Key Features • Quickstart • Agent Catalog • Agent Ideas • Documentation
Agent Stack is open infrastructure for taking AI agents from prototype to production—no matter how you built them. It includes everything you need to make your agents usable by others: hosting, web UI, runtime services, and multi-tenancy—all without vendor lock-in.
Built on the Agent2Agent (A2A) Protocol and hosted by the Linux Foundation, Agent Stack bridges the gap between different agent ecosystems.
| Feature | Description |
|---|---|
| 🎯 Instant Agent UI | Generate a shareable front-end from your code in minutes. Focus on your agent's logic, not UI frameworks. |
| 🚀 Effortless Deployment | Go from container to production-ready. We handle database, storage, scaling, and RAG so you can focus on your agent. |
| 🔄 Multi-Provider Playground | Test across OpenAI, Anthropic, Gemini, IBM watsonx, Ollama and more. Instantly compare performance and cost to find the optimal model. |
| 🔧 Framework-Agnostic | Run agents from LangChain, CrewAI, and more on a single platform. Enable cross-framework collaboration without rewriting your code. |
sh -c "$(curl -LsSf https://raw.githubusercontent.com/i-am-bee/agentstack/HEAD/install.sh)"Tip
The one-line script works on Linux and macOS. For manual setup or experimental Windows support, see the quickstart guide.
agentstack ui # Launch web interface
agentstack list # See what agents are available
agentstack run chat "Hi!" # Send a message to chat agent
agentstack run chat # Try interactive mode
agentstack info chat # View agent details
agentstack --help # See all optionsgit clone https://github.com/i-am-bee/agentstack-starter my-agent
cd my-agent
uv run server # Start your agentThen in another terminal:
agentstack run example_agent "Alice" # Test your agentYou should see: "Ciao Alice!" 🎉
Tip
Check out Start Building Agents for a complete step-by-step guide to creating your first agent.
Reference implementations demonstrating core Agent Stack capabilities.
- BeeAI Showcase Agent - Full-featured chat assistant demonstrating RequirementAgent with conditional tool use, web search (DuckDuckGo), advanced reasoning (ThinkTool), file handling (PDF/CSV/JSON), streaming, UI feature toggles, trajectory logging, and citation extraction.
- Serper Search Agent - Web search agent showing runtime secrets management (Secrets Extension), custom tool creation (SerperSearchTool), automatic query term extraction, and structured results with citations.
- GitHub Issue Writer - Single-turn workflow using the Form Extension for multi-field input, AI-enhanced issue drafting with ThinkTool, and professional Markdown formatting.
- Chat Agent - Multi-turn conversational agent using RequirementAgent, ActTool for reasoning sequences, and ClarificationTool for ambiguous queries. Integrates DuckDuckGo, Wikipedia, OpenMeteo, and file tools with UnconstrainedMemory, streaming, citation extraction, and OpenTelemetry instrumentation.
- Form Agent - Single-turn form interaction using Form Extension with multiple field types, customizable layouts, file uploads, validation, and structured output.
- RAG Agent - Retrieval-Augmented Generation agent supporting 12+ file formats, dynamic vector stores, semantic search (VectorSearchTool), document summaries (FileReaderTool), intelligent tool selection, and citation tracking with document URLs.
- OAuth Agent - OAuth Extension demo with MCP integration, browser-based authorization, secure token management, and Stripe MCP server access.
- Dynamic Form Request Agent - Multi-step form workflow showing both static and dynamic form generation, where the agent conditionally requests additional input mid-conversation.
A growing collection of community-built agents showcasing various use cases and integrations.
Note
Community agents are maintained by their respective authors. Please review each agent's documentation before use.
- Coming soon! You can add your agent here via a PR to be featured.
Tip
Before contributing, please review our Contribution Guidelines to ensure a smooth experience.
Have an idea for an agent that could solve a real problem? Want to build something that others will find useful? Agent Ideas is where the community proposes, discusses, and builds the next generation of agents together.
- 💡 Propose - Share your agent idea using our Agent Idea template
- 👍 Vote - Browse proposed ideas and upvote the ones you find exciting
- 🛠️ Build - Pick an idea (yours or someone else's) and bring it to life
- 🎉 Share - Show off your creation and help others benefit from your work
- Build real agents - Create practical agentic AI systems that solve actual problems
- Grow your AI engineering skills - Develop expertise through hands-on projects
- Shape the ecosystem - Help define what agents the community needs most
- Connect with others - Collaborate with fellow builders and learn together
Vote with 👍 on the ideas you find most valuable!
| Agent | Description |
|---|---|
| Q&A Builder Agent | Watches Slack and Discord for questions, retrieves answers from docs, and proposes new Q&A entries |
| Issue Sizing Agent | Reviews GitHub issues and provides size estimates (S, M, L, XL) based on complexity and scope |
| Issue Labeling Agent | Automatically assigns the correct issue type and labels to GitHub issues and PRs |
Whether you're proposing your first idea or implementing someone else's vision, we'd love to have you involved!
Visit agentstack.beeai.dev for full documentation.
The Agent Stack community is active on GitHub Discussions where you can ask questions, voice ideas, and share your projects.
To chat with other community members, you can join the Agent Stack Discord server.
Please note that our Code of Conduct applies to all Agent Stack community channels. We strongly encourage you to read and follow it.
For information about maintainers, see MAINTAINERS.md.
Contributions to Agent Stack are always welcome and greatly appreciated. Before contributing, please review our Contribution Guidelines to ensure a smooth experience.
Special thanks to our contributors for helping us improve Agent Stack.
Agent builds upon the foundations established by several pioneering projects in the agent and protocol ecosystem:
- Agent2Agent (A2A) Protocol - The open standard enabling cross-framework agent communication
- Model Context Protocol - Advancing how AI models interact with context
We're grateful to these communities for advancing the state of agent infrastructure and interoperability.
Developed by contributors to the BeeAI project, this initiative is part of the Linux Foundation AI & Data program. Its development follows open, collaborative, and community-driven practices.