# agent-framework

MCP, ACP and Skills support for building production coding agents — interactive or automated.

fast-agent is an Apache-licensed Python framework for building and running LLM agents with full MCP (Model Context Protocol) and ACP support. It ships with an interactive shell mode, Skills management, and multi-model routing — making it a practical platform for coding agents, workflow automation, and agent evaluation across Claude, Codex, HuggingFace, and local models.

Open-source toolkit for building AI SRE incident response agents

OpenSRE is an open-source Python toolkit from Tracer Cloud for building AI SRE agents that investigate and respond to production incidents. It ships with connectors to Prometheus, Grafana, Kubernetes and incident platforms, plus a simulation harness that replays past incidents so teams can benchmark agent accuracy before trusting it on live pager rotations.

Self-evolution engine for AI agents with auditable updates

Evolver is an open-source self-evolution engine for AI agents that turns run logs into auditable, reviewable updates via its Genome Evolution Protocol. Instead of ad hoc prompt tweaking, teams collect traces and Evolver proposes versioned diffs to prompts, tools and workflows that engineers can approve, reject or roll back like code.

Self-evolving local computer agent with a reusable skill tree

GenericAgent is a minimal, self-evolving autonomous agent in roughly 3K lines of Python that gives LLMs system-level control of a local computer. It writes files, runs shell commands, and browses the web, but its defining feature is skill crystallization: successful task runs are saved as reusable skills inside a growing skill tree that cuts token cost on repeats.

Open-source async coding agent you can run in your own sandbox

Open-source framework from LangChain AI for building your organization's internal coding agent — the same pattern Stripe's Minions, Ramp's Inspect, and Coinbase's Cloudbot follow. Built on LangGraph and Deep Agents, Open SWE runs each task in an isolated cloud sandbox (Modal, Daytona, Runloop, or LangSmith), invokes from Slack, Linear, or GitHub, orchestrates subagents, and opens pull requests autonomously — customizable end-to-end for your codebase and conventions.

Build modular, scalable LLM applications in Rust

Open-source Rust library for building scalable, modular, and ergonomic LLM-powered applications. Rig unifies 20+ model providers (OpenAI, Anthropic, Mistral, DeepSeek, Ollama, and more) and 10+ vector stores behind one trait-based interface, supports completion and embedding workflows, multi-turn streaming, and transcription/audio/image generation, with full GenAI Semantic Convention compatibility and WASM-ready core library — production agentic infra for Rust teams.

Fork, customize, and ship AI agents on Vercel in minutes

Open Agents is a Vercel Labs open-source template for building and deploying cloud-hosted AI agents. It provides a production-ready Next.js starter with built-in tool use, streaming responses, multi-model support, and deployment on Vercel infrastructure. Developers can fork, customize agent behavior and tools, then ship agent-backed apps in minutes with automatic scaling and edge routing.

Human-in-the-loop web agent you can co-pilot in real time

Magentic-UI is a Microsoft Research web agent with a human-in-the-loop interface for browsing, coding, and file tasks. It plans multi-step actions, asks for approval before executing, and lets users co-pilot by taking over the browser mid-task. Built on AutoGen, it runs a team of specialized agents for web browsing, file handling, and code execution with full action transparency and safety guardrails.

LangChain-powered agent harness with planning and subagents

Deep Agents is a production-ready agent framework built on LangChain and LangGraph for complex agentic workflows. It features a planning system for task decomposition, a filesystem backend for persistent operations, sandboxed shell execution, and isolated subagents with independent context windows. Automatic context summarization keeps agents coherent across long sessions, while smart defaults simplify prompt engineering for multi-step autonomous tasks.

Thread-based AI assistant API with tools and file support

OpenAI's platform API for building stateful AI assistants. Manages conversation threads, supports function calling, code interpreter, and file search (RAG) out of the box. Usage-based pricing makes it accessible for startups and enterprises alike, with built-in memory and tool orchestration for production-grade conversational applications.