Agno is an open-source framework designed to simplify the development and deployment of multi-agent AI systems. It provides a unified stack that combines agent construction, stateful orchestration, and a production runtime—all with a focus on privacy, performance, and scalability. Unlike traditional frameworks that require manual setup of APIs, memory, and monitoring, Agno bundles these components into a cohesive system that can be deployed in minutes. This makes it ideal for developers and teams building complex AI workflows involving multiple autonomous agents that need to communicate, share memory, and operate securely in production environments.
Agno targets developers who need to move beyond single-agent prototypes into multi-agent systems with real-world requirements: persistent state, human-in-the-loop controls, tool integrations, and scalable infrastructure. Its architecture is built around a stateless FastAPI backend that connects to an intuitive control plane UI, enabling teams to test, monitor, and manage agent workflows without vendor lock-in or data retention concerns.
What You Get
- Model-agnostic agents - Supports OpenAI, Anthropic, Google, and local LLMs with consistent interfaces; use Claude-sonnet-4-5 or any compatible model without rewriting agent logic.
- Type-safe I/O with schemas - Define input and output schemas to ensure structured, predictable agent interactions and reduce runtime errors.
- Persistent memory & Agentic RAG - Store session history and user state via SQLite or 20+ vector stores with hybrid search and reranking for accurate knowledge retrieval.
- Built-in MCP and A2A support - Native integration with Model Control Protocol (MCP) tools and agent-to-agent communication for complex workflows.
- Production-ready FastAPI runtime - Generate a fully functional web server with SSE endpoints in one line of code using AgentOS().get_app() and serve() methods.
- Integrated control plane UI - Monitor agent performance, view execution logs, and manage permissions through the web-based AgentOS UI (os.agno.com) without external services.
- Human-in-the-loop workflows - Insert confirmation steps and approvals into agent chains to ensure safety and compliance in critical operations.
- Guardrails & lifecycle hooks - Apply pre/post execution validation, security checks, and custom logic to every agent step.
- Multi-modal input handling - Process text, images, audio, video, and files natively within agent prompts and tools.
- Durable execution & resumable workflows - Agents can pause, resume, and recover from failures without losing state or context.
- RBAC and per-agent permissions - Control access to agents, tools, and data at a granular level for team collaboration and security.
- 100+ built-in toolkits - Access pre-built integrations for databases, APIs, and external services to accelerate development.
Common Use Cases
- Building a multi-tenant SaaS dashboard with personalized AI assistants - Each tenant gets an isolated agent with its own memory and tools, managed via RBAC and served through a shared FastAPI instance.
- Creating a research assistant that aggregates data from PDFs, databases, and web APIs - Use Agno’s RAG with hybrid search to retrieve context from multiple sources, then synthesize answers using Claude-sonnet-4-5 with type-safe outputs.
- Problem: Long-running agent workflows fail due to crashes → Solution: Agno’s durable execution resumes failed tasks from the last checkpoint without reprocessing entire history.
- Team: DevOps engineers deploying AI agents across AWS and GCP → Agno’s stateless runtime allows horizontal scaling with zero config; control plane provides centralized monitoring across cloud environments.
- Developing a customer support bot that escalates to humans for approvals - Use human-in-the-loop hooks to pause responses and wait for team confirmation before sending sensitive information.
Under The Hood
The agno project is a Python-based framework designed for building and deploying AI agents with a strong emphasis on modularity, extensibility, and integration capabilities. It enables developers to construct intelligent systems that can interact with various tools and platforms through a structured, agent-oriented architecture.
Architecture
This project adopts a layered architecture that promotes clear separation between agent logic, tooling, and infrastructure components.
- The system is organized into distinct modules with well-defined roles for configuration, core functionality, and integration points
- Design patterns such as strategy and middleware support are applied to enable flexible agent composition and custom FastAPI app extensions
- Component interactions rely on standardized interfaces, with dependency injection and configuration-driven agent building as key mechanisms
Tech Stack
Built entirely in Python, the framework leverages modern libraries and frameworks to support agent-based AI automation.
- The primary language is Python, with agno as the core framework for constructing intelligent agents
- Key dependencies include OpenAI integration, database connectors like PostgreSQL and SQLite, web scraping tools, and domain-specific agent libraries
- The tech stack supports standard Python packaging, includes setup verification scripts, and enables multi-backend database support
Code Quality
Code quality in the agno framework is moderate, reflecting a balance between practicality and maintainability.
- A comprehensive set of tests ensures reliability across core agent behaviors and session management features
- Error handling practices include custom exceptions and standard try/except blocks for input validation and runtime issues
- The codebase adheres to consistent naming conventions, modular structure, and standard Python idioms throughout
- Technical debt is minimal, with most components well-organized and presented in a clean, readable format
What Makes It Unique
This project stands out through its modular agent framework and practical application across diverse use cases.
- Structured, typed agent interactions with clear schema definitions support robust and maintainable agent development
- Extensive integration support for A2A communication, Discord, and observability platforms offers a unified interface for agent-to-agent and external system interaction
- Built-in guardrails and state management provide out-of-the-box safety and context preservation for complex agent workflows
- Showcase examples across domains like document summarization, inbox handling, and invoice analysis demonstrate real-world applicability