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Awesome List
An awesome list of Agent Harness engineering resources, including GitHub projects, tools, benchmarks, and practical guides.
GitHub stars and default-branch commits for Picrew/awesome-agent-harness.
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PraisonAI 🦞 — Hire a 24/7 AI Workforce. Stop writing boilerplate and start shipping autonomous self-improving agents that research, plan, code, and execute tasks. Deployed in 5 lines of code with built-in memory, RAG, and support for 100+ LLMs.
Build effective agents using Model Context Protocol and simple workflow patterns
✨ Build AI agents and web apps — with a single binary.
Build an agent harness and control it end-to-end. Open-source SDK for production AI agents in Python & TypeScript - any model, any cloud.
The 100 line AI agent that solves GitHub issues or helps you in your command line. Radically simple, no huge configs, no giant monorepo—but scores >74% on SWE-bench verified!
A model-driven approach to building AI agents in just a few lines of code.
Python SDK for AI agent monitoring, LLM cost tracking, benchmarking, and more. Integrates with most LLMs and agent frameworks including CrewAI, Agno, OpenAI Agents SDK, Langchain, Autogen, AG2, and CamelAI
Nexent is a zero-code platform for auto-generating production-grade AI agents using Harness Engineering principles — unified tools, skills, memory, and orchestration with built-in constraints, feedback loops, and control planes.
Next-generation AI Agent Optimization Platform: Cozeloop addresses challenges in AI agent development by providing full-lifecycle management capabilities from development, debugging, and evaluation to monitoring.
Open-source All in One AI agent workspace. Run any agent — Claude Code, Codex — across your tools (100+ integrations + MCP), apps, browser, and files, with shared memory. Built-in models or BYOK.
Build and deploy AI Agents on Cloudflare
ByteRover CLI (brv) - The portable memory layer for autonomous coding agents (formerly Cipher)
Enterprise AI Platform with guardrails, MCP registry, gateway & orchestrator
Next Generation Agentic Proxy for AI Agents and MCP servers
An AI Gateway, registry, and proxy that sits in front of any MCP, A2A, or REST/gRPC APIs, exposing a unified endpoint with centralized discovery, guardrails and management. Optimizes Agent & Tool calling, and supports plugins.
Cascading runtime for AI agents. Optimize cost, latency, quality, and policy decisions inside the agent loop.
Agent framework for the JVM. Pronounced Em-BAY-bel /ɛmˈbeɪbəl/
Devon: An open-source pair programmer
The platform for LLM evaluations and AI agent testing
AI Agent Builder and Runtime by Docker Engineering
Laminar - open-source observability platform purpose-built for AI agents. YC S24.
Security scanner for AI agents, MCP servers and agent skills.
Build applications that make decisions (chatbots, agents, simulations, etc...). Monitor, trace, persist, and execute on your own infrastructure.
A production-oriented multi-agent orchestration framework.
Official Microsoft Learn MCP Server and CLI tool – powering LLMs and AI agents with real-time, trusted Microsoft docs & code samples.
A Go framework for building production agent systems with graph workflows, tools, memory, A2A, AG-UI, MCP, evaluation, and observability.
τ-Bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domains
Run Coding Agents in Sandboxes. Control Them Over HTTP. Supports Claude Code, Codex, OpenCode, and Amp.
The batteries included agent harness.
E2B Desktop Sandbox for LLMs. E2B Sandbox with desktop graphical environment that you can connect to any LLM for secure computer use.
LLM powered fuzzing via OSS-Fuzz.
The Cloud Sandbox Built for AI Agents
A desktop multi-agent harness built with Rust, Tauri, and React, powered by langgraph-rust.
Minimal and readable coding agent harness implementation in Python to explain the core components of coding agents.
Ultimate Context Engineering Infrastructure, starting from MCPs and Integrations
An agent benchmark with tasks in a simulated software company.
Agent harness and tookit for building AI agents and agentic applications. CLI and SDKs included
Sandboxed code execution for AI agents, locally or on the cloud. Massively parallel, easy to extend. Powering SWE-agent and more.