This is a test project for showcasing Agno and Agno UI. It is not intended for production use.

1. Install Node dependencies with npm install
2. Setup your virtual environment:

python3 -m venv .venv
source .venv/bin/activate

3. Install Python dependencies:

uv pip install -U openai sqlalchemy 'fastapi[standard]' agno

4. Export your OPENAI_API_KEY as an environment variable:

export OPENAI_API_KEY=sk-***

1. Start the agent with npm run agent:playground
2. Run the app with npm run dev
3. Open http://localhost:3000 with your browser

1. Start the agent with npm run agent:api
2. Go to http://localhost:8001/docs to view the API documentation
3. Use the POST /runs endpoint to run the agent. Use the agent_id from the console output.

INFO Starting API on localhost:8001                                                                                                                                                                      
INFO:     Will watch for changes in these directories:
INFO:     Uvicorn running on http://localhost:8001 (Press CTRL+C to quit)
INFO:     Started reloader process [1545] using WatchFiles
Planning Agent ID:623550a9-0331-48f1-8fff-44f5b81800fc 
Fitness Agent ID:006a7ce6-99b6-432f-8619-7b192c88a360
INFO:     Started server process [1550]
INFO:     Waiting for application startup.
INFO:     Application startup complete.

• https://github.com/agno-agi/agno
• https://github.com/agno-agi/agent-ui
• https://github.com/agno-agi/agent-api
• https://docs.agno.com/agent-ui/introduction#connect-to-local-agents

Why Agno? Agno will help you build best-in-class, highly-performant agentic systems, saving you hours of research and boilerplate. Here are some key features that set Agno apart:

• 30k+ Github Stars
• Backed by a company
• 79 releases since start. Now on major version 1.7.2
• Model Agnostic: Agno provides a unified interface to 23+ model providers, no lock-in.
• Highly performant: Agents instantiate in ~3μs and use ~6.5Kib memory on average.
• Reasoning is a first class citizen: Reasoning improves reliability and is a must-have for complex autonomous agents. Agno supports 3 approaches to reasoning: Reasoning Models, ReasoningTools or our custom chain-of-thought approach.
• Natively Multi-Modal: Agno Agents are natively multi-modal, they accept text, image, audio and video as input and generate text, image, audio and video as output.
• Advanced Multi-Agent Architecture: Agno provides an industry leading multi-agent architecture (Agent Teams) with reasoning, memory, and shared context.
• Built-in Agentic Search: Agents can search for information at runtime using 20+ vector databases. Agno provides state-of-the-art Agentic RAG, fully async and highly performant.
• Built-in Memory & Session Storage: Agents come with built-in Storage & Memory drivers that give your Agents long-term memory and session storage.
• Structured Outputs: Agno Agents can return fully-typed responses using model provided structured outputs or json_mode.
• Pre-built FastAPI Routes: After building your Agents, serve them using pre-built FastAPI routes. 0 to production in minutes.

• Agent Coordination
• Reasoning
• Memory
• Streaming
• Tracing
• Monitoring
• Human-in-the-Loop
• Workflows
• Scheduling
• HTTP/API Integration
• WebSocket Connections
• Native MCP Support
• Context/Short-term Memory
• Long-term Memory Management
• Tool Calls
• Tool Hooks (for custom logging, etc.)
• Structured Outputs
• Agentic Search
• Multi-Modal
• Multi-Agent Architecture
• Pre-built FastAPI Routes