Agno, formerly known as Phidata, is a Python agent framework developed by Ashpreet Bedi and the Phidata team, rebranded in 2025 to reflect a broader product vision. The framework is built around three core design goals: exceptional performance (agent instantiation benchmarked at under 3 microseconds), first-class multimodal support, and built-in infrastructure for memory and knowledge management. Unlike frameworks that treat memory and storage as optional add-ons, Agno considers persistent agent state a core capability — every agent can be configured with long-term memory, session storage, and a knowledge base from the same Agent constructor. The result is a framework that handles the full stack of agent infrastructure with minimal configuration.
Key Features#
Multimodal Agent Support Agno agents can natively process text, images, audio, and video inputs in a single, unified agent loop. Developers do not need separate agent types or custom preprocessing pipelines for different modalities — the same agent definition handles all of them. This is particularly valuable for applications like document analysis agents that might receive PDFs (with embedded images), voice notes, and structured data in the same session.
Built-in Memory and Storage Every Agno agent can be configured with three distinct memory layers: short-term memory (the conversation buffer), long-term memory (facts about users or contexts persisted across sessions), and agent memory (the agent's own knowledge about previous runs). Storage backends include PostgreSQL, SQLite, and MongoDB for session data, and vector stores like PgVector, LanceDB, and Qdrant for semantic search.
Knowledge Base Integration
Agno provides a Knowledge abstraction that connects agents to document stores, databases, and custom data sources. Agents can retrieve relevant context from their knowledge base before responding, implementing RAG patterns without requiring the developer to manage embedding generation, chunking, or retrieval ranking manually.
Model-Agnostic Architecture Agno supports over 20 LLM providers including OpenAI, Anthropic, Google Gemini, Groq, Mistral, Cohere, AWS Bedrock, Azure OpenAI, and Ollama for local models. The provider interface is consistent, making it straightforward to test the same agent logic against multiple models or implement multi-provider fallback strategies.
Agno Cloud Platform The optional Agno Cloud platform provides a hosted environment for running, monitoring, and managing agents in production. Features include agent session inspection, performance analytics, team collaboration, and a playground for testing agents without deploying code. The cloud platform is what differentiates Agno from purely framework-only offerings.
Pricing#
The Agno open-source framework is free under the Mozilla Public License 2.0. Agno Cloud offers a free tier supporting up to a limited number of agent sessions and team members, suitable for development and small-scale production. Paid plans scale with usage and team size, providing unlimited sessions, advanced analytics, and priority support. Specific pricing tiers are available on the Agno website. LLM API costs are billed separately by each provider.
Who It's For#
Agno is the right choice for:
- Teams building multimodal applications: Developers creating agents that process images, audio, or video alongside text, without wanting to manage separate processing pipelines for each modality.
- Developers who need batteries-included infrastructure: Teams who want built-in memory, storage, and knowledge base capabilities without assembling a stack of separate libraries and services.
- Startups moving quickly: Organizations that need to go from concept to production agent with minimal infrastructure setup, leveraging Agno Cloud for managed deployment.
It is less suitable for teams with highly custom infrastructure requirements, those who prefer complete control over every layer of the stack, or organizations with strict open-source license requirements that may conflict with the MPL 2.0.
Strengths#
Exceptional performance. Agno's benchmark of sub-3-microsecond agent instantiation is not just a marketing claim — it reflects a codebase designed for high-throughput scenarios where agent spin-up time contributes to overall latency.
Integrated memory architecture. Having short-term, long-term, and agent memory all configured in one place, with multiple storage backend options, removes one of the most common infrastructure headaches in production agent development.
Broad model support. With over 20 providers supported, Agno gives developers genuine flexibility. Switching from OpenAI to Anthropic or adding Groq as a low-latency alternative requires minimal code changes.
Limitations#
Smaller community than LangChain. While growing rapidly, Agno's ecosystem of community plugins, tutorials, and third-party integrations is smaller than LangChain's, which has several years of community development behind it.
Cloud platform lock-in risk. Teams that build around Agno Cloud for production monitoring and session management may find migration difficult if they later need a different observability stack or multi-cloud strategy.
Related Resources#
Browse the full AI Agent Tools Directory for a side-by-side look at Python agent frameworks.
- Learn the fundamentals in our AI Agent Framework overview
- Understand memory patterns in the multi-agent system glossary entry
- Compare top Python frameworks in our LangChain vs CrewAI comparison
- Follow our hands-on build an AI agent with LangChain tutorial to see the difference in approach
- See the LangChain directory entry for an alternative with a larger ecosystem
- Explore tool use patterns central to all agent frameworks including Agno