Awesome Agent Harness

Open-source Python library for AI browser agents, plus Browser Use Cloud: stealth browsers and a hosted agent API.

An open-source long-horizon SuperAgent harness that researches, codes, and creates. With the help of sandboxes, memories, tools, skill, subagents and message gateway, it handles different levels of tasks that could take minutes to hours.

Bash is all you need - A nano claude code–like 「agent harness」, built from 0 to 1

Compress tool outputs, logs, files, and RAG chunks before they reach the LLM. 20% fewer tokens for coding agents, 60-95% fewer tokens for JSON, same answers. Library, proxy, MCP server.

A coding agent for open models like Kimi K3 and GLM 5.3

The Memory Layer for AI Agents - Drop-in memory infrastructure for AI agents and apps. Context that persists. Built for production.

The best-benchmarked open-source AI memory system. And it's free.

Ultra-lightweight, open-source, self-hosted personal AI agent framework in Python with WebUI, tools, memory, MCP, multi-agent workflows, automation, and chat apps

Build resilient agents.

Build, run, and manage agent platforms.

Local-first code intelligence graph for MCP and CLI. Builds a persistent map of your codebase so AI coding tools read only what matters, with benchmarked context reductions on reviews and large-repo workflows.

A lightweight, powerful framework for multi-agent workflows

The batteries-included agent harness.

🚀 The fast, Pythonic way to build MCP servers and clients.

The official Python SDK for Model Context Protocol servers and clients

How Python does AI. Agents, realtime voice, image generation, embeddings. Every model, every interface, typed end to end.

The LLM Evaluation Framework

Build reliable customer-facing AI agents with Parlant: an interaction control harness optimized for controlled, consistent, and predictable LLM interactions.

Memori is agent-native memory infrastructure. A LLM-agnostic layer that turns agent execution and conversation into structured, persistent state for production systems. Built for enterprise, Memori works with the data infrastructure you already run, no rip-and-replace, and deploys across managed cloud, single-tenant cloud, VPC, and on-premises.

Open-source, secure environment with real-world tools for enterprise-grade agents.

A framework for building, orchestrating and deploying AI agents and multi-agent workflows with support for Python and .NET.

TensorZero is an open-source LLMOps platform that unifies an LLM gateway, observability, evaluation, optimization, and experimentation.

Multi-Agent Harness for Production AI

Omnigent is an open-source AI agent framework and meta-harness: orchestrate Claude Code, Codex, Cursor, Pi, and custom agents — swap harnesses without rewriting, enforce policies and sandboxing, and collaborate in real time from any device.

Build an agent harness and control it end-to-end. Open-source SDK for production AI agents in Python & TypeScript - any model, any cloud.

Build effective agents using Model Context Protocol and simple workflow patterns

🧱 Easy fast local-first microVM runtime and library

Flexible and powerful framework for managing multiple AI agents and handling complex conversations

Open-source observability for your GenAI or LLM application, based on OpenTelemetry

AI Agent Governance Toolkit — Policy enforcement, zero-trust identity, execution sandboxing, and reliability engineering for autonomous AI agents. Covers 10/10 OWASP Agentic Top 10.

A model-driven approach to building AI agents in just a few lines of code.

Your agent in your terminal, equipped with local tools: writes code, uses the terminal, browses the web. Make your own persistent autonomous agent on top!

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.

LangChain 🔌 MCP

Minimal and readable coding agent harness implementation in Python to explain the core components of coding agents.

CheetahClaws: A Fast and Easy-to-Use Agent Harness Infrastructure for Long-Horizon, Multi-Model, and Tool-Using AI Systems

Secure runtime to sandbox AI agent tasks. Run untrusted code in isolated WebAssembly environments.

Open Python agent harness for production AI apps: tools, MCP, memory, workspace, telemetry, subagents, background tasks, and OmniServe APIs.