Agno
by Agno Inc.
Pure-Python agent framework plus a runtime you host in your own cloud
Agno is an open-source Python framework for building multi-agent systems, paired with AgentOS, a runtime and control plane that customers deploy into their own cloud. It gives teams agents, teams, workflows, memory, knowledge and tracing without graphs or chains, and keeps agent data inside the customer's own infrastructure.
Agno is an Apache 2.0-licensed Python framework and runtime for building and operating multi-agent systems. It was announced on 29 January 2025 by Ashpreet Bedi as the rename of Phidata, with a deliberate design thesis stated in that post: 'Simplicity: No graphs, chains, or convoluted patterns - just pure Python,' plus being 'Truly Agnostic: Any model, any provider, any modality.' The vendor's benchmark claims 5,000x faster agent instantiation than LangGraph and 50x less memory; practitioners on Hacker News cite roughly 3 microseconds to instantiate an agent at about 6.6 KiB, and note the contrast with LangChain where 'Agent is the base class and is 1 file' rather than five to seven layers. The product ships in three parts: the Agno SDK for building agents, teams and workflows; AgentOS, a stateless runtime exposing a production API with 50-plus endpoints over SSE and WebSockets; and the AgentOS UI control plane for chatting with agents, inspecting sessions, memory, knowledge and traces. AgentOS adds per-session isolation, JWT-based RBAC with multi-tenant separation, human-in-the-loop approval flows, guardrails, OpenTelemetry observability, cron scheduling and native Slack, Telegram, WhatsApp, Discord and AG-UI interfaces, and it runs on Docker, Railway, AWS, GCP, Azure, Fly, Render, Modal or Helm — including air-gapped. It supports 30-plus model providers and 100-plus integrations, and can host agents built with the Claude Agent SDK, LangGraph or DSPy alongside its own. The repository carries roughly 41.6k stars and 5.7k forks; the current PyPI release is 2.8.7, published 5 August 2026, supporting Python 3.9 to 3.13.
Python-native platform and ML engineering teams that need multi-agent systems running in their own AWS, GCP or air-gapped environment, and do not want a managed SaaS holding their agent memory and traces.
A production runtime, API and control plane for agents that would otherwise take months of internal platform work, deployed into infrastructure the customer already owns.
At a Glance
- Category
- Agent Development
- Pricing
- Freemium, Subscription, Free
- Target Market
- CTOs, Enterprise Developers, Data Scientists, Platform Engineers
- Deployment
- Open-source, Self-hosted, Multi-cloud, Hybrid
- Team Size
- 1-10
Key Features
- ✓Pure-Python agent SDK
Agents, teams and workflows are plain Python classes with no graph or chain DSL, so debugging uses ordinary stack traces.
- ✓AgentOS runtime
A stateless, horizontally scalable runtime exposing more than 50 API endpoints over SSE and WebSockets for runs, sessions, memory and traces.
- ✓Runs in your own cloud
Deploys to Docker, Railway, AWS, GCP, Azure, Fly, Render, Modal or Helm including air-gapped, so no agent data leaves the customer environment.
- ✓Control plane UI
A web console for chatting with agents, replaying run history and managing memory, knowledge and sessions without writing custom admin tooling.
- ✓Governance and human-in-the-loop
JWT-based RBAC with multi-tenant isolation, guardrails, administrative tool blocking, approval flows and audit logs for agents that take real actions.
- ✓OpenTelemetry observability
Structured tracing and run history export through OpenTelemetry, so agent behaviour lands in the observability stack a team already runs.
- ✓Model and framework agnostic
Supports 30-plus model providers through one interface and can host agents written with the Claude Agent SDK, LangGraph or DSPy.
- ✓Built-in scheduling and channel interfaces
Cron jobs, background tasks and native Slack, Telegram, WhatsApp, Discord and AG-UI front ends ship with the runtime rather than as add-ons.
Capabilities
Use Cases
- •Internal agent platform for a Python engineering org
Replaces months of in-house platform work with a runtime, API and control plane that engineering teams share across agent projects.
- •Agentic RAG over private knowledge
Combines vector knowledge stores with hybrid search so agents answer from internal documents without those documents leaving the environment.
- •Regulated or air-gapped deployments
Runs entirely inside a customer VPC or disconnected network, which is what makes it viable for finance, defence and healthcare workloads.
- •Multi-agent research and analysis workflows
Delegates subtasks across specialised agents in a team, with shared session state and traces so failures are attributable to a specific agent.
- •Operational agents behind chat channels
Exposes the same agents in Slack, Telegram, WhatsApp or Discord with approval gates before any tool call touches production systems.
Ideal For
Best For
- ✓Python teams building multi-agent systems without adopting a graph or chain abstraction
- ✓Running agents inside your own cloud account or an air-gapped environment where no data may leave
- ✓Standing up a production agent API with sessions, memory, knowledge and traces without building the platform layer first
- ✓Adding observability, RBAC and human-approval gates to agents that already exist in another framework
- ✓Exposing the same agents through Slack, Telegram, WhatsApp, Discord and a web UI from one runtime
Not Ideal For
- ✗Non-Python engineering organisations — the SDK is Python-only, so TypeScript-first teams are better served by Mastra or the Vercel AI SDK
- ✗Teams that want a fully managed SaaS runtime with no infrastructure to own; AgentOS is designed to run in the customer's own cloud and the operational burden comes with it
- ✗Buyers who need vendor-issued SOC 2 or ISO 27001 attestations, since Agno advertises none and compliance is inherited from whatever the customer hosts on
- ✗Simple single-prompt applications where a direct model API call is cheaper and clearer than adopting any agent framework
Integrations
Deployment
Market Analysis
Pros
- ✓Very low overhead per agent — the vendor claims 5,000x faster instantiation and 50x less memory than LangGraph, with HN practitioners reporting roughly 3 microseconds and 6.6 KiB
- ✓The whole platform layer (API, RBAC, tracing, approvals, scheduling, chat channels) ships with the runtime rather than being assembled from parts
- ✓Apache 2.0 with fewer than ten required dependencies, so it is genuinely adoptable in restrictive environments
- ✓Model- and framework-agnostic: 30-plus providers, and it will run agents built in the Claude Agent SDK, LangGraph or DSPy
- ✓Active release cadence — 2.8.7 shipped 5 August 2026 against roughly 41.6k GitHub stars
Cons
- ✗Documentation is the recurring practitioner complaint: one Hacker News developer reported 'plenty of times I literally could not find the API for some object' and that the docs chatbot returned an 'outdated function signature'
- ✗Anonymous run telemetry is enabled by default and must be explicitly disabled with AGNO_TELEMETRY=false
- ✗Pro at $150/month includes just one live connection and four seats, and extra connections at $95/month each make multi-environment setups expensive quickly
- ✗Roughly 606 open issues against a fast-moving 2.x line, and the framework sits in a crowded field against LangGraph, CrewAI, Mastra and OpenAI's own Agents SDK
- ✗The headline speed numbers measure agent instantiation in a vendor micro-benchmark, not end-to-end system throughput
- ✗Python-only, and the company publishes no funding, headcount or compliance detail for procurement teams to assess
Pricing
Free (Open Source)
$0
- ✓Apache 2.0 framework
- ✓Build multi-agent systems
- ✓AgentOS Control Plane with session monitoring, knowledge management and evaluations
- ✓Community support
Pro
From $150/mo
- ✓Control Plane for live AgentOS instances
- ✓1 live connection and 4 seats included
- ✓Unlimited usage, monitoring, retention, memories and chats
- ✓$30/mo per extra seat, $95/mo per extra live connection
Enterprise
Contact for pricing
- ✓Dedicated Slack channel and technical lead
- ✓Support SLA
- ✓Custom SSO and RBAC configuration
- ✓Self-hosted Control Plane and custom agent solutions
The framework and AgentOS runtime are Apache 2.0 and free; what Agno sells is the hosted Control Plane. Pro is $150 a month and includes only one live connection plus four seats, with additional seats at $30 a month and additional live connections at $95 a month — so a team running separate dev, staging and production instances pays for each. The vendor markets 'No per-event fees, no surprise egress costs' because data stays in the customer's own system, which also means infrastructure and model costs are entirely separate and self-incurred.
Security & Compliance
Connect
Sources
This page was written from 7 sources, 4 on domains other than agno.com.
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