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title: "Link Check"
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task: ""
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upstream_source: https://github.com/ARUNAGIRINATHAN-K/awesome-ai-agents-2026/blob/539ec1dc/.github/workflows/link-check.yml
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upstream_sha: 539ec1dc
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imported_at: 2026-06-26
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upstream_source: https://github.com/ARUNAGIRINATHAN-K/awesome-ai-agents-2026/blob/d1ce3b4a/.github/workflows/link-check.yml
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upstream_sha: d1ce3b4a
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imported_at: 2026-08-08
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prompt_class: unknown
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upstream_changes: accepted
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author: upstream
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args: >-
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--verbose
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--no-progress
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--accept 200,204,301,302,403,429
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--accept 200,204,301,302,307,308,403,429
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--exclude-path node_modules
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--exclude "star-history.com"
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--exclude "shields.io"
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--exclude "stableaudio.com"
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--exclude "mckinsey.com"
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--exclude "servicenow.com"
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--exclude "agentage.io"
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--exclude "agentwork-api.yfoob.chatgpt.site"
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--timeout 30
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README.md
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fail: true
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title: "Awesome AI Agents 2026"
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task: ""
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lineage_type: import
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upstream_source: https://github.com/ARUNAGIRINATHAN-K/awesome-ai-agents-2026/blob/bd38c30b/README.md
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upstream_sha: bd38c30b
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imported_at: 2026-07-26
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upstream_source: https://github.com/ARUNAGIRINATHAN-K/awesome-ai-agents-2026/blob/d1ce3b4a/README.md
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upstream_sha: d1ce3b4a
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imported_at: 2026-08-08
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prompt_class: catalogue
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upstream_changes: accepted
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author: upstream
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@@ -234,9 +234,8 @@ Sandboxes, web scrapers, browser automation, and networking layers that agents d
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|
||||
- [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.
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- [AgentDock](https://github.com/agentdock/agentdock) `🚀` `[Python]` `[Docker]` - Framework for building and deploying production-ready AI agents with composable node architecture.
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- [AgentServices](https://agentservices.to) `🚀` `[Python]` `[x402]` - Paid data APIs for AI agents with 54 services, 37 MCP tools, and x402 nanopayments on Base. Market data, onchain analytics, AI inference, and research.
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- [AgentWork](https://agentwork-api.yfoob.chatgpt.site/) `🔬` `[Cloud]` `[x402]` - Aggregates verified paid work opportunities for autonomous agents, with a 0.005 Polygon USDC/hour x402 API for full decision context.
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- [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.
|
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- [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.
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- [Crawl4AI](https://github.com/unclecode/crawl4ai) `🌱` `[Python]` `[Multi-Agent]` - Extracts structured data from web pages using LLM-friendly output formats optimized for agent ingestion.
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- [Docling](https://github.com/docling-project/docling) `🌱` `[Python]` `[IDE]` - Parses PDFs, DOCX, and slides into structured text with deep layout understanding for document agents.
|
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- [E2B](https://github.com/e2b-dev/e2b) `🌱` `[TypeScript]` `[Multi-Agent]` - Cloud sandboxes for AI agents to run code securely in isolated environments.
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@@ -263,8 +262,8 @@ Sandboxes, web scrapers, browser automation, and networking layers that agents d
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- [Flowise](https://github.com/FlowiseAI/Flowise) `🌱` `[TypeScript]` `[RAG]` - Open-source drag-and-drop UI to build customized LLM flows, RAG pipelines, and agent systems.
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- [Langflow](https://github.com/langflow-ai/langflow) `🌱` `[Python]` `[RAG]` - Visual drag-and-drop builder for LLM workflows, RAG agents, and multi-step pipelines.
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- [Lindy](https://www.lindy.ai) `🌱` `[Cloud]` `[No-Code]` - No-code AI agent platform with 3000+ app integrations for business workflow automation.
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- [LlamaIndex Workflows](https://docs.llamaindex.ai/en/stable/module_guides/workflow/) `🌱` `[Python]` `[RAG]` - Event-driven orchestration framework for building complex agentic systems.
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- [LLMGraph](https://llmgraph.ai/home) `🔬` `[Cloud]` `[RAG]` - Visual canvas for building RAG chatbots and AI agents with one-click deploy to REST API or chat widget.
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- [LlamaIndex Workflows](https://developers.llamaindex.ai/python/llamaagents/workflows/) `🌱` `[Python]` `[RAG]` - Event-driven orchestration framework for building complex agentic systems.
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- [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.
|
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- [Make](https://www.make.com/en) `🌱` `[Cloud]` `[RAG]` - Visual workflow automation platform with AI capabilities and drag-and-drop scenario builder.
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- [n8n](https://github.com/n8n-io/n8n) `🌱` `[TypeScript]` `[Docker]` - Open-source workflow automation with AI agent nodes combining visual and code-based orchestration.
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- [Relevance AI](https://relevanceai.com) `🌱` `[Cloud]` `[No-Code]` - No-code AI agent builder for sales, support, and research use cases with team collaboration.
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@@ -281,6 +280,7 @@ Sandboxes, web scrapers, browser automation, and networking layers that agents d
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- [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.
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- [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.
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- [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.
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- [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.
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- [Rasa](https://github.com/RasaHQ/rasa) `🌱` `[Python]` `[Self-Hosted]` - Open-source conversational AI framework with self-hosted NLU training and dialogue management.
|
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- [simulate-sdk](https://github.com/future-agi/simulate-sdk) `🌱` `[Python]` `[Voice]` - Persona- and scenario-driven SDK for simulating voice and text AI agents.
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- [Vapi](https://github.com/VapiAI/server-sdk-python) `🌱` `[Python]` `[Voice]` - Platform for building voice AI agents with low-latency speech-to-speech capabilities.
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@@ -317,7 +317,6 @@ Sandboxes, web scrapers, browser automation, and networking layers that agents d
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- [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).
|
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- [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.
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- [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.
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- [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).
|
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- [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.
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## Agent Interfaces and UIs
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@@ -352,6 +351,7 @@ Frontend workspaces and chat interfaces with built-in agent plugins and tool-use
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- [AgentBench](https://github.com/THUDM/AgentBench) `🌱` `[Python]` `[Benchmark]` - Comprehensive benchmark for evaluating LLMs as agents across 8 distinct environments.
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- [ARC-AGI-2](https://arcprize.org) `🌱` `[Python]` `[Benchmark]` - Frontier benchmark for measuring general intelligence capabilities in AI agents beyond pattern matching.
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- [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.
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- [GAIA Benchmark](https://huggingface.co/papers/2311.12983) `🌱` `[Python]` `[Benchmark]` - Benchmark for General AI Assistants measuring real-world reasoning and tool use.
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- [Inspect AI](https://github.com/UKGovernmentBEIS/inspect_ai) `🌱` `[Python]` `[Evaluation]` - Framework for evaluating large language models with composable tasks and scoring.
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- [SWE-bench](https://github.com/SWE-bench/SWE-bench) `🚀` `[Python]` `[GitHub]` - Benchmark for evaluating LLMs on real-world software engineering tasks from GitHub issues.
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@@ -407,7 +407,7 @@ Curated list of vertical agent solutions for finance, healthcare, legal, manufac
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### Government & Compliance
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- Anthropic Government Agents - Policy analysis and public sector agents for regulated workflows (🏷️ `Cloud` `Government` `Enterprise`).
|
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- [Leyna](https://www.leyna.ai) `🌱` `[Cloud]` `[Government]` - Public records request agent for government agencies to parse, redact, and track requests.
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- [Leyna](https://www.atom.com/name/Leyna.ai) `🌱` `[Cloud]` `[Government]` - Public records request agent for government agencies to parse, redact, and track requests.
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- Tax & Insurance Agent Platforms - Generic category placeholder for compliance-focused tax and underwriting agents (🏷️ `Cloud` `Compliance` `Enterprise`).
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## Learning Resources
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@@ -572,9 +572,9 @@ AI agents that automate customer support, CRM workflows, sales outreach, and tic
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- [Ada](https://www.ada.cx) `🚀` `[Cloud]` `[Multi-Agent]` - Resolves 60% of customer support tickets automatically with complex multi-turn query understanding.
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- [Assembled](https://www.assembled.com) `🚀` `[Cloud]` `[Multi-Agent]` - Routes support tickets with workforce-aware scheduling and intelligent handoff to human agents.
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- [ChatBotKit](https://chatbotkit.com) `🌱` `[Cloud]` `[RAG]` - Deploys AI agents that answer customer questions from business data and hand conversations to humans.
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- [Dixa](https://www.dixa.com) `🚀` `[Cloud]` `[Multi-Agent]` - CRM-first conversational support platform with AI-powered routing and customer context enrichment.
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- [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.
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- [Hellomatik](https://hellomatik.com) `🌱` `[Cloud]` `[Multi-Agent]` - Turns company knowledge into agents that answer, book, and sell across WhatsApp, phone, email, and web.
|
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- [Intercom Fin](https://fin.ai) `🚀` `[Cloud]` `[Multi-Agent]` - Resolves 50% of SaaS support tickets by learning directly from your help center and knowledge base.
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- [Zendesk AI](https://www.zendesk.com/service/ai/) `🚀` `[Cloud]` `[Multi-Agent]` - Automates 30% of enterprise support tickets with deep integration into the existing Zendesk ecosystem.
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@@ -623,6 +623,7 @@ Platforms for building, deploying, and scaling voice-based AI agents across call
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- [Synthesia](https://www.synthesia.io) `🌱` `[Cloud]` `[IDE]` - Generates AI video avatars that speak in 120+ languages for training and communication agents.
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- [Synthflow](https://synthflow.ai) `🌱` `[Cloud]` `[No-Code]` - No-code voice agent builder with pre-built templates for SMBs to deploy phone agents quickly.
|
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- [Voiceflow](https://www.voiceflow.com) `🌱` `[Cloud]` `[No-Code]` - No-code builder for voice and chat agents with visual conversation design and team collaboration.
|
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- [Workforce Wave](https://www.workforcewave.com/) `🔬` `[Cloud]` `[Voice]` - AI voice receptionist for SMBs handling 24/7 call answering, appointment booking, and lead capture.
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## Deep Research Agents
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@@ -834,7 +835,6 @@ Curated newsletters, podcasts, and communities for staying current with AI agent
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- [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.
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- [The Rundown AI](https://www.therundown.ai) `🌱` `[Python]` `[RAG]` - Daily AI digest reaching 600K+ subscribers with concise coverage of agent news and launches.
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- [Agents Launchpad](https://launchpad.smartbizcalc.com) `🌱` `[Python]` `[Multi-Agent]` - Community-curated directory of indie AI agents and tools.
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## Changelog
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See [CHANGELOG.md](CHANGELOG.md) for the full update history.
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title: "Awesome AI Agent Papers"
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task: ""
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lineage_type: import
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upstream_source: https://github.com/VoltAgent/awesome-ai-agent-papers/blob/c8502b6a/README.md
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upstream_sha: c8502b6a
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imported_at: 2026-08-08
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upstream_source: https://github.com/VoltAgent/awesome-ai-agent-papers/blob/aa50c0c3/README.md
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upstream_sha: aa50c0c3
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imported_at: 2026-07-03
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prompt_class: catalogue
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upstream_changes: accepted
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author: upstream
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<br>
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<details open id="multi-agent">
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<summary><h3 style="display:inline">Multi-Agent (54)</h3></summary>
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<summary><h3 style="display:inline">Multi-Agent (53)</h3></summary>
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<br>
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| Paper | arXiv ID |
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|---|:---:|
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| **[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> |
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| **[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> |
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| **[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> |
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| **[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> |
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@@ -207,7 +206,7 @@ Hundreds of papers are published on arXiv every week, and a growing number of th
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<br>
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<details id="eval--observability">
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<summary><h3 style="display:inline">Eval & Observability (81)</h3></summary>
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<summary><h3 style="display:inline">Eval & Observability (80)</h3></summary>
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<br>
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@@ -215,7 +214,6 @@ Hundreds of papers are published on arXiv every week, and a growing number of th
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|---|:---:|
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| **[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> |
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| **[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> |
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| **[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> |
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| **[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> |
|
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| **[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> |
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| **[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> |
|
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@@ -260,7 +258,6 @@ Hundreds of papers are published on arXiv every week, and a growing number of th
|
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| **[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> |
|
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| **[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> |
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| **[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> |
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| **[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> |
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| **[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> |
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Reference in New Issue
Block a user