Build, run, manage multi-agent systems.
What is Agno?
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 |
Why Agno?
- 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 →
Example
An agent with MCP tools, persistent state, served via FastAPI:
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://github.com/user-attachments/assets/feb23db8-15cc-4e88-be7c-01a21a03ebf6
Features
Core
- Model-agnostic: OpenAI, Anthropic, Google, local models
- Type-safe I/O with
input_schemaandoutput_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
Getting Started
- Follow the getting started guide
- Browse the cookbook for real-world examples
- Read the docs to go deeper
Performance
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 | 6.6 KiB | 161 KiB (24×) | 29 KiB (4×) | 66 KiB (10×) |
Apple M4 MacBook Pro, Oct 2025. Run benchmarks yourself →
https://github.com/user-attachments/assets/54b98576-1859-4880-9f2d-15e1a426719d
IDE Integration
Add our docs to your AI-enabled editor:
Cursor: Settings → Indexing & Docs → Add https://docs.agno.com/llms-full.txt
Also works with VSCode, Windsurf, and similar tools.
Contributing
We welcome contributions. See the contributing guide.
Telemetry
Agno logs which model providers are used to prioritize updates. Disable with AGNO_TELEMETRY=false.