Agno
Open-source Python SDK plus AgentOS runtime for serving agents as a REST API and MCP server you host yourself.
Agno suits a Python team whose roadmap says "our own agent platform." SDK, AgentOS runtime, and Control Plane are one coherent story, and the MCP server puts your agent inside Claude and ChatGPT without extra plumbing. It is not a shortcut: you still own deployment, databases, and upgrades, and the documented setup flow is a coding agent cloning a template repo and running the setup-platform skill, not a wizard. Choose it for that control. If you want a managed runtime and someone else on call, look at a managed agent builder instead.
Verified 2d ago · liveness 74/100 · cite: rightaichoice.com/tools/agno
- Python product teams serving agents inside their own app via the REST API
- Teams that need the same agent in Slack, WhatsApp, or Telegram
- Developers who want agents usable in Claude and ChatGPT through the MCP server
- Platform teams self-hosting documentation, support, or internal agents
- Anyone who wants a fully managed, no-code agent platform
- Teams unwilling to deploy and operate their own infrastructure
- Quick one-off prototypes where a runtime and Control Plane are overkill
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Skip Agno if you want a managed agent runtime with someone else on call for databases, secrets, and upgrades rather than a repo you deploy and operate yourself.
You supply the databases: the docs pair Postgres for sessions with pgvector for knowledge once you leave the local SQLite and ChromaDB demo, so database hosting is a bill you carry.
Agno is the open-source SDK and AgentOS runtime; the pricing page was not reachable for this pass, so treat cost as engineering time plus your own cloud bill rather than a seat price. That usually works out cheaper than managed agent-builder subscriptions once you are serving production traffic, and more expensive than a weekend prototype tool when you only need a demo.
In short
Agno — Open-source Python SDK plus AgentOS runtime for serving agents as a REST API and MCP server you host yourself. Best for Python product teams serving agents inside their own app via the REST API, Teams that need the same agent in Slack, WhatsApp, or Telegram, Developers who want agents usable in Claude and ChatGPT through the MCP server. Free to use.
No head-to-head comparison for Agno yet — the alternatives below are the closest matches we list.
What people actually say about Agno — is it worth it?
We ran a structured research pass across product reviews, community discussions, and post-purchase forum threads to surface the patterns vendors won't publish themselves. Below: the recurring strengths, the hidden costs people mention most, and the cohort that consistently regrets adopting this tool.
18 mentions across 4 sources (Hacker News, Product Hunt, GitHub, Lemmy), 43 more we could not attribute · researched Oct 14, 2026.
Weighted by the 61 posts each of 4 sources contributed.
- +Production teams publicly confirm sustained use — HuntYourTribe runs on Agno and it 'works out well'
- +Control plane plus isolation architecture repeatedly praised as the standout differentiator
- +Minimum viable agent is genuinely simple; Product Hunt users call it easy even for low-dev experience
- +Model-agnostic design lets teams swap LLM providers without rewriting agent code
- +Memory with CRUD, topic filters, and token-usage optimization baked in, not bolted on
- −1,902 open GitHub issues signals maintenance bandwidth is not keeping pace with adoption
- −Internals described as messy and inconsistent by a developer who migrated off OpenAI Agents
- −PII guardrail flattens non-string input, silently corrupting structured or multimodal payloads
- −Requeued non-retryable jobs fail on claim without actually running — retry flow broken
- −No TypeScript SDK, despite repeated Product Hunt requests going unanswered
- • Self-hosting means you pay for compute, vector storage, and LLM API calls directly — no bundled credit
- • Operational cost of running the runtime, MCP server, and control plane is real headcount time
- • No priced enterprise tier is published, so budgeting for team features requires a sales conversation
Viability Score
How well maintained and how widely used is Agno? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: October 2026
How we score →Key Features
- Python SDK for building agents, teams, and workflows
- Build with the Agno SDK, Claude Agent SDK, or Codex SDK
- AgentOS runtime with durable execution and multi-replica scaling
- Agents served as a REST API with SSE streaming
- MCP server to make agents available in Claude and ChatGPT
- A2A interface for agent-to-agent exposure
- Slack, WhatsApp, and Telegram interfaces
- Control Plane (AgentOS UI) for monitoring and managing the platform
- Memory with CRUD, topic filters, per-user stats, and token optimization
- Knowledge uploads from files, text, URLs, S3, GCS, SharePoint, and GitHub
- Vector, keyword, and hybrid knowledge search
- Guardrails for input and output validation
- Session management with per-user isolation
- Background runs returning 202 Accepted with run and session IDs
- Evals for accuracy, agent-as-judge, performance, and reliability
About Agno
Agno is an open-source stack for teams that want to run agents as a service instead of renting a managed platform. It has three layers. You build agents with the Agno SDK, the Claude Agent SDK, the Codex SDK, or another supported harness. AgentOS runs them as a durable service with multi-replica scaling. The Control Plane (the AgentOS UI at os.agno.com) lets you monitor runs, inspect sessions, and manage the platform. The distribution story is the differentiator. Define an agent once, then expose it through the REST API inside your own product, through the MCP server inside Claude and ChatGPT, and through Interfaces inside Slack, WhatsApp, and Telegram. The documented use cases cover agents as API, agents as MCP, structured output, product agents, data labeling, data and analytics agents, workflow automation, and full multi-agent platforms. The SDK carries the operational pieces: knowledge uploads from files, URLs, S3, GCS, SharePoint, and GitHub; vector, keyword, and hybrid search; memory with CRUD and per-user stats; guardrails; scheduling; observability; and evaluation. Deployment is bring-your-own-infrastructure, with templates for Railway, Docker, AWS, Fly, GCP, Kubernetes, Azure, Render, and Modal. Setup is designed to be driven by a coding agent that clones a template and runs the setup-platform skill. That is the trade: managed agent builders get you to a demo faster, while Agno hands you the runtime, the data, and the upgrade path.
Behind the Verdict
Agno's strongest idea is that the same agent should not be rebuilt per surface. You define a product agent in Python, register it with AgentOS, and then reach it three ways: HTTP from your own web or mobile app, MCP from Claude and ChatGPT, and Slack, WhatsApp, or Telegram through Interfaces. The docs walk you through exactly this with a product agent over Linear's public docs, using ChromaDB for vectors and SQLite for sessions locally, and Postgres plus pgvector for a deployed service. That level of concreteness is unusual and makes the first hour predictable. The runtime provides the items you would otherwise build yourself: durable execution, multi-replica scaling, background runs that return 202 Accepted with run and session IDs, session management with per-user isolation, memory with CRUD and per-user stats, knowledge ingestion from S3, GCS, SharePoint, and GitHub, hybrid search, guardrails for input and output validation, scheduling, tracing with span-tree inspection, and evals covering accuracy, agent-as-judge, performance, and reliability. The framework choice is also open: the Agno SDK, the Claude Agent SDK, and the Codex SDK are all documented harnesses, so you are not locked into Agno's agent authoring layer to get AgentOS. The costs are real. Everything is bring-your-own-infrastructure: nine deployment targets are documented, and someone on your team has to own databases, migrations, secrets, and upgrades. The setup flow assumes a coding agent with access to the docs and a template repo; if your team would rather click through a console, this is friction, not a feature. The scraped docs name specific models only inside code samples (openai:gpt-5.6 with the text-embedding-3-small embedder in the product agent example), so treat model support as something to verify against your own provider list rather than a published compatibility matrix. And if you want a catalog of pre-built no-code tools, this is the wrong layer entirely. Where it fits: platform teams self-hosting documentation, support, or internal agents, and product teams that need one backend serving a web app, a chat app, and an AI assistant at once. Where it does not: solo builders prototyping an idea over a weekend, or anyone who does not want to operate infrastructure.
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Real-world workflow fit
Concrete scenarios for the personas Agno actually fits — and what changes day-one when you adopt it.
You install agno with the os, openai, sqlite, chromadb, and markdown extras, define a product agent over your docs URL, load the knowledge base, and serve it with AgentOS on localhost:7777.
Outcome: You call the agent with curl at /agents/product-agent/runs, open /docs to see the generated endpoints, and connect os.agno.com to inspect sessions and traces.
You clone an agentos-railway or agentos-aws template into an agent-platform folder and run the setup-platform skill with your coding agent to register the runtime and first agent.
Outcome: AgentOS is running as a durable service you own, with registered agents discoverable at /agents and available over the REST API and MCP server.
You register a knowledge agent over S3 and SharePoint sources, enable hybrid search, and expose it through Interfaces and the MCP server.
Outcome: The same agent answers customers in Slack, WhatsApp, or Telegram, and appears inside Claude and ChatGPT, with runs and traces visible in the Control Plane.
Use Cases
- Serve a product agent that answers from your documentation over HTTP, with follow-up questions kept in the same session.
- Expose one registered agent in your own app, in Claude and ChatGPT via MCP, and in Slack at the same time.
- Orchestrate a multi-agent team where a leader agent coordinates specialist sub-agents for research.
- Run a scheduled weekly data quality audit agent that checks logs and sends a report.
- Automate data labeling and classification of text, image, audio, and video at scale.
- Build a customer support agent that remembers user preferences across sessions.
- Deploy a document extraction agent that handles images and text in one multipart request.
- Version and roll back agent configurations, then promote a validated version to production.
Models Under the Hood
as of 2026-10-16
Limitations
- Agno is developer-oriented: a Python SDK plus an AgentOS runtime you deploy and operate yourself, so it assumes coding and infrastructure knowledge.
- The documented deployment targets are Railway, Docker, AWS, Fly, GCP, Kubernetes, Azure, Render, and Modal — nine templates you maintain.
- Setup is designed for a coding agent to clone a template repo and run the setup-platform skill, not a click-through wizard.
- The docs name specific models only inside code samples (an openai:gpt-5.6 agent with a text-embedding-3-small embedder), so confirm provider and model support against your own requirements before committing.
as of 2026-10-20
Verification history
We have re-verified Agno 21 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-checked, vendor evidence unchanged
- — re-checked, vendor evidence unchanged
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
Showing the 6 most recent of 21 verification passes.
Free to cite with attribution — this page re-verifies continuously.
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Where the pricing makes sense
The company stage and team size where Agno's pricing actually pencils out — and where peers do it cheaper.
Agno is the open-source SDK and AgentOS runtime; the pricing page was not reachable for this pass, so treat cost as engineering time plus your own cloud bill rather than a seat price. That usually works out cheaper than managed agent-builder subscriptions once you are serving production traffic, and more expensive than a weekend prototype tool when you only need a demo.
Setup time & first value
How long it actually takes to get something useful out of Agno — broken out by persona, not the marketing-page minute.
With a coding agent and a deployment template, the scaffold is a clone plus running the setup-platform skill, so expect hours rather than days to a running AgentOS. Manually, the documented product-agent walkthrough (uv init, install extras, define the agent, load knowledge) is a short afternoon; add time for Postgres and pgvector before production.
Switching to or from Agno
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From a hand-rolled FastAPI agent service: wrap the existing agent in AgentOS and keep the REST API, gaining sessions, tracing, and the MCP server.
- →From the Claude Agent SDK or Codex SDK alone: register those agents with AgentOS to add durable execution, an API, and the Control Plane UI.
- →From a local SQLite and ChromaDB prototype: swap in Postgres for sessions and pgvector for knowledge per the deployed-service guidance.
- ↗To a managed agent builder: reimplement agent definitions in the vendor's console, since AgentOS runtime, sessions, and traces do not transfer.
- ↗To a different orchestrator: export knowledge sources and re-ingest them, as the knowledge layer is tied to Agno's vector and keyword search.
Integrations
Resources & Guides
Tutorials & Learning
YouTube returned 6 videos for “Agno”, and we withheld 6: 6 could not be judged, because “Agno” is a single word that other videos use for other things. We are showing none, because we could not prove any of them are about Agno.
Official links
Tools that pair well with Agno
Common stack mates teams adopt alongside Agno, with the specific reason each pairing earns its keep.
Phidata
Agno is an open-source Python agent SDK plus AgentOS, a runtime that serves agents as a REST API and an MCP server in your own cloud.
TaskWeaver
Microsoft's open-source, code-first Python framework for building stateful, data-intensive conversational AI agents.
OpenAgents
Open-source research platform (COLM 2024) shipping three language agents — Data, Plugins and Web — in one chat UI, for free.
Alternatives to Agno
View all
Phidata
Agno is an open-source Python agent SDK plus AgentOS, a runtime that serves agents as a REST API and an MCP server in your own cloud.
TaskWeaver
Microsoft's open-source, code-first Python framework for building stateful, data-intensive conversational AI agents.
OpenAgents
Open-source research platform (COLM 2024) shipping three language agents — Data, Plugins and Web — in one chat UI, for free.
Frequently Asked Questions
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