[upstream-sync] README.md from ARUNAGIRINATHAN-K/awesome-ai-agents-2026@4387b454 [catalogue]

Merge upstream sync PR #31 : resolve conflicts keeping most recent upstream version
Merge pull request '[Upstream sync] ARUNAGIRINATHAN-K/awesome-ai-agents-2026 (github) — 0 added, 2 modified' (#34 ) from upstream-sync/awesome-ai-agents-2026-20260808-d1ce3b-nigj into main
2026-08-13 16:08:26 +00:00 · 2026-08-09 22:38:26 +00:00 · 2026-08-09 22:25:22 +00:00 · 2026-08-09 22:25:09 +00:00 · 2026-08-08 15:47:35 +00:00 · 2026-07-25 05:45:39 +00:00
3 changed files with 20 additions and 14 deletions
@@ -2,9 +2,9 @@
 title: "Link Check"
 task: ""
 lineage_type: import
-upstream_source: https://github.com/ARUNAGIRINATHAN-K/awesome-ai-agents-2026/blob/d1ce3b4a/.github/workflows/link-check.yml
-upstream_sha: d1ce3b4a
-imported_at: 2026-08-08
+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
@@ -40,15 +40,13 @@ jobs:
          args: >-
            --verbose
            --no-progress
-            --accept 200,204,301,302,307,308,403,429
+            --accept 200,204,301,302,308,403,429
            --exclude-path node_modules
            --exclude "star-history.com"
            --exclude "shields.io"
            --exclude "stableaudio.com"
            --exclude "mckinsey.com"
            --exclude "servicenow.com"
-            --exclude "agentage.io"
-            --exclude "agentwork-api.yfoob.chatgpt.site"
            --timeout 30
            README.md
          fail: true
@@ -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/d1ce3b4a/README.md
-upstream_sha: d1ce3b4a
-imported_at: 2026-08-08
+upstream_source: https://github.com/ARUNAGIRINATHAN-K/awesome-ai-agents-2026/blob/4387b454/README.md
+upstream_sha: 4387b454
+imported_at: 2026-08-13
 prompt_class: catalogue
 upstream_changes: accepted
 author: upstream
@@ -95,6 +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.
@@ -106,6 +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.
@@ -126,7 +128,9 @@ validated: false
 - [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.
@@ -155,6 +159,7 @@ validated: false
 - [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.
@@ -2,9 +2,9 @@
 title: "Awesome AI Agent Papers"
 task: ""
 lineage_type: import
-upstream_source: https://github.com/VoltAgent/awesome-ai-agent-papers/blob/aa50c0c3/README.md
-upstream_sha: aa50c0c3
-imported_at: 2026-07-03
+upstream_source: https://github.com/VoltAgent/awesome-ai-agent-papers/blob/c8502b6a/README.md
+upstream_sha: c8502b6a
+imported_at: 2026-08-08
 prompt_class: catalogue
 upstream_changes: accepted
 author: upstream
@@ -69,12 +69,13 @@ Hundreds of papers are published on arXiv every week, and a growing number of th
 <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> |
 | **[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> |
@@ -206,7 +207,7 @@ 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>

@@ -214,6 +215,7 @@ Hundreds of papers are published on arXiv every week, and a growing number of th
 |---|:---:|
 | **[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> |
 | **[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> |
@@ -258,6 +260,7 @@ 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> |
Author	SHA1	Message	Date
promptadmin	7a404a4fea	[upstream-sync] README.md from ARUNAGIRINATHAN-K/awesome-ai-agents-2026@4387b454 [catalogue]	2026-08-13 16:08:26 +00:00
promptadmin	5630c7eda3	Merge upstream sync PR #31 : resolve conflicts keeping most recent upstream version	2026-08-09 22:38:26 +00:00
promptadmin	2f62d46a10	Merge pull request '[Upstream sync] ARUNAGIRINATHAN-K/awesome-ai-agents-2026 (github) — 0 added, 2 modified' (#34 ) from upstream-sync/awesome-ai-agents-2026-20260808-d1ce3b-nigj into main Reviewed-on: #34	2026-08-09 22:25:22 +00:00
promptadmin	39b05f2a41	Merge pull request '[Upstream sync] VoltAgent/awesome-ai-agent-papers (github) — 0 added, 1 modified' (#33 ) from upstream-sync/awesome-ai-agent-papers-20260808-c8502b-qogc into main Reviewed-on: #33	2026-08-09 22:25:09 +00:00
promptadmin	8f2270c6c0	[upstream-sync] README.md from VoltAgent/awesome-ai-agent-papers@c8502b6a [catalogue]	2026-08-08 15:47:35 +00:00
promptadmin	62047676bd	[upstream-sync] README.md from ARUNAGIRINATHAN-K/awesome-ai-agents-2026@117f624b [catalogue]	2026-07-25 05:45:39 +00:00
promptadmin	b9ecbc7feb	[upstream-sync] .github/workflows/link-check.yml from ARUNAGIRINATHAN-K/awesome-ai-agents-2026@117f624b [unknown]	2026-07-25 05:45:28 +00:00