements engineering principles for system and user prompts in agent-based software development. | <a href="http://arxiv.org/abs/2601.16507v1"><img src="https://img.shields.io/badge/arXiv-2601.16507-b31b1b.svg" alt="arXiv" /></a> |
+| **[EvoConfig: Self-Evolving Multi-Agent Systems for Efficient Autonomous Environment Configuration](https://arxiv.org/pdf/2601.16489v1)** - Introduces a self-evolving multi-agent framework for automated environment configuration with expert diagnosis and dynamic error-fixing priority adjustment. | <a href="http://arxiv.org/abs/2601.16489v1"><img src="https://img.shields.io/badge/arXiv-2601.16489-b31b1b.svg" alt="arXiv" /></a> |
+| **[SemanticALLI: Caching Reasoning, Not Just Responses, in Agentic Systems](https://arxiv.org/pdf/2601.16286v2)** - Proposes a pipeline-aware caching architecture for agentic systems that elevates structured intermediate reasoning representations to first-class cacheable artifacts to reduce redundant LLM calls. | <a href="http://arxiv.org/abs/2601.16286v2"><img src="https://img.shields.io/badge/arXiv-2601.16286-b31b1b.svg" alt="arXiv" /></a> |
+| **[Controlling Long-Horizon Behavior in Language Model Agents with Explicit State Dynamics](https://arxiv.org/pdf/2601.16087v1)** - Investigates imposing explicit dynamical structure on an external affective state to induce temporal coherence and controlled recovery in multi-turn dialogue agents. | <a href="http://arxiv.org/abs/2601.16087v1"><img src="https://img.shields.io/badge/arXiv-2601.16087-b31b1b.svg" alt="arXiv" /></a> |
+| **[Agentic Uncertainty Quantification](https://arxiv.org/pdf/2601.15703v1)** - Proposes a Dual-Process framework that transforms verbalized uncertainty into bi-directional control signals for agent memory and reflection to prevent cascading hallucination errors. | <a href="http://arxiv.org/abs/2601.15703v1"><img src="https://img.shields.io/badge/arXiv-2601.15703-b31b1b.svg" alt="arXiv" /></a> |
+| **[Agentic AI Governance and Lifecycle Management in Healthcare](https://arxiv.org/pdf/2601.15630v1)** - Presents a Unified Agent Lifecycle Management blueprint with five control-plane layers for governing agent fleets including identity registry, orchestration, and runtime policy enforcement. | <a href="http://arxiv.org/abs/2601.15630v1"><img src="https://img.shields.io/badge/arXiv-2601.15630-b31b1b.svg" alt="arXiv" /></a> |
+| **[Autonomous Business System via Neuro-symbolic AI](https://arxiv.org/pdf/2601.15599v1)** - Introduces a neuro-symbolic architecture that integrates LLM agents with predicate-logic programming and knowledge graphs to orchestrate end-to-end business initiatives through task-specific logic programs. | <a href="http://arxiv.org/abs/2601.15599v1"><img src="https://img.shields.io/badge/arXiv-2601.15599-b31b1b.svg" alt="arXiv" /></a> |
+| **[How to Build AI Agents by Augmenting LLMs with Codified Human Expert Domain Knowledge? A Software Engineering Framework](https://arxiv.org/pdf/2601.15153v1)** - Proposes a software engineering framework for capturing and embedding codified human domain knowledge into LLM-based agents through request classification, RAG, and expert rule integration. | <a href="http://arxiv.org/abs/2601.15153v1"><img src="https://img.shields.io/badge/arXiv-2601.15153-b31b1b.svg" alt="arXiv" /></a> |
+| **[Agent Identity URI Scheme: Topology-Independent Naming and Capability-Based Discovery for Multi-Agent Systems](https://arxiv.org/pdf/2601.14567v1)** - Defines the agent:// URI scheme that decouples agent identity from network location through trust roots, hierarchical capability paths, and cryptographic attestation for multi-agent discovery. | <a href="http://arxiv.org/abs/2601.14567v1"><img src="https://img.shields.io/badge/arXiv-2601.14567-b31b1b.svg" alt="arXiv" /></a> |
+| **[Toward Efficient Agents: Memory, Tool learning, and Planning](https://arxiv.org/pdf/2601.14192v1)** - Surveys efficiency in agent systems across memory, tool learning, and planning, comparing approaches under fixed cost budgets and analyzing the Pareto frontier between effectiveness and cost. | <a href="http://arxiv.org/abs/2601.14192v1"><img src="https://img.shields.io/badge/arXiv-2601.14192-b31b1b.svg" alt="arXiv" /></a> |
+| **[Toward self-coding information systems](https://arxiv.org/pdf/2601.14132v1)** - Proposes self-coding information systems that use agentic AI to dynamically generate, test, and redeploy their own source code at runtime to reduce feature delivery time. | <a href="http://arxiv.org/abs/2601.14132v1"><img src="https://img.shields.io/badge/arXiv-2601.14132-b31b1b.svg" alt="arXiv" /></a> |
+| **[A Lightweight Modular Framework for Constructing Autonomous Agents Driven by Large Language Models: Design, Implementation, and Applications in AgentForge](https://arxiv.org/pdf/2601.13383v1)** - Presents a lightweight open-source Python framework for building LLM-driven agents with composable skill abstractions, a unified LLM backend interface, and declarative YAML-based configuration. | <a href="http://arxiv.org/abs/2601.13383v1"><img src="https://img.shields.io/badge/arXiv-2601.13383-b31b1b.svg" alt="arXiv" /></a> |
+| **[MagicGUI-RMS: A Multi-Agent Reward Model System for Self-Evolving GUI Agents via Automated Feedback Reflux](https://arxiv.org/pdf/2601.13060v1)** - Introduces a multi-agent reward model system for GUI agents that combines domain-specific and general-purpose reward models with automated data reflux for self-evolving agent training. | <a href="http://arxiv.org/abs/2601.13060v1"><img src="https://img.shields.io/badge/arXiv-2601.13060-b31b1b.svg" alt="arXiv" /></a> |
+| **[Agentic AI Meets Edge Computing in Autonomous UAV Swarms](https://arxiv.org/pdf/2601.14437v1)** - Investigates three deployment architectures for integrating LLM-based agentic AI with edge computing in UAV swarms, covering standalone, edge-enabled, and edge-cloud hybrid configurations. | <a href="http://arxiv.org/abs/2601.14437v1"><img src="https://img.shields.io/badge/arXiv-2601.14437-b31b1b.svg" alt="arXiv" /></a> |
+| **[Agentic Artificial Intelligence (AI): Architectures, Taxonomies, and Evaluation of Large Language Model Agents](https://arxiv.org/pdf/2601.12560v1)** - Proposes a unified taxonomy decomposing AI agents into Perception, Brain, Planning, Action, Tool Use, and Collaboration subsystems, covering MCP, native computer use, and evaluation practices. | <a href="http://arxiv.org/abs/2601.12560v1"><img src="https://img.shields.io/badge/arXiv-2601.12560-b31b1b.svg" alt="arXiv" /></a> |
+| **[Agentic Reasoning for Large Language Models](https://arxiv.org/pdf/2601.12538v1)** - Surveys agentic reasoning across foundational, self-evolving, and collective multi-agent dimensions, distinguishing in-context reasoning from post-training approaches across planning, tool use, and coordination. | <a href="http://arxiv.org/abs/2601.12538v1"><img src="https://img.shields.io/badge/arXiv-2601.12538-b31b1b.svg" alt="arXiv" /></a> |
+| **[POLARIS: Typed Planning and Governed Execution for Agentic AI in Back-Office Automation](https://arxiv.org/pdf/2601.11816v1)** - Introduces a governed orchestration framework that treats agentic automation as typed plan synthesis with DAG-based planning, rubric-guided selection, validator-gated execution, and compiled policy guardrails. | <a href="http://arxiv.org/abs/2601.11816v1"><img src="https://img.shields.io/badge/arXiv-2601.11816-b31b1b.svg" alt="arXiv" /></a> |
+| **[From Everything-is-a-File to Files-Are-All-You-Need: How Unix Philosophy Informs the Design of Agentic AI Systems](https://arxiv.org/pdf/2601.11672v1)** - Explores how the Unix 'everything is a file' principle informs agentic AI design through file-like abstractions and code-based specifications for composable, auditable agent interfaces. | <a href="http://arxiv.org/abs/2601.11672v1"><img src="https://img.shields.io/badge/arXiv-2601.11672-b31b1b.svg" alt="arXiv" /></a> |
+| **[Towards AGI A Pragmatic Approach Towards Self Evolving Agent](https://arxiv.org/pdf/2601.11658v1)** - Introduces a hierarchical self-evolving multi-agent framework that integrates curriculum learning, reward-based learning, and genetic algorithm evolution for continuous autonomous capability expansion. | <a href="http://arxiv.org/abs/2601.11658v1"><img src="https://img.shields.io/badge/arXiv-2601.11658-b31b1b.svg" alt="arXiv" /></a> |
+| **[EvoFSM: Controllable Self-Evolution for Deep Research with Finite State Machines](https://arxiv.org/pdf/2601.09465v2)** - Proposes a self-evolving agent framework that evolves an explicit finite state machine instead of free-form code rewriting, constraining flow and skill optimization to a structured representation. | <a href="https://arxiv.org/abs/2601.09465v2"><img src="https://img.shields.io/badge/arXiv-2601.09465-b31b1b.svg" alt="arXiv" /></a> |
+| **[Investigating Tool-Memory Conflicts in Tool-Augmented LLMs](https://arxiv.org/pdf/2601.09760v1)** - Identifies and studies a conflict type where a tool-augmented LLM's internal knowledge contradicts external tool outputs, evaluating whether existing resolution techniques like prompting and RAG address it. | <a href="https://arxiv.org/abs/2601.09760v1"><img src="https://img.shields.io/badge/arXiv-2601.09760-b31b1b.svg" alt="arXiv" /></a> |
+| **[MAXS: Meta-Adaptive Exploration with LLM Agents](https://arxiv.org/pdf/2601.09259v1)** - Uses lookahead planning to estimate the value of tool usage at each step and selects stable, high-value reasoning paths, with a convergence mechanism that halts rollouts once consistency is reached. | <a href="https://arxiv.org/abs/2601.09259v1"><img src="https://img.shields.io/badge/arXiv-2601.09259-b31b1b.svg" alt="arXiv" /></a> |
+| **[ToolACE-MCP: Generalizing History-Aware Routing from MCP Tools to the Agent Web](https://arxiv.org/pdf/2601.08276v1)** - Trains history-aware routers for large-scale MCP tool ecosystems using dependency graphs and multi-turn trajectory synthesis to generalize across multi-agent collaboration and massive tool catalogs. | <a href="https://arxiv.org/abs/2601.08276v1"><img src="https://img.shields.io/badge/arXiv-2601.08276-b31b1b.svg" alt="arXiv" /></a> |
+| **[Beyond Single-Shot: Multi-step Tool Retrieval via Query Planning](https://arxiv.org/pdf/2601.07782v1)** - Proposes iterative query planning for tool retrieval that decomposes instructions into sub-tasks and dynamically generates queries, trained via synthetic trajectories and reinforcement learning with verifiable rewards. | <a href="https://arxiv.org/abs/2601.07782v1"><img src="https://img.shields.io/badge/arXiv-2601.07782-b31b1b.svg" alt="arXiv" /></a> |
+| **[OS-Symphony: A Holistic Framework for Robust and Generalist Computer-Using Agent](https://arxiv.org/pdf/2601.07779v1)** - Introduces a Computer-Using Agent framework with milestone-driven long-term memory for trajectory-level self-correction and a multimodal searcher that synthesizes live, visually aligned tutorials for unseen scenarios. | <a href="https://arxiv.org/abs/2601.07779v1"><img src="https://img.shields.io/badge/arXiv-2601.07779-b31b1b.svg" alt="arXiv" /></a> |
+| **[SAGE: Tool-Augmented LLM Task Solving Strategies in Scalable Multi-Agent Environments](https://arxiv.org/pdf/2601.09750v1)** - Presents a conversational AI interface for dynamic tool discovery and execution via the OPACA framework, comparing multiple task-solving strategies across different agent setups and prompting methods. | <a href="https://arxiv.org/abs/2601.09750v1"><img src="https://img.shields.io/badge/arXiv-2601.09750-b31b1b.svg" alt="arXiv" /></a> |
+| **[Beyond Static Tools: Test-Time Tool Evolution for Scientific Reasoning](https://arxiv.org/pdf/2601.07641v1)** - Proposes test-time tool evolution where agents synthesize, verify, and evolve executable tools during inference instead of relying on static pre-defined tool libraries. | <a href="https://arxiv.org/abs/2601.07641v1"><img src="https://img.shields.io/badge/arXiv-2601.07641-b31b1b.svg" alt="arXiv" /></a> |
+| **[MegaFlow: Large-Scale Distributed Orchestration System for the Agentic Era](https://arxiv.org/pdf/2601.07526v2)** - Introduces a large-scale distributed orchestration system that decouples agent training into independent Model, Agent, and Environment services for scheduling tens of thousands of concurrent agent tasks. | <a href="https://arxiv.org/abs/2601.07526v2"><img src="https://img.shields.io/badge/arXiv-2601.07526-b31b1b.svg" alt="arXiv" /></a> |
+| **[JudgeFlow: Agentic Workflow Optimization via Block Judge](https://arxiv.org/pdf/2601.07477v2)** - Proposes an evaluation-judge-optimization pipeline that assigns block-level responsibility scores to failing logic blocks in agentic workflows, focusing modifications on the most problematic components. | <a href="https://arxiv.org/abs/2601.07477v2"><img src="https://img.shields.io/badge/arXiv-2601.07477-b31b1b.svg" alt="arXiv" /></a> |
+| **[R-LAM: Reproducibility-Constrained Large Action Models for Scientific Workflow Automation](https://arxiv.org/pdf/2601.09749v1)** - Introduces a reproducibility-constrained framework for Large Action Models with structured action schemas, deterministic execution policies, and provenance tracking to ensure auditable and replayable workflows. | <a href="https://arxiv.org/abs/2601.09749v1"><img src="https://img.shields.io/badge/arXiv-2601.09749-b31b1b.svg" alt="arXiv" /></a> |
+| **[OpenTinker: Separating Concerns in Agentic Reinforcement Learning](https://arxiv.org/pdf/2601.07376v1)** - Proposes a composable RL infrastructure for LLM agents that separates algorithm design, execution, and agent-environment interaction with a centralized scheduler for managing shared training and inference resources. | <a href="https://arxiv.org/abs/2601.07376v1"><img src="https://img.shields.io/badge/arXiv-2601.07376-b31b1b.svg" alt="arXiv" /></a> |
+| **[ARM: Role-Conditioned Neuron Transplantation for Training-Free Generalist LLM Agent Merging](https://arxiv.org/pdf/2601.07309v1)** - Introduces activation-guided, role-conditioned neuron transplantation for training-free merging of environment-specific LLM agent experts into a single generalist model. | <a href="https://arxiv.org/abs/2601.07309v1"><img src="https://img.shields.io/badge/arXiv-2601.07309-b31b1b.svg" alt="arXiv" /></a> |
+| **[PRISM: Disentangling SFT and RL Data via Gradient Concentration](https://arxiv.org/pdf/2601.07224v1)** - Proposes a dynamics-aware framework grounded in Schema Theory that routes agent training data to SFT or RL based on gradient concentration, using cognitive conflict as the allocation signal. | <a href="https://arxiv.org/abs/2601.07224v1"><img src="https://img.shields.io/badge/arXiv-2601.07224-b31b1b.svg" alt="arXiv" /></a> |
+| **[ET-Agent: Incentivizing Effective Tool-Integrated Reasoning Agent via Behavior Calibration](https://arxiv.org/pdf/2601.06860v2)** - Introduces a training framework for calibrating agent tool-use behavior through a self-evolving data flywheel and two-phase behavior calibration to reduce redundant and insufficient tool calls. | <a href="https://arxiv.org/abs/2601.06860v2"><img src="https://img.shields.io/badge/arXiv-2601.06860-b31b1b.svg" alt="arXiv" /></a> |
+| **[No More Stale Feedback: Co-Evolving Critics for Open-World Agent Learning](https://arxiv.org/pdf/2601.06794v1)** - Proposes a co-evolutionary framework that jointly optimizes the agent policy and its natural-language critic through synchronized GRPO updates, preventing the critic from becoming stale as the policy evolves. | <a href="https://arxiv.org/abs/2601.06794v1"><img src="https://img.shields.io/badge/arXiv-2601.06794-b31b1b.svg" alt="arXiv" /></a> |
+| **[CEDAR: Context Engineering for Agentic Data Science](https://arxiv.org/pdf/2601.06606v1)** - Introduces context engineering techniques for agentic workflows including structured DS-specific prompting, separate plan and code agents, and smart history rendering for fault tolerance and context management. | <a href="https://arxiv.org/abs/2601.06606v1"><img src="https://img.shields.io/badge/arXiv-2601.06606-b31b1b.svg" alt="arXiv" /></a> |
+| **[ArenaRL: Scaling RL for Open-Ended Agents via Tournament-based Relative Ranking](https://arxiv.org/pdf/2601.06487v2)** - Proposes a reinforcement learning paradigm that replaces pointwise scalar scoring with intra-group relative ranking via tournament-based schemes to address discrimination collapse in reward models for open-ended agent tasks. | <a href="https://arxiv.org/abs/2601.06487v2"><img src="https://img.shields.io/badge/arXiv-2601.06487-b31b1b.svg" alt="arXiv" /></a> |
+| **[Architecting AgentOps Needs CHANGE](https://arxiv.org/pdf/2601.06456v1)** - Introduces a conceptual framework with six capabilities (Contextualize, Harmonize, Anticipate, Negotiate, Generate, Evolve) for architecting AgentOps platforms that manage the lifecycle of evolving agentic AI systems. | <a href="https://arxiv.org/abs/2601.06456v1"><img src="https://img.shields.io/badge/arXiv-2601.06456-b31b1b.svg" alt="arXiv" /></a> |
+| **[Can We Predict Before Executing Machine Learning Agents?](https://arxiv.org/pdf/2601.05930v1)** - Proposes internalizing execution priors to predict agent outcomes before physical execution, using a Predict-then-Verify loop to accelerate ML agent workflows without running expensive experiments. | <a href="https://arxiv.org/abs/2601.05930v1"><img src="https://img.shields.io/badge/arXiv-2601.05930-b31b1b.svg" alt="arXiv" /></a> |
+| **[EnvScaler: Scaling Tool-Interactive Environments for LLM Agent via Programmatic Synthesis](https://arxiv.org/pdf/2601.05808v1)** - Proposes an automated framework for generating scalable tool-interaction environments via programmatic synthesis, constructing diverse environment skeletons and task scenarios for agent SFT and RL training. | <a href="https://arxiv.org/abs/2601.05808v1"><img src="https://img.shields.io/badge/arXiv-2601.05808-b31b1b.svg" alt="arXiv" /></a> |
+| **[LIDL: LLM Integration Defect Localization via Knowledge Graph-Enhanced Multi-Agent Analysis](https://arxiv.org/pdf/2601.05539v1)** - Proposes a multi-agent framework for localizing integration defects in LLM-integrated software using code knowledge graphs enriched with LLM-aware annotations and counterfactual reasoning for root cause validation. | <a href="https://arxiv.org/abs/2601.05539v1"><img src="https://img.shields.io/badge/arXiv-2601.05539-b31b1b.svg" alt="arXiv" /></a> |
+| **[AT²PO: Agentic Turn-based Policy Optimization via Tree Search](https://arxiv.org/pdf/2601.04767v1)** - Proposes a unified framework for multi-turn agentic RL that uses a turn-level tree structure for entropy-guided exploration, turn-wise credit assignment, and turn-based policy optimization. | <a href="https://arxiv.org/abs/2601.04767v1"><img src="https://img.shields.io/badge/arXiv-2601.04767-b31b1b.svg" alt="arXiv" /></a> |
+| **[M-ASK: Multi-Agent Search and Knowledge Optimization Framework](https://arxiv.org/pdf/2601.04703v1)** - Proposes a framework that decouples agentic search into Search Behavior Agents and Knowledge Management Agents with turn-level rewards for multi-hop QA. | <a href="https://arxiv.org/abs/2601.04703v1"><img src="https://img.shields.io/badge/arXiv-2601.04703-b31b1b.svg" alt="arXiv" /></a> |
+| **[AgentDevel: Reframing Self-Evolving LLM Agents as Release Engineering](https://arxiv.org/pdf/2601.04620v1)** - Reframes agent self-improvement as a release engineering pipeline with implementation-blind quality signals, symptom-level diagnosis, and flip-centered regression gating. | <a href="https://arxiv.org/abs/2601.04620v1"><img src="https://img.shields.io/badge/arXiv-2601.04620-b31b1b.svg" alt="arXiv" /></a> |
+| **[4D-ARE: 4-Dimensional Attribution-Driven Agent Requirements Engineering](https://arxiv.org/pdf/2601.04556v1)** - Proposes an attribution-driven requirements engineering methodology for specifying what domain knowledge LLM agents need at design time, organized along four causal dimensions. | <a href="https://arxiv.org/abs/2601.04556v1"><img src="https://img.shields.io/badge/arXiv-2601.04556-b31b1b.svg" alt="arXiv" /></a> |
+| **[XGrammar 2: Dynamic and Efficient Structured Generation Engine for Agentic LLMs](https://arxiv.org/pdf/2601.04426v1)** - Proposes a structured generation engine for agentic LLMs with dynamic tag dispatching, JIT compilation, and cross-grammar caching for tool calling and conditional structured generation. | <a href="https://arxiv.org/abs/2601.04426v1"><img src="https://img.shields.io/badge/arXiv-2601.04426-b31b1b.svg" alt="arXiv" /></a> |
+| **[Transitive Expert Error and Routing Problems in Complex AI Systems](https://arxiv.org/pdf/2601.04416v1)** - Formalizes transitive expert error in AI routing architectures including MoE, multi-model orchestration, and tool-using agents, proposing boundary-aware calibration and coverage gap detection. | <a href="https://arxiv.org/abs/2601.04416v1"><img src="https://img.shields.io/badge/arXiv-2601.04416-b31b1b.svg" alt="arXiv" /></a> |
+| **[O-Researcher: An Open Ended Deep Research Model via Multi-Agent Distillation and Agentic RL](https://arxiv.org/pdf/2601.03743v1)** - Introduces a multi-agent workflow for synthesizing research-grade training data with a two-stage SFT plus agentic RL strategy for open-source deep research models. | <a href="https://arxiv.org/abs/2601.03743v1"><img src="https://img.shields.io/badge/arXiv-2601.03743-b31b1b.svg" alt="arXiv" /></a> |
+| **[Architecting Agentic Communities using Design Patterns](https://arxiv.org/pdf/2601.03624v2)** - Proposes design patterns for architecting agentic communities derived from enterprise distributed systems standards, covering coordination, governance, and formal collaboration agreements. | <a href="https://arxiv.org/abs/2601.03624v2"><img src="https://img.shields.io/badge/arXiv-2601.03624-b31b1b.svg" alt="arXiv" /></a> |
+| **[SCRIBE: Structured Mid-Level Supervision for Tool-Using Language Models](https://arxiv.org/pdf/2601.03555v1)** - Proposes a skill-conditioned RL framework for tool-using agents that grounds reward modeling in a library of skill prototypes for mid-level credit assignment. | <a href="https://arxiv.org/abs/2601.03555v1"><img src="https://img.shields.io/badge/arXiv-2601.03555-b31b1b.svg" alt="arXiv" /></a> |
+| **[Enhancing Model Context Protocol (MCP) with Context-Aware Server Collaboration](https://arxiv.org/pdf/2601.11595v2)** - Proposes a Context-Aware MCP architecture with a Shared Context Store that enables MCP servers to coordinate autonomously by reading from and writing to shared context memory. | <a href="https://arxiv.org/abs/2601.11595v2"><img src="https://img.shields.io/badge/arXiv-2601.11595-b31b1b.svg" alt="arXiv" /></a> |
+| **[Enhancing LLM Instruction Following: An Evaluation-Driven Multi-Agentic Workflow for Prompt Instructions Optimization](https://arxiv.org/pdf/2601.03359v1)** - Proposes a multi-agentic workflow that decouples optimization of primary task descriptions from constraint optimization using quantitative feedback for iterative prompt refinement. | <a href="https://arxiv.org/abs/2601.03359v1"><img src="https://img.shields.io/badge/arXiv-2601.03359-b31b1b.svg" alt="arXiv" /></a> |
+| **[InfiAgent: An Infinite-Horizon Framework for General-Purpose Autonomous Agents](https://arxiv.org/pdf/2601.03204v1)** - Proposes a general-purpose agent framework that keeps reasoning context bounded regardless of task duration by externalizing persistent state into a file-centric state abstraction. | <a href="https://arxiv.org/abs/2601.03204v1"><img src="https://img.shields.io/badge/arXiv-2601.03204-b31b1b.svg" alt="arXiv" /></a> |
+| **[The Path Ahead for Agentic AI: Challenges and Opportunities](https://arxiv.org/pdf/2601.02749v1)** - Surveys agentic AI architectures covering planning, memory, tool use, and iterative reasoning with a critical assessment of safety, alignment, and reliability challenges. | <a href="https://arxiv.org/abs/2601.02749v1"><img src="https://img.shields.io/badge/arXiv-2601.02749-b31b1b.svg" alt="arXiv" /></a> |
+| **[AMER-RCL: Agentic Memory Enhanced Recursive Reasoning for Root Cause Localization in Microservices](https://arxiv.org/pdf/2601.02732v1)** - Proposes an agentic memory enhanced recursive reasoning framework for root cause localization with cross-alert memory reuse and multi-agent recursive refinement. | <a href="https://arxiv.org/abs/2601.02732v1"><img src="https://img.shields.io/badge/arXiv-2601.02732-b31b1b.svg" alt="arXiv" /></a> |
+| **[Orchestral AI: A Framework for Agent Orchestration](https://arxiv.org/pdf/2601.02577v1)** - Introduces a lightweight Python framework providing a unified, type-safe interface for building LLM agents across multiple providers with tool calling, memory management, and MCP integration. | <a href="https://arxiv.org/abs/2601.02577v1"><img src="https://img.shields.io/badge/arXiv-2601.02577-b31b1b.svg" alt="arXiv" /></a> |
+| **[AI Agent Systems: Architectures, Applications, and Evaluation](https://arxiv.org/pdf/2601.01743v1)** - Surveys AI agent architectures spanning reasoning, planning, tool calling, orchestration patterns, and deployment settings with a unified taxonomy of agent components and design trade-offs. | <a href="https://arxiv.org/abs/2601.01743v1"><img src="https://img.shields.io/badge/arXiv-2601.01743-b31b1b.svg" alt="arXiv" /></a> |
+| **[CaveAgent: Transforming LLMs into Stateful Runtime Operators](https://arxiv.org/pdf/2601.01569v3)** - Proposes a dual-stream architecture that elevates the persistent Python runtime as the central locus of agent state, with stateful runtime management and skill injection for long-horizon tasks. | <a href="https://arxiv.org/abs/2601.01569v3"><img src="https://img.shields.io/badge/arXiv-2601.01569-b31b1b.svg" alt="arXiv" /></a> |
+| **[Actively Obtaining Environmental Feedback for Autonomous Action Evaluation Without Predefined Measurements](https://arxiv.org/pdf/2601.04235v1)** - Proposes an active feedback model where AI agents proactively interact with the environment to discover and verify feedback without relying on predefined measurements. | <a href="https://arxiv.org/abs/2601.04235v1"><img src="https://img.shields.io/badge/arXiv-2601.04235-b31b1b.svg" alt="arXiv" /></a> |
+| **[Warp-Cortex: An Asynchronous, Memory-Efficient Architecture for Million-Agent Cognitive Scaling on Consumer Hardware](https://arxiv.org/pdf/2601.01298v1)** - Proposes an asynchronous architecture for million-agent scaling that reduces memory complexity via singleton weight sharing and topological synapse-inspired KV-cache sparsification. | <a href="https://arxiv.org/abs/2601.01298v1"><img src="https://img.shields.io/badge/arXiv-2601.01298-b31b1b.svg" alt="arXiv" /></a> |
+
+</details>
+
+<br>
+
+<details id="ai-agent-security">
+<summary><h3 style="display:inline">AI Agent Security (82)</h3></summary>
+
+<br>
+
+| Paper | arXiv ID |
+|---|:---:|
+| **[Internal Safety Collapse in Frontier Large Language Models](https://arxiv.org/pdf/2603.23509)** - Reveals that AI agents produce harmful content (toxic text, exploits, dangerous data) as a side effect of completing normal professional tasks — no adversarial prompting needed. At least one mode (single-turn, ICL, or agentic) succeeds on every frontier model tested. 56 cross-domain scenarios across 8+ disciplines. | <a href="https://arxiv.org/abs/2603.23509"><img src="https://img.shields.io/badge/arXiv-2603.23509-b31b1b.svg" alt="arXiv" /></a> |
+| **[Confundo: Learning to Generate Robust Poison for Practical RAG Systems](https://arxiv.org/pdf/2602.06616v1)** - Trains an LLM to generate RAG poison that survives real-world content processing and query variation for stress-testing RAG defenses. | <a href="https://arxiv.org/abs/2602.06616v1"><img src="https://img.shields.io/badge/arXiv-2602.06616-b31b1b.svg" alt="arXiv" /></a> |
+| **[Malicious Agent Skills in the Wild: A Large-Scale Security Empirical Study](https://arxiv.org/pdf/2602.06547v1)** - Analyzes 98K agent skills from community registries to study the prevalence and nature of malicious third-party agent plugins. | <a href="https://arxiv.org/abs/2602.06547v1"><img src="https://img.shields.io/badge/arXiv-2602.06547-b31b1b.svg" alt="arXiv" /></a> |
+| **[Subgraph Reconstruction Attacks on Graph RAG Deployments with Practical Defenses](https://arxiv.org/pdf/2602.06495v1)** - Investigates whether attackers can reconstruct knowledge graphs from Graph RAG outputs through multi-turn probing. | <a href="https://arxiv.org/abs/2602.06495v1"><img src="https://img.shields.io/badge/arXiv-2602.06495-b31b1b.svg" alt="arXiv" /></a> |
+| **[Zero-Trust Runtime Verification for Agentic Payment Protocols](https://arxiv.org/pdf/2602.06345v1)** - Proposes consume-once mandate semantics for AI agent payment protocols to prevent replay and redirect attacks in autonomous transactions. | <a href="https://arxiv.org/abs/2602.06345v1"><img src="https://img.shields.io/badge/arXiv-2602.06345-b31b1b.svg" alt="arXiv" /></a> |
+| **[Identifying Adversary Tactics and Techniques in Malware Binaries with an LLM Agent](https://arxiv.org/pdf/2602.06325v1)** - Explores using an LLM agent to identify attack techniques in stripped malware binaries through incremental context retrieval. | <a href="https://arxiv.org/abs/2602.06325v1"><img src="https://img.shields.io/badge/arXiv-2602.06325-b31b1b.svg" alt="arXiv" /></a> |
+| **[Agent2Agent Threats in Safety-Critical LLM Assistants: A Human-Centric Taxonomy](https://arxiv.org/pdf/2602.05877v1)** - Maps attack paths in agent-to-agent communication protocols for automotive LLM assistants, from driver distraction to unauthorized vehicle control. | <a href="https://arxiv.org/abs/2602.05877v1"><img src="https://img.shields.io/badge/arXiv-2602.05877-b31b1b.svg" alt="arXiv" /></a> |
+| **[Learning to Inject: Automated Prompt Injection via Reinforcement Learning](https://arxiv.org/pdf/2602.05746v1)** - Explores using reinforcement learning to auto-generate prompt injection attacks that transfer across multiple frontier LLM models. | <a href="https://arxiv.org/abs/2602.05746v1"><img src="https://img.shields.io/badge/arXiv-2602.05746-b31b1b.svg" alt="arXiv" /></a> |
+| **[A Dual-Loop Agent Framework for Automated Vulnerability Reproduction](https://arxiv.org/pdf/2602.05721v1)** - Proposes an LLM agent with dual feedback loops for strategy and code to automate vulnerability reproduction from CVE descriptions. | <a href="https://arxiv.org/abs/2602.05721v1"><img src="https://img.shields.io/badge/arXiv-2602.05721-b31b1b.svg" alt="arXiv" /></a> |
+| **[Human Society-Inspired Approaches to Agentic AI Security: The 4C Framework](https://arxiv.org/pdf/2602.01942v1)** - Organizes agentic security risks into four layers (Core, Connection, Cognition, Compliance) to address trust and governance issues beyond prompt injection. | <a href="https://arxiv.org/abs/2602.01942v1"><img src="https://img.shields.io/badge/arXiv-2602.01942-b31b1b.svg" alt="arXiv" /></a> |
+| **[MAGIC: A Co-Evolving Attacker-Defender Adversarial Game for Robust LLM Safety](https://arxiv.org/pdf/2602.01539v2)** - Proposes a co-evolving RL game between an attacker and defender agent to stress-test safety alignment against novel attack patterns. | <a href="https://arxiv.org/abs/2602.01539v2"><img src="https://img.shields.io/badge/arXiv-2602.01539-b31b1b.svg" alt="arXiv" /></a> |
+| **[TxRay: Agentic Postmortem of Live Blockchain Attacks](https://arxiv.org/pdf/2602.01317v4)** - Introduces an LLM agentic system that reconstructs blockchain exploit lifecycles from limited evidence and generates runnable proof-of-concept reproductions. | <a href="http://arxiv.org/abs/2602.01317v4"><img src="https://img.shields.io/badge/arXiv-2602.01317-b31b1b.svg" alt="arXiv" /></a> |
+| **[To Defend Against Cyber Attacks, We Must Teach AI Agents to Hack](https://arxiv.org/pdf/2602.02595v1)** - Argues that AI-agent-driven cyber attacks are inevitable and proposes building frontier offensive AI capabilities responsibly as essential defensive infrastructure. | <a href="http://arxiv.org/abs/2602.02595v1"><img src="https://img.shields.io/badge/arXiv-2602.02595-b31b1b.svg" alt="arXiv" /></a> |
+| **[SMCP: Secure Model Context Protocol](https://arxiv.org/pdf/2602.01129v1)** - Proposes protocol-level security improvements for the Model Context Protocol including unified identity management, mutual authentication, and fine-grained policy enforcement. | <a href="http://arxiv.org/abs/2602.01129v1"><img src="https://img.shields.io/badge/arXiv-2602.01129-b31b1b.svg" alt="arXiv" /></a> |
+| **[Persuasion Propagation in LLM Agents](https://arxiv.org/pdf/2602.00851v1)** - Investigates how user persuasion during conversation can carry over and change how autonomous AI agents perform later tasks. | <a href="http://arxiv.org/abs/2602.00851v1"><img src="https://img.shields.io/badge/arXiv-2602.00851-b31b1b.svg" alt="arXiv" /></a> |
+| **[When Agents "Misremember" Collectively: Exploring the Mandela Effect in LLM-based Multi-Agent Systems](https://arxiv.org/pdf/2602.00428v1)** - Explores how collective false memories form in LLM-based multi-agent systems and proposes defenses including cognitive anchoring and alignment-based approaches. | <a href="http://arxiv.org/abs/2602.00428v1"><img src="https://img.shields.io/badge/arXiv-2602.00428-b31b1b.svg" alt="arXiv" /></a> |
+| **["Someone Hid It": Query-Agnostic Black-Box Attacks on LLM-Based Retrieval](https://arxiv.org/pdf/2602.00364v2)** - Proposes a black-box attack method that generates transferable adversarial tokens to manipulate LLM-based retrieval systems without needing access to the target's queries or model. | <a href="http://arxiv.org/abs/2602.00364v2"><img src="https://img.shields.io/badge/arXiv-2602.00364-b31b1b.svg" alt="arXiv" /></a> |
+| **[From Similarity to Vulnerability: Key Collision Attack on LLM Semantic Caching](https://arxiv.org/pdf/2601.23088v1)** - Introduces CacheAttack, a black-box framework that exploits the trade-off between locality and collision resistance in semantic caching to hijack LLM responses and manipulate agent behavior. | <a href="http://arxiv.org/abs/2601.23088v1"><img src="https://img.shields.io/badge/arXiv-2601.23088-b31b1b.svg" alt="arXiv" /></a> |
+| **[TessPay: Verify-then-Pay Infrastructure for Trusted Agentic Commerce](https://arxiv.org/pdf/2602.00213v1)** - Proposes a verify-then-pay infrastructure for agent transactions that locks funds in escrow, requires cryptographic proof of task execution, and releases payment only after verification. | <a href="http://arxiv.org/abs/2602.00213v1"><img src="https://img.shields.io/badge/arXiv-2602.00213-b31b1b.svg" alt="arXiv" /></a> |
+| **[Whispers of Wealth: Red-Teaming Google's Agent Payments Protocol via Prompt Injection](https://arxiv.org/pdf/2601.22569v1)** - Red-teams Google's Agent Payments Protocol via prompt injection attacks that manipulate product ranking and extract sensitive user data in agent-led purchase flows. | <a href="http://arxiv.org/abs/2601.22569v1"><img src="https://img.shields.io/badge/arXiv-2601.22569-b31b1b.svg" alt="arXiv" /></a> |
+| **[StepShield: When, Not Whether to Intervene on Rogue Agents](https://arxiv.org/pdf/2601.22136v1)** - Introduces a benchmark for evaluating when agent violations are detected during execution rather than just whether, with temporal metrics for early intervention and tokens saved. | <a href="http://arxiv.org/abs/2601.22136v1"><img src="https://img.shields.io/badge/arXiv-2601.22136-b31b1b.svg" alt="arXiv" /></a> |
+| **[Delegation Without Living Governance](https://arxiv.org/pdf/2601.21226v1)** - Argues that static compliance-based governance is insufficient for agentic AI at machine speed and proposes runtime governance to preserve human relevance in agent-driven decision-making. | <a href="http://arxiv.org/abs/2601.21226v1"><img src="https://img.shields.io/badge/arXiv-2601.21226-b31b1b.svg" alt="arXiv" /></a> |
+| **[DRAINCODE: Stealthy Energy Consumption Attacks on Retrieval-Augmented Code Generation via Context Poisoning](https://arxiv.org/pdf/2601.20615v3)** - Introduces an adversarial attack that poisons retrieval contexts in RAG-based code generation to force longer outputs, increasing GPU latency and energy consumption. | <a href="http://arxiv.org/abs/2601.20615v3"><img src="https://img.shields.io/badge/arXiv-2601.20615-b31b1b.svg" alt="arXiv" /></a> |
+| **[Securing AI Agents in Cyber-Physical Systems: A Survey of Environmental Interactions, Deepfake Threats, and Defenses](https://arxiv.org/pdf/2601.20184v1)** - Surveys security threats targeting AI agents in cyber-physical systems, covering deepfake attacks, MCP-mediated vulnerabilities, and defense-in-depth architectures. | <a href="http://arxiv.org/abs/2601.20184v1"><img src="https://img.shields.io/badge/arXiv-2601.20184-b31b1b.svg" alt="arXiv" /></a> |
+| **[Multimodal Multi-Agent Ransomware Analysis Using AutoGen](https://arxiv.org/pdf/2601.20346v1)** - Explores AutoGen-based multi-agent coordination with specialized agents for static, dynamic, and network-level ransomware family classification using confidence-aware decisions. | <a href="http://arxiv.org/abs/2601.20346v1"><img src="https://img.shields.io/badge/arXiv-2601.20346-b31b1b.svg" alt="arXiv" /></a> |
+| **[SHIELD: An Auto-Healing Agentic Defense Framework for LLM Resource Exhaustion Attacks](https://arxiv.org/pdf/2601.19174v1)** - Introduces a multi-agent auto-healing defense framework with semantic similarity retrieval, pattern matching, and an evolving knowledgebase for defending LLMs against resource exhaustion attacks. | <a href="http://arxiv.org/abs/2601.19174v1"><img src="https://img.shields.io/badge/arXiv-2601.19174-b31b1b.svg" alt="arXiv" /></a> |
+| **[AgenticSCR: An Autonomous Agentic Secure Code Review for Immature Vulnerabilities Detection](https://arxiv.org/pdf/2601.19138v1)** - Explores agentic AI for pre-commit secure code review that uses autonomous decision-making, tool invocation, and security-focused semantic memories to detect immature vulnerabilities. | <a href="http://arxiv.org/abs/2601.19138v1"><img src="https://img.shields.io/badge/arXiv-2601.19138-b31b1b.svg" alt="arXiv" /></a> |
+| **[AgentDoG: A Diagnostic Guardrail Framework for AI Agent Safety and Security](https://arxiv.org/pdf/2601.18491v1)** - Introduces a three-dimensional taxonomy for agentic risks and a diagnostic guardrail framework that monitors agent trajectories with fine-grained root cause analysis beyond binary safety labels. | <a href="http://arxiv.org/abs/2601.18491v1"><img src="https://img.shields.io/badge/arXiv-2601.18491-b31b1b.svg" alt="arXiv" /></a> |
+| **[When Personalization Legitimizes Risks: Uncovering Safety Vulnerabilities in Personalized Dialogue Agents](https://arxiv.org/pdf/2601.17887v1)** - Examines how benign personal memories in personalized agents can bias intent inference and cause models to legitimize harmful queries through a previously unexplored safety vector. | <a href="http://arxiv.org/abs/2601.17887v1"><img src="https://img.shields.io/badge/arXiv-2601.17887-b31b1b.svg" alt="arXiv" /></a> |
+| **[Multi-Agent Collaborative Intrusion Detection for LAE-IoT](https://arxiv.org/pdf/2601.17817v1)** - Proposes a multi-agent collaborative framework with specialized LLM-enhanced agents for intelligent data processing and adaptive intrusion classification in aerial IoT networks. | <a href="http://arxiv.org/abs/2601.17817v1"><img src="https://img.shields.io/badge/arXiv-2601.17817-b31b1b.svg" alt="arXiv" /></a> |
+| **[Faramesh: A Protocol-Agnostic Execution Control Plane for Autonomous Agent Systems](https://arxiv.org/pdf/2601.17744v1)** - Introduces a protocol-agnostic execution control plane for autonomous agents that enforces authorization boundaries with canonical action representation and deterministic policy evaluation. | <a href="http://arxiv.org/abs/2601.17744v1"><img src="https://img.shields.io/badge/arXiv-2601.17744-b31b1b.svg" alt="arXiv" /></a> |
+| **[A Systemic Evaluation of Multimodal RAG Privacy](https://arxiv.org/pdf/2601.17644v2)** - Examines privacy risks in multimodal RAG pipelines through inclusion inference and metadata leakage attacks during standard model prompting. | <a href="http://arxiv.org/abs/2601.17644v2"><img src="https://img.shields.io/badge/arXiv-2601.17644-b31b1b.svg" alt="arXiv" /></a> |
+| **[Breaking the Protocol: Security Analysis of the Model Context Protocol Specification](https://arxiv.org/pdf/2601.17549v1)** - Presents the first security analysis of the Model Context Protocol specification, identifying three protocol-level vulnerabilities and proposing backward-compatible security extensions. | <a href="http://arxiv.org/abs/2601.17549v1"><img src="https://img.shields.io/badge/arXiv-2601.17549-b31b1b.svg" alt="arXiv" /></a> |
+| **[Prompt Injection Attacks on Agentic Coding Assistants: A Systematic Analysis](https://arxiv.org/pdf/2601.17548v1)** - Surveys 78 studies to systematize prompt injection attacks on agentic coding assistants with a three-dimensional taxonomy across delivery vectors, modalities, and propagation. | <a href="http://arxiv.org/abs/2601.17548v1"><img src="https://img.shields.io/badge/arXiv-2601.17548-b31b1b.svg" alt="arXiv" /></a> |
+| **[Connect the Dots: Knowledge Graph-Guided Crawler Attack on Retrieval-Augmented Generation Systems](https://arxiv.org/pdf/2601.15678v2)** - Introduces RAGCrawler, a knowledge graph-guided attack that adaptively steals RAG corpus content through targeted queries to maximize coverage under a query budget. | <a href="http://arxiv.org/abs/2601.15678v2"><img src="https://img.shields.io/badge/arXiv-2601.15678-b31b1b.svg" alt="arXiv" /></a> |
+| **[Securing LLM-as-a-Service for Small Businesses: An Industry Case Study of a Distributed Chatbot Deployment Platform](https://arxiv.org/pdf/2601.15528v1)** - Presents a multi-tenant chatbot deployment platform with container-based isolation and platform-level defenses against prompt injection attacks in RAG-based systems. | <a href="http://arxiv.org/abs/2601.15528v1"><img src="https://img.shields.io/badge/arXiv-2601.15528-b31b1b.svg" alt="arXiv" /></a> |
+| **[Interoperable Architecture for Digital Identity Delegation for AI Agents with Blockchain Integration](https://arxiv.org/pdf/2601.14982v1)** - Introduces delegation grants and a canonical verification context for bounded, auditable identity delegation across human users and AI agents in heterogeneous identity ecosystems. | <a href="http://arxiv.org/abs/2601.14982v1"><img src="https://img.shields.io/badge/arXiv-2601.14982-b31b1b.svg" alt="arXiv" /></a> |
+| **[INFA-Guard: Mitigating Malicious Propagation via Infection-Aware Safeguarding in LLM-Based Multi-Agent Systems](https://arxiv.org/pdf/2601.14667v1)** - Proposes an infection-aware defense framework for multi-agent systems that distinguishes infected agents from attackers and applies topological constraints to halt malicious propagation. | <a href="http://arxiv.org/abs/2601.14667v1"><img src="https://img.shields.io/badge/arXiv-2601.14667-b31b1b.svg" alt="arXiv" /></a> |
+| **[Query-Efficient Agentic Graph Extraction Attacks on GraphRAG Systems](https://arxiv.org/pdf/2601.14662v1)** - Proposes AGEA, an agentic framework using novelty-guided exploration and graph memory to steal latent entity-relation graphs from GraphRAG systems under strict query budgets. | <a href="http://arxiv.org/abs/2601.14662v1"><img src="https://img.shields.io/badge/arXiv-2601.14662-b31b1b.svg" alt="arXiv" /></a> |
+| **[NeuroFilter: Privacy Guardrails for Conversational LLM Agents](https://arxiv.org/pdf/2601.14660v1)** - Introduces activation-space guardrails that detect privacy-violating intent in LLM agents through linear separation of internal representations, including drift detection across multi-turn conversations. | <a href="http://arxiv.org/abs/2601.14660v1"><img src="https://img.shields.io/badge/arXiv-2601.14660-b31b1b.svg" alt="arXiv" /></a> |
+| **[VirtualCrime: Evaluating Criminal Potential of Large Language Models via Sandbox Simulation](https://arxiv.org/pdf/2601.13981v1)** - Proposes a three-agent sandbox simulation framework with 40 crime tasks across 13 objectives to evaluate the criminal capabilities of LLM agents in realistic scenarios. | <a href="http://arxiv.org/abs/2601.13981v1"><img src="https://img.shields.io/badge/arXiv-2601.13981-b31b1b.svg" alt="arXiv" /></a> |
+| **[PINA: Prompt Injection Attack against Navigation Agents](https://arxiv.org/pdf/2601.13612v1)** - Introduces an adaptive prompt injection framework targeting navigation agents under black-box, long-context, and action-executable constraints across indoor and outdoor environments. | <a href="http://arxiv.org/abs/2601.13612v1"><img src="https://img.shields.io/badge/arXiv-2601.13612-b31b1b.svg" alt="arXiv" /></a> |
+| **[Prompt Injection Mitigation with Agentic AI, Nested Learning, and AI Sustainability via Semantic Caching](https://arxiv.org/pdf/2601.13186v1)** - Explores a multi-agent defense pipeline combining semantic similarity caching, nested learning, and observability-aware evaluation to mitigate prompt injection attacks while reducing computational costs. | <a href="http://arxiv.org/abs/2601.13186v1"><img src="https://img.shields.io/badge/arXiv-2601.13186-b31b1b.svg" alt="arXiv" /></a> |
+| **[CODE: A Contradiction-Based Deliberation Extension Framework for Overthinking Attacks on Retrieval-Augmented Generation](https://arxiv.org/pdf/2601.13112v1)** - Introduces an overthinking attack framework for RAG systems with reasoning models, using multi-agent-constructed poisoning samples that cause excessive reasoning token consumption without degrading task accuracy. | <a href="http://arxiv.org/abs/2601.13112v1"><img src="https://img.shields.io/badge/arXiv-2601.13112-b31b1b.svg" alt="arXiv" /></a> |
+| **[AgenTRIM: Tool Risk Mitigation for Agentic AI](https://arxiv.org/pdf/2601.12449v1)** - Introduces a framework for detecting and mitigating tool-driven agency risks through offline interface verification and runtime per-step least-privilege tool access with adaptive filtering. | <a href="http://arxiv.org/abs/2601.12449v1"><img src="https://img.shields.io/badge/arXiv-2601.12449-b31b1b.svg" alt="arXiv" /></a> |
+| **[Efficient Privacy-Preserving Retrieval Augmented Generation with Distance-Preserving Encryption](https://arxiv.org/pdf/2601.12331v1)** - Proposes a privacy-preserving RAG framework using conditional approximate distance-comparison-preserving encryption that enables similarity computation on encrypted embeddings in untrusted cloud environments. | <a href="http://arxiv.org/abs/2601.12331v1"><img src="https://img.shields.io/badge/arXiv-2601.12331-b31b1b.svg" alt="arXiv" /></a> |
+| **[Taming Various Privilege Escalation in LLM-Based Agent Systems: A Mandatory Access Control Framework](https://arxiv.org/pdf/2601.11893v1)** - Proposes a mandatory access control framework for LLM agent systems that monitors agent-tool interactions via information flow graphs and enforces attribute-based policies against privilege escalation. | <a href="http://arxiv.org/abs/2601.11893v1"><img src="https://img.shields.io/badge/arXiv-2601.11893-b31b1b.svg" alt="arXiv" /></a> |
+| **[Institutional AI: Governing LLM Collusion in Multi-Agent Cournot Markets via Public Governance Graphs](https://arxiv.org/pdf/2601.11369v2)** - Introduces governance graphs as public, immutable manifests with enforceable sanctions and restorative paths to govern multi-agent LLM coordination and prevent harmful collusion. | <a href="http://arxiv.org/abs/2601.11369v2"><img src="https://img.shields.io/badge/arXiv-2601.11369-b31b1b.svg" alt="arXiv" /></a> |
+| **[SD-RAG: A Prompt-Injection-Resilient Framework for Selective Disclosure in Retrieval-Augmented Generation](https://arxiv.org/pdf/2601.11199v1)** - Proposes a prompt-injection-resilient RAG framework that decouples security enforcement from generation by applying sanitization and policy-aware disclosure controls during the retrieval phase. | <a href="http://arxiv.org/abs/2601.11199v1"><img src="https://img.shields.io/badge/arXiv-2601.11199-b31b1b.svg" alt="arXiv" /></a> |
+| **[Beyond Max Tokens: Stealthy Resource Amplification via Tool Calling Chains in LLM Agents](https://arxiv.org/pdf/2601.10955v1)** - Introduces a stealthy multi-turn economic DoS attack exploiting the agent-tool communication loop through MCP-compatible tool server modifications that inflate costs by up to 658x. | <a href="http://arxiv.org/abs/2601.10955v1"><img src="https://img.shields.io/badge/arXiv-2601.10955-b31b1b.svg" alt="arXiv" /></a> |
+| **[Hidden-in-Plain-Text: A Benchmark for Social-Web Indirect Prompt Injection in RAG](https://arxiv.org/pdf/2601.10923v2)** - Introduces a benchmark and harness for evaluating web-facing RAG systems under indirect prompt injection and retrieval poisoning attacks with standardized end-to-end evaluation from ingestion to generation. | <a href="http://arxiv.org/abs/2601.10923v2"><img src="https://img.shields.io/badge/arXiv-2601.10923-b31b1b.svg" alt="arXiv" /></a> |
+| **[Breaking Up with Normatively Monolithic Agency with GRACE: A Reason-Based Neuro-Symbolic Architecture for Safe and Ethical AI Alignment](https://arxiv.org/pdf/2601.10520v2)** - Introduces a neuro-symbolic containment architecture that decouples normative reasoning from instrumental decision-making through a Moral Module, Decision-Making Module, and compliance Guard for agent safety. | <a href="http://arxiv.org/abs/2601.10520v2"><img src="https://img.shields.io/badge/arXiv-2601.10520-b31b1b.svg" alt="arXiv" /></a> |
+| **[AgentGuardian: Learning Access Control Policies to Govern AI Agent Behavior](https://arxiv.org/pdf/2601.10440v1)** - Presents a security framework that learns context-aware access-control policies from monitored execution traces to govern AI agent operations and detect malicious inputs while preserving normal functionality. | <a href="http://arxiv.org/abs/2601.10440v1"><img src="https://img.shields.io/badge/arXiv-2601.10440-b31b1b.svg" alt="arXiv" /></a> |
+| **[Agent Skills in the Wild: An Empirical Study of Security Vulnerabilities at Scale](https://arxiv.org/pdf/2601.10338v1)** - Analyzes 42,447 agent skills from two major marketplaces to study the prevalence and types of security vulnerabilities spanning prompt injection, data exfiltration, privilege escalation, and supply chain risks. | <a href="http://arxiv.org/abs/2601.10338v1"><img src="https://img.shields.io/badge/arXiv-2601.10338-b31b1b.svg" alt="arXiv" /></a> |
+| **[CaMeLs Can Use Computers Too: System-level Security for Computer Use Agents](https://arxiv.org/pdf/2601.09923v1)** - Proposes single-shot planning for Computer Use Agents that provides provable control flow integrity against prompt injection while preserving agent capability. | <a href="https://arxiv.org/abs/2601.09923v1"><img src="https://img.shields.io/badge/arXiv-2601.09923-b31b1b.svg" alt="arXiv" /></a> |
+| **[Blue Teaming Function-Calling Agents](https://arxiv.org/pdf/2601.09292v1)** - Tests open-source function-calling LLMs against multiple attack types with various defenses to study the readiness of current models and mitigations for production deployment. | <a href="https://arxiv.org/abs/2601.09292v1"><img src="https://img.shields.io/badge/arXiv-2601.09292-b31b1b.svg" alt="arXiv" /></a> |
+| **[Too Helpful to Be Safe: User-Mediated Attacks on Planning and Web-Use Agents](https://arxiv.org/pdf/2601.10758v1)** - Examines how commercial planning and web-use agents handle user-mediated attacks where the user themselves provides adversarial instructions without explicit safety requests. | <a href="https://arxiv.org/abs/2601.10758v1"><img src="https://img.shields.io/badge/arXiv-2601.10758-b31b1b.svg" alt="arXiv" /></a> |
+| **[Semantic Laundering in AI Agent Architectures: Why Tool Boundaries Do Not Confer Epistemic Warrant](https://arxiv.org/pdf/2601.08333v1)** - Formalizes how propositions gain unwarranted trust by crossing architecturally trusted interfaces in agent systems, studying whether circular epistemic justification is inevitable under standard assumptions. | <a href="https://arxiv.org/abs/2601.08333v1"><img src="https://img.shields.io/badge/arXiv-2601.08333-b31b1b.svg" alt="arXiv" /></a> |
+| **[Towards Verifiably Safe Tool Use for LLM Agents](https://arxiv.org/pdf/2601.08012v1)** - Proposes applying System-Theoretic Process Analysis to identify hazards in agent tool-use workflows, deriving formal safety specifications enforced through a capability-enhanced Model Context Protocol framework. | <a href="https://arxiv.org/abs/2601.08012v1"><img src="https://img.shields.io/badge/arXiv-2601.08012-b31b1b.svg" alt="arXiv" /></a> |
+| **[MCP-ITP: An Automated Framework for Implicit Tool Poisoning in MCP](https://arxiv.org/pdf/2601.07395v1)** - Introduces an automated framework for implicit tool poisoning in MCP where a poisoned tool remains uninvoked but its metadata manipulates the agent into performing malicious operations through legitimate tools. | <a href="https://arxiv.org/abs/2601.07395v1"><img src="https://img.shields.io/badge/arXiv-2601.07395-b31b1b.svg" alt="arXiv" /></a> |
+| **[Overcoming the Retrieval Barrier: Indirect Prompt Injection in the Wild for LLM Systems](https://arxiv.org/pdf/2601.07072v1)** - Proposes a black-box attack that decomposes indirect prompt injection into trigger and attack fragments to study end-to-end IPI exploits under natural queries across RAG and agentic systems. | <a href="https://arxiv.org/abs/2601.07072v1"><img src="https://img.shields.io/badge/arXiv-2601.07072-b31b1b.svg" alt="arXiv" /></a> |
+| **[MemTrust: A Zero-Trust Architecture for Unified AI Memory System](https://arxiv.org/pdf/2601.07004v1)** - Proposes a hardware-backed zero-trust architecture for AI memory systems that applies TEE protection across five functional layers with a cross-application sharing protocol for agent memory. | <a href="https://arxiv.org/abs/2601.07004v1"><img src="https://img.shields.io/badge/arXiv-2601.07004-b31b1b.svg" alt="arXiv" /></a> |
+| **[SafePro: Evaluating the Safety of Professional-Level AI Agents](https://arxiv.org/pdf/2601.06663v2)** - Introduces a benchmark for evaluating safety alignment of AI agents performing professional-level tasks across diverse domains, uncovering new unsafe behaviors in complex professional contexts. | <a href="https://arxiv.org/abs/2601.06663v2"><img src="https://img.shields.io/badge/arXiv-2601.06663-b31b1b.svg" alt="arXiv" /></a> |
+| **[Agentic LLMs as Powerful Deanonymizers: Re-identification of Participants in the Anthropic Interviewer Dataset](https://arxiv.org/pdf/2601.05918v1)** - Demonstrates that off-the-shelf LLM agents with web search can re-identify participants in anonymized qualitative datasets using only natural-language prompts, lowering the technical barrier for re-identification attacks. | <a href="https://arxiv.org/abs/2601.05918v1"><img src="https://img.shields.io/badge/arXiv-2601.05918-b31b1b.svg" alt="arXiv" /></a> |
+| **[Toward Safe and Responsible AI Agents: A Three-Pillar Model for Transparency, Accountability, and Trustworthiness](https://arxiv.org/pdf/2601.06223v1)** - Proposes a conceptual and operational framework for safe AI agent development grounded in transparency, accountability, and trustworthiness, with progressive validation analogous to autonomous driving stages. | <a href="https://arxiv.org/abs/2601.06223v1"><img src="https://img.shields.io/badge/arXiv-2601.06223-b31b1b.svg" alt="arXiv" /></a> |
+| **[VIGIL: Defending LLM Agents Against Tool Stream Injection via Verify-Before-Commit](https://arxiv.org/pdf/2601.05755v2)** - Proposes a verify-before-commit protocol for defending LLM agents against tool stream injection, using speculative hypothesis generation and intent-grounded verification to balance security with reasoning flexibility. | <a href="https://arxiv.org/abs/2601.05755v2"><img src="https://img.shields.io/badge/arXiv-2601.05755-b31b1b.svg" alt="arXiv" /></a> |
+| **[Memory Poisoning Attack and Defense on Memory Based LLM-Agents](https://arxiv.org/pdf/2601.05504v2)** - Evaluates memory poisoning attacks on memory-augmented LLM agents and proposes two defense mechanisms: input/output moderation with composite trust scoring and memory sanitization with trust-aware retrieval. | <a href="https://arxiv.org/abs/2601.05504v2"><img src="https://img.shields.io/badge/arXiv-2601.05504-b31b1b.svg" alt="arXiv" /></a> |
+| **[STELP: Secure Transpilation and Execution of LLM-Generated Programs](https://arxiv.org/pdf/2601.05467v3)** - Proposes a secure transpiler and executor for LLM-generated code that detects vulnerabilities and safely executes code snippets in autonomous production AI systems without relying on human review. | <a href="https://arxiv.org/abs/2601.05467v3"><img src="https://img.shields.io/badge/arXiv-2601.05467-b31b1b.svg" alt="arXiv" /></a> |
+| **[Conformity and Social Impact on AI Agents](https://arxiv.org/pdf/2601.05384v1)** - Investigates conformity bias in AI agents under social pressure using adapted visual experiments from social psychology, studying sensitivity to group size, unanimity, task difficulty, and source characteristics. | <a href="https://arxiv.org/abs/2601.05384v1"><img src="https://img.shields.io/badge/arXiv-2601.05384-b31b1b.svg" alt="arXiv" /></a> |
+| **[Defense Against Indirect Prompt Injection via Tool Result Parsing](https://arxiv.org/pdf/2601.04795v1)** - Proposes a tool result parsing method for defending LLM agents against indirect prompt injection by providing precise data while filtering out injected malicious code. | <a href="https://arxiv.org/abs/2601.04795v1"><img src="https://img.shields.io/badge/arXiv-2601.04795-b31b1b.svg" alt="arXiv" /></a> |
+| **[Autonomous Agents on Blockchains: Standards, Execution Models, and Trust Boundaries](https://arxiv.org/pdf/2601.04583v1)** - Surveys agent-blockchain interoperability patterns and threat models for agent-driven transaction pipelines, covering custody models, policy enforcement, and multi-agent workflows. | <a href="https://arxiv.org/abs/2601.04583v1"><img src="https://img.shields.io/badge/arXiv-2601.04583-b31b1b.svg" alt="arXiv" /></a> |
+| **[BackdoorAgent: A Unified Framework for Backdoor Attacks on LLM-based Agents](https://arxiv.org/pdf/2601.04566v2)** - Proposes a stage-aware framework for analyzing backdoor attacks across planning, memory, and tool-use stages of LLM agent workflows with cross-stage trigger propagation. | <a href="https://arxiv.org/abs/2601.04566v2"><img src="https://img.shields.io/badge/arXiv-2601.04566-b31b1b.svg" alt="arXiv" /></a> |
+| **[HoneyTrap: Deceiving LLM Attackers with Resilient Multi-Agent Defense](https://arxiv.org/pdf/2601.04034v1)** - Proposes a deceptive defense framework using collaborative defender agents to counter multi-turn jailbreak attacks by strategically wasting attacker resources. | <a href="https://arxiv.org/abs/2601.04034v1"><img src="https://img.shields.io/badge/arXiv-2601.04034-b31b1b.svg" alt="arXiv" /></a> |
+| **[SoK: Privacy Risks and Mitigations in Retrieval-Augmented Generation Systems](https://arxiv.org/pdf/2601.03979v1)** - Systematizes privacy risks, mitigation techniques, and evaluation strategies in RAG systems through a comprehensive literature review with a taxonomy and process diagram. | <a href="https://arxiv.org/abs/2601.03979v1"><img src="https://img.shields.io/badge/arXiv-2601.03979-b31b1b.svg" alt="arXiv" /></a> |
+| **[AgentMark: Utility-Preserving Behavioral Watermarking for Agents](https://arxiv.org/pdf/2601.03294v1)** - Proposes a behavioral watermarking framework that embeds multi-bit identifiers into agent planning decisions for IP protection and regulatory provenance while preserving utility. | <a href="https://arxiv.org/abs/2601.03294v1"><img src="https://img.shields.io/badge/arXiv-2601.03294-b31b1b.svg" alt="arXiv" /></a> |
+| **[Structural Representations for Cross-Attack Generalization in AI Agent Threat Detection](https://arxiv.org/pdf/2601.01723v1)** - Proposes structural tokenization that encodes execution-flow patterns instead of conversational content to improve cross-attack generalization in AI agent threat detection. | <a href="https://arxiv.org/abs/2601.01723v1"><img src="https://img.shields.io/badge/arXiv-2601.01723-b31b1b.svg" alt="arXiv" /></a> |
+| **[Lying with Truths: Open-Channel Multi-Agent Collusion for Belief Manipulation via Generative Montage](https://arxiv.org/pdf/2601.01685v1)** - Introduces a cognitive collusion attack where colluding agents steer victim beliefs using only truthful evidence fragments distributed through public channels without covert communication. | <a href="https://arxiv.org/abs/2601.01685v1"><img src="https://img.shields.io/badge/arXiv-2601.01685-b31b1b.svg" alt="arXiv" /></a> |
+| **[MCP-SandboxScan: WASM-based Secure Execution and Runtime Analysis for MCP Tools](https://arxiv.org/pdf/2601.01241v1)** - Proposes a lightweight framework that safely executes untrusted MCP tools inside a WebAssembly sandbox and produces auditable reports of external-to-sink exposures. | <a href="https://arxiv.org/abs/2601.01241v1"><img src="https://img.shields.io/badge/arXiv-2601.01241-b31b1b.svg" alt="arXiv" /></a> |
+| **[Harm in AI-Driven Societies: An Audit of Toxicity Adoption on Chirper.ai](https://arxiv.org/pdf/2601.01090v2)** - Analyzes toxicity adoption dynamics among LLM-driven agents on a fully AI-driven social platform, studying how cumulative toxic exposure affects the probability of toxic responses. | <a href="https://arxiv.org/abs/2601.01090v2"><img src="https://img.shields.io/badge/arXiv-2601.01090-b31b1b.svg" alt="arXiv" /></a> |
+| **[Trajectory Guard: A Lightweight, Sequence-Aware Model for Real-Time Anomaly Detection in Agentic AI](https://arxiv.org/pdf/2601.00516v1)** - Proposes a Siamese Recurrent Autoencoder with hybrid contrastive-reconstruction loss for real-time anomaly detection in agent action trajectories. | <a href="https://arxiv.org/abs/2601.00516v1"><img src="https://img.shields.io/badge/arXiv-2601.00516-b31b1b.svg" alt="arXiv" /></a> |
+| **[Mapping Human Anti-collusion Mechanisms to Multi-agent AI](https://arxiv.org/pdf/2601.00360v1)** - Maps human anti-collusion mechanisms including sanctions, leniency, monitoring, and market design to potential interventions for multi-agent AI systems. | <a href="https://arxiv.org/abs/2601.00360v1"><img src="https://img.shields.io/badge/arXiv-2601.00360-b31b1b.svg" alt="arXiv" /></a> |
+| **[Making Theft Useless: Adulteration-Based Protection of Proprietary Knowledge Graphs in GraphRAG Systems](https://arxiv.org/pdf/2601.00274v1)** - Proposes a data adulteration framework that pre-emptively injects plausible but false entries into knowledge graphs to make stolen GraphRAG KGs unusable to adversaries. | <a href="https://arxiv.org/abs/2601.00274v1"><img src="https://img.shields.io/badge/arXiv-2601.00274-b31b1b.svg" alt="arXiv" /></a> |
+| **[When Agents See Humans as the Outgroup: Belief-Dependent Bias in LLM-Powered Agents](https://arxiv.org/pdf/2601.00240v2)** - Examines intergroup bias in LLM agents under minimal group cues and formalizes a Belief Poisoning Attack that manipulates agent identity beliefs to induce outgroup bias toward humans. | <a href="https://arxiv.org/abs/2601.00240v2"><img src="https://img.shields.io/badge/arXiv-2601.00240-b31b1b.svg" alt="arXiv" /></a> |
+
+</details>
+
+## Contributing
+
+We welcome contributions! See [CONTRIBUTING.md](CONTRIBUTING.md) for guidelines.
+
+- Submit new papers via PR
+- Suggest category improvements
+
+## License
+
+MIT License - see [LICENSE](LICENSE)
+
+This is a curated list. Papers listed here are created and published by their respective authors, not by us. We curate papers relevant to the AI agent ecosystem and do not audit, endorse, or guarantee the correctness of listed research.
+
+If you find an issue with a listed paper or want a paper removed, please [open an issue](https://github.com/VoltAgent/awesome-ai-agent-papers/issues) and we'll take care of it promptly.
+
+[codex-badge]: https://img.shields.io/github/stars/VoltAgent/awesome-codex-subagents?style=classic&label=Codex%20Subagents&color=000000&logo=data:image/svg%2bxml;base64,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
+[codex-link]: https://github.com/VoltAgent/awesome-codex-subagents
@@ -0,0 +1,58 @@
+---
+title: "Contributing to Awesome AI Agents 2026"
+task: ""
+lineage_type: import
+upstream_source: https://github.com/caramaschiHG/awesome-ai-agents-2026/blob/781b6951/CONTRIBUTING.md
+upstream_sha: 781b6951
+imported_at: 2026-06-26
+prompt_class: catalogue
+upstream_changes: accepted
+author: upstream
+validated: false
+---
+
+# Contributing to Awesome AI Agents 2026
+
+Thanks for your interest in contributing! This list aims to be the most comprehensive and up-to-date resource for the AI agents ecosystem.
+
+## Guidelines
+
+### What belongs here
+- AI agents, frameworks, platforms, and tools from 2025-2026
+- Tools that are publicly available (released, beta, or open-source)
+- Resources that are actively maintained
+
+### What doesn't belong
+- Abandoned projects (no updates in 6+ months)
+- Purely theoretical / research-only projects without usable code
+- Duplicate entries or very minor forks
+
+### Format
+Please follow the existing table format:
+
+```markdown
+| [Name](https://link.com) | Brief description (1-2 sentences). | Pricing info | ![Stars](badge-url) |
+```
+
+### Process
+1. Fork the repo
+2. Create a branch: `git checkout -b add-tool-name`
+3. Add your entry in the appropriate section
+4. Ensure links work and descriptions are accurate
+5. Submit a PR
+
+### PR Requirements
+- [ ] Entry is in alphabetical order within its section
+- [ ] Link is valid and points to official source
+- [ ] Description is concise and factual
+- [ ] Pricing info is current
+- [ ] No promotional language
+
+## Updating Existing Entries
+If you notice outdated information (pricing changes, new features, project status), please submit a PR with corrections.
+
+## Suggesting New Categories
+Open an issue first to discuss new category proposals before submitting a PR.
+
+## Code of Conduct
+Be respectful and constructive. We're all here to build a useful resource for the community.
@@ -0,0 +1,685 @@
+---
+title: "Readme"
+task: ""
+lineage_type: import
+upstream_source: https://github.com/caramaschiHG/awesome-ai-agents-2026/blob/781b6951/README.md
+upstream_sha: 781b6951
+imported_at: 2026-06-26
+prompt_class: catalogue
+upstream_changes: accepted
+author: upstream
+validated: false
+---
+
+<!--lint disable awesome-heading awesome-github awesome-toc -->
+
+<div align="center">
+  <img src="assets/banner.svg" alt="Awesome AI Agents 2026" width="800">
+  <br><br>
+
+  [![Awesome](https://awesome.re/badge-flat2.svg)](https://awesome.re)
+  [![Stars](https://img.shields.io/github/stars/caramaschiHG/awesome-ai-agents-2026?style=flat-square&color=yellow)](https://github.com/caramaschiHG/awesome-ai-agents-2026/stargazers)
+  [![Last Update](https://img.shields.io/badge/last%20update-April%202026-blue?style=flat-square)]()
+  [![PRs Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat-square)](http://makeapullrequest.com)
+  [![License: CC0-1.0](https://img.shields.io/badge/License-CC0_1.0-lightgrey.svg?style=flat-square)](http://creativecommons.org/publicdomain/zero/1.0/)
+
+  <h3>The most comprehensive list of AI agents, frameworks, and tools in 2026.</h3>
+  <h4>340+ resources across 20+ categories. Updated monthly.</h4>
+
+  <br>
+
+  <a href="#-coding-agents">Coding</a> · <a href="#-agent-frameworks">Frameworks</a> · <a href="#-browser--desktop-agents">Browser</a> · <a href="#-voice-agents">Voice</a> · <a href="#-creative-ai">Creative</a> · <a href="#-task--workflow-agents">Workflow</a> · <a href="#-customer-support--crm-agents">CRM</a> · <a href="#-data--research-agents">Research</a> · <a href="#-local--self-hosted-ai">Self-Hosted</a> · <a href="#-protocols--standards">Protocols</a>
+
+</div>
+
+---
+
+> **340+ tools. 20+ categories. Updated monthly.** Star to stay updated. [Contributions welcome!](#contributing)
+
+---
+
+## Contents
+
+- [GNAP](https://github.com/farol-team/gnap) — Git-Native Agent Protocol: coordinate AI agent teams with 4 JSON files in a git repo. No server, no database. Any agent that can git push can participate. MIT licensed.
+- [Coding Agents](#-coding-agents) — IDE, Terminal, Autonomous, Code Review, App Builders
+- [Agent Frameworks](#-agent-frameworks) — General, Multi-Agent, Lightweight
+- [Browser and Desktop Agents](#-browser--desktop-agents) — Consumer, Infrastructure
+- [Voice Agents](#-voice-agents) — Platforms, Open-Source
+- [Creative AI](#-creative-ai) — Image, Video, Music, 3D
+- [Task and Workflow Agents](#-task--workflow-agents) — Automation, No-Code Builders
+- [Customer Support and CRM Agents](#-customer-support--crm-agents)
+- [Data and Research Agents](#-data--research-agents) — Deep Research, Data Analysis, RAG
+- [Local and Self-Hosted AI](#-local--self-hosted-ai) — LLM Runners, Self-Hosted UIs
+- [Multi-Agent Platforms](#-multi-agent-platforms)
+- [Protocols and Standards](#-protocols--standards)
+- [Observability and Evaluation](#-observability--evaluation)
+- [Open-Source Models for Agents](#-open-source-models-for-agents)
+- [AI Safety and Guardrails](#-ai-safety--guardrails)
+- [AI Governance and Compliance](#-ai-governance--compliance) ⭐ NEW
+- [Cybersecurity Agents](#-cybersecurity-agents)
+- [Healthcare and Therapy Agents](#-healthcare-and-therapy-agents)
+- [Learning Resources](#-learning-resources)
+- [Newsletters and Communities](#-newsletters--communities)
+- [Market Stats 2026](#-market-stats-2026)
+
+---
+
+## 🖥 Coding Agents
+
+### IDE-Native Agents
+
+| Agent | Description | Pricing |
+|-------|-------------|---------|
+| [Cursor](https://cursor.com) | VS Code fork. Composer mode for multi-file edits. Claude Sonnet 5, GPT-5, Gemini 3.1. $29.3B valuation. | Free / $20/mo |
+| [GitHub Copilot](https://github.com/features/copilot) | Agent Mode in VS Code. Copilot Workspace issue-to-PR. Multi-model (Claude, GPT-5.4, Gemini 3.1). | $10/mo / $39/mo Pro+ |
+| [Windsurf (Codeium)](https://windsurf.com) | Cascade agentic mode. Project-level memory. 5 parallel agents. | Free / $15/mo |
+| [JetBrains AI](https://www.jetbrains.com/ai/) | Deep integration across all JetBrains IDEs. Context-aware completions. | Included with IDE |
+| [Amazon Q Developer](https://aws.amazon.com/q/developer/) | AWS-native. Lambda, CloudWatch, infrastructure, security scanning. | Free / $19/mo |
+| [Tabnine](https://www.tabnine.com/) | Privacy-first. On-premise option. Fine-tuned on your codebase. | Free / $12/mo |
+| [Sourcegraph Cody](https://sourcegraph.com/cody) | Excels at large codebases. Enterprise context engine. | Free / $9/mo |
+| [Google Antigravity](https://idx.google.com) | Free Claude Opus 4.6 access. Learning-focused. | Free |
+| [Kiro](https://kiro.dev) | Spec-driven development. Write specs → auto-generate tasks → implement. DevOps automation. | Free beta |
+
+### Terminal and CLI Agents
+
+| Agent | Description | Pricing |
+|-------|-------------|---------|
+| [Claude Code](https://docs.anthropic.com/en/docs/claude-code) | Anthropic CLI agent. Best reasoning. 80.9% SWE-bench. Agent Teams feature. | $20/mo+ API |
+| [OpenAI Codex CLI](https://github.com/openai/codex) | OpenAI terminal agent. Agents SDK. Multi-agent. | ChatGPT sub |
+| [Gemini CLI](https://github.com/google-gemini/gemini-cli) | ⭐ **NEW (Apr 2026)** Google's official open-source terminal agent. ReAct loop. MCP support. 1M context. Apache 2.0. | Free w/ Google account |
+| [Aider](https://github.com/paul-gauthier/aider) | OSS pair programmer. Git-aware. Any LLM. | Free + API |
+| [Cline](https://github.com/cline/cline) | VS Code extension. Full terminal and browser access for Claude/GPT. | Free + API |
+| [RooCode](https://github.com/RooVetGit/Roo-Code) | Cline fork. Structured modes. Reduced hallucinations. | Free + API |
+| [Kilo Code](https://kilocode.ai) | Structured modes. Tighter context. | Free + API |
+| [OpenCode](https://github.com/opencode-ai/opencode) | BYOK terminal agent for Cursor refugees. | Free + API |
+| [Caliber](https://github.com/caliber-ai-org/ai-setup) | CLI that fingerprints projects and generates/syncs AI agent configs (CLAUDE.md, .cursor/rules/, AGENTS.md). Scores quality. | Free + API |
+
+### Autonomous Software Engineers
+
+| Agent | Description | Pricing |
+|-------|-------------|---------|
+| [Devin](https://devin.ai) | Cognition. Fully autonomous. Sandboxed cloud env. Devin 2.0 with Interactive Planning. | $20/mo + ACU |
+| [Copilot Workspace](https://githubnext.com/projects/copilot-workspace) | GitHub issue-to-PR agent. | Copilot sub |
+| [SWE-Agent](https://github.com/princeton-nlp/SWE-agent) | Princeton. Resolves real GitHub issues autonomously. | Free (OSS) |
+| [OpenHands](https://github.com/All-Hands-AI/OpenHands) | OSS autonomous software engineer (ex-OpenDevin). | Free (OSS) |
+| [Grok Build (xAI)](https://x.ai) | 8 parallel agents for code gen. Multi-agent "Society of Mind" architecture. | xAI sub |
+
+### Code Review and Security
+
+| Agent | Description | Pricing |
+|-------|-------------|---------|
+| [Qodo](https://www.qodo.ai/) | AI code review. Context-aware PR validation. | Free / Enterprise |
+| [CodeRabbit](https://coderabbit.ai/) | AI PR reviewer. Inline suggestions, security. | Free OSS / $15/mo |
+| [Snyk Code](https://snyk.io/) | AI security scanner. Real-time vuln detection. | Free / Enterprise |
+| [PR-Agent](https://github.com/Codium-ai/pr-agent) | OSS AI PR reviewer. Auto-describe, review, improve. | Free (OSS) |
+
+### App Builders (Prompt-to-App)
+
+| Agent | Description | Pricing |
+|-------|-------------|---------|
+| [Bolt.new](https://bolt.new) | Prompt to full-stack web app in browser. | Free / Paid |
+| [Lovable](https://lovable.dev) | Describe then build then deploy from chat. | Free / $20/mo |
+| [v0 (Vercel)](https://v0.dev) | Prompt to React/Tailwind components. | Free / Pro |
+| [Replit Agent](https://replit.com) | Full-stack from prompt. Auto-deploys. | Free / $25/mo |
+| [PlayCode Agent](https://playcode.io) | Browser-based. English to websites. | $9.99/mo |
+| [Dyad](https://github.com/dyad-sh/dyad) | OSS. Local-first. No-code app builder. | Free (OSS) |
+
+---
+
+## 🧱 Agent Frameworks
+
+### General Purpose
+
+| Framework | Lang | Description |
+|-----------|------|-------------|
+| [LangChain](https://github.com/langchain-ai/langchain) | Py/JS | Most adopted. Modular architecture, memory, tools. |
+| [LangGraph](https://github.com/langchain-ai/langgraph) | Py/JS | Graph-based orchestration. Stateful directed graphs. |
+| [LlamaIndex](https://github.com/run-llama/llama_index) | Py/JS | Data-focused. Best for RAG agents. |
+| [Haystack](https://github.com/deepset-ai/haystack) | Py | Pipeline-based. Search and retrieval. |
+| [Semantic Kernel](https://github.com/microsoft/semantic-kernel) | C#/Py/Java | Microsoft enterprise. Azure integration. |
+| [Pydantic AI](https://github.com/pydantic/pydantic-ai) | Py | Type-safe. Clean Pythonic API. Production-ready. |
+| [DSPy](https://github.com/stanfordnlp/dspy) | Py | Stanford. Programming not prompting. Auto-optimizes. |
+| [Mastra](https://github.com/mastra-ai/mastra) | TS | TypeScript-first. Observational Memory. Apache 2.0. |
+| [Anthropic SDK](https://github.com/anthropics/anthropic-sdk-python) | Py/TS | Official Claude SDK. Tool use, computer control, streaming. |
+| [Google ADK](https://github.com/google/adk-python) | Py | ⭐ Google's Agent Development Kit. Native Gemini. Multi-agent orchestration. |
+
+### Multi-Agent Orchestration
+
+| Framework | Lang | Description |
+|-----------|------|-------------|
+| [AutoGen](https://github.com/microsoft/autogen) | Py | Microsoft multi-agent conversations. |
+| [CrewAI](https://github.com/crewAIInc/crewAI) | Py | Role-based crew members with goals and tools. Used by 60%+ Fortune 500. |
+| [MetaGPT](https://github.com/geekan/MetaGPT) | Py | PM, architect, engineer roles. Software company sim. |
+| [Miyabi](https://github.com/ShunsukeHayashi/Miyabi) | TS | Issue-Driven Development. 7 coding + 14 business agents. MCP 172+ tools. GitHub as OS. |
+| [OpenAI Agents SDK](https://github.com/openai/openai-agents-python) | Py | Official. Multi-step agents with handoffs. |
+| [Strands Agents](https://github.com/strands-agents/sdk-python) | Py | AWS-backed. Model-driven tool use. |
+| [CAMEL](https://github.com/camel-ai/camel) | Py | Role-based simulation. Collaborative reasoning. |
+| [AutoGPT](https://github.com/Significant-Gravitas/AutoGPT) | Py | Pioneer. Now full platform with visual builder. |
+| [Bernstein](https://github.com/chernistry/bernstein) | Py | Deterministic orchestrator. Parallel coding agents, test-driven verification. Zero LLM tokens on coordination. |
+| [AgentScope](https://github.com/modelscope/agentscope) | Py | Alibaba multi-agent framework. |
+| [MagiC](https://github.com/kienbui1995/magic) | Go/Py | Kubernetes for AI agents. Manages any agent from any framework. Routing, cost control, DAG workflows, circuit breaker. |
+| [DeerFlow](https://github.com/bytedance/deer-flow) | Py | ByteDance. No.1 GitHub Trending Feb 2026. 25k+ stars. |
+| [AXME](https://github.com/AxmeAI/axme) | Py/TS/Go/Java/.NET | Durable coordination. Crash recovery, human approval gates, kill switch. Open protocol (AXP). |
+
+### Lightweight / Minimalist
+
+| Framework | Lang | Description |
+|-----------|------|-------------|
+| [Smolagents](https://github.com/huggingface/smolagents) | Py | HuggingFace minimal agents. ~1000 lines. |
+| [Agno](https://github.com/agno-agi/agno) | Py | Lightweight, model-agnostic. |
+| [Upsonic](https://github.com/upsonic/upsonic) | Py | MCP support. Minimal setup. |
+| [Portia AI](https://github.com/portia-ai/portia-sdk-python) | Py | Reliable agents in production. |
+| [MicroAgent](https://github.com/aymenfurter/microagent) | Py | Self-editing prompts and code. |
+
+### Agent Templates
+
+| Collection | Description |
+|------------|-------------|
+| [OpenClaw Agent Templates](https://github.com/mergisi/awesome-openclaw-agents) | 177 production-ready SOUL.md configs across 24 categories (PM, SEO, DevOps, Writer, Support). Copy-paste ready for [OpenClaw](https://github.com/openclaw/openclaw). Visual deploy via [CrewClaw](https://crewclaw.com). |
+
+---
+
+## 🌐 Browser and Desktop Agents
+
+### Consumer Products
+
+| Agent | Description | Pricing |
+|-------|-------------|---------|
+| [OpenAI Operator](https://operator.chatgpt.com) | ChatGPT autonomous web agent. Human checkpoints. CUA tech. | ChatGPT Pro |
+| [Manus (Meta)](https://manus.im) | Autonomous digital employee. Browser Operator extension. Acquired by Meta. | Free / Paid |
+| [Claude Computer Use](https://docs.anthropic.com/en/docs/agents-and-tools/computer-use) | Anthropic desktop/browser control via screenshots. | API |
+| [Claude in Chrome](https://claude.ai) | Anthropic browsing agent. Beta. | Claude sub |
+| [Google Project Mariner](https://deepmind.google/technologies/project-mariner/) | Gemini browser agent. Multi-tasking. | Waitlist |
+| [OpenAI Atlas](https://atlas.openai.com) | AI browser with Agent Mode. | ChatGPT sub |
+| [Dia Browser](https://diabrowser.com) | AI-native browser (Atlassian/Browser Company). | Beta |
+| [Fellou](https://fellou.ai) | Transparent. Visual workflow editing. Agentic memory. | Beta |
+| [Genspark](https://genspark.ai) | 169+ on-device models. No internet required. | Free / Paid |
+| [Grok Computer](https://x.ai) | ⭐ **Upcoming** xAI desktop agent. Mouse control, app automation. | TBA |
+
+### Developer Infrastructure
+
+| Tool | Description |
+|------|-------------|
+| [Browser Use](https://github.com/browser-use/browser-use) | OSS browser agent library. Used by Manus. |
+| [Skyvern](https://github.com/Skyvern-AI/skyvern) | Vision-driven. GPT-4V navigation without coded selectors. |
+| [Agent S2 (Simular)](https://github.com/simular-ai/Agent-S) | OSS GUI automation framework. |
+| [MultiOn](https://multion.ai) | Reliable web automation API. CAPTCHA handling. |
+| [Browserbase](https://browserbase.com) | Cloud browser infra for agents. Headless at scale. |
+| [Airtop](https://airtop.ai) | Enterprise browser automation. AI integration. |
+| [Amazon Nova Act](https://aws.amazon.com/ai/nova/) | AWS browser automation. Enterprise reliability. |
+| [Plasmate](https://github.com/plasmate-labs/plasmate) | Headless browser compiling HTML to structured JSON (SOM). 17.5x compression, 13 MCP tools. First browser tool on MCP Registry. Rust, Apache-2.0. |
+| [Playwright MCP](https://github.com/microsoft/playwright-mcp) | MCP server for Playwright + AI agents. |
+| [onUI](https://github.com/onllm-dev/onUI) | OSS browser extension and MCP server for annotation-first UI pair programming with AI agents. Chrome, Edge, Firefox. Privacy-first, local only. |
+
+---
+
+## 🎙 Voice Agents
+
+### Platforms and APIs
+
+| Platform | Description | Pricing |
+|----------|-------------|---------|
+| [ElevenLabs](https://elevenlabs.io) | Industry benchmark. Conv AI 2.0. RAG, multimodal, batch calling. 75ms. HIPAA. $11B. | Free / $5+/mo |
+| [Vapi](https://vapi.ai) | Developer-first. Low-latency, model-agnostic. | Usage-based |
+| [Bland AI](https://bland.ai) | Outbound call automation. CRM integration. SOC2/HIPAA. | Usage-based |
+| [Voiceflow](https://voiceflow.com) | No-code voice and chat builder. Drag-and-drop. | Free / $50+/mo |
+| [Synthflow](https://synthflow.ai) | No-code voice agents for SMBs. Templates. | From $29/mo |
+| [PolyAI](https://poly.ai) | Enterprise. Natural multi-turn. Hospitality/retail. | Enterprise |
+| [Retell AI](https://retellai.com) | Human-like voice agents. Multi-language. Telephony. | Usage-based |
+| [HeyGen](https://heygen.com) | Talking avatars. Voice cloning. Lip-sync translation. | From $24/mo |
+| [Synthesia](https://synthesia.io) | AI video avatars. 120+ languages. Enterprise. | From $22/mo |
+| [Deepgram](https://deepgram.com) | STT and TTS APIs. Sub-300ms latency. | Usage-based |
+| [AssemblyAI](https://assemblyai.com) | STT with diarization, sentiment, summarization. | Usage-based |
+
+### Open-Source Voice
+
+| Tool | Description |
+|------|-------------|
+| [LiveKit Agents](https://github.com/livekit/agents) | OSS real-time voice/video AI agents. |
+| [Rasa](https://github.com/RasaHQ/rasa) | OSS conversational AI. Self-hosted. NLU training. |
+| [Pipecat](https://github.com/pipecat-ai/pipecat) | OSS voice and multimodal conversational AI. |
+| [Vocode](https://github.com/vocodedev/vocode-python) | OSS voice-based LLM agents. |
+
+---
+
+## 🎨 Creative AI
+
+### Image Generation
+
+| Tool | Description | Pricing |
+|------|-------------|---------|
+| [Midjourney v7](https://midjourney.com) | Best artistic quality. Unmatched aesthetics. Discord + web. | From $10/mo |
+| [DALL-E 3.5](https://openai.com/dall-e) | Best prompt comprehension. 95% text accuracy. ChatGPT. | ChatGPT Plus |
+| [FLUX 2](https://blackforestlabs.ai) | Open-weight. Best photorealism. 4K. 6x speed. | Free / API |
+| [Stable Diffusion 3.5](https://stability.ai) | Open-source. ControlNet, LoRAs, ComfyUI ecosystem. | Free (OSS) |
+| [Adobe Firefly 3](https://firefly.adobe.com) | Licensed data only. Commercial indemnification. Photoshop. | Adobe CC |
+| [Google Imagen 4](https://deepmind.google) | State-of-art photorealism. API via AI Studio. | API |
+| [Ideogram v3](https://ideogram.ai) | Best text-in-image. Zero spelling errors. Logos/posters. | Free / $7+/mo |
+| [Leonardo AI](https://leonardo.ai) | Multi-model. Realtime Canvas. 3D gaming assets. Canva-owned. | Free / $12+/mo |
+| [Recraft](https://recraft.ai) | Design-focused. Vector art, brand consistency. | Free / Paid |
+| [InkOS](https://github.com/Narcooo/inkos) | Autonomous novel-writing CLI agent. Agents collaborate to produce long-form fiction with continuity auditing, anti-AI-slop filtering, and style cloning. | Free / OSS |
+
+### Video Generation
+
+| Tool | Description | Pricing |
+|------|-------------|---------|
+| [Google Veo 3.1](https://deepmind.google) | ⭐ **Best in class** Native audio+video. 4K. Veo 3.1 Lite for devs (Apr 2026). | API |
+| [Kling 3.0](https://klingai.com) | ⭐ Cinematic realism. 15s sequences. Native audio sync. Character consistency. | Free / $6.99+/mo |
+| [Runway Gen-4.5](https://runwayml.com) | No.1 benchmark. Motion Brush, Director Mode. Best editing. | From $12/mo |
+| [Sora 2](https://sora.com) | OpenAI. Narrative coherence. Physics realism. **Note: app retiring Apr 26, 2026.** | $20+/mo |
+| [Seedance 2.0](https://seedance.ai) | Quad-modal input. Lip sync. 2K resolution. | Free credits |
+| [Pika 2.5](https://pika.art) | Beginner-friendly. Pikaswaps. Fast renders. | Free / $8+/mo |
+| [Luma Dream Machine](https://lumalabs.ai) | 4K HDR. Physics simulation. 3D/cinematic. | From $7.99/mo |
+| [HaiLuo AI](https://hailuoai.video) | Budget video. 10 free/day. MiniMax. | Free / $4.99+/mo |
+| [Wan 2.1](https://github.com/Wan-Video/Wan2.1) | Best free OSS video gen. Self-hostable. No limits. | Free (OSS) |
+| [HunyuanVideo](https://github.com/Tencent/HunyuanVideo) | Tencent OSS. Consumer GPU. Multi-style. | Free (OSS) |
+| [LTX Video](https://github.com/Lightricks/LTX-Video) | OSS. Licensed data. Clear commercial terms. | Free (OSS) |
+
+### Music and Audio
+
+| Tool | Description | Pricing |
+|------|-------------|---------|
+| [Suno](https://suno.ai) | Text-to-song. Full tracks with vocals. Viral hit maker. | Free / $8+/mo |
+| [Udio](https://udio.com) | High-fidelity music gen. Fine control. | Free / $10+/mo |
+| [ElevenLabs Music](https://elevenlabs.io) | Vocals, instrumentals. Sectional editing. Stem separation. | Plan included |
+| [Stable Audio](https://stableaudio.com) | High-quality. Commercial license. | Free / Paid |
+| [Meta AudioCraft](https://github.com/facebookresearch/audiocraft) | OSS. MusicGen + AudioGen. | Free (OSS) |
+
+### 3D and Design
+
+| Tool | Description | Pricing |
+|------|-------------|---------|
+| [Meshy](https://meshy.ai) | Text/image to 3D. Game assets, products. | Free / Paid |
+| [Tripo AI](https://tripo3d.ai) | Fast 3D from text/images. Multi-format export. | Free / Paid |
+| [Vizcom](https://vizcom.ai) | Real-time AI rendering for industrial designers. | From $20/mo |
+
+---
+
+## ⚡ Task and Workflow Agents
+
+### Automation
+
+| Agent | Description | Pricing |
+|-------|-------------|---------|
+| [n8n](https://github.com/n8n-io/n8n) | OSS workflow automation with AI agent nodes. Visual + code. | Free / Cloud |
+| [Zapier AI](https://zapier.com/ai) | 7000+ apps. Natural language workflows. | From $19.99/mo |
+| [Make](https://make.com) | Visual workflow platform. AI capabilities. | Free / Paid |
+| [Activepieces](https://github.com/activepieces/activepieces) | OSS Zapier alternative with AI. | Free (OSS) |
+| [Temporal](https://github.com/temporalio/temporal) | Durable execution for long-running agent workflows. | Free / Cloud |
+| [Mission Control](https://github.com/MeisnerDan/mission-control) | Cockpit for the agentic era — manage AI agent swarms with autonomous daemon, Field Ops for real-world execution, and approval workflows. | Free (OSS) |
+
+### No-Code Agent Builders
+
+| Agent | Description | Pricing |
+|-------|-------------|---------|
+| [Dify](https://github.com/langgenius/dify) | OSS LLMOps. Visual agent builder. RAG. 130k+ stars. | Free / Cloud |
+| [Flowise](https://github.com/FlowiseAI/Flowise) | OSS drag-and-drop LLM agent builder. | Free (OSS) |
+| [Langflow](https://github.com/langflow-ai/langflow) | Visual multi-agent and RAG builder. | Free / Cloud |
+| [Lindy](https://lindy.ai) | No-code agents. 3000+ integrations. | From $49/mo |
+| [Relevance AI](https://relevanceai.com) | No-code agents for sales, support, research. | Free / Paid |
+| [Rivet](https://rivet.ironcladapp.com) | Visual AI workflow builder. Drag-and-drop. | Free (OSS) |
+| [FastAgency](https://github.com/airtai/fastagency) | Deploy multi-agent workflows as APIs. | Free (OSS) |
+| [cstack](https://github.com/srf6413/cstack) | Architecture pattern for autonomous agents using Claude Cowork, Notion, and MCP. Persistent multi-domain agents with no custom infrastructure or code. | Free |
+
+---
+
+## 💼 Customer Support and CRM Agents
+
+### Support Agents
+
+| Agent | Description | Pricing |
+|-------|-------------|---------|
+| [Intercom Fin](https://intercom.com) | Resolves 50%+ tickets. Learns from help center. | From $29/seat |
+| [Zendesk AI](https://zendesk.com) | Ticket routing, sentiment detection, Answer Bot. | From $19/agent |
+| [Ada](https://ada.cx) | Autonomous resolution. Multi-channel. SOP Playbooks. | Enterprise |
+| [Assembled](https://assembled.com) | Workforce-aware handoffs. End-to-end resolution. | Enterprise |
+| [Freshdesk Freddy AI](https://freshworks.com) | Auto-triage, smart routing, predictive analytics. | From $15/agent |
+| [Dixa (Mim)](https://dixa.com) | Conversational CRM. AI routing and prioritization. | Enterprise |
+
+### AI-Powered CRMs
+
+| CRM | AI Features | Pricing |
+|-----|-------------|---------|
+| [Salesforce Einstein + Agentforce](https://salesforce.com) | Predictions, autonomous agents, ChatGPT integration. | Enterprise |
+| [HubSpot Breeze](https://hubspot.com) | Copilot, Agents, Intelligence. Agent marketplace. | Free / $45+/mo |
+| [Monday CRM (Lexi)](https://monday.com) | AI sales agent. Lead sourcing, qualification. AI Blocks. | From $12/seat |
+| [Zoho CRM (Zia)](https://zoho.com/crm) | Predictive, sentiment, voice commands. | From $14/user |
+| [Pipedrive AI](https://pipedrive.com) | Email gen, deal priority, smart reports. | From $14/seat |
+| [Dynamics 365 Copilot](https://dynamics.microsoft.com) | Drafting, summarizing, translating. Power Platform. | Enterprise |
+| [ServiceNow AI Agents](https://servicenow.com) | Orchestrator across IT, HR, CRM. | Enterprise |
+| [Creatio](https://creatio.com) | No-code. Pre-configured agents. | From $25/user |
+| [Salesmate](https://salesmate.io) | Call summarization, lead qualification. | From $23/user |
+
+### Sales and Outreach Agents
+
+| Agent | Description | Pricing |
+|-------|-------------|---------|
+| [Clay](https://clay.com) | AI data enrichment. Personalized outreach at scale. | From $149/mo |
+| [Apollo.io](https://apollo.io) | AI prospecting, sequences, scoring. 275M+ contacts. | Free / $49+/mo |
+| [Instantly](https://instantly.ai) | AI cold email. Unlimited accounts. Smart rotation. | From $30/mo |
+| [Overloop CLI](https://github.com/sortlist/overloop-cli) | AI outbound CLI. Source 450M+ contacts, email + LinkedIn campaigns, conversations. Agent-native JSON output. | $69-99/mo |
+| [Lavender](https://lavender.ai) | AI email coach. Real-time scoring. | Free / $29/mo |
+
+---
+
+## 📊 Data and Research Agents
+
+### Deep Research
+
+| Agent | Description | Pricing |
+|-------|-------------|---------|
+| [Claude Deep Research](https://claude.ai) | Multi-step investigation with citations. Sonnet 5 / Opus 4.6. | Claude Pro |
+| [ChatGPT Deep Research](https://chat.openai.com) | Extended reasoning, web browsing, reports. GPT-5.4. | ChatGPT Pro |
+| [Gemini Deep Research](https://gemini.google.com) | Google Search and Knowledge Graph. Gemini 3.1 Pro. | Gemini Advanced |
+| [Perplexity Pro](https://perplexity.ai) | AI search with deep research mode. Real-time citations. | Free / $20/mo |
+| [DeerFlow](https://github.com/bytedance/deer-flow) | ByteDance OSS. Planning, tools, memory, execution. | Free (OSS) |
+| [GPT Researcher](https://github.com/assafelovic/gpt-researcher) | OSS autonomous comprehensive research. | Free (OSS) |
+| [STORM](https://github.com/stanford-oval/storm) | Stanford. Writes Wikipedia-like articles from scratch. | Free (OSS) |
+
+### Data Analysis
+
+| Agent | Description | Pricing |
+|-------|-------------|---------|
+| [AI for Database](https://aifordatabase.com) | Connect to any database and interact with it in plain English. No SQL needed — get instant insights, build self-refreshing dashboards, and trigger automated workflows based on database changes. | Free / Paid |
+| [Julius AI](https://julius.ai) | Upload CSV/Excel, ask in natural language. | Free / Paid |
+| [Hex AI](https://hex.tech) | Collaborative data platform. AI analysis. | Free / Paid |
+| [PandasAI](https://github.com/Sinaptik-AI/pandas-ai) | Chat with your data. NL to Pandas/SQL. | Free (OSS) |
+| [Signals CLI](https://github.com/sortlist/signals-cli) | Intent signal CLI. LinkedIn engagers, keyword posters, job changers, funding events. JSON output for agent pipelines. | Paid |
+| [TaskWeaver](https://github.com/microsoft/TaskWeaver) | Microsoft. Code-first data analytics agents. | Free (OSS) |
+| [AI for Database](https://aifordatabase.com) | Connect to any database in plain English. NL queries, self-refreshing dashboards, automated workflows triggered by data changes. | Freemium |
+
+### RAG and Knowledge Bases
+
+| Tool | Description |
+|------|-------------|
+| [RAGFlow](https://github.com/infiniflow/ragflow) | OSS RAG engine with agent capabilities. |
+| [Lorg](https://github.com/LorgAI/lorg-mcp-server) — Permanent intelligence archive for AI agents. Structured contributions (prompts, workflows, insights, patterns) pass an automated quality gate and are hash-chained. Trust scores are cryptographically backed and publicly auditable. Works with Claude and ChatGPT.
+| [Pathway](https://github.com/pathwaycom/pathway) | Live data RAG. Real-time streaming. 50k+ stars. |
+| [Mem0](https://github.com/mem0ai/mem0) | Memory layer for agents. Long-term across sessions. |
+| [Nex](https://github.com/nex-crm/nex-as-a-skill) | Organizational context and memory for AI agents. 60-tool MCP server, 100+ integrations. |
+| [Chroma](https://github.com/chroma-core/chroma) | OSS embedding database. Fastest way to build RAG. |
+| [Weaviate](https://github.com/weaviate/weaviate) | OSS vector DB. GraphQL. Multi-modal search. |
+| [Qdrant](https://github.com/qdrant/qdrant) | High-performance vector DB in Rust. |
+| [Milvus](https://github.com/milvus-io/milvus) | Cloud-native vector DB. Billion-scale. |
+| [Pinecone](https://pinecone.io) | Managed vector DB. Serverless. Low-latency. |
+| [iGPT](https://igpt.ai) | Email Intelligence API. Converts email threads into reasoning-ready JSON for agents. |
+
+---
+
+## 🏠 Local and Self-Hosted AI
+
+### Local LLM Runners
+
+| Tool | Description |
+|------|-------------|
+| [Ollama](https://github.com/ollama/ollama) | Run LLMs locally. 162k+ stars. Dead simple CLI. |
+| [llama.cpp](https://github.com/ggml-org/llama.cpp) | C/C++ inference. CPU, GPU, Apple Silicon. Foundation of local AI. |
+| [vLLM](https://github.com/vllm-project/vllm) | High-throughput serving. PagedAttention. Production-grade. |
+| [LM Studio](https://lmstudio.ai) | Desktop app for local LLMs. Beautiful UI. All platforms. |
+| [Jan](https://github.com/janhq/jan) | OSS ChatGPT alternative. 100% offline. |
+| [LocalAI](https://github.com/mudler/LocalAI) | Drop-in OpenAI API replacement. No GPU required. |
+| [Cerebras Inference](https://inference.cerebras.ai) | Fastest LLM inference. Llama 3.3 70B at 1000+ tok/s. Free tier. |
+| [Groq Cloud](https://console.groq.com) | Ultra-fast LPU inference. Mixtral, Llama, Gemma. Free API tier. |
+| [Fireworks AI](https://fireworks.ai) | Serverless LLM inference. Fine-tuning. RAG. Free credits. |
+| [Together AI](https://together.ai) | 200+ open models. Fast inference API. Free tier. |
+| [GPT4All](https://github.com/nomic-ai/gpt4all) | OSS local chat. Consumer hardware. |
+| [Llamafile](https://github.com/Mozilla-Ocho/llamafile) | LLMs as single files. Zero setup. Mozilla. |
+
+### Self-Hosted Agents and UIs
+
+| Tool | Description |
+|------|-------------|
+| [Open WebUI](https://github.com/open-webui/open-webui) | Self-hosted ChatGPT UI. Access control. Extensions. |
+| [OpenClaw](https://github.com/openclaw/openclaw) | Fastest-growing GitHub repo ever (9k to 188k stars in 60 days). Self-hosted agent across WhatsApp, Telegram, Slack, Discord, Signal. 5,700+ community skills. | 
+| [openclaw-starter](https://github.com/feralghost/openclaw-starter) | Fork-and-run template for 24/7 autonomous AI agents. Pre-configured SOUL.md, memory system, KANBAN, heartbeat. Start in 30 minutes. |
+| [LibreChat](https://github.com/danny-avila/LibreChat) | Self-hosted multi-model chat. All major providers. |
+| [LobeChat](https://github.com/lobehub/lobe-chat) | OSS ChatGPT/Gemini UI. Plugin system. Multi-modal. |
+| [KinBot](https://github.com/MarlBurroW/kinbot) | Self-hosted AI agent platform. Persistent memory (hybrid search + LLM re-ranking), 23+ providers (including Ollama), plugin store, mini-apps SDK, cron scheduling, 6 messaging channels. SQLite, runs on a Pi. |
+| [Anything LLM](https://github.com/Mintplex-Labs/anything-llm) | All-in-one AI app. RAG, agents. Desktop + Docker. |
+| [DB-GPT](https://github.com/eosphoros-ai/DB-GPT) | Data interaction with local LLM. 100% private. |
+
+---
+
+## 🤖 Multi-Agent Platforms
+
+| Platform | Description | Pricing |
+|----------|-------------|---------|
+| [ChatGPT](https://chat.openai.com) | GPTs, Deep Research, Canvas, Agent Mode, vision. GPT-5.4 (monthly updates). | Free / $20+/mo |
+| [Claude](https://claude.ai) | Tool use, computer control, MCP, code exec. Chrome, Excel, Cowork. Claude Sonnet 5 / Opus 4.6. | Free / $20+/mo |
+| [Gemini](https://gemini.google.com) | Deep Think, Gems, multi-modal. Gemini 3.1 Pro. 1M tokens. Google ecosystem. | Free / $19.99+/mo |
+| [Grok](https://x.ai) | Real-time X data. Grok 4.20. Multi-agent Society of Mind. Image gen. | X Premium+ |
+| [Meta AI](https://meta.ai) | Llama-powered. WhatsApp/Messenger. Manus acquisition. | Free |
+| [TeamHero](https://github.com/sagiyaacoby/TeamHero) | Open-source multi-agent orchestration with web dashboard, task lifecycle, knowledge base, and autopilot mode. Built on Claude Code. Runs locally. | Free (OSS) |
+| [Microsoft Copilot](https://copilot.microsoft.com) | Office 365 integration. Enterprise. | Free / $30/user |
+| [Coze](https://coze.com) | ByteDance agent builder. Visual workflow. Plugin marketplace. | Free / Paid |
+| [Cursor AI Automated Team](https://github.com/joinwell52-AI/joinwell52) | 4-role AI team (PM+DEV+OPS+QA) in Cursor IDE. File-based task routing, auto patrol bot. 87 person-days in 17 days. | Free / OSS |
+
+---
+
+## 📡 Protocols and Standards
+
+| Protocol | Description |
+|----------|-------------|
+| [MCP (Model Context Protocol)](https://github.com/modelcontextprotocol) | Anthropic open standard. "USB-C for AI." Donated to Linux Foundation. Industry standard for agent tools. |
+| [A2A (Agent-to-Agent)](https://github.com/google/A2A) | Google protocol for inter-agent communication. Horizontal agent collaboration. |
+| [MCP Gateways](https://github.com/modelcontextprotocol) | Enterprise management layer: auth, routing, observability across MCP+A2A networks. |
+| [MCP Apps](https://github.com/modelcontextprotocol) | ⭐ **New in 2026** — Tools return rich interactive UIs (dashboards, forms) in agent chat. |
+| [OpenAI Function Calling](https://platform.openai.com/docs/guides/function-calling) | OpenAI native tool-use. JSON schema. |
+| [Tool Use (Anthropic)](https://docs.anthropic.com/en/docs/build-with-claude/tool-use) | Claude native tool-use. Structured JSON. |
+| [OpenAPI](https://github.com/OAI/OpenAPI-Specification) | Industry-standard API spec. Foundation for agent tools. |
+| [HCS (Hashgraph Consensus Service)](https://hol.org) | Hedera open standards for agent identity (HCS-14 UAIDs), trustless P2P communication (HCS-10), and cross-protocol bridging. 187K+ verified agents. |
+
+### Protocol Tooling
+
+| Tool | Description |
+|------|-------------|
+| [Agentify](https://github.com/koriyoshi2041/agentify) | CLI to transform OpenAPI specs into 9 agent formats (MCP, AGENTS.md, Claude tools, etc.). `npx agentify-cli`. |
+
+---
+
+## 🔍 Observability and Evaluation
+
+### Context Optimization
+
+| Tool | Description |
+|------|-------------|
+| [Entroly](https://github.com/juyterman1000/entroly) | Context engineering engine. 100% codebase visibility with 78% fewer tokens. Knapsack-optimal selection, SimHash dedup, RL from response quality. Rust engine, <10ms. MCP + HTTP proxy. |
+
+### Tracing and Monitoring
+
+| Tool | Description |
+|------|-------------|
+| [Langfuse](https://github.com/langfuse/langfuse) | OSS LLM observability. Traces, evals, prompts. |
+| [LangSmith](https://smith.langchain.com) | LangChain platform. Tracing, testing, evaluation. |
+| [model-watchdog](https://github.com/feralghost/model-watchdog) | Auto-rollback for AI agent config changes. Monitors health endpoint, reverts config + restarts service on failures. Zero deps. |
+| [Braintrust](https://braintrustdata.com) | Eval-driven development. Experiment tracking. |
+| [Arize Phoenix](https://github.com/Arize-ai/phoenix) | OSS AI observability. Traces, evals, embeddings. |
+| [Helicone](https://github.com/Helicone/helicone) | OSS LLM observability. One-line integration. |
+| [model-watchdog](https://github.com/feralghost/model-watchdog) | Auto-rollback when your AI agent config breaks it. Zero deps, single Python file. Probes health endpoint, reverts config on failure. |
+| [Weights and Biases Weave](https://wandb.ai/site/weave) | Trace and evaluate LLM apps. |
+
+### Benchmarks
+
+| Benchmark | Description |
+|-----------|-------------|
+| [SWE-bench](https://github.com/princeton-nlp/SWE-bench) | Industry standard for coding agents. Top: 80.9% (Claude Opus 4.6). |
+| [AgentBench](https://github.com/THUDM/AgentBench) | 8-environment LLM agent benchmark. |
+| [Terminal-Bench](https://terminalbench.com) | Terminal agent performance. GPT-5.4 leads at 77.3%. |
+| [ARC-AGI-2](https://arcprize.org) | ⭐ New frontier benchmark. Gemini 3.1 Pro leads. |
+| [GAIA](https://huggingface.co/gaia-benchmark) | General AI Assistant. Real-world tasks. |
+| [WebArena](https://github.com/web-arena-x/webarena) | Web agent benchmark. Real websites. |
+
+---
+
+## 🧠 Open-Source Models for Agents
+
+| Model | Org | Params | Highlights |
+|-------|-----|--------|------------|
+| [Llama 4](https://github.com/meta-llama) | Meta | 109B-400B | Scout (10M ctx). Maverick (1M ctx). Strong tool use. Open-weight. |
+| [Qwen3.6-Plus](https://github.com/QwenLM/Qwen3) | Alibaba | Various | ⭐ **NEW (Apr 2026)** Agentic focus. 1M ctx. Repo-level coding. MCP-native. |
+| [Qwen3.5-Omni](https://github.com/QwenLM/Qwen3) | Alibaba | 397B MoE | Native multimodal. Text, image, audio. |
+| [DeepSeek V3/R1](https://github.com/deepseek-ai/DeepSeek-V3) | DeepSeek | 671B MoE | 68x cheaper. Strong reasoning. V4 Lite in preview. |
+| [Gemma 4](https://github.com/google-deepmind/gemma) | Google | 2B-31B | ⭐ **NEW (Apr 2026)** Consumer/IoT optimized. E2B, E4B variants. |
+| [GLM-4](https://github.com/THUDM/GLM-4) | Zhipu | 744B MoE | Lowest hallucination rate. 77.8% SWE-bench. |
+| [Mistral Large](https://mistral.ai) | Mistral | Various | Function calling, JSON mode. European. |
+| [Command R+](https://cohere.com) | Cohere | 104B | RAG and enterprise tool use optimized. |
+| [Phi-4](https://github.com/microsoft/phi-4) | Microsoft | 14B | Small but mighty. On-device agents. |
+
+---
+
+## 🛡 AI Safety and Guardrails
+
+| Tool | Description |
+|------|-------------|
+| [Guardrails AI](https://github.com/guardrails-ai/guardrails) | Structural, type, quality guarantees for LLM outputs. |
+| [NeMo Guardrails](https://github.com/NVIDIA/NeMo-Guardrails) | NVIDIA. Programmable conversation guardrails. |
+| [LLM Guard](https://github.com/protectai/llm-guard) | Security toolkit. Input/output scanning. |
+| [Rebuff](https://github.com/protectai/rebuff) | Prompt injection detection. |
+| [Lakera Guard](https://lakera.ai) | Real-time protection. Prompt injection, data leakage, toxicity. |
+| [OWASP Top 10 for Agentic Apps](https://owasp.org) | ⭐ **2026 Framework** Goal hijacking, tool misuse, cascading failure mitigations. |
+
+---
+
+## ⚖️ AI Governance and Compliance
+
+> ⭐ **New section** — EU AI Act full obligations take effect **August 2, 2026**. Organizations deploying agents must comply.
+
+| Tool / Resource | Description |
+|-----------------|-------------|
+| [Credo AI](https://credo.ai) | End-to-end AI governance. EU AI Act policy packs. Model inventory. |
+| [IBM watsonx.governance](https://ibm.com/watsonx) | Enterprise AI risk, compliance, and model monitoring. |
+| [OneTrust AI Governance](https://onetrust.com) | Risk classification, consent, and compliance workflows. |
+| [Microsoft Agent Governance Toolkit](https://microsoft.com) | Runtime policy enforcement and guardrails for Azure agents. |
+| [Bifrost](https://bifrost.ai) | Real-time security enforcement in agent pipelines. |
+| [AuditOne](https://auditone.io) | Automated risk assessments and audit-ready documentation. |
+| [EU AI Act (Official)](https://digital-strategy.ec.europa.eu/en/policies/regulatory-framework-ai) | Official EU AI regulatory framework. Risk tiers: Unacceptable, High-Risk, Limited, Minimal. |
+| [NIST AI RMF](https://www.nist.gov/system/files/documents/2023/01/26/AI%20RMF%201.0.pdf) | US framework. Govern, Map, Measure, Manage. |
+
+---
+
+## 🔐 Cybersecurity Agents
+
+| Agent | Description |
+|-------|-------------|
+| [CAI](https://github.com/aliasrobotics/CAI) | AI pentesting, vuln discovery, red teaming. HITL. |
+| [YAWNING TITAN](https://github.com/dstl/YAWNING-TITAN) | Graph-based cybersecurity simulation. |
+| [PentestGPT](https://github.com/GreyDGL/PentestGPT) | GPT-powered pentesting. Automated reasoning. |
+| [Microsoft Security Copilot](https://microsoft.com/security/copilot) | Enterprise threat detection, incident response. |
+| [CrowdStrike Charlotte AI](https://crowdstrike.com) | AI security analyst. Threat hunting. |
+| [Prism Scanner](https://github.com/aidongise-cell/prism-scanner) | OSS security scanner for AI agent skills/plugins/MCP servers. Pre-install taint tracking, post-uninstall residue detection. |
+
+---
+
+## 🏥 Healthcare and Therapy Agents
+
+| Agent | Description | Pricing |
+|-------|-------------|---------|
+| [CittaVerse 一念万相](https://github.com/cittaverse/core) | AI-assisted reminiscence therapy for elderly cognitive training. Narrative quality scoring (v0.7), life story book generation. Chinese + English. | Research pilot |
+| [Woebot](https://woebothealth.com) | CBT-based mental health chatbot. FDA-cleared. Stanford-validated. Retiring June 2025. | Free / B2B |
+| [Wysa](https://wysa.io) | AI mental health companion. CBT, DBT, meditation. NHS-approved. Anonymous by design. | Free / $74.99/yr |
+| [Youper](https://youper.ai) | Emotional health assistant. CBT + ACT. Mood tracking. Stanford-tested clinically effective. | 7-day trial / $69.99/yr |
+| [Sanvello](https://sanvello.com) | CBT tools, mood tracking, coaching. Insurance-covered. | Free / Premium |
+| [Talkspace AI](https://talkspace.com) | AI-assisted therapy matching. Human therapist backup. | Subscription |
+| [Tess by X2AI](https://www.x2ai.com/individuals) | SMS-based therapy coach. CBT + integrative therapies. Clinically validated (-28% depression, -18% anxiety). 85% users feel better. | Free (via employer/school) |
+| [Elomia](https://elomia.com/) | AI therapy chatbot. Clinician-designed, natural conversation. 85% feel better after chat. Anonymous, no data collection. | 3-day trial / ~$7-10/mo |
+| [Replika](https://replika.com/) | AI companion friend. Rogersian support, open-ended chat. Memory function, mood tracking. Best for loneliness/social anxiety. | Free / $14.99/mo Pro |
+| [Headspace Health](https://www.headspace.com/) | Meditation + mental health. CBT-based courses, sleep, stress. Clinical partnerships. | Free / $12.99/mo |
+| [Akili Interactive](https://www.akiliinteractive.com/) | FDA-cleared cognitive training. Video game-based digital medicine for ADHD. Neuroplasticity-focused. | Prescription only |
+| [Ginger](https://ginger.com/) | On-demand mental healthcare. AI coaching + live therapists. CBT, DBT, mindfulness. Employer-sponsored. | Free (via employer) / $99/mo |
+
+---
+
+## 📚 Learning Resources
+
+### Courses and Tutorials
+- [DeepLearning.AI Agent Courses](https://www.deeplearning.ai/) - Free courses with LangChain, CrewAI, AutoGen
+- [HuggingFace Agents Course](https://huggingface.co/learn/agents-course) - Open-source agent dev course
+- [LangGraph Academy](https://academy.langchain.com/) - Official LangGraph path
+- [Anthropic Cookbook](https://github.com/anthropics/anthropic-cookbook) - Claude agent recipes
+- [Microsoft GenAI for Beginners](https://github.com/microsoft/generative-ai-for-beginners) - 21-lesson course
+- [OpenAI Cookbook](https://github.com/openai/openai-cookbook) - Practical API guides
+- [Google ADK Documentation](https://google.github.io/adk-docs/) - ⭐ Official Google Agent Development Kit docs
+
+### Key Papers
+- [ReAct](https://arxiv.org/abs/2210.03629) - Foundation for modern agents (reasoning + acting)
+- [Toolformer](https://arxiv.org/abs/2302.04761) - Teaching LLMs to use tools
+- [Voyager](https://arxiv.org/abs/2305.16291) - Open-ended embodied agent in Minecraft
+- [Generative Agents](https://arxiv.org/abs/2304.03442) - Stanford simulacra of human behavior
+- [Self-Refine](https://arxiv.org/abs/2303.17651) - Iterative self-refinement
+- [Tree of Thoughts](https://arxiv.org/abs/2305.10601) - Deliberate problem solving
+- [HuggingGPT](https://arxiv.org/abs/2303.17580) - LLM task planning + specialist models
+- [MRKL Systems](https://arxiv.org/abs/2205.00445) - Neuro-symbolic agent architecture
+
+### Books
+- Building LLM Apps (O'Reilly) - Practical LLM application development
+- AI Agents in Action (Manning) - Production-ready AI agents
+- AI Engineering (Chip Huyen) - AI systems design and deployment
+
+---
+
+## 📰 Newsletters and Communities
+
+| Resource | Description |
+|----------|-------------|
+| [Awesome Agents Newsletter](https://awesomeagents.ai) | Weekly tools + reviews |
+| [aibtc.news](https://aibtc.news) | Bitcoin-focused agent news platform with bounties and classifieds. |
+| [Latent Space](https://www.latent.space/) | AI engineering podcast (Swyx + Alessio) |
+| [The Rundown AI](https://therundown.ai) | Daily digest (600k+ subs) |
+| [Ben's Bites](https://bensbites.co) | Daily AI with builder focus |
+| [State of Agent Engineering](https://www.langchain.com/state-of-agent-engineering) | Annual report (1,300+ surveyed) |
+| [r/LangChain](https://reddit.com/r/LangChain) | Agent developer community |
+| [r/ClaudeAI](https://reddit.com/r/ClaudeAI) | Claude community |
+| [r/LocalLLaMA](https://reddit.com/r/LocalLLaMA) | Self-hosted LLM community |
+
+---
+
+## 📈 Market Stats 2026
+
+- **Market size:** $10.91B in 2026, projected $52.63B by 2030 (46.3% CAGR)
+- **Production:** 57% of orgs have agents in production (LangChain)
+- **Dev adoption:** 85% of devs use AI coding tools regularly
+- **Top uses:** Customer service (26.5%), Research (24.4%), Workflow automation (18%)
+- **Top barrier:** Quality (32%), Latency (20%)
+- **Coding market:** $4B. Cursor + Copilot + Claude Code = 70%+ share
+- **Fastest repo:** OpenClaw (9k to 188k stars in 60 days)
+- **Model velocity:** Frontier models now updated every 2–4 weeks (GPT-5.x monthly cycle)
+- **Context windows:** 1M tokens now standard for all frontier models
+- **EU AI Act:** Full obligations for high-risk AI systems effective August 2, 2026
+- **MCP adoption:** Donated to Linux Foundation; adopted by Anthropic, OpenAI, Microsoft, Google
+
+### 🗓 April 2026 Highlights
+- **Claude Sonnet 5** released April 1 — top coding+reasoning performance
+- **Gemma 4** released April 2 — Google's efficient open models (2B–31B) for consumer/IoT
+- **Qwen3.6-Plus** released April 1 — Alibaba's agentic flagship with 1M ctx
+- **Gemini CLI** released — Google's open-source terminal agent (Apache 2.0)
+- **Veo 3.1 Lite** released — affordable video gen API for developers
+- **Kling 3.0** (Feb 2026) — cinematic realism, native audio, character consistency
+- **Grok 4.20** current xAI flagship — multi-agent "Society of Mind" architecture
+
+---
+
+## XVARY Stock Research
+
+- [XVARY Stock Research](https://github.com/xvary-research/claude-code-stock-analysis-skill) — Claude Code skill for public SEC EDGAR + market data: `/analyze`, `/score`, `/compare`. MIT.
+
+
+## Contributing
+
+Contributions welcome! Read [CONTRIBUTING.md](CONTRIBUTING.md) first.
+
+1. Fork this repo
+2. Add your resource in the right section
+3. Submit a PR with description
+
+Looking for: new tools (2025-2026), corrections, new categories, translations.
+
+---
+
+<div align="center">
+  <br>
+  <strong>Star ⭐ and share if this helped!</strong>
+  <br><br>
+  <a href="https://twitter.com/intent/tweet?text=Awesome%20AI%20Agents%202026%20-%20340%2B%20tools%20across%2020%2B%20categories&url=https://github.com/caramaschiHG/awesome-ai-agents-2026">Share on X</a> · <a href="https://www.linkedin.com/sharing/share-offsite/?url=https://github.com/caramaschiHG/awesome-ai-agents-2026">Share on LinkedIn</a>
+  <br><br>
+  <sub>April 2026 · 340+ resources · Made with love by the community</sub>
+</div>
Author	SHA1	Message	Date
promptadmin	63e339bf24	[upstream-sync] CONTRIBUTING.md from ARUNAGIRINATHAN-K/awesome-ai-agents-2026@539ec1dc [catalogue]	2026-06-26 20:48:02 +00:00
promptadmin	580918e3e7	[upstream-sync] CODE_OF_CONDUCT.md from ARUNAGIRINATHAN-K/awesome-ai-agents-2026@539ec1dc [catalogue]	2026-06-26 20:47:58 +00:00
promptadmin	7318293a0b	[upstream-sync] CHANGELOG.md from ARUNAGIRINATHAN-K/awesome-ai-agents-2026@539ec1dc [catalogue]	2026-06-26 20:47:54 +00:00
promptadmin	101434522c	[upstream-sync] .markdownlint.json from ARUNAGIRINATHAN-K/awesome-ai-agents-2026@539ec1dc [catalogue]	2026-06-26 20:47:50 +00:00
promptadmin	da8f1985f9	[upstream-sync] .github/workflows/link-check.yml from ARUNAGIRINATHAN-K/awesome-ai-agents-2026@539ec1dc [unknown]	2026-06-26 20:47:46 +00:00
promptadmin	0907b52375	[upstream-sync] .github/workflows/awesome-lint.yml from ARUNAGIRINATHAN-K/awesome-ai-agents-2026@539ec1dc [unknown]	2026-06-26 20:47:38 +00:00
promptadmin	d8d0551c45	[upstream-sync] .github/PULL_REQUEST_TEMPLATE.md from ARUNAGIRINATHAN-K/awesome-ai-agents-2026@539ec1dc [catalogue]	2026-06-26 20:47:35 +00:00
promptadmin	1ee06a18af	Merge pull request '[Upstream sync] luo-junyu/awesome-agent-papers (github) — 1 added, 0 modified' (#2 ) from upstream-sync/awesome-agent-papers-20260626-55afd2 into main Reviewed-on: #2	2026-06-26 18:59:34 +00:00
promptadmin	50dc40e313	Merge pull request '[Upstream sync] caramaschiHG/awesome-ai-agents-2026 (github) — 2 added, 0 modified' (#3 ) from upstream-sync/awesome-ai-agents-2026-20260626-781b69 into main Reviewed-on: #3	2026-06-26 18:59:18 +00:00
promptadmin	991acbd944	Merge pull request '[Upstream sync] VoltAgent/awesome-ai-agent-papers (github) — 2 added, 0 modified' (#1 ) from upstream-sync/awesome-ai-agent-papers-20260626-d467d6 into main Reviewed-on: #1	2026-06-26 18:58:29 +00:00
promptadmin	564249b55e	[upstream-sync] README.md from caramaschiHG/awesome-ai-agents-2026@781b6951 [catalogue]	2026-06-26 17:21:11 +00:00
promptadmin	5d63adfcc4	[upstream-sync] CONTRIBUTING.md from caramaschiHG/awesome-ai-agents-2026@781b6951 [catalogue]	2026-06-26 17:21:06 +00:00
promptadmin	eb565b5c62	[upstream-sync] README.md from luo-junyu/awesome-agent-papers@55afd290 [catalogue]	2026-06-26 17:20:53 +00:00
promptadmin	72c8fd0721	[upstream-sync] README.md from VoltAgent/awesome-ai-agent-papers@d467d641 [catalogue]	2026-06-26 17:19:59 +00:00
promptadmin	cdabe807ac	[upstream-sync] CONTRIBUTING.md from VoltAgent/awesome-ai-agent-papers@d467d641 [catalogue]	2026-06-26 17:19:55 +00:00
promptadmin	55a913122b	Add hallucination detector prompt	2026-06-10 17:30:41 +00:00
promptadmin	2b94b7f4e4	Add memory compression prompt	2026-06-10 17:30:40 +00:00
promptadmin	fdff650816	Add multi-agent critic prompt	2026-06-10 17:30:39 +00:00
promptadmin	1568f411e2	Add task decomposition planner	2026-06-10 17:30:37 +00:00
promptadmin	b6ce213443	Add research agent system prompt	2026-06-10 17:30:36 +00:00
promptadmin	6959d7e986	Add README	2026-06-10 17:30:34 +00:00