git-workflow

Agno is a framework, runtime, and control plane for multi-agent systems.

| Layer | What it does |

|-------|--------------|

| Framework | Build agents, teams, and workflows with memory, knowledge, guardrails, and 100+ integrations |

| AgentOS Runtime | Run your system in production with a stateless, secure FastAPI backend |

| Control Plane | Test, monitor, and manage your system using the [AgentOS UI](https://os.agno.com) |

• Private by design. AgentOS runs in your cloud. The control plane connects directly to your runtime from your browser. No retention costs, no vendor lock-in, no compliance headaches.
• Production-ready on day one. Pre-built FastAPI runtime with SSE endpoints, ready to deploy.
• Fast. 529× faster instantiation than LangGraph. 24× lower memory. [See benchmarks →](#performance)

An agent with MCP tools, persistent state, served via FastAPI:

```python

from agno.agent import Agent

from agno.db.sqlite import SqliteDb

from agno.models.anthropic import Claude

from agno.os import AgentOS

from agno.tools.mcp import MCPTools

agno_agent = Agent(

name="Agno Agent",

model=Claude(id="claude-sonnet-4-5"),

db=SqliteDb(db_file="agno.db"),

tools=[MCPTools(transport="streamable-http", url="https://docs.agno.com/mcp")],

add_history_to_context=True,

markdown=True,

)

agent_os = AgentOS(agents=[agno_agent])

app = agent_os.get_app()

if __name__ == "__main__":

agent_os.serve(app="agno_agent:app", reload=True)

```

Run this and connect to the [AgentOS UI](https://os.agno.com):

https://github.com/user-attachments/assets/feb23db8-15cc-4e88-be7c-01a21a03ebf6

Core

• Model-agnostic: OpenAI, Anthropic, Google, local models
• Type-safe I/O with input_schema and output_schema
• Async-first, built for long-running tasks
• Natively multimodal (text, images, audio, video, files)

Memory & Knowledge

• Persistent storage for session history and state
• User memory across sessions
• Agentic RAG with 20+ vector stores, hybrid search, reranking
• Culture: shared long-term memory across agents

Orchestration

• Human-in-the-loop (confirmations, approvals, overrides)
• Guardrails for validation and security
• Pre/post hooks for the agent lifecycle
• First-class MCP and A2A support
• 100+ built-in toolkits

Production

• Ready-to-use FastAPI runtime
• Integrated control plane UI
• Evals for accuracy, performance, latency
• Durable execution for resumable workflows
• RBAC and per-agent permissions

1. Follow the [quickstart guide](https://github.com/agno-agi/agno/tree/main/cookbook/00_quickstart)
2. Browse the [cookbook](https://github.com/agno-agi/agno/tree/main/cookbook) for real-world examples
3. Read the [docs](https://docs.agno.com) to go deeper

Agent workloads spawn hundreds of instances. Stateless, horizontal scalability isn't optional.

| Metric | Agno | LangGraph | PydanticAI | CrewAI |

|--------|------|-----------|------------|--------|

| Instantiation | 3μs | 1,587μs (529×) | 170μs (57×) | 210μs (70×) |

| Memory | *