Agno

Agno is a framework-agnostic agent platform maintained by Agno Inc. It solves a specific problem that has plagued production AI teams: every agent framework handles development differently, but none provide a dedicated production runtime. You build agents in LangGraph, DSPy, or the Claude Agent SDK, then stitch together sessions, storage, RBAC, and observability yourself. Agno replaces that glue with a single platform.

Released in 2024 under Apache 2.0, Agno (rebranded from Phidata in early 2025) has quickly accumulated over 40,000 GitHub stars. The project is written entirely in Python and targets teams that need to run multiple agent types — built with different frameworks — on one infrastructure stack. Its design philosophy is pragmatic: don’t lock users into a proprietary agent API. Instead, let them write agents in whatever framework makes sense for the task, then deploy them through AgentOS, the production runtime that handles everything else.

Agno occupies a unique category: it is not just an orchestration or RAG framework, but a full agent runtime that spans orchestration, agent lifecycle management, workflow execution, and retrieval-augmented generation. It competes most directly with building custom infrastructure on top of LangGraph or CrewAI, and with managed platforms that do not offer self-hosting.

Agno separates agent development from agent operations. The core abstraction is the Agent object, which you define in code using Agno’s own SDK or any supported third-party framework. An agent has a model, tools, memory, knowledge base, and optional human-in-the-loop guardrails. Teams are coordinated through a Team abstraction that supports hierarchical delegation and parallel execution. Workflows are defined as sequences of steps (the Workflow class) with branching, retries, and conditional logic.

The programming model is code-first and imperative. You write Python classes or functions that configure agents, attach tools (including MCP tools), and define how they interact. Here is a conceptual fifteen-line example:

1from agno.agent import Agent
2from agno.models.anthropic import Claude
3from agno.tools.duckduckgo import DuckDuckGoTools
4from agno.team import Team
5
6researcher = Agent(
7    name="Researcher",
8    model=Claude(id="claude-sonnet-4-5"),
9    tools=[DuckDuckGoTools()],
10    description="You research topics and provide summaries."
11)
12
13writer = Agent(
14    name="Writer",
15    model=Claude(id="claude-sonnet-4-5"),
16    description="You write polished articles based on research."
17)
18
19team = Team(
20    name="Content Team",
21    members=[researcher, writer],
22    instructions="First research, then write."
23)

Control flow is managed by the runtime. When you serve the agent or team via AgentOS, it exposes a FastAPI backend with session-scoped endpoints. Each session maintains its own memory and knowledge filters. The runtime handles streaming over SSE and websockets, stores traces in your database, and enforces RBAC. You never write a REST API — AgentOS generates the 50+ endpoints automatically.

Multi-framework support. This is Agno’s headline feature. You can build agents in Agno’s native SDK, LangGraph, DSPy, or the Claude Agent SDK, and run all of them on a single AgentOS instance. Teams that have legacy LangGraph agents or Claude-based prototypes can standardize on one platform without rewriting anything.

AgentOS production runtime. Out of the box, Agno provides a production API with sessions, streaming (SSE and websockets), storage, scheduling, RBAC, and a control plane UI. The UI lets you test agents, inspect sessions, view traces, and manage users. You get this without deploying separate services for each concern.

Knowledge and memory. Attach knowledge bases with static or agentic filters — the agent can dynamically decide which subset of the knowledge to use per session. Per-session memory and history are stored in your own database, enabling continuity across user interactions.

Native type safety and multi-modal inputs. Agents accept text, image, audio, and video inputs and produce structured outputs (typed Pydantic models). This is especially useful for ML teams doing data labeling, document extraction, or synthetic data generation where input format variance is high.

Tracing and evaluations. Every run is traced and stored. You can export traces for evaluation, build datasets from production sessions, and run automated evals against ground truth. This creates a feedback loop: agents improve over time using real usage data.

Self-host or cloud. Deploy AgentOS in your own cloud (AWS, GCP, Azure) or use Agno’s managed cloud. Self-hosting means data, sessions, and traces never leave your infrastructure — a requirement for enterprise teams with compliance obligations.

Multi-framework production runtime. A company with three teams using LangGraph (one team), Claude SDK (another), and a custom framework can deploy all their agents through Agno. The runtime provides unified observability, access control, and session management. Teams keep their preferred development tooling; the platform standardizes the production layer.

Private-by-default enterprise agents. Financial services, healthcare, and defense organizations run agents on their own cloud. Agno stores every session, memory, and trace in the customer’s database. No data flows through a third-party API. This is a direct requirement for SOC 2 and HIPAA-compliant deployments.

Multi-modal agent platforms. An ML team builds a document extraction agent that accepts PDFs, audio recordings, and video files. The agent classifies documents, extracts structured fields, and outputs JSON. Multi-modal input plus type-safe output eliminates the need for separate pipelines per modality.

Product copilots and conversational agents. Product teams build internal copilots that answer questions about company data, generate reports, and execute actions (e.g., creating Jira tickets). Agno’s session memory enables natural conversations; human-in-the-loop guardrails prevent unintended actions.

Automated data workflows. Data engineering teams run agents that detect data quality issues, analyze failure logs, and generate weekly reports. Agents are scheduled using AgentOS’s built-in scheduler, with traces stored for audit.

Install the Python package:

1pip install agno

The smallest meaningful example is a single agent with a tool and structured output:

1from agno.agent import Agent
2from agno.models.openai import GPT4o
3from agno.tools.duckduckgo import DuckDuckGoTools
4from agno.db.sqlite import SqliteDb
5from agno.os import AgentOS
6
7agent = Agent(
8    name="Web Researcher",
9    model=GPT4o(id="gpt-4o"),
10    tools=[DuckDuckGoTools()],
11    db=SqliteDb("agent.db")
12)
13
14agent.print_response("What is the latest on Agno framework?", markdown=True)

To serve it as a production API, wrap it with AgentOS:

1os = AgentOS(agents=[agent])
2os.serve()

You need an LLM provider key (OpenAI, Anthropic, etc.) and optionally a vector store for knowledge bases. For evaluation and tracing, no additional tools are required — AgentOS stores everything in your database.

Documentation is at [docs.agno.com](https://docs.agno.com). The cookbook on GitHub ([github.com/agno-agi/agno](https://github.com/agno-agi/agno/tree/main/cookbook)) contains full examples for teams, workflows, multi-modal agents, and MCP tools. Community support is on Discord.

Agno vs. LangGraph. LangGraph gives you fine-grained control over graph-based agent logic. It is ideal when you need custom graph topologies, complex branching, or state machines. However, LangGraph provides no production runtime — you must build your own serving, storage, and observability layer. Agno trades graph flexibility for an opinionated runtime. Choose LangGraph if you need arbitrary graph structures and are willing to build infrastructure. Choose Agno if you want a batteries-included platform and are happy to use its agent/team/workflow abstractions.

Agno vs. CrewAI. CrewAI focuses on role-based agent teams with a simple API. It is great for prototyping and small deployments. But CrewAI lacks production features like session management, RBAC, multi-modal support, and self-hosting. Agno is better suited for enterprise-grade, long-running deployments where you need audit trails, data isolation, and a control plane.

Agno vs. building custom. The main alternative is to assemble a stack manually: a vector store, a message queue, a database, an auth service, and a monitoring solution. Agno bundles these into one platform. The cost is lock-in to its opinionated runtime — you must adopt AgentOS’s session model and API conventions. If you already have a custom infrastructure stack, adding Agno may not be worth it. If you are starting fresh, Agno saves months of engineering time.