https://github.com/agno-agi/agno
Agno is a lightweight library for building Agents with memory, knowledge, tools and reasoning.
https://github.com/agno-agi/agno
agents agi ai developer-tools framework python
Last synced: 14 days ago
JSON representation
Agno is a lightweight library for building Agents with memory, knowledge, tools and reasoning.
- Host: GitHub
- URL: https://github.com/agno-agi/agno
- Owner: agno-agi
- License: mpl-2.0
- Created: 2022-05-04T15:23:02.000Z (almost 4 years ago)
- Default Branch: main
- Last Pushed: 2025-05-12T13:52:02.000Z (9 months ago)
- Last Synced: 2025-05-12T14:25:56.845Z (9 months ago)
- Topics: agents, agi, ai, developer-tools, framework, python
- Language: Python
- Homepage: https://docs.agno.com
- Size: 194 MB
- Stars: 26,528
- Watchers: 178
- Forks: 3,358
- Open Issues: 151
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
- Codeowners: CODEOWNERS
Awesome Lists containing this project
- awesome-LLM-resources - Agno
- awesome-ai-agents - Agno - agent runtime and AgentOS for secure and production-ready systems. (Frameworks)
- awesomeLibrary - agno - Agno is a lightweight library for building multi-modal Agents (语言资源库 / python)
- awesome-safety-critical-ai - `agno-agi/agno` - modal agents (<a id="tools"></a>🛠️ Tools / Bleeding Edge ⚗️)
- awesome-local-llm - agno - a full-stack framework for building Multi-Agent Systems with memory, knowledge and reasoning (Tools / Agent Frameworks)
- awesome - agno-agi/agno - Build multi-agent systems that learn and improve with every interaction. (Python)
- my-awesome-list - agno - agent systems that learn and improve with every interaction. | agno-agi | 37644 | (Python)
- awesome-github-repos - agno-agi/agno - Build multi-agent systems that learn and improve with every interaction. (Python)
- definitive-opensource - Agno - agent systems that learn and improve with every interaction. | `Cross` | **37.6k** | (Table of Contents / Model Tools)
- AiTreasureBox - agno-agi/agno - 11-03_34823_3](https://img.shields.io/github/stars/agno-agi/agno.svg)|Agno is a lightweight library for building Multimodal Agents. It exposes LLMs as a unified API and gives them superpowers like memory, knowledge, tools and reasoning.| (Repos)
- awesome-ai-agents - Agno - agi/agno) |Multi-agent framework, runtime, and control plane for AI products | (🌟 Core Frameworks)
- awesome-nlp-llm-spanish-espa-ol - Agno
- awesome-ai-agents - agno-agi/agno - Agno is a lightweight, model-agnostic library for building advanced AI agents with memory, knowledge, tools, and reasoning capabilities, supporting multi-modal inputs and outputs and featuring an advanced multi-agent architecture. (AI Agent Frameworks & SDKs / Multi-Agent Collaboration Systems)
- Awesome-LLMOps - Agno - source framework for building multi-agent systems with memory, knowledge and reasoning.    (Orchestration / Agent Framework)
- awesome - agno-agi/agno - The complete stack for AI Engineers: framework, runtime and control plane. (<a name="Python"></a>Python)
- awesome-context-engineering - agno - agi`)  Full-stack framework for building Multi-Agent Systems with memory, knowledge and reasoning. (📦 Isolate Context / Multi-Agent Frameworks)
README
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](https://os.agno.com) |
## 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 →](#performance)
## Example
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
## Features
**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
## Getting Started
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
## 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/agno-agi/agno/tree/main/cookbook/12_evals/performance)
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](https://github.com/agno-agi/agno/blob/v2.0/CONTRIBUTING.md).
## Telemetry
Agno logs which model providers are used to prioritize updates. Disable with `AGNO_TELEMETRY=false`.