Agno
Build, run, and manage agent platforms.
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What is Agno?
Agno is an open-source SDK, runtime, and control plane for building, running, and managing multi-agent systems in your own cloud infrastructure. It combines a high-performance Python agent framework with AgentOS, a production-ready runtime that turns agents into API services with tracing, scheduling, RBAC, and audit logs. The framework supports building individual agents, multi-agent teams, and step-based agentic workflows using any model provider (OpenAI, Claude, Gemini, Groq, and open-source LLMs) while maintaining model-agnostic flexibility.
Key features include industry-leading performance with ~3μs agent instantiation (529× faster than LangGraph), built-in memory and knowledge systems with agentic RAG over 20+ vector stores, multimodal support for text/images/audio/video, structured outputs with Pydantic schema validation, and a powerful control plane UI for real-time monitoring and management. Agno provides APIs out of the box for sessions, memory, knowledge, and traces, plus governance features like guardrails, human-in-the-loop approval flows, and JWT-based RBAC.
Agno is designed for product teams building in-product agents and chat copilots, ML teams doing data labeling and extraction, AI teams generating synthetic data and automation, data science teams handling enrichment and training data curation, and data engineering teams automating quality audits. It's ideal for engineering teams that need production-ready performance, complete data privacy (no data leaves your cloud), and want to avoid maintaining infrastructure like model providers and MCP integrations.
The platform runs entirely in your infrastructure (AWS, GCP, Railway) with your database, giving you ownership of session, memory, and trace data for auto-improving agents. Teams use it for everything from AI-native software like agent widgets to document processing, employee assistants, and multi-agent research systems.
Agno pricing
Pricing model: Freemium
Free tier: Open-source, build multi-agent systems, run agent systems using AgentOS, Control Plane for local AgentOS, chat with agents/teams/workflows, session monitoring & metrics, knowledge & memory management, system evaluations, Jumpstart & community, pre-built production-ready codebases, community support and forums, docs. Pro tier: $150/month for managing production systems, includes everything in Free plus Control Plane for live AgentOS, 1 live connection, 4 total seats included, unlimited usage, unlimited monitoring, unlimited retention, unlimited knowledge, unlimited memories, unlimited chats. Add-ons: $30/month per seat, $95/month per live connection. Enterprise: Custom solutions with dedicated Slack channel, dedicated technical lead, support SLA, custom SSO and RBAC, custom agent solutions, self-hosted Control Plane. Pro is free for students, educators, and startups with less than $2M in funding.
Agno pros
- 529× faster agent instantiation than LangGraph (~3μs vs seconds)
- 70× faster than CrewAI in benchmarks
- ~50× lower memory per agent vs LangGraph (~5KB average)
- Open-source framework with free tier for building
- Runs entirely in your cloud - complete data privacy
- No data egress costs or retention limits
- Built-in memory system with persistent storage across conversations
- Agentic RAG over 20+ vector stores with hybrid search and reranking
- Supports 23+ model providers (OpenAI, Claude, Gemini, Groq, open-source)
- Multimodal support for text, images, audio, and video
- Structured outputs with Pydantic schema validation (auto-validated)
- AgentOS control plane with real-time monitoring UI
- APIs available immediately for sessions, memory, knowledge, traces
- JWT-based RBAC, guardrails, and human-in-the-loop approval flows
- Audit logs and post-execution hooks for governance
- Stateless runtime scales horizontally like your other services
- Intuitive Python API easier than LangChain/CrewAI
- Multi-agent teams with automatic coordination and delegation
- Pre-built production-ready codebases (Jumpstart)
- Unlimited usage, monitoring, retention on Pro plan
- Supports any framework (native Agno, LangGraph, DSPy, Claude)
- Async support with unified API for sync and async
- Session isolation with per-session data separation
- Custom tools as first-class pluggable Python classes
Agno cons
- Relatively newer framework with smaller ecosystem than LangChain/LangGraph
- Python-only (no native support for other programming languages)
- Pro tier at $150/month may be expensive for small teams
- Limited third-party integrations compared to established frameworks
- Documentation can lag behind rapid framework updates
- Breaking changes can occur as framework is still evolving
- Debugging multi-agent failures requires patience
- Token usage may spike if agents over-communicate
- Best suited for structured problems, less optimal for creative/open-ended tasks
- 4 seats included on Pro plan may be limiting for larger teams
- $30/month per seat addon cost adds up for large teams
- $95/month per live connection addon for additional connections
- Steep learning curve for complex multi-agent orchestration
- Custom RAG specifics and context management may still require manual coding
- Lower brand recognition compared to established competitors
Frequently asked questions about Agno
What is Agno and what does it do?
Agno is an SDK and runtime for building your own agent platform. It lets you build agents, multi-agent teams, and step-based agentic workflows using the Agno SDK, run agents as a service using AgentOS runtime (every agent becomes an API with multi-user isolated sessions, tracing, scheduling, RBAC, and audit logs), and manage everything from a unified control plane. Your platform runs in your cloud with your database, and no data leaves your environment.
How does Agno compare to LangGraph and CrewAI in performance?
Agno delivers industry-leading performance: 529× faster agent instantiation than LangGraph (~3μs vs seconds), 70× faster than CrewAI (CrewAI takes 70 seconds to instantiate agents, Agno does it in 1 second), and ~50× lower memory per agent (~5KB average vs 24× more for PydanticAI). This measurable performance matters when running agents at scale.
What models does Agno support?
Agno is model-agnostic and supports 23+ model providers including OpenAI (GPT-4, GPT-4o, GPT-4o-mini, GPT-5-mini), Anthropic Claude, Google Gemini, Groq, and open-source LLMs. You can use any model in your preferred provider while maintaining the same Python API.
What is AgentOS and how does it work?
AgentOS is Agno's production-ready runtime that turns any agent into an API service. It provides multi-user isolated sessions, automatic tracing, scheduling, JWT-based RBAC, and audit logs. AgentOS is a stateless FastAPI application that fits operational patterns of your existing services and scales horizontally. It runs entirely in your cloud (AWS, GCP, Railway) with your database, and includes a control plane UI for chat, tracing, monitoring, and management.
How does Agno's knowledge and memory system work?
Knowledge gives agents access to information beyond training data by loading files, URLs, or raw text into a vector database for search and retrieval. Memory provides persistent storage across conversations with user memory and shared 'Culture' knowledge. The system supports agentic RAG over 20+ vector stores with hybrid search (semantic + keyword using Reciprocal Rank Fusion) and reranking. Agents automatically search knowledge when relevant.
What are Structured Outputs and when should I use them?
Structured Outputs is the preferred and most reliable way to extract schema-compliant responses from models. You provide a schema using Pydantic or JSON Schema, and the model's response is guaranteed to strictly follow that schema, eliminating missing fields, invalid enum values, or inconsistent formatting. Agno uses Structured Outputs by default if the model supports it. Use JSON mode (use_json_mode=True) when the model doesn't support structured outputs, you need broader compatibility, or the model doesn't support tools with structured outputs.
Can I use Agno with frameworks other than the native SDK?
Yes, Agno lets you build agents using any framework including native Agno SDK, LangGraph, DSPy, or Claude Code. You can build agent platforms managed entirely by Claude Code, and AgentOS productionizes agents built with any framework, any model, on any cloud. This flexibility lets you use existing frameworks while gaining AgentOS's production features.
What security and governance features does Agno provide?
Agno provides governance as a first-class concept with guardrails, human-in-the-loop (HITL) and admin approval flows built into the runtime. It includes JWT-based RBAC, audit logs, and post-execution hooks for sensitive actions. The platform is private by default - usage, logs, metrics, traces, memory, knowledge, sessions, and user data stay in your environment with no data egress. Per-session isolation ensures data separation.
Who should use Agno and what are the main use cases?
Product teams use Agno for in-product agents and chat copilots (many power entire products with AgentOS). ML teams use it for labeling text, image, audio, and video data (natively typesafe and multimodal). AI teams generate synthetic data and preference pairs for training/evals, automate document processing and knowledge organization. Data science teams handle enrichment, segmentation, and training data curation. Data engineering teams automate quality audits, failure log analysis, and weekly reports.
What are the pricing tiers and what's included in each?
Free tier is open-source for building agent systems with local AgentOS Control Plane, session monitoring, knowledge/memory management, system evaluations, community support, and docs. Pro tier is $150/month for production systems with live AgentOS Control Plane, 1 live connection, 4 seats included, unlimited usage/monitoring/retention/knowledge/memories/chats. Add-ons: $30/month per seat, $95/month per live connection. Enterprise offers custom solutions with dedicated support, SLA, custom SSO/RBAC, and self-hosted Control Plane. Pro is free for students, educators, and startups under $2M funding.