Agno

Agno is an open-source SDK and runtime for building production-grade agentic software. It provides everything needed to take an AI agent from prototype to production: a Python SDK for defining stateful agents with memory, knowledge, and tools; an AgentOS runtime that serves agents as scalable FastAPI services with streaming, scheduling, and RBAC; and a web-based control plane UI for monitoring, testing, and managing agents at scale.

Agno’s architecture is built around the principle of full ownership — your data, your context, your infrastructure. Agents persist state in databases you control (PostgreSQL, SQLite, MongoDB), access knowledge through vector stores you choose, and run in your own cloud. The framework supports composing individual agents into coordinated teams and declarative workflows with built-in support for loops, parallel execution, conditional branching, and human-in-the-loop approval gates.

The platform integrates natively with 40+ LLM providers (OpenAI, Anthropic, Google Gemini, AWS Bedrock, Groq, Ollama, and many more) and 100+ external tool integrations via pre-built toolkits. Beyond tool use, Agno adds context from live sources — Slack, Google Drive, wikis, MCP servers — and exposes agents through Slack, Telegram, WhatsApp, Discord, AG-UI, and Agent-to-Agent (A2A) protocols.

Designed for engineering teams shipping real AI products, Agno handles the operational complexity of multi-user, multi-tenant agentic deployments: JWT-based RBAC, per-session isolation, OpenTelemetry tracing, cron-based scheduling, and horizontal scaling with stateless architecture. At version 2.6, the project has 40,000+ GitHub stars, 380+ contributors, and ships multiple releases per week.

What You Get

• Composable Agent Primitives - Define agents, teams, and workflows using Python dataclasses with a clean, type-safe API; compose them with Steps, Loop, Parallel, Router, and Condition primitives for arbitrarily complex orchestration.
• AgentOS Production Runtime - Serve agents and teams as FastAPI applications with 50+ REST and WebSocket endpoints, SSE streaming, JWT-based RBAC, multi-tenant session isolation, and built-in OpenTelemetry tracing.
• Persistent Memory and Session Management - Agents maintain user-specific memories and conversation history across sessions via Memori, backed by SQLite, PostgreSQL, MongoDB, or any SQLAlchemy-compatible database.
• 100+ Tool Integrations - Connect agents to web search, code execution, email, calendar, Slack, GitHub, databases, image generation, and dozens more via pre-built Toolkit classes.
• Multi-Provider Model Support - Switch between OpenAI, Anthropic, Google Gemini, AWS Bedrock, Groq, Ollama, LiteLLM, and 35+ other providers using a unified interface with automatic fallback configuration.
• Human-in-the-Loop Governance - Pause agent runs for user confirmation or admin approval at the tool level, workflow step level, or any arbitrary point, with full audit trails and approval state management.
• Scheduling and Background Jobs - Run agents on cron schedules or as background tasks without external infrastructure, powered by Croniter with environment-configurable endpoints.
• Multi-Interface Exposure - Expose the same agent through Slack, WhatsApp, Telegram, Discord, AG-UI, and Agent-to-Agent (A2A) protocol without changing any agent code.
• Knowledge and RAG - Ground agent responses in your own documents via built-in RAG support across 15+ vector databases (pgvector, Chroma, Qdrant, Pinecone, MongoDB, Milvus, and more).
• LearningMachine and Cultural Context - Enable agents to accumulate and apply domain-specific learnings and cultural knowledge across runs for continuously improving behavior.

Common Use Cases

• Customer support agent with persistent memory - A SaaS company deploys an Agno agent that remembers user preferences, past tickets, and product context across sessions using Memori and PostgreSQL, served through Slack with real-time streaming responses.
• Multi-agent research and investment committee - A financial firm builds a team of specialized agents (market analyst, risk assessor, allocator) that collaborate via Agno Teams, debate recommendations, and submit high-stakes decisions for human approval before execution.
• Enterprise knowledge assistant with RAG - An organization ingests internal documentation, wikis, and Slack history into a vector store; an Agno agent retrieves relevant context on demand and improves over time using the LearningMachine system.
• Self-improving developer coding assistant - A development team builds a coding agent that references internal APIs, remembers past code patterns via agentic memory, and uses tools for code execution and GitHub access, deployed as a persistent service.
• Automated workflow with HITL checkpoints - A business process automation pipeline uses Agno Workflows with Steps and Loop, pausing at critical decision points for human review via the approval system before proceeding with irreversible actions.
• Scheduled data collection and reporting agent - An analytics team uses Agno’s built-in scheduler to run a research agent nightly, aggregating data from web search and APIs, storing structured outputs, and delivering summaries via email or Slack.

Under The Hood

Architecture

Agno is organized as three distinct but unified layers: the SDK (define), the AgentOS runtime (serve), and the control plane (manage). At the SDK layer, the core Agent dataclass is kept intentionally lean, with execution logic split into focused private modules — run lifecycle, session management, tool dispatch, message handling — rather than a monolithic class. A Protocol-based interface (AgentProtocol, KnowledgeProtocol) allows local and remote variants of each component to be substituted transparently. The Team layer composes agents into coordinated systems, while the Workflow engine provides declarative orchestration via composable primitives: Steps, Loop, Parallel, Router, and CEL-based Condition. A global Registry provides loose coupling between components without tight dependencies. This clean separation of SDK definition, runtime serving, and management UI gives the system excellent testability and deployment flexibility across from a single developer laptop to a horizontally-scaled cloud deployment.

Tech Stack

Agno is a Python 3.7+ library built on Pydantic for schema validation and Pydantic Settings for configuration management. FastAPI with Uvicorn powers the AgentOS runtime, providing REST and WebSocket endpoints with SSE streaming. SQLAlchemy handles database persistence with support for PostgreSQL, SQLite, and MySQL via both sync and async drivers. OpenTelemetry SDK provides distributed tracing with structured span schemas for agents, teams, and workflows. The model provider layer spans 40+ integrations — OpenAI, Anthropic, Google Gemini, AWS Bedrock, Groq, Ollama, LiteLLM, xAI, and many more — behind a unified interface. Code quality is enforced by Ruff for linting and formatting, Mypy for type checking in strict mode, and GitHub Actions CI with parallel test jobs split by subsystem.

Code Quality

The test suite is extensive and well-structured, with unit, integration, and system test categories covering agents, teams, workflows, memory, knowledge, vectordb, OS routers, and individual model providers. Pytest is used with pytest-asyncio for async coverage and pytest-mock for dependency isolation. Type annotations are comprehensive throughout the codebase, enforced by Mypy in strict mode. Error handling uses typed custom exceptions (InputCheckError, OutputCheckError, RunCancelledException) rather than generic catches. The private module pattern for agent internals enforces a clean public API surface. Inline comments in core modules are informative and dense, and the cookbook directory contains abundant, runnable examples organized as progressive learning journeys.

What Makes It Unique

Agno’s most distinctive contribution is treating agents, teams, and workflows as first-class composable primitives that share a single execution interface — enabling arbitrary nesting without framework friction. The LearningMachine subsystem allows agents to autonomously accumulate and apply domain knowledge over time, which is rare in open-source agent frameworks. The AgentOS runtime takes an unusually complete stance on production requirements from the start: built-in RBAC with JWT, multi-tenancy, session isolation, scheduling, approval workflows, and OpenTelemetry tracing are all included rather than delegated to external systems. The multi-interface exposure layer — serving the same agent through Slack, WhatsApp, Telegram, Discord, AG-UI, and A2A from a single runtime — eliminates the typical friction of adapting agents for different deployment channels.

Self-Hosting

Agno is released under the Apache License 2.0, one of the most permissive open-source licenses available. This means you can use it freely in commercial products, modify the source code, and distribute modified versions without any copyleft obligations. There are no usage fees, no license keys, and no restrictions on the number of agents or users you deploy. The Apache license does require that you retain the original copyright notices and the Apache license text when redistributing, but this has no impact on typical self-hosted deployments.

Running Agno in production requires infrastructure you provision and maintain yourself. The minimum footprint is a Python environment and a database (SQLite works for development; PostgreSQL is recommended for production multi-user deployments). Serving agents via AgentOS adds a FastAPI process and optionally a reverse proxy. For knowledge and RAG capabilities, you will need a vector database (pgvector is the simplest choice if you are already running PostgreSQL). Horizontal scaling is straightforward because the AgentOS runtime is stateless — session data lives in the database, so you can run multiple instances behind a load balancer. Backups, upgrades, and operational monitoring are your responsibility, though OpenTelemetry integration and the built-in audit log make observability achievable without additional tooling.

Agno itself does not offer a managed cloud tier at the time of this writing — the entire stack runs self-hosted. This means you get full data sovereignty and no vendor lock-in, but you forgo managed database backups, high-availability failover, automatic scaling, and a dedicated support SLA. The project is actively developed with multiple releases per week, a public CONTRIBUTING.md, and an active community. Enterprise teams should plan for the operational overhead of maintaining their own deployment, monitoring, and upgrade cycles, and should evaluate whether they need to build additional tooling around the control plane UI for internal governance.