Agno
Officially listedProduction-ready full-stack framework for multi-agent systems, with 3-microsecond agent instantiation and a built-in control plane.
Agno
Agno (formerly Phidata) is a full-stack platform for production multi-agent systems, built by former Airbnb engineers with 40K+ GitHub stars. It covers the complete path from local development to production deployment — a three-in-one stack of SDK for building, the AgentOS runtime, and a visual control plane — with your first agent live in 20 lines of Python.
Core Capabilities
- Extreme performance: Agent instantiation takes only 3μs — 529x faster than LangGraph — supporting high-concurrency scenarios
- Full-stack architecture: SDK + AgentOS + Control Plane in three integrated layers, covering the entire path from development to production
- Privacy-first: All data stays in the user's own cloud, with built-in JWT, RBAC, and multi-tenant isolation
- Knowledge base integration: Built-in RAG, vector storage, and 100+ toolkits, ready out of the box for knowledge-intensive scenarios
- Multi-model support: Compatible with 30+ providers, including OpenAI, Claude, and Gemini
- Production-grade API: AgentOS provides 50+ REST endpoints, SSE/WebSocket, and session management
Use Cases The first choice for Python developers building production-grade agent systems. Best for knowledge-intensive applications (legal research, financial analysis, enterprise knowledge base Q&A) and enterprise customers with strict data privacy requirements. Non-developers or scenarios that need fine-grained state machines should consider other options.
Unique Advantages The only choice among the four major frameworks that offers a complete Control Plane — not just a code library, but an end-to-end platform. Its 529x instantiation speed makes it a natural fit for high concurrency, whereas the LangChain ecosystem requires far more self-built infrastructure to reach the same production readiness.
Editor's Review Recommendation index 4/5. Developer feedback includes "abandoned LangGraph and switched fully to Agno — everything is more logical." You can start with the free open-source SDK, and the Pro plan unlocks the production control console. One caution: the v2.0 upgrade drew community complaints, so new projects should pin versions before going live.
Pricing
### 💰 定价模式：免费增值（开源 SDK + 付费控制台） **起步价**：$0（开源 SDK 完整可用） #### 主要方案 - **Free（开源）**：$0 - 完整 SDK、本地 AgentOS 运行时、本地控制台、社区支持、100+ 工具集成 - **Pro**：$150/月 - Free 全部功能 + 生产环境控制台、1个实时连接、4个席位、无限监控/存储/知识/记忆 - **Enterprise**：联系定价 - Pro 全部功能 + 专属 Slack 支持、技术 SLA、自定义 SSO/RBAC、自托管控制台 #### 试用/其他信息 Pro 套餐附加费用：额外席位 $30/月/人，额外实时连接 $95/月/个。开源 SDK 永久免费，无需注册即可使用。 — Visit website
FAQ
Is Agno free?
The Agno core SDK is fully open source and free, usable with no registration. The production-grade control console (Pro) starts at $150/month; Enterprise is contact-for-pricing.
What can Agno be used for?
Agno is used to build production-grade multi-agent systems, supporting agent orchestration, knowledge base Q&A (RAG), workflow automation, and multi-model invocation, covering the full path from development to deployment.
How is Agno different from LangChain?
Agno is lighter and higher-performance (529x instantiation speed) and ships with a complete Control Plane; LangChain has a larger ecosystem and richer community resources, better suited to complex state machine scenarios.
Is Agno's claim of being 529x faster than LangGraph real?
That figure comes from official benchmarks (agent instantiation speed) and has been partially verified by third parties. But real production bottlenecks usually lie in LLM inference latency rather than instantiation, so evaluate it against your specific scenario.
How does Agno ensure data privacy and security?
Agno uses a privacy-first architecture: all data (logs, memory, knowledge, sessions) stays in the user's own cloud environment (AWS/GCP/Railway) and never flows to third parties, with built-in JWT and RBAC access control.
Is Agno suitable for users without Python experience?
Not really. Agno is a developer framework that requires a Python foundation. No-code/low-code users should consider products like Dify or Coze; developers with Python basics can usually run their first agent within 2 minutes.