Agno
FreemiumAgent framework and high-performance runtime for multi-agent systems.
Agno is an open-source Python SDK and runtime for building, running, and managing your own agent platform. It gives developers ready-made components for agents, multi-agent teams, and step-based workflows, then wraps them in AgentOS, a stateless FastAPI runtime with session isolation, RBAC, tracing, and scheduling. Teams keep data in their own databases while deploying on AWS, GCP, Railway, or air-gapped infrastructure.
What is it
Agno is an open-source Python SDK and runtime that combines a lightweight agent-building framework with AgentOS, a production-ready FastAPI service layer.
What it can do
Build agents, teams, and workflows; connect 30+ model providers and 100+ tool integrations; deploy as APIs with auth, memory, knowledge, and observability out of the box.
Who is it for
Engineering teams and AI-native product builders who want to ship private, production-grade agent systems without rebuilding infrastructure.
Key Features
Agent, Team, and Workflow Primitives — Python-first building blocks
Build autonomous agents, coordinate multi-agent teams, and run deterministic step-based pipelines with a clean Python API.
Model-Agnostic Architecture — Swap providers without rewrites
Use 30+ LLM providers including OpenAI, Anthropic, Google, Ollama, Groq, Cerebras, DeepSeek, Mistral, AWS Bedrock, Azure, and vLLM without changing agent logic.
Built-in Memory and Knowledge — Persistent context for every agent
Store session history, per-user memory, and RAG-ready knowledge bases over documents, URLs, and databases.
100+ Tool Integrations + MCP — Connect to live data sources
Leverage pre-built toolkits plus Model Context Protocol support for secure, real-time access to Slack, Drive, wikis, and custom systems.
Use Cases
Build In-Product Copilots
Product teams embed conversational agents into their apps with persistent user sessions, memory, and knowledge retrieval.
Run Multi-Agent Research Pipelines
Research teams compose agent teams that search the web, synthesize findings, and draft reports autonomously.
Label and Extract Data
ML teams process text, image, audio, and video datasets using multimodal agents and Pydantic-typed structured outputs.
Process Documents at Scale
Operations teams route contracts, medical records, and financial reports through RAG pipelines over vector stores.
Pricing plans
Frequently Asked Questions
Discussion
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