Agno is an open-source framework and runtime for developing and operating autonomous AI agents at scale. It enables developers to build stateful, tool-using agents and multi-agent systems that stream reasoning, manage session memory, and enforce governance rules — all while running in your own infrastructure. Designed for engineers and AI product teams, Agno solves the challenge of moving from experimental AI prototypes to reliable, auditable, production-grade agentic applications.
Built with Python and FastAPI, Agno provides a modular stack: the SDK for defining agents with memory and tools, the AgentOS runtime for serving APIs with per-session isolation, and the AgentOS UI for monitoring and control. It integrates with 100+ tools and model providers (OpenAI, Anthropic, Bedrock, Gemini), stores data in your choice of database (PostgreSQL, SQLite, MongoDB, etc.), and supports horizontal scaling with stateless architecture.
What You Get
- Stateful Agents with Persistent Memory - Agents maintain conversation history and user preferences across sessions using Memori, with support for SQLite, PostgreSQL, MongoDB, and more via SQLAlchemy.
- Multi-Agent Teams & Workflows - Build coordinated agent teams (like Investment Team or Scout) that collaborate, debate, and execute complex workflows using a unified architecture.
- Production-Ready FastAPI Backend - Deploy agents as scalable, stateless HTTP APIs with streaming responses, per-user session isolation, and built-in tracing in under 20 lines of code.
- AgentOS Control Plane - Monitor, test, and manage agents in real time via the web-based AgentOS UI, with connection to local or remote runtimes and full audit logs.
- Human-in-the-Loop Governance - Enforce approval workflows, audit trails, and runtime guardrails to control high-risk actions without rewriting agent logic.
- LLM-Agnostic Model Support - Seamlessly switch between OpenAI, Anthropic, Bedrock, Gemini, Grok, and other providers with unified streaming, async, and sync interfaces.
- MCP Tools Integration - Connect agents to external APIs and knowledge bases via the MCP protocol (e.g., Agno’s own documentation server) for grounded, real-time responses.
- Session & Context Management - Automatically compress chat history, manage context windows, and inject relevant memory without manual prompt engineering.
Common Use Cases
- Running a customer support chatbot with memory - A SaaS company uses Agno to build a support agent that remembers past interactions, preferences, and order history across sessions using Memori and SQLite.
- Building a multi-agent investment committee - A hedge fund deploys a team of agents (Investment Team) that analyze market data, debate recommendations, and allocate capital with audit trails and human approval gates.
- Developing a self-learning enterprise knowledge agent - An enterprise uses Scout to continuously ingest, summarize, and retrieve internal documentation, improving over time without retraining models.
- Creating a post-IDE coding assistant - Developers integrate Gcode to auto-suggest code improvements, reference internal APIs, and learn from past commits — all as a production-grade agent service.
Under The Hood
Architecture
- Fragmented structure with no unified abstraction layer, leading to duplicated logic across domains like StockAnalysis and CompanyAnalysis
- Absence of dependency injection and inversion of control, with components hardcoding their dependencies
- Monolithic request handling via a single endpoint without strategy or routing patterns to decouple concerns
- Poor modularity with mixed responsibilities in utility modules and no clear package-level encapsulation
- Incomplete or abandoned architectural patterns, such as the unused BaseConnector
Tech Stack
- Modern Python 3.9+ codebase with strong emphasis on type safety using Pydantic and typing
- Custom agent frameworks built as modular, plugin-like components with memory and structured output capabilities
- SQLAlchemy for ORM-based database interactions, with a dedicated internal db module
- Rich library used to enhance CLI experience with interactive output
- CLI-first design with no external web frameworks or deployment tools detected
- Consistent code formatting enforced via EditorConfig
Code Quality
- Extensive test suite covering unit, integration, and edge cases with clear separation of agent behavior and tool interactions
- Strong type safety through Pydantic schemas ensuring consistent data contracts across components
- Robust mocking patterns that isolate external dependencies for reliable state-based assertions
- Comprehensive error handling with validation, though lacking domain-specific exception classes
- Consistent naming aligned with domain language and behavior-focused test naming
- Clean, structured code suggests good practices, but no explicit linting or CI enforcement is visible
What Makes It Unique
- Built-in, automatic telemetry system that captures OS-level, team, and workflow events into a unified schema without external instrumentation
- SDK-level auto-instrumentation that dynamically embeds run metadata (including SDK version) at execution time
- Unified event schema enabling cross-domain observability across agentic systems—a rare and powerful pattern
- Decoupled API schemas serving dual roles as data contracts and telemetry contracts, creating a self-documenting pipeline
- Telemetry deeply integrated into the core agent lifecycle, making observability intrinsic rather than additive