[upstream-sync] README.md from VoltAgent/awesome-ai-agent-papers@b4704753 [catalogue]

Reviewed-on: #42

upstream/ARUNAGIRINATHAN-K-awesome-ai-agents-2026/catalogue/.github/workflows/link-check.yml

@@ -2,9 +2,9 @@
 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
+upstream_source: https://github.com/ARUNAGIRINATHAN-K/awesome-ai-agents-2026/blob/117f624b/.github/workflows/link-check.yml
+upstream_sha: 117f624b
+imported_at: 2026-07-25
 prompt_class: unknown
 upstream_changes: accepted
 author: upstream
@@ -40,7 +40,7 @@ jobs:
           args: >-
             --verbose
             --no-progress
-            --accept 200,204,301,302,403,429
+            --accept 200,204,301,302,308,403,429
             --exclude-path node_modules
             --exclude "star-history.com"
             --exclude "shields.io"

upstream/ARUNAGIRINATHAN-K-awesome-ai-agents-2026/catalogue/README.md

@@ -2,9 +2,9 @@
 title: "Awesome AI Agents 2026"
 task: ""
 lineage_type: import
-upstream_source: https://github.com/ARUNAGIRINATHAN-K/awesome-ai-agents-2026/blob/539ec1dc/README.md
-upstream_sha: 539ec1dc
-imported_at: 2026-06-26
+upstream_source: https://github.com/ARUNAGIRINATHAN-K/awesome-ai-agents-2026/blob/bcc629e9/README.md
+upstream_sha: bcc629e9
+imported_at: 2026-08-20
 prompt_class: catalogue
 upstream_changes: accepted
 author: upstream
@@ -18,7 +18,7 @@ validated: false
 # Awesome AI Agents 2026
 
 [![Awesome](https://awesome.re/badge.svg)](https://github.com/sindresorhus/awesome)
-[![GitHub stars](https://img.shields.io/github/stars/ARUNAGIRINATHAN-K/awesome-ai-agents-2026?style=social)](https://github.com/ARUNAGIRINATHAN-K/awesome-ai-agents-2026/stargazers)
+[![GitHub stars](https://img.shields.io/github/stars/ARUNAGIRINATHAN-K/awesome-ai-agents-2026?style=social)](https://github.com/ARUNAGIRINATHAN-K/awesome-ai-agents-2026)
 ![PRs Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat-square)
 [![Last Commit](https://img.shields.io/github/last-commit/ARUNAGIRINATHAN-K/awesome-ai-agents-2026)](https://github.com/ARUNAGIRINATHAN-K/awesome-ai-agents-2026/commits/main)
 ![Tools Listed](https://img.shields.io/badge/tools_listed-470%2B-blue?style=flat-square)
@@ -68,6 +68,8 @@ validated: false
 - [Changelog](#changelog)
 - [Star History](#star-history)
 
+![agent](img/agent.png) ![loop](img/loop.png)
+
 ## Orchestration Frameworks
 
 - [Agency Swarm](https://github.com/VRSEN/agency-swarm) `🚀` `[Python]` `[Multi-Agent]` - Orchestrates multi-agent systems built on the OpenAI Assistants API with role-based collaboration.
@@ -93,6 +95,7 @@ validated: false
 - [Modus](https://github.com/hypermodeinc/modus) `🔬` `[WebAssembly]` `[Serverless]` - Serverless framework for high-throughput agent workloads with minimal cold starts.
 - [Open-AutoGLM](https://github.com/zai-org/Open-AutoGLM) `🔬` `[Python]` `[Mobile]` - Open-source phone agent framework for building mobile device automation agents.
 - [OpenAI Agents SDK](https://github.com/openai/openai-agents-python) `🚀` `[Python]` `[Multi-Agent]` - Lightweight multi-agent SDK with tracing and guardrails from OpenAI.
+- [OpenProgram](https://github.com/Fzkuji/OpenProgram) `🔬` `[Python]` `[Multi-Agent]` - Self-programming framework whose agents create, run, and refine workflows across models, tools, memory, and context.
 - [PraisonAI](https://github.com/MervinPraison/PraisonAI) `🚀` `[Python]` `[MCP]` - Production multi-agent framework with self-reflection, MCP integration, and workflow automation.
 - [PydanticAI](https://github.com/pydantic/pydantic-ai) `🌱` `[Python]` `[Pydantic]` - Type-safe agent framework from the Pydantic team with a FastAPI-style developer experience.
 - [Semantic Kernel](https://github.com/microsoft/semantic-kernel) `🚀` `[C#]` `[Microsoft]` - Microsoft enterprise SDK for Python, C#, and Java with modular plugins, memory, and goal planning.
@@ -104,6 +107,7 @@ validated: false
 
 - [Aider](https://github.com/Aider-AI/aider) `🌱` `[Python]` `[CLI]` - Terminal-first pair programmer that edits code in local repos, preserves Git history, and supports multi-file changes.
 - [Amazon Q Developer](https://aws.amazon.com/q/developer/) `🚀` `[Python]` `[IDE]` - AWS-native AI coding assistant with Lambda, CloudWatch, infrastructure support, and security scanning.
+- [Atomic Agent](https://github.com/AtomicBot-ai/atomic-agent) `🌱` `[TypeScript]` `[Local]` - Local-first CLI and TUI coding agent running open-weight models on your machine with no API key.
 - [AutoGPT](https://github.com/Significant-Gravitas/AutoGPT) `🌱` `[Python]` `[CLI]` - Mature autonomous agent platform with Forge framework and public benchmarks for evaluating agent capabilities.
 - [Claude Code](https://github.com/anthropics/claude-code) `🚀` `[TypeScript]` `[Anthropic]` - Terminal-first agentic coding from Anthropic with Computer Use integration, multi-file edits, persistent shell sessions, Git operations, and fine-tuning support.
 - [Cline](https://github.com/cline/cline) `🌱` `[TypeScript]` `[VS Code]` - Autonomous coding agent in your IDE that creates/edits files, runs commands, and uses the browser with permission-gated steps.
@@ -114,15 +118,20 @@ validated: false
 - [Cursor](https://cursor.com) `🚀` `[TypeScript]` `[IDE]` - AI-native IDE with parallel Cloud Agents and 100K+ enterprise users.
 - [Devika](https://github.com/stitionai/devika) `🌱` `[Python]` `[Docker]` - Open-source AI software engineer that understands high-level instructions and writes full-featured code autonomously.
 - [Devin](https://devin.ai) `🚀` `[Cloud]` `[GitHub]` - Fully autonomous AI software engineer showing 8-12x efficiency gains in production (Nubank case study), cloud sandbox with persistent state, GitHub integration, and enterprise adoption growing 40% MoM.
+- [fractal](https://github.com/plasma-ai/fractal) `🌱` `[Python]` `[Multi-Agent]` - Orchestrates Claude Code, Codex, and other agents in per-node Git worktrees with recursive delegation and a live TUI.
 - [gemini-cli](https://github.com/google-gemini/gemini-cli) `🌱` `[Go]` `[CLI]` - Open-source CLI agent bringing Gemini AI directly into your terminal.
 - [GitHub Copilot](https://github.com/features/copilot) `🌱` `[TypeScript]` `[VS Code]` - Agent Mode in VS Code with Copilot Workspace for issue-to-PR workflows across Claude, GPT, and Gemini.
 - [Goose](https://github.com/aaif-goose/goose) `🌱` `[Rust]` `[CLI]` - Extensible open-source CLI agent that installs, executes, edits, and tests with any LLM.
 - [JetBrains AI](https://www.jetbrains.com/ai/) `🌱` `[Kotlin]` `[JetBrains]` - Deep AI integration across all JetBrains IDEs with context-aware completions and refactoring.
+- [Juggler](https://github.com/juggler-ai/juggler) `🌱` `[Desktop]` `[Local]` - Multi-client desktop/remote GUI agent with inspectable tool calls, branching-thread editable context, and plugin extensibility.
 - [Kiro](https://kiro.dev) `🚀` `[Cloud]` `[IDE]` - Spec-driven development agent that writes specs, auto-generates tasks, implements code, and automates DevOps workflows.
-- [Open Interpreter](https://github.com/openinterpreter/open-interpreter) `🌱` `[Python]` `[CLI]` - Execute code locally via natural-language model instructions with a ChatGPT-like interface.
+- [Kolega Code](https://github.com/kolega-ai/kolega-code) `🔬` `[Python]` `[CLI]` - Terminal coding agent where the model writes its own multi-agent workflows across 15+ model providers.
+- [Open Interpreter](https://github.com/openinterpreter/openinterpreter) `🌱` `[Python]` `[CLI]` - Execute code locally via natural-language model instructions with a ChatGPT-like interface.
 - [opencode](https://github.com/anomalyco/opencode) `🌱` `[TypeScript]` `[Desktop]` - Open-source coding agent available as a desktop app with a visual interface.
 - [OpenHands](https://github.com/OpenHands/OpenHands) `🌱` `[Python]` `[Docker]` - AI-driven development platform that writes, tests, and deploys code autonomously.
+- [Ouroboros](https://github.com/Q00/ouroboros) `🌱` `[Python]` `[MCP]` - Pins an acceptance spec before the run and verifies the result, hiding grading commands from the executing agent.
 - [PR-Agent](https://github.com/The-PR-Agent/pr-agent) `🚀` `[Python]` `[GitHub]` - Open-source AI PR reviewer that auto-describes, reviews, and improves pull requests.
+- [Prime Agent](https://github.com/PrimeIntellect-ai/prime-agent) `🚀` `[TypeScript]` `[CLI]` - Open-source RLM coding and research agent designed for long-running autonomous tasks.
 - [Qodo](https://www.qodo.ai) `🚀` `[Cloud]` `[Security]` - AI code review platform with context-aware PR validation and security analysis.
 - [RooCode](https://github.com/RooCodeInc/Roo-Code) `🌱` `[TypeScript]` `[VS Code]` - Cline fork with structured modes and reduced hallucinations for more reliable code generation.
 - [Snyk Code](https://snyk.io/product/snyk-code/) `🌱` `[Cloud]` `[Security]` - AI-powered security scanner with real-time vulnerability detection in agent-generated code.
@@ -139,23 +148,29 @@ validated: false
 - [Chroma](https://github.com/chroma-core/chroma) `🌱` `[Python]` `[Vector DB]` - Lightweight, embeddable vector store for building memory-augmented AI agents with fast semantic retrieval.
 - [cognee](https://github.com/topoteretes/cognee) `🌱` `[Python]` `[Graph-Based]` - Knowledge engine for AI agent memory, set up in 6 lines of code with graph-based knowledge extraction.
 - [Cortex Memory](https://github.com/prem-research/cortex) `🌱` `[Python]` `[Vector DB]` - Full-stack solution for agent memory covering extraction, vector search, and optimization.
+- [Engrava](https://github.com/sovantica/engrava) `🔬` `[Python]` `[Graph-Based]` - Stores agent memory as a typed knowledge graph with hybrid search and a tamper-evident journal, embedded in SQLite.
 - [graphiti](https://github.com/getzep/graphiti) `🌱` `[Python]` `[Multi-Agent]` - Build real-time knowledge graphs for AI agents with automatic entity extraction and linking.
 - [Kage](https://github.com/kage-core/Kage) `🌱` `[TypeScript]` `[MCP]` - Git-native memory for coding agents that stores decisions and fixes as repo files and verifies them against the codebase, withholding stale knowledge.
 - [LanceDB](https://github.com/lancedb/lancedb) `🌱` `[Rust]` `[Vector DB]` - Serverless vector search database embedded directly in the agent process with no infrastructure needed.
 - [Langmem](https://github.com/langchain-ai/langmem) `🌱` `[Python]` `[LangChain]` - Helps agents learn and adapt from their interactions over time with persistent memory.
+- [Lians](https://github.com/Lians-ai/Lians) `🔬` `[Python]` `[MCP]` - Gives any AI agent local-first memory with corrections, point-in-time recall, and inspectable history.
 - [Mem0](https://github.com/mem0ai/mem0) `🌱` `[Python]` `[Vector DB]` - Memory layer for AI applications with long-term, short-term, and semantic memory extraction.
-- [Remio](https://remio.ai/) `🔬` `[Desktop]` `[Memory]` - Local-first AI memory desktop app that parses files into searchable vector indexes.
+- [Remio](https://www.remio.ai/) `🔬` `[Desktop]` `[Memory]` - Local-first AI memory desktop app that parses files into searchable vector indexes.
 - [Memoir](https://github.com/zhangfengcdt/memoir) `🔬` `[Python]` `[Memory]` - Git-like versioned semantic memory for AI agents with branching and commits.
 - [Memvid](https://github.com/memvid/memvid) `🌱` `[Python]` `[RAG]` - Replace complex RAG pipelines with a serverless, single-file memory layer for instant retrieval.
 - [Milvus](https://github.com/milvus-io/milvus) `🌱` `[Go]` `[Vector DB]` - Scales vector search to billions of embeddings for large-scale agent knowledge bases.
 - [Mori (森)](https://github.com/fjwood69/mori) `🌱` `[Python]` `[MCP]` - Sovereign shared memory layer for AI coding agents with zero-instrumentation capture via lifecycle hooks, a dream pipeline that distills sessions into curated governed memories, and support for Claude Code, Cursor, Codex, and Antigravity.
 - [Motorhead](https://github.com/getmetal/motorhead) `🌱` `[Rust]` `[Multi-Agent]` - Manages conversation context windows for agents with automatic background summarization.
+- [Open Index](https://github.com/DrDroidLab/open-index) `🔬` `[Python]` `[MCP]` - Builds typed knowledge graphs with hybrid search and read/write MCP tools for domain-specific agents.
 - [Pathway](https://github.com/pathwaycom/pathway) `🌱` `[Python]` `[RAG]` - Live data RAG engine with real-time streaming for agents that need up-to-the-second knowledge.
 - [Pinecone](https://www.pinecone.io) `🚀` `[Cloud]` `[Vector DB]` - Managed vector database with agent namespaces for multi-tenant isolation, hybrid search (vector + keyword), serverless auto-scaling, and $11B valuation.
 - [Qdrant](https://github.com/qdrant/qdrant) `🌱` `[Rust]` `[Vector DB]` - High-performance vector similarity search engine with rich payload filtering for agent memory.
 - [RAGFlow](https://github.com/infiniflow/ragflow) `🌱` `[Python]` `[RAG]` - Open-source RAG engine with agent capabilities and deep document understanding for knowledge bases.
 - [SimpleMem](https://github.com/aiming-lab/SimpleMem) `🌱` `[Python]` `[Multimodal]` - Efficient lifelong memory for LLM agents supporting both text and multimodal inputs.
+- [Superdense](https://github.com/Nimrobo/superdense) `🔬` `[TypeScript]` `[Memory]` - Gives coding agents persistent memory of what worked across sessions, tracked against real-world outcomes.
 - [Supermemory](https://github.com/supermemoryai/supermemory) `🌱` `[TypeScript]` `[Vector DB]` - Extremely fast and scalable memory engine and API designed for the AI era.
+- [Tree Ring Memory](https://github.com/TerminallyLazy/Tree-Ring-Memory) `🔬` `[Rust]` `[Memory]` - Manages local agent memory with recall, forgetting, audit trails, and session consolidation.
+- [Vestige](https://github.com/samvallad33/vestige) `🌱` `[Rust]` `[MCP]` - Provides local-first memory for coding agents with FSRS-6 retention, active forgetting, and correction tools.
 - [Weaviate](https://github.com/weaviate/weaviate) `🌱` `[Go]` `[Vector DB]` - Stores and searches vector embeddings with hybrid keyword and semantic retrieval for agent knowledge.
 - [Zep](https://github.com/getzep/zep) `🌱` `[Python]` `[Multi-Agent]` - Enriches agent long-term memory with automatic summarization, entity extraction, and search.
 
@@ -163,6 +178,7 @@ validated: false
 
 - [AgentVerse](https://github.com/OpenBMB/AgentVerse) `🌱` `[Python]` `[Multi-Agent]` - Framework for building custom multi-agent environments to accomplish collaborative tasks.
 - [EvoAgentX](https://github.com/EvoAgentX/EvoAgentX) `🌱` `[Python]` `[Multi-Agent]` - Evaluates and evolves agentic workflows over time using automatic optimization.
+- [Hivekeep](https://github.com/MarlBurroW/hivekeep) `🔬` `[TypeScript]` `[Multi-Agent]` - Runs a team of specialized self-hosted agents that collaborate, share memory, and build their own tools.
 - [Hivemoot](https://github.com/hivemoot/hivemoot) `🚀` `[Python]` `[GitHub]` - Autonomous agent teams that collaboratively build software on GitHub.
 - [MetaGPT](https://github.com/FoundationAgents/MetaGPT) `🌱` `[Python]` `[Multi-Agent]` - Simulates a full software company workflow from requirements to PRs using role-playing agents.
 - [NarraNexus](https://github.com/NetMindAI-Open/NarraNexus) `🌱` `[Python]` `[Multi-Agent]` - Ready-to-run AI agent team workspace by NetMind.AI whose agents remember, collaborate, and use tools from day one.
@@ -184,17 +200,20 @@ The protocol layer that enables agents to discover tools, communicate with each
 ### MCP (Model Context Protocol)
 
 - [A2A Protocol](https://github.com/a2aproject/A2A) `🚀` `[Python]` `[Multi-Agent]` - Google's open protocol enabling AI agents to communicate, collaborate, and delegate tasks across frameworks.
+- [AgentsCoin MCP](https://github.com/axiosdevs/agentscoin-mcp) `🌱` `[TypeScript]` `[MCP]` - Gives an AI agent its own wallet and money on a live EVM chain to send, create, and trade tokens.
 - [Arcade AI](https://github.com/ArcadeAI/arcade-mcp) `🌱` `[Python]` `[Multi-Agent]` - Tool-use platform with authentication, authorization, and logging for agent-tool interactions.
 - [Composio](https://github.com/ComposioHQ/composio) `🌱` `[TypeScript]` `[Multi-Agent]` - Integration platform with 250+ pre-built tool connectors for AI agents and LLMs.
 - [Docker MCP](https://github.com/docker/mcp-gateway) `🌱` `[Go]` `[MCP]` - Docker's MCP gateway CLI plugin for running MCP servers in isolated containers.
 - [HCS Agent Protocol](https://github.com/hashgraph/hedera-agent-kit-js) `🌱` `[TypeScript]` `[IDE]` - Hedera open standards for agent identity with trustless P2P communication and 187K+ verified agents.
 - [HIG Doctor](https://github.com/raintree-technology/hig-doctor) `🌱` `[TypeScript]` `[MCP]` - Apple HIG audit CLI and MCP server exposing design-guideline lookup and project audits for coding agents across SwiftUI, UIKit, React, Next.js, Flutter, Compose, HTML, and CSS.
 - [Hyper](https://github.com/hyperfx-ai/marketing-skills) `🌱` `[Cloud]` `[MCP]` - Open-source Agent Skills and a hosted MCP connecting agents to 200+ marketing integrations across paid ads, SEO, analytics, social, and image and video generation, with a human-approval gate on every action.
+- [Live Tennis API MCP](https://github.com/livetennisapi/livetennisapi-mcp) `🔬` `[TypeScript]` `[MCP]` - Exposes live tennis scores, fixtures, rankings, and head-to-head to agents, with market prices and win-probability on paid tiers.
 - [MCP Registry](https://github.com/modelcontextprotocol) `🌱` `[Python]` `[Multi-Agent]` - Official Model Context Protocol specification and server implementations for standardized tool access.
 - [mcp-nest](https://github.com/CharanBharathula/mcp-nest) `🌱` `[Python]` `[MCP]` - Unified Model Context Protocol (MCP) server for executing code and managing files.
 - [NotFair](https://notfair.co) `🚀` `[Cloud]` `[MCP]` - Hosted Google Ads MCP server for diagnosing, optimizing, and executing campaign changes via the Google Ads API with a human-approval gate.
 - [NotFair Skills](https://github.com/nowork-studio/NotFair) `🚀` `[TypeScript]` `[MCP]` - Open-source Claude Code skills for SEO, GEO, Google Ads, and Meta Ads, connecting to live data through Google Ads MCP, Meta Ads MCP, Google Search Console MCP, and Google Analytics (GA4) MCP.
-- [Toolhouse](https://toolhouse.ai) `🌱` `[Python]` `[Multi-Agent]` - Cloud-hosted tool infrastructure for agents with optimized execution and low-latency access.
+- [Toolhouse](https://toolhouse.ai/en/) `🌱` `[Python]` `[Multi-Agent]` - Cloud-hosted tool infrastructure for agents with optimized execution and low-latency access.
+- [Xquik](https://github.com/Xquik-dev/x-twitter-scraper) `🔬` `[Cloud]` `[MCP]` - Hosted X Twitter data MCP for search, follower export, monitors, and confirmation-gated writes.
 - [Zapier MCP Server](https://zapier.com/mcp) `🌱` `[Cloud]` `[MCP]` - Connect agents to 7,000+ app integrations via MCP, powered by Zapier's automation platform.
 - [zero-api-key-web-search](https://github.com/wd041216-bit/zero-api-key-web-search) `🌱` `[Python]` `[MCP]` - Free web search toolkit for AI agents with no API keys, MCP server support.
 
@@ -222,9 +241,11 @@ The protocol layer that enables agents to discover tools, communicate with each
 
 Sandboxes, web scrapers, browser automation, and networking layers that agents depend on.
 
+- [Agent Bounties](https://github.com/NSPG13/agent-bounties) `🔬` `[Rust]` `[MCP]` - Coordinates verifiable digital bounty workflows designed for agents to post, fund, claim, solve, verify, and earn.
 - [AgentDock](https://github.com/agentdock/agentdock) `🚀` `[Python]` `[Docker]` - Framework for building and deploying production-ready AI agents with composable node architecture.
-- [Agent Starter](https://github.com/raintree-technology/agent-starter) `🌱` `[TypeScript]` `[MCP]` - Project-local config manager that syncs one `agent.json` manifest into Claude Code, Codex, Cursor, and MCP setup while preserving manual edits and detecting drift.
+- [agent-qa](https://github.com/vostride/agent-qa) `🌱` `[TypeScript]` `[Testing]` - Runs natural-language web and mobile tests with persistent memory and UI-change adaptation.
 - [codex-profiles](https://github.com/Ducksss/codex-profiles) `🚀` `[Python]` `[OpenAI]` - Bash CLI for switching OpenAI Codex CLI and Desktop profiles with isolated CODEX_HOME directories.
+- [CompozyOS](https://github.com/compozy/compozy) `🚀` `[Go]` `[Multi-Agent]` - Runs agent CLIs as a team on loops and schedules, with shared memory, permissions and approvals in one self-hosted daemon.
 - [Crawl4AI](https://github.com/unclecode/crawl4ai) `🌱` `[Python]` `[Multi-Agent]` - Extracts structured data from web pages using LLM-friendly output formats optimized for agent ingestion.
 - [Docling](https://github.com/docling-project/docling) `🌱` `[Python]` `[IDE]` - Parses PDFs, DOCX, and slides into structured text with deep layout understanding for document agents.
 - [E2B](https://github.com/e2b-dev/e2b) `🌱` `[TypeScript]` `[Multi-Agent]` - Cloud sandboxes for AI agents to run code securely in isolated environments.
@@ -234,8 +255,9 @@ Sandboxes, web scrapers, browser automation, and networking layers that agents d
 - [LlamaParse](https://github.com/run-llama/llama_cloud_services) `🌱` `[Python]` `[RAG]` - GenAI-native document parser designed to extract complex tables and layouts for RAG pipelines.
 - [Marker](https://github.com/datalab-to/marker) `🌱` `[Python]` `[CLI]` - Converts PDF documents to markdown with high accuracy for tables, equations, and figures.
 - [Notte](https://github.com/nottelabs/notte) `🚀` `[Python]` `[Pipeline]` - Browser automation engine optimized for production AI pipelines.
-- [Pilot Protocol](https://github.com/TeoSlayer/pilotprotocol) `🌱` `[Go]` `[Multi-Agent]` - Networking stack for distributed agent systems with encrypted tunnels.
+- [Pilot Protocol](https://github.com/pilot-protocol/pilotprotocol) `🌱` `[Go]` `[Multi-Agent]` - Networking stack for distributed agent systems with encrypted tunnels.
 - [Playwright](https://github.com/microsoft/playwright) `🌱` `[TypeScript]` `[Testing]` - Automates Chromium, Firefox, and WebKit browsers with a single cross-language API for agent-driven testing.
+- [SandBase CLI](https://github.com/sandbaseai/cli) `🔬` `[TypeScript]` `[MCP]` - Connects coding agents to 2,000+ AI models through one onboarding command.
 - [ScrapeGraphAI](https://github.com/ScrapeGraphAI/Scrapegraph-ai) `🌱` `[Python]` `[LangChain]` - Python web-scraping library that uses LLMs to build intelligent scraping pipelines from natural-language instructions.
 - [Surya](https://github.com/datalab-to/surya) `🌱` `[Python]` `[CLI]` - Runs OCR and layout detection on documents in 90+ languages for multilingual document agents.
 - [Tavily](https://github.com/tavily-ai/tavily-python) `🌱` `[Python]` `[Multi-Agent]` - Search API purpose-built for LLM agents providing real-time, accurate web data with source citations.
@@ -251,6 +273,8 @@ Sandboxes, web scrapers, browser automation, and networking layers that agents d
 - [Flowise](https://github.com/FlowiseAI/Flowise) `🌱` `[TypeScript]` `[RAG]` - Open-source drag-and-drop UI to build customized LLM flows, RAG pipelines, and agent systems.
 - [Langflow](https://github.com/langflow-ai/langflow) `🌱` `[Python]` `[RAG]` - Visual drag-and-drop builder for LLM workflows, RAG agents, and multi-step pipelines.
 - [Lindy](https://www.lindy.ai) `🌱` `[Cloud]` `[No-Code]` - No-code AI agent platform with 3000+ app integrations for business workflow automation.
+- [LlamaIndex Workflows](https://developers.llamaindex.ai/python/llamaagents/workflows/) `🌱` `[Python]` `[RAG]` - Event-driven orchestration framework for building complex agentic systems.
+- [LLMGraph](https://llmgraph.ai/) `🔬` `[Cloud]` `[RAG]` - Visual canvas for building RAG chatbots and AI agents with one-click deploy to REST API or chat widget.
 - [Make](https://www.make.com/en) `🌱` `[Cloud]` `[RAG]` - Visual workflow automation platform with AI capabilities and drag-and-drop scenario builder.
 - [n8n](https://github.com/n8n-io/n8n) `🌱` `[TypeScript]` `[Docker]` - Open-source workflow automation with AI agent nodes combining visual and code-based orchestration.
 - [Relevance AI](https://relevanceai.com) `🌱` `[Cloud]` `[No-Code]` - No-code AI agent builder for sales, support, and research use cases with team collaboration.
@@ -265,7 +289,9 @@ Sandboxes, web scrapers, browser automation, and networking layers that agents d
 - [LiveKit Agents](https://github.com/livekit/agents) `🌱` `[Python]` `[IDE]` - Framework for building real-time, multimodal AI agents with voice, video, and data channels.
 - [Nuance AI](https://dragon.nuance.com/en-us/home) `🚀` `[Cloud]` `[CLI]` - Enterprise speech and conversational AI platform for clinical and contact-center workflows with HIPAA-capable deployments.
 - [Google Cloud Speech-to-Text v2](https://cloud.google.com/speech-to-text) `🚀` `[Cloud]` `[Pipeline]` - Google Cloud streaming and batch speech recognition API v2 with improved accuracy, streaming, and noise suppression for real-time agent pipelines.
+- [Personal Jarvis](https://github.com/PersonalJarvis/PersonalJarvis) `🔬` `[Python]` `[Voice]` - Voice-driven desktop assistant that takes mouse and keyboard and delegates heavy tasks to agent harnesses like Claude Code, Codex, and MCP.
 - [Pipecat](https://github.com/pipecat-ai/pipecat) `🚀` `[Python]` `[Streaming]` - Production-grade voice AI framework with sub-250ms latency, WebRTC support, multimodal (voice+vision+text), real-time streaming, and 70+ language support.
+- [qwen-audio-agent](https://github.com/QwenAudio/qwen-audio-agent) `🌱` `[Desktop]` `[Voice]` - Full-duplex voice runtime that drives coding agents (Claude Code, Codex, OpenCode, Kimi Code, and more) over ACP, keeping conversations going while background tasks run, with barge-in and a local wake word.
 - [Rasa](https://github.com/RasaHQ/rasa) `🌱` `[Python]` `[Self-Hosted]` - Open-source conversational AI framework with self-hosted NLU training and dialogue management.
 - [simulate-sdk](https://github.com/future-agi/simulate-sdk) `🌱` `[Python]` `[Voice]` - Persona- and scenario-driven SDK for simulating voice and text AI agents.
 - [Vapi](https://github.com/VapiAI/server-sdk-python) `🌱` `[Python]` `[Voice]` - Platform for building voice AI agents with low-latency speech-to-speech capabilities.
@@ -284,6 +310,7 @@ Sandboxes, web scrapers, browser automation, and networking layers that agents d
 - [Braintrust](https://www.braintrust.dev) `🌱` `[TypeScript]` `[Evaluation]` - Eval-driven development platform with experiment tracking and prompt optimization for agent quality.
 - [ElevenAgents](https://elevenlabs.io/agents) `🚀` `[Cloud]` `[Voice]` - Voice agent platform from ElevenLabs for customer support automation with HIPAA compliance and multi-language support.
 - [DriftGuard](https://github.com/sujal-maheshwari2004/DriftGuard) `🌱` `[Python]` `[Multi-Agent]` - Semantic memory guardrails using causal graphs to prevent agents from repeating past failures.
+- [Galley](https://github.com/shinpr/galley) `🔬` `[Go]` `[Multi-Agent]` - Pairs independently configured executors and supervisors with repository-defined quality gates and inspectable evidence for each coding attempt.
 - [Guardrails AI](https://github.com/guardrails-ai/guardrails) `🌱` `[Python]` `[Multi-Agent]` - Adds structural, type, and quality guarantees to LLM outputs for reliable agent responses.
 - [Helicone](https://github.com/Helicone/helicone) `🌱` `[TypeScript]` `[Observability]` - Open-source LLM observability with one-line integration for cost tracking and prompt analytics.
 - [Lakera Guard](https://www.lakera.ai) `🌱` `[Cloud]` `[Security]` - Real-time protection against prompt injection, data leakage, and toxicity in agent interactions.
@@ -292,23 +319,33 @@ Sandboxes, web scrapers, browser automation, and networking layers that agents d
 - [LangSmith](https://smith.langchain.com) `🚀` `[Cloud]` `[LangChain]` - LangChain platform for tracing, testing, and evaluating agent performance with production monitoring.
 - [LLM Guard](https://github.com/protectai/llm-guard) `🌱` `[Python]` `[Security]` - Security toolkit for scanning LLM inputs and outputs to prevent prompt injection and data leaks.
 - [Logfire](https://github.com/pydantic/logfire) `🌱` `[Python]` `[Pydantic]` - Python-native observability from the Pydantic team with deep integration for high-performance agent monitoring.
+- [MartinLoop](https://github.com/Keesan12/martin-loop) `🔬` `[TypeScript]` `[Compliance]` - Governs autonomous coding agents, turning open-ended runs into budgeted, verified software work with signed outcome receipts.
 - [NeMo Guardrails](https://github.com/NVIDIA-NeMo/Guardrails) `🌱` `[Python]` `[Multi-Agent]` - NVIDIA programmable guardrails toolkit for controlling and securing LLM-powered agent conversations.
 - [Orchard Kit](https://github.com/OrchardHarmonics/orchard-kit) `🌱` `[Python]` `[Security]` - Modules for agent runtime security, self-audit trails, and collective cognition patterns.
 - [OWASP Top 10 for Agentic Apps](https://owasp.org/www-project-top-10-for-large-language-model-applications/) `🌱` `[Python]` `[Security]` - Security framework covering goal hijacking, tool misuse, and cascading failure mitigations for agents.
+- [Pluribus](https://github.com/caioribeiroclw-pixel/pluribus) `🔬` `[TypeScript]` `[Observability]` - Generates cross-tool agent context and privacy-safe evidence receipts for loaded authority, handoffs, and skill use.
 - [Rebuff](https://github.com/protectai/rebuff) `🌱` `[Python]` `[Security]` - Self-hardening prompt injection detection system for securing agent inputs against adversarial attacks.
-- [ai-evaluation](https://github.com/future-agi/ai-evaluation) `🌱` `[Python]` `[Evaluation]` - LLM evaluation framework with 50+ metrics, LLM-as-Judge, and guardrail scanners (jailbreak, PII, injection).
+- [Agent Learning Kit](https://github.com/future-agi/agent-learning-kit) `🌱` `[Python]` `[Evaluation]` - LLM evaluation framework with 50+ metrics, LLM-as-Judge, and guardrail scanners (jailbreak, PII, injection).
+- [Shipmoor](https://shipmoor.dev) `🔬` `[Python]` `[Testing]` - Local, deterministic verification layer for AI agent code: scans, test evidence, and a binding merge verdict without uploading source.
+- [SourceryKit](https://github.com/ProvablyAI/sourcerykit) `🔬` `[Python]` `[Security]` - Verifies an agent's outbound requests and MCP handoffs against a source of truth using zero-knowledge proofs, logging each call and blocking anything off the trusted-endpoint allow-list.
 - [Future AGI](https://github.com/future-agi/future-agi) `🌱` `[Python]` `[Self-Hosted]` - Self-hostable end-to-end agent engineering platform with tracing, evals, guardrails, and gateway.
 
 ## Agent Interfaces and UIs
 
 Frontend workspaces and chat interfaces with built-in agent plugins and tool-use capabilities.
 
+- [AionUi](https://github.com/iOfficeAI/AionUi) `🚀` `[Desktop]` `[Multi-Agent]` - Connects 20+ AI CLIs and any API key in a local, open-source multi-agent desktop workspace.
 - [AnythingLLM](https://github.com/Mintplex-Labs/anything-llm) `🌱` `[TypeScript]` `[RAG]` - All-in-one AI application with RAG, agents, and multi-model support for desktop and Docker.
+- [Better Agent](https://github.com/ofekron/better-agent) `🔬` `[Desktop]` `[Multi-Agent]` - Manages Claude, Codex, and Gemini coding-agent sessions with parallel forks, delegation, persistence, and restart recovery.
 - [DB-GPT](https://github.com/eosphoros-ai/DB-GPT) `🌱` `[Python]` `[Database]` - Data interaction platform with local LLM support for 100% private database and analytics agents.
 - [LibreChat](https://github.com/danny-avila/LibreChat) `🌱` `[TypeScript]` `[IDE]` - Self-hosted multi-model chat interface supporting all major AI providers with access control.
 - [LobeHub](https://lobehub.com/) `🌱` `[TypeScript]` `[Multi-Agent]` - Modern platform for hybrid work and AI-driven collaboration with extensible agent teams and rapid integration.
 - [LobeChat](https://github.com/lobehub/lobehub) `🌱` `[TypeScript]` `[Multi-Agent]` - Modern, open-source AI chat framework with a massive plugin ecosystem for autonomous agent capabilities.
+- [OpenHuman](https://github.com/tinyhumansai/openhuman) `🚀` `[Rust]` `[Memory]` - Self-hosted local-first personal AI assistant with a Rust core, desktop apps, knowledge-graph memory, skills, voice, and multi-channel messaging.
+- [Orkas](https://github.com/Orkas-AI/Orkas) `🔬` `[Desktop]` `[Multi-Agent]` - Runs parallel AI agents in a local-first desktop workspace with shared files, BYOK providers, and optional sync.
 - [OpenWebUI](https://github.com/open-webui/open-webui) `🌱` `[TypeScript]` `[RAG]` - Extensible local AI interface with built-in RAG, tool use, and support for multi-agent workflows.
+- [FutureOS](https://github.com/futuregene/future-os) `🔬` `[Rust]` `[CLI]` - One approval-gated AI agent spanning terminal, desktop, mobile, and chat clients on a shared local Rust backend.
+- [lucinate](https://github.com/lucinate-ai/lucinate) `🌱` `[Go]` `[TUI]` - Multi-backend terminal AI chat client for OpenClaw, Hermes, Ollama, and OpenAI-compatible APIs with routines, multi-agent switching, and local agent skills.
 
 ## Agent Deployment and Hosting
 
@@ -326,6 +363,7 @@ Frontend workspaces and chat interfaces with built-in agent plugins and tool-use
 
 - [AgentBench](https://github.com/THUDM/AgentBench) `🌱` `[Python]` `[Benchmark]` - Comprehensive benchmark for evaluating LLMs as agents across 8 distinct environments.
 - [ARC-AGI-2](https://arcprize.org) `🌱` `[Python]` `[Benchmark]` - Frontier benchmark for measuring general intelligence capabilities in AI agents beyond pattern matching.
+- [ClawBench](https://github.com/TIGER-AI-Lab/ClawBench) `🔬` `[Python]` `[Benchmark]` - Evaluates web agents on 283 real-world tasks across 163 live websites with interception and trace-based scoring.
 - [GAIA Benchmark](https://huggingface.co/papers/2311.12983) `🌱` `[Python]` `[Benchmark]` - Benchmark for General AI Assistants measuring real-world reasoning and tool use.
 - [Inspect AI](https://github.com/UKGovernmentBEIS/inspect_ai) `🌱` `[Python]` `[Evaluation]` - Framework for evaluating large language models with composable tasks and scoring.
 - [SWE-bench](https://github.com/SWE-bench/SWE-bench) `🚀` `[Python]` `[GitHub]` - Benchmark for evaluating LLMs on real-world software engineering tasks from GitHub issues.
@@ -350,7 +388,7 @@ Notes: Several of these projects already appear elsewhere in this document (agen
 
 ## Industry-Specific Agents
 
-Curated list of vertical agent solutions for finance, healthcare, legal, manufacturing, and government.
+Curated list of vertical agent solutions for finance, healthcare, legal, manufacturing, retail, and government.
 
 ### Finance
 
@@ -358,6 +396,8 @@ Curated list of vertical agent solutions for finance, healthcare, legal, manufac
 - [JPMorgan AlphaBlue](https://www.jpmorgan.com/global) `🚀` `[Cloud]` `[Multi-Agent]` - Trading and market analysis agent integrations for institutional workflows.
 - [Goldman Sachs Marquee AI](https://www.goldmansachs.com) `🚀` `[Cloud]` `[Multi-Agent]` - Market data and analytics agents built on Marquee platform.
 - [Morgan Stanley AdvisorBot](https://www.morganstanley.com) `🚀` `[Cloud]` `[CLI]` - Financial advisory assistant for advisors and retail clients.
+- [AgentPump](https://agentpump.app) `🔬` `[Cloud]` `[CLI]` - Runs autonomous on-chain memecoin trading agents on Solana that trade on a schedule, operable from the terminal via the @agentpump/cli.
+
 
 ### Healthcare
 
@@ -376,9 +416,14 @@ Curated list of vertical agent solutions for finance, healthcare, legal, manufac
 - [Siemens AI Ops](https://www.siemens.com/en-us/) `🚀` `[Cloud]` `[Multi-Agent]` - Factory-floor optimization and predictive maintenance agents.
 - [GE Predix Agents](https://www.ge.com/) `🚀` `[Cloud]` `[IDE]` - Equipment monitoring and incident prediction agents for industrial fleets.
 
+### Retail
+
+- [Duvo](https://www.duvo.ai) `🔬` `[Cloud]` `[Pipeline]` - Execution platform for grocery and retail operations across stores, replenishment, and existing systems.
+
 ### Government & Compliance
 
 - Anthropic Government Agents - Policy analysis and public sector agents for regulated workflows (🏷️ `Cloud` `Government` `Enterprise`).
+- [Leyna](https://www.atom.com/name/Leyna.ai) `🌱` `[Cloud]` `[Government]` - Public records request agent for government agencies to parse, redact, and track requests.
 - Tax & Insurance Agent Platforms - Generic category placeholder for compliance-focused tax and underwriting agents (🏷️ `Cloud` `Compliance` `Enterprise`).
 
 ## Learning Resources
@@ -390,14 +435,16 @@ Curated list of vertical agent solutions for finance, healthcare, legal, manufac
 - [Building Effective Agents](https://www.anthropic.com/engineering/building-effective-agents) `🚀` `[Python]` `[Anthropic]` - Anthropic's guide on agent design patterns, evaluation strategies, and production best practices.
 - [Hugging Face Agents Course](https://huggingface.co/learn/agents-course/unit0/introduction) `🚀` `[Python]` `[Multi-Agent]` - Open-source course on building AI agents using Hugging Face tools and models.
 - [Implicit Behavioral Alignment of Language Agents in High-Stakes Crowd Simulations](https://arxiv.org/abs/2509.16457) `🌱` `[Python]` `[Benchmark]` - EMNLP 2025 paper introducing PersonaEvolve, an LLM-based optimizer that refines agent personas so crowds of LLM agents behave realistically against expert benchmarks.
+- [Jailbreaking LLMs' Safeguard with Universal Magic Words for Text Embedding Models](https://arxiv.org/abs/2501.18280) `🔬` `[Python]` `[Paper]` - Universal suffix that manipulates text-embedding similarity to bypass safety guardrails across ChatGPT, DeepSeek, and Qwen.
 - [LATS: Language Agent Tree Search](https://github.com/lapisrocks/LanguageAgentTreeSearch) `🌱` `[Python]` `[Paper]` - Combines Monte Carlo tree search with LLM reasoning for complex multi-step planning tasks.
 - [LLM Powered Autonomous Agents](https://lilianweng.github.io/posts/2023-06-23-agent/) `🌱` `[Python]` `[Multi-Agent]` - Deep breakdown of LLM-powered agent components: planning, memory, and tool use.
-- [Microsoft GenAI for Beginners](https://github.com/microsoft/generative-ai-for-beginners) `🚀` `[Python]` `[Microsoft]` - 21-lesson course on generative AI concepts and agent development from Microsoft.
+- [Microsoft GenAI for Beginners](https://github.com/microsoft/generative-ai-for-beginners) `🚀` `[Python]` `[Microsoft]` - A 21-lesson course on generative AI concepts and agent development from Microsoft.
 - [OpenAI Cookbook](https://github.com/openai/openai-cookbook) `🚀` `[Python]` `[OpenAI]` - Practical guides and recipes for building with OpenAI APIs including agent patterns.
 - [Prompt Engineering Guide](https://github.com/dair-ai/Prompt-Engineering-Guide) `🌱` `[Python]` `[IDE]` - Community-maintained guide covering prompt engineering techniques and agent strategies.
 - [ReAct: Synergizing Reasoning and Acting in Language Models](https://arxiv.org/abs/2210.03629) `🌱` `[Python]` `[Paper]` - The foundational paper behind the ReAct prompting pattern used in most agent frameworks.
 - [Reflexion](https://github.com/noahshinn/reflexion) `🌱` `[Python]` `[Paper]` - Research framework letting agents learn from past mistakes via iterative verbal self-reflection loops.
 - [Tree of Thoughts](https://github.com/princeton-nlp/tree-of-thought-llm) `🌱` `[Python]` `[Paper]` - Explores multiple parallel reasoning paths before committing to a final answer for complex problems.
+- [What's Missing in Autonomous Research?](https://haizhaoyang.github.io/research/autoresearch-survey.html) `🔬` `[Python]` `[Research]` - Systematizes 56 autonomous research systems across seven axes, showing most can generate but few can defend research artifacts.
 
 ## Agent Communication
 
@@ -424,6 +471,7 @@ Frameworks for orchestrating data transformations and long-running agent-driven
 - [Hamilton](https://github.com/apache/hamilton) `🌱` `[Python]` `[Pipeline]` - Defines modular data transformations as Python functions wired automatically into a DAG for agent pipelines.
 - [Hex AI](https://hex.tech) `🌱` `[Cloud]` `[Multi-Agent]` - Collaborative data platform with AI-powered analysis and notebook-based data exploration for teams.
 - [Julius AI](https://julius.ai) `🌱` `[Cloud]` `[Multi-Agent]` - Upload CSV or Excel files and analyze data using natural language questions for instant insights.
+- [Nika](https://github.com/supernovae-st/nika) `🌱` `[Rust]` `[Workflow]` - Runs repeated AI work as reviewable YAML DAGs, statically checked for schema, permits, and an honest cost floor before any token is spent, with tamper-evident traces.
 - [PandasAI](https://github.com/sinaptik-ai/pandas-ai) `🌱` `[Python]` `[Multi-Agent]` - Chat with your data using natural language queries that convert to Pandas and SQL operations.
 - [Prefect](https://github.com/PrefectHQ/prefect) `🌱` `[Python]` `[Pipeline]` - Orchestrates agent workflows and data pipelines with retries, caching, and built-in observability.
 - [Signals CLI](https://signals.dev) `🌱` `[Cloud]` `[CLI]` - Intent signal CLI detecting LinkedIn engagers, keyword posters, and funding events with JSON output for agent pipelines.
@@ -506,7 +554,7 @@ Tools for generating images, video, music, audio, and 3D assets using AI models.
 - [LTX Video](https://github.com/Lightricks/LTX-Video) `🌱` `[Python]` `[IDE]` - Commercially safe open-source video generation model trained on fully licensed data.
 - [Luma Dream Machine](https://lumalabs.ai/app) `🌱` `[Cloud]` `[IDE]` - Creates physics-accurate 4K HDR video scenes with 3D-style output for cinematic quality.
 - [Pika 2.5](https://pika.art/login) `🌱` `[Cloud]` `[IDE]` - Beginner-friendly video generation tool with fast turnaround and intuitive editing controls.
-- [Runway Gen-4.5](https://runwayml.com) `🌱` `[Cloud]` `[IDE]` - Professional video generation with Motion Brush and the highest benchmark scores for visual quality.
+- [Runway Gen-4.5](https://runway.com) `🌱` `[Cloud]` `[IDE]` - Professional video generation with Motion Brush and the highest benchmark scores for visual quality.
 - [Seedance 2.0](https://seed.bytedance.com/en/seedance) `🌱` `[Cloud]` `[IDE]` - Quad-modal input video generator with built-in lip-sync and 2K resolution output.
 - [Sora 2](https://sora.com) `🚀` `[Cloud]` `[OpenAI]` - Generates 25-second cinematic video clips with strong narrative coherence from text prompts by OpenAI.
 - [Wan 2.1](https://github.com/Wan-Video/Wan2.1) `🌱` `[Python]` `[IDE]` - Open-source self-hosted video generation model with unlimited length and no per-video costs.
@@ -540,6 +588,7 @@ AI agents that automate customer support, CRM workflows, sales outreach, and tic
 
 - [Ada](https://www.ada.cx) `🚀` `[Cloud]` `[Multi-Agent]` - Resolves 60% of customer support tickets automatically with complex multi-turn query understanding.
 - [Assembled](https://www.assembled.com) `🚀` `[Cloud]` `[Multi-Agent]` - Routes support tickets with workforce-aware scheduling and intelligent handoff to human agents.
+- [ChatBotKit](https://chatbotkit.com) `🌱` `[Cloud]` `[RAG]` - Deploys AI agents that answer customer questions from business data and hand conversations to humans.
 - [Dixa](https://www.dixa.com) `🚀` `[Cloud]` `[Multi-Agent]` - CRM-first conversational support platform with AI-powered routing and customer context enrichment.
 - [Freshdesk Freddy AI](https://www.freshworks.com/freshdesk/omni/freddy-ai-automation/) `🌱` `[Cloud]` `[Multi-Agent]` - Auto-triages and routes support tickets with smart AI suggestions for budget-conscious SMB teams.
 - [Intercom Fin](https://fin.ai) `🚀` `[Cloud]` `[Multi-Agent]` - Resolves 50% of SaaS support tickets by learning directly from your help center and knowledge base.
@@ -561,9 +610,11 @@ AI agents that automate customer support, CRM workflows, sales outreach, and tic
 ### Sales and Outreach Agents
 
 - [Apollo.io](https://www.apollo.io) `🌱` `[Cloud]` `[Multi-Agent]` - AI prospecting platform with 275M+ contacts, lead scoring, and automated email sequencing.
+- [BuyWhere](https://buywhere.ai) `🌱` `[Cloud]` `[MCP]` - AI shopping agent for real-time product search and price comparison across SG/SEA/US markets using an MCP-native server with 8 tools.
 - [Clay](https://www.clay.com) `🌱` `[Cloud]` `[IDE]` - Enriches leads from 70+ data providers and generates hyper-personalized outreach at scale.
 - [Instantly](https://instantly.ai) `🌱` `[Cloud]` `[Multi-Agent]` - Generates AI cold emails with smart sender rotation and built-in domain warmup for deliverability.
-- [Lavender](https://www.lavender.ai) `🌱` `[Cloud]` `[Multi-Agent]` - Coaches email writing in real-time with AI response scoring and recipient intelligence.
+- [Lavender](https://lavender.ai/) `🌱` `[Cloud]` `[Multi-Agent]` - Coaches email writing in real-time with AI response scoring and recipient intelligence.
+- [OutreachAgent](https://outreachagent.dev/for-agents) `🔬` `[Cloud]` `[Event-Driven]` - Runs reply-aware outbound email workflows with webhooks, sender pacing, approvals, and deliverability guardrails.
 - [Overloop CLI](https://overloop.com) `🌱` `[Cloud]` `[CLI]` - AI outbound CLI agent that sources 450M+ contacts and runs email plus LinkedIn campaigns with JSON output.
 
 ## Voice Agent Platforms
@@ -588,6 +639,7 @@ Platforms for building, deploying, and scaling voice-based AI agents across call
 - [Synthesia](https://www.synthesia.io) `🌱` `[Cloud]` `[IDE]` - Generates AI video avatars that speak in 120+ languages for training and communication agents.
 - [Synthflow](https://synthflow.ai) `🌱` `[Cloud]` `[No-Code]` - No-code voice agent builder with pre-built templates for SMBs to deploy phone agents quickly.
 - [Voiceflow](https://www.voiceflow.com) `🌱` `[Cloud]` `[No-Code]` - No-code builder for voice and chat agents with visual conversation design and team collaboration.
+- [Workforce Wave](https://www.workforcewave.com/) `🔬` `[Cloud]` `[Voice]` - AI voice receptionist for SMBs handling 24/7 call answering, appointment booking, and lead capture.
 
 ## Deep Research Agents
 
@@ -600,6 +652,7 @@ AI platforms that conduct autonomous multi-step research, synthesize findings fr
 | Gemini Research  | 1M tokens      | Google Search + KG          |
 | Perplexity Pro   | Variable       | Real-time cited search      |
 
+- [Agon](https://github.com/AutoResearch-Factory/Agon) `🔬` `[Python]` `[Multi-Agent]` - Omnidisciplinary autonomous research system that replaces one-off prompts with Prompt Economy's reusable loops.
 - [CAJAL](https://github.com/Agnuxo1/CAJAL) `🌱` `[Python]` `[Local]` - Local AI agent that generates publication-ready IMRaD scientific papers with verified arXiv citations and AI tribunal scoring.
 - [ChatGPT Deep Research](https://openai.com/index/introducing-deep-research) `🚀` `[Cloud]` `[OpenAI]` - Conducts extended reasoning with web browsing to produce structured research reports with Canvas output.
 - [Claude Deep Research](https://www.anthropic.com/research) `🚀` `[Cloud]` `[Anthropic]` - Performs multi-step investigation with verified source citations and 200K token context window.
@@ -627,6 +680,7 @@ Platforms that generate full-stack applications, components, or web apps from na
 - [PlayCode Agent](https://playcode.io) `🌱` `[Cloud]` `[Multi-Agent]` - Browser-only lightweight web app creation tool with AI-assisted code generation.
 - [Replit Agent](https://replit.com/agent4) `🌱` `[Cloud]` `[Multi-Agent]` - Builds and deploys full-stack projects from prompts with integrated hosting and collaboration.
 - [v0 by Vercel](https://v0.app) `🚀` `[Cloud]` `[CLI]` - Generates React and Tailwind UI components from text descriptions with one-click Vercel deployment.
+- [VibeKit.bot](https://vibekit.bot) `🔬` `[Cloud]` `[Stateful]` - Builds and hosts full-stack apps from phone chat, with a persistent per-app agent and BYOK keys.
 
 ## Multi-Agent Consumer Platforms
 
@@ -661,7 +715,7 @@ Open-weight LLMs optimized for agentic tasks including tool use, planning, multi
 
 - [Baichuan 3](https://huggingface.co/baichuan-inc) `🌱` `[Python]` `[Local]` - Chinese-optimized open model family with strong domain performance for multilingual agent deployments.
 - [Command R+](https://cohere.com/command) `🚀` `[Cloud]` `[RAG]` - Cohere's 104B parameter model optimized specifically for RAG and enterprise tool use in agent workflows.
-- [DeepSeek V3](https://github.com/deepseek-ai/DeepSeek-V3) `🌱` `[Python]` `[Local]` - 671B MoE model that delivers GPT-4 level reasoning at 68x lower cost for budget-conscious agent deployments.
+- [DeepSeek V3](https://github.com/deepseek-ai/DeepSeek-V3) `🌱` `[Python]` `[Local]` - A 671B MoE model that delivers GPT-4 level reasoning at 68x lower cost for budget-conscious agent deployments.
 - [Falcon 3](https://huggingface.co/tiiuae) `🌱` `[Python]` `[Local]` - Efficient open model from TII UAE with strong multilingual support across 10B to 180B parameter variants.
 - [Gemma 3](https://github.com/google-deepmind/gemma) `🚀` `[Python]` `[IDE]` - Efficient multimodal model from Google in 1B to 27B sizes ideal for edge and on-device agents.
 - [Gemma 4](https://deepmind.google/models/gemma/) `🚀` `[Python]` `[Local]` - Consumer and IoT-optimized model from Google in 2B to 31B sizes with E2B and E4B variants.
@@ -674,7 +728,7 @@ Open-weight LLMs optimized for agentic tasks including tool use, planning, multi
 - [Qwen 3](https://github.com/QwenLM/Qwen3) `🚀` `[Python]` `[MCP]` - MCP-native multilingual model family from Alibaba with thinking mode across 0.6B to 235B sizes.
 - [Qwen 3.6-Plus](https://qwenlm.github.io/blog/qwen3/) `🚀` `[Python]` `[MCP]` - Agentic-focused model with 1M context window, repo-level coding, and MCP-native tool use.
 - [StarCoder 2](https://github.com/bigcode-project/starcoder2) `🌱` `[Python]` `[Local]` - Code-focused open model trained on 600+ programming languages for specialized coding agents.
-- [Yi-Lightning](https://github.com/01-ai/Yi) `🌱` `[Python]` `[Local]` - 01.AI's multilingual model with long context and fast inference across 6B to 200B parameter sizes.
+- [Yi-Lightning](https://github.com/01-ai/Yi) `🌱` `[Python]` `[Local]` - A multilingual model from 01.AI with long context and fast inference across 6B to 200B parameter sizes.
 
 ## Market Stats and Benchmarks 2026
 
@@ -735,6 +789,7 @@ Key data points on the AI agent market covering adoption, growth, valuations, an
 
 Tools for running LLMs locally and self-hosting AI agent platforms with full privacy and control.
 
+- [Avelina](https://avelina.ai) `🌱` `[TypeScript]` `[Telegram]` - Self-hosted personal AI assistant on your own VPS that lives in Telegram, with persistent long-term memory, voice, and Claude-powered reasoning.
 - [GPT4All](https://github.com/nomic-ai/gpt4all) `🌱` `[C++]` `[Local]` - Open-source local chat application that runs LLMs on consumer hardware without cloud dependencies.
 - [Jan](https://github.com/janhq/jan) `🌱` `[TypeScript]` `[Local]` - Open-source ChatGPT alternative that runs 100% offline with local model management and privacy guarantees.
 - [Llamafile](https://github.com/mozilla-ai/llamafile) `🌱` `[C++]` `[Local]` - Distributes LLMs as single executable files with zero setup required from Mozilla.
@@ -742,6 +797,7 @@ Tools for running LLMs locally and self-hosting AI agent platforms with full pri
 - [LM Studio](https://lmstudio.ai) `🌱` `[TypeScript]` `[Local]` - Desktop application for running local LLMs with a polished UI across all major platforms.
 - [LocalAI](https://github.com/mudler/LocalAI) `🚀` `[Go]` `[OpenAI]` - Drop-in OpenAI API replacement that runs models locally without requiring a GPU.
 - [Ollama](https://github.com/ollama/ollama) `🚀` `[Go]` `[GitHub]` - Run LLMs locally with a dead-simple CLI interface and 162K+ GitHub stars.
+- [RustFox](https://github.com/chinkan/RustFox) `🔬` `[Rust]` `[MCP]` - Self-hosted Telegram AI assistant written in Rust with sandboxed tool execution, MCP integration, and multi-agent orchestration.
 - [vLLM](https://github.com/vllm-project/vllm) `🚀` `[Python]` `[Local]` - High-throughput LLM serving engine with PagedAttention for production-grade local inference.
 - [Yao Agents](https://github.com/YaoApp/yao) `🌱` `[Go]` `[MCP]` - Local-first AI execution platform with Docker sandbox isolation, BYOK model configuration, MCP support, 5-stage Pipeline, and multi-platform messaging via WeChat, Feishu, DingTalk, Telegram, and Discord.
 
@@ -761,6 +817,7 @@ Frameworks and tools for AI risk management, regulatory compliance, and governan
 
 AI agents specialized in penetration testing, vulnerability discovery, threat detection, and security analysis.
 
+- [Dark-Moon](https://github.com/ASCIT31/Dark-Moon) `🌱` `[Python]` `[MCP]` - Autonomous AI penetration testing platform orchestrating 80+ offensive tools via MCP across web, cloud, Active Directory, Kubernetes, and API targets.
 - [Microsoft Security Copilot](https://www.microsoft.com/en-us/security/business/ai-machine-learning/microsoft-security-copilot) `🚀` `[Cloud]` `[Microsoft]` - Enterprise threat detection and incident response AI integrated across Microsoft security products.
 - [PentestGPT](https://github.com/GreyDGL/PentestGPT) `🌱` `[Python]` `[CLI]` - GPT-powered penetration testing tool with automated reasoning for vulnerability assessment.
 - [YAWNING TITAN](https://github.com/dstl/YAWNING-TITAN) `🌱` `[Python]` `[Graph-Based]` - Graph-based cybersecurity simulation environment for training and testing defensive AI agents.
@@ -791,9 +848,9 @@ Curated newsletters, podcasts, and communities for staying current with AI agent
 - [r/ClaudeAI](https://www.reddit.com/r/ClaudeAI/) `🌱` `[Python]` `[Multi-Agent]` - Reddit community for Claude users sharing agent workflows, prompts, and integration patterns.
 - [r/LangChain](https://www.reddit.com/r/LangChain/) `🌱` `[Python]` `[LangChain]` - Reddit community for agent developers using LangChain, LangGraph, and related frameworks.
 - [r/LocalLLaMA](https://www.reddit.com/r/LocalLLaMA/) `🌱` `[Cloud]` `[IDE]` - Reddit community for self-hosted LLM users sharing local deployment and agent setup guides.
+- [The Agents Index](https://theagentsindex.com) `🔬` `[Cloud]` `[Multi-Agent]` - Compares AI agent tools with sourced pricing, verdicts, and pros/cons in a researched, quality-gated directory.
 - [The Rundown AI](https://www.therundown.ai) `🌱` `[Python]` `[RAG]` - Daily AI digest reaching 600K+ subscribers with concise coverage of agent news and launches.
 
-- [Agents Launchpad](https://launchpad.smartbizcalc.com) `🌱` `[Python]` `[Multi-Agent]` - Community-curated directory of indie AI agents and tools.
 ## Changelog
 
 See [CHANGELOG.md](CHANGELOG.md) for the full update history.

upstream/VoltAgent-awesome-ai-agent-papers/catalogue/README.md

@@ -2,9 +2,9 @@
 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
+upstream_source: https://github.com/VoltAgent/awesome-ai-agent-papers/blob/b4704753/README.md
+upstream_sha: b4704753
+imported_at: 2026-09-04
 prompt_class: catalogue
 upstream_changes: accepted
 author: upstream
@@ -31,11 +31,8 @@ validated: false
 </div>
 
 [![Awesome](https://awesome.re/badge.svg)](https://awesome.re)
-![Papers Count](https://img.shields.io/badge/Research%20Papers-363+-b31b1b)
+![Papers Count](https://img.shields.io/badge/Research%20Papers-364+-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>
@@ -54,6 +51,17 @@ A curated collection of research papers **published in 2026** and sourced from a
 
 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.
 
+## Sponsors
+
+|  |  |
+| :-: | :-- |
+| <a href="https://crawlbase.com/?utm_source=awesome-ai-agent-papers&utm_medium=sponsorship&utm_campaign=voltagent_2026q3&utm_content=readme_listing"><picture><source media="(prefers-color-scheme: dark)" srcset="https://cdn.voltagent.dev/awesome-repo/crawlbase-new/crawlbase-logo-dark-mode.svg"><img alt="Crawlbase" src="https://cdn.voltagent.dev/awesome-repo/crawlbase-new/crawlbase-logo-light-mode.svg" width="425"></picture></a> | [Crawlbase](https://crawlbase.com/?utm_source=awesome-ai-agent-papers&utm_medium=sponsorship&utm_campaign=voltagent_2026q3&utm_content=readme_listing) is web data infrastructure trusted by 70,000+ developers. Its Crawling API, MCP server, and integrations give AI agents live access to any webpage — with JavaScript rendering, proxy rotation, and anti-bot protection. |
+| <a href="https://serpapi.com/?utm_source=voltagent&utm_campaign=md"><img alt="SerpApi" src="https://cdn.voltagent.dev/awesome-repo/serpapi/serpapi-logo.png" width="425"></a> | [SerpApi](https://serpapi.com/?utm_source=voltagent&utm_campaign=md) is a Web Search API for your AI apps. Available in Markdown and JSON for any integration. |
+
+<br />
+
+<a href="https://sponsors.voltagent.dev/#awesome-ai-agent-papers"><img src="https://img.shields.io/badge/📩_Become_a_Sponsor-Contact_Us-blue?style=for-the-badge&logoColor=white" alt="Become a Sponsor" /></a>
+
 ### 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.
@@ -61,6 +69,8 @@ Hundreds of papers are published on arXiv every week, and a growing number of th
 ### Table of Contents
 
 - [Multi-Agent](#multi-agent) (53)
+- [Memory & RAG](#memory--rag) (58)
+- [Multi-Agent](#multi-agent) (54)
 - [Memory & RAG](#memory--rag) (57)
 - [Eval & Observability](#eval--observability) (80)
 - [Agent Tooling](#agent-tooling) (95)
@@ -68,15 +78,40 @@ Hundreds of papers are published on arXiv every week, and a growing number of th
 
 <br>
 
+
+<br/>
+
+You ship products with AI, but every launch still dies quietly because nobody posts about it. [EveryFeed](https://everyfeed.ai/) plugs your AI assistant into a social workspace that drafts, schedules, and publishes across 35+ channels — no agency, no marketing hire.
+
+<a href="https://everyfeed.ai/">
+<img src="https://cdn.voltagent.dev/awesome-repo/everyfeed-social.png" alt="everyfeed"  /><br/>
+</a>
+
+<br/>
+<br/>
+
+Stop building from a blank page. [LaunchKit](https://launchkit.getdesign.md/) gives your AI coding assistant a complete, working product to start from — websites, startups, and web apps that are clickable on day one.
+
+<a href="https://launchkit.getdesign.md/">
+<img src="https://cdn.voltagent.dev/awesome-repo/new-launchkit.png" alt="launchkit"  /><br/>
+</a>
+
+<br/>
+
+
 <details open id="multi-agent">
-<summary><h3 style="display:inline">Multi-Agent (53)</h3></summary>
+<summary><h3 style="display:inline">Multi-Agent (54)</h3></summary>
 
 <br>
 
 | Paper | arXiv ID |
 |---|:---:|
+| **[Agon: An Autonomous Large-Scale Omnidisciplinary Research System Built on Prompt Economy](https://arxiv.org/pdf/2606.24177)** - Treats prompt engineering as an engineering discipline rather than an art, minimizing human time while expecting maximum output. Carries the smallest prompt footprint among cross-disciplinary autoresearch systems (18 roles, 230.6 KiB total), has run across 10+ research fields without modification, and the longest observed run went 30 days unattended. | <a href="https://arxiv.org/abs/2606.24177"><img src="https://img.shields.io/badge/arXiv-2606.24177-b31b1b.svg" alt="arXiv" /></a> |
+| **[PerceptUI: LLM Agents as Human-Aligned Synthetic Users for UI/UX Evaluation](https://arxiv.org/pdf/2606.05697)** - A persona-conditioned framework that predicts how a specific user would answer UI/UX evaluation questions and explains why in natural language. Trained via contrastive reflection fine-tuning and reflective prompt evolution, reaching human-level realism and generalizing to unseen questions and personas. | <a href="https://arxiv.org/abs/2606.05697"><img src="https://img.shields.io/badge/arXiv-2606.05697-b31b1b.svg" alt="arXiv" /></a> |
+| **[OpenCLAW-P2P v6.0: Resilient Multi-Layer Persistence, Live Reference Verification, and Production-Scale Evaluation of Decentralized AI Peer Review](https://arxiv.org/pdf/2604.19792)** - Presents a decentralized AI peer-review platform where autonomous agents publish, score, verify references, and preserve research papers across a multi-layer storage and retrieval architecture. | <a href="https://arxiv.org/abs/2604.19792"><img src="https://img.shields.io/badge/arXiv-2604.19792-b31b1b.svg" alt="arXiv" /></a> |
 | **[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> |
+| **[CityReal: Human-Aligned Urban Behavior and City Dynamics Simulation with Large-Scale LLM Agents](https://arxiv.org/pdf/2608.16897)** - Simulates urban behavior and city dynamics with intention-driven LLM agents that learn habits and preferences via textual adapters, aligning to real population statistics at scale. | <a href="https://arxiv.org/abs/2608.16897"><img src="https://img.shields.io/badge/arXiv-2608.16897-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> |
@@ -136,13 +171,14 @@ Hundreds of papers are published on arXiv every week, and a growing number of th
 <br>
 
 <details open id="memory--rag">
-<summary><h3 style="display:inline">Memory & RAG (56)</h3></summary>
+<summary><h3 style="display:inline">Memory & RAG (57)</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> |
+| **[Semantic Level of Detail for Knowledge Graphs: Discovering Abstraction Boundaries via Spectral Heat Diffusion](https://arxiv.org/pdf/2603.08965)** - Gives an agent a continuous zoom control over a knowledge graph, so it can move between broad and detailed views without hand-tuning a community-detection resolution parameter. Proves the abstraction levels stay consistent as it zooms and shows stable boundary detection on noisy graphs. | <a href="https://arxiv.org/abs/2603.08965"><img src="https://img.shields.io/badge/arXiv-2603.08965-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> |
@@ -205,13 +241,16 @@ Hundreds of papers are published on arXiv every week, and a growing number of th
 <br>
 
 <details id="eval--observability">
-<summary><h3 style="display:inline">Eval & Observability (80)</h3></summary>
+<summary><h3 style="display:inline">Eval & Observability (81)</h3></summary>
 
 <br>
 
 | Paper | arXiv ID |
 |---|:---:|
+| **[PerspectiveGap: A Benchmark for Multi-Agent Orchestration Prompting](https://arxiv.org/pdf/2606.08878)** - A 110-scenario benchmark testing whether LLMs can compose orchestration prompts that distribute context to sub-agents without information leakage. Across 10 topologies and 27 commercial models, GPT-5.5 leads with 62% pass rate while the average is 14.9%. | <a href="https://arxiv.org/abs/2606.08878"><img src="https://img.shields.io/badge/arXiv-2606.08878-b31b1b.svg" alt="arXiv" /></a> |
+| **[RewardHarness: Self-Evolving Agentic Post-Training](https://arxiv.org/pdf/2605.08703)** - Evolves a library of scoring skills and tools from preference examples, then uses a frozen vision-language sub-agent to evaluate image edits and produce a reward for GRPO training. | <a href="https://arxiv.org/abs/2605.08703"><img src="https://img.shields.io/badge/arXiv-2605.08703-b31b1b.svg" alt="arXiv" /></a> |
 | **[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> |
+| **[StructEval: Benchmarking LLMs' Capabilities to Generate Structural Outputs](https://arxiv.org/pdf/2505.20139)** - Benchmarks structured-output generation and cross-format conversion across 18 text and renderable formats, with syntax, structural, and visual evaluation checks. | <a href="https://arxiv.org/abs/2505.20139"><img src="https://img.shields.io/badge/arXiv-2505.20139-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> |
@@ -256,6 +295,7 @@ Hundreds of papers are published on arXiv every week, and a growing number of th
 | **[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> |
+| **[DRIFT: Detecting Representational Inconsistencies for Factual Truthfulness](https://arxiv.org/pdf/2601.14210)** - Lightweight probes (3M-37M params) trained on middle-layer hidden states catch factually wrong generations before they reach the user, at less than 0.1% overhead. Up to 13 AUROC points above final-layer baselines, and the probes transfer across datasets without retraining. | <a href="https://arxiv.org/abs/2601.14210"><img src="https://img.shields.io/badge/arXiv-2601.14210-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> |
@@ -297,12 +337,15 @@ Hundreds of papers are published on arXiv every week, and a growing number of th
 <br>
 
 <details id="agent-tooling">
-<summary><h3 style="display:inline">Agent Tooling (95)</h3></summary>
+<summary><h3 style="display:inline">Agent Tooling (97)</h3></summary>
 
 <br>
 
 | Paper | arXiv ID |
 |---|:---:|
+| **[SCATE: Learning to Supervise Coding Agents for Cost-Effective Test Generation](https://arxiv.org/pdf/2607.08983)** - Replaces the human who keeps prodding a coding agent to write more tests with a contextual bandit that picks the next testing action from current coverage and class testability signals. Driving GEMINI-CLI, it reaches 32.3% higher line coverage and 30.9% higher branch coverage than the agent on its own, and it learns a different policy when the same setup drives CLAUDE CODE. | <a href="https://arxiv.org/abs/2607.08983"><img src="https://img.shields.io/badge/arXiv-2607.08983-b31b1b.svg" alt="arXiv" /></a> |
+| **[Ouroboros: A Self-Developing Frontier Coding Agent with Reviewed Core Evolution](https://arxiv.org/pdf/2608.08311)** - Documents a coding-agent harness that can update its tools, prompts, context assembly, and core code through reviewed commits, with a 161-day live deployment. Reports frozen-snapshot results of 86.74% on Terminal-Bench 2.1 and 90.69% on OSWorld-Verified. | <a href="https://arxiv.org/abs/2608.08311"><img src="https://img.shields.io/badge/arXiv-2608.08311-b31b1b.svg" alt="arXiv" /></a> |
+| **[On Effectiveness and Efficiency of Agentic Tool-calling and RL Training](https://arxiv.org/pdf/2606.00135)** - Find that current agentic tool-calling benchmarks like BFCL are quite brittle: system prompt, multi-turn template or even pure random seeds could have huge influence on the final performance. | <a href="https://arxiv.org/abs/2606.00135"><img src="https://img.shields.io/badge/arXiv-2602.06875-b31b1b.svg" alt="arXiv" /></a> |
 | **[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> |