fast-agent

Composable agent orchestration via MCP servers

mcp-agent is an open-source framework with 8K+ GitHub stars for building AI agents that leverage MCP (Model Context Protocol) servers as composable tool providers. Agents connect to multiple MCP servers simultaneously, gaining access to diverse capabilities without custom integrations. Supports multi-agent workflows, parallel tool execution, and automatic server discovery. Designed to make MCP the universal interface between AI agents and external tools, databases, and services.

Hugging Face's lightweight agent framework

smolagents is Hugging Face's lightweight agent framework for building AI agents that can use tools, write and execute code, and collaborate in multi-agent setups. Designed for simplicity with minimal abstractions — agents are just LLMs that write Python code to orchestrate tool calls rather than using JSON-based function calling. Supports any LLM provider, integrates with Hugging Face Hub for sharing tools and agents, and runs with as few as 1,000 lines of core library code.

Lightweight multi-modal agent framework

Fast, lightweight Python framework for building multi-modal AI agents, formerly known as Phidata. Includes built-in memory, knowledge bases, tools, and reasoning capabilities with 40K+ GitHub stars. Designed for developers who want to build production-ready agents quickly with minimal boilerplate, supporting structured outputs and multi-agent coordination out of the box.

Python agent framework by Pydantic team

Agent framework built on Pydantic for type-safe AI applications. Provides structured outputs, dependency injection, and multi-model support. Created by the Pydantic team, it brings the same validation and typing philosophy that made Pydantic essential for Python APIs to the world of AI agents, ensuring reliable data flow between LLMs and application logic.

Open-source natural-language browser automation framework for AI agents

An AGPL-3.0 TypeScript framework that drives a real browser from natural-language tasks — the self-hostable OSS framework from Hyperbrowser, distinct from the vendor's paid cloud browser product.

JavaScript framework for building and visualizing multi-agent workflows on a Kanban board

KaibanJS is an MIT-licensed JavaScript framework for defining AI agents, tasks, tools, and teams, then orchestrating their work through a Kanban-inspired runtime and visual board. It can run inside Node.js, React, or Next.js projects, supports custom UIs and headless workflows, and provides real-time task-state visibility for multi-agent applications.

Multi-agent model API that orchestrates frontier models behind one OpenAI-compatible endpoint

Sakana Fugu is a hosted model-provider API that exposes a learned multi-agent system as one OpenAI-compatible model. It dynamically routes coding, code review, research, and reasoning tasks across a frontier-model pool, with Fugu for lower-latency work and Fugu Ultra for harder workloads where answer quality matters more than cost or speed.

LangChain CLI for maintaining agent-friendly codebase documentation

OpenWiki is LangChain’s open-source CLI for generating and maintaining an agent-focused wiki inside a codebase. It can create an openwiki/ documentation folder, update it from repository changes, add guidance to AGENTS.md or CLAUDE.md, and run via an interactive CLI or daily GitHub Action so coding agents have durable context without stuffing every detail into prompts.

Open-source desktop AI coworker for browsing and code execution.

Accomplish Coworker is an MIT-licensed open-source AI coworker that runs on the desktop, combining computer-use style browsing with code execution so agents can research, implement, run, and debug workflows in one local environment.

Context compression for LLM apps and coding agents

Headroom is an Apache-2.0 context compression layer for LLM apps and coding agents. It compresses tool output, logs, files, RAG chunks, and agent history through a local library, proxy, wrapper, or MCP server, with retrieval hooks for bringing originals back when needed. Treat its savings numbers as Headroom-reported benchmarks, not independent aicoolies measurements.