Agno
Also known as: Phidata, AgentOS
High-performance agent runtime and framework (formerly Phidata) with multi-agent support, memory, and Apache 2.0 self-hosting.
Agno is an open-source Python framework for building, running, and managing AI agents and multi-agent systems. It began as Phidata, a developer library for retrieval-augmented and tool-using assistants, and was renamed Agno in early 2025 as its focus shifted from and RAG toward a runtime built for production multi-agent systems. A defining theme is performance: agents instantiate near-instantly and use little memory, which the project treats as a precondition for running many agents reliably at scale rather than a nice-to-have.
The framework is organized around three building blocks. Agents are lightweight, near-dependency-free objects that combine a model, tools, and instructions; Agno automatically turns plain Python functions into the tool definitions a model can call, and can return structured, validated output using Pydantic schemas instead of brittle string parsing. Teams coordinate several agents that work together under delegation or a supervisor. Workflows compose deterministic multi-step pipelines with steps, loops, and parallel branches for cases that need predictable control flow rather than open-ended reasoning.
Agno ships with batteries included. Memory and database awareness are first-class, so agents can persist what they learn. Knowledge bases support retrieval, and Agno was an early proponent of agentic retrieval, where the agent searches its own knowledge base for exactly what it needs instead of always stuffing context into every prompt, saving tokens and improving answers. It also handles structured output, reasoning, and multimodal input across text, images, audio, and video, and treats the Model Context Protocol as a first-class concern: an Agno application can both consume MCP servers and expose itself as one.
The operational half of the framework is AgentOS, a production runtime built on a FastAPI server, introduced with version 2.0. It lets teams run agents as real services with tracing, scheduling, and role-based access control, managed through a single control plane and a management interface. Its central promise is that you own your agent stack: data, context, tools, permissions, memory, and human-review loops all stay inside your own cloud. Among Python agent frameworks, Agno's distinguishing bet is this pairing of a minimal, fast SDK with a bundled runtime for taking agents to production.
Vendor details
Canonical URL
https://www.agno.com/
Category
Agent infrastructure
Company status
independent
Use cases & customers
Target customers
Deployment options
In practice
You're running many agents and heavier frameworks feel sluggish and memory-hungry. Agno's agents instantiate near-instantly with a small footprint, and turn plain Python functions into callable tools so you write minimal code.
A single agent isn't enough; your task needs research, analysis, and writing handled by different specialists. Agno lets you compose agents into a coordinated team, or a deterministic workflow with steps, loops, and parallel branches.
You've prototyped an agent but need to run it as a real service in your own cloud. Agno's AgentOS runtime serves agents over FastAPI with tracing, scheduling, and role-based access, keeping your data and permissions in your environment.
Agentic Index coverage score
12.5 / 14 capabilities · 89%
| Integrations & Tool CallingAgentic AI Index Expansion Evidence Report | Full |
|---|---|
| Workflow OrchestrationAgentic AI Index Expansion Evidence Report | Full |
| Knowledge Grounding & RAGAgentic AI Index Expansion Evidence Report | Full |
| Human Oversight & GuardrailsAgentic AI Index Expansion Evidence Report | Full |
| Security, Identity & GovernanceAgentic AI Index Expansion Evidence Report | Full |
| Observability & AuditabilityAgentic AI Index Expansion Evidence Report | Full |
| Memory & State PersistenceAgentic AI Index Expansion Evidence Report | Full |
| Deployment & Data ResidencyAgentic AI Index Expansion Evidence Report | Full |
| Prebuilt Agents, Templates & PacksAgentic AI Index Expansion Evidence Report | Partial |
| Triggers & Channel CoverageAgentic AI Index Expansion Evidence Report | Full |
| Model Flexibility & RoutingAgentic AI Index Expansion Evidence Report | Full |
| APIs, SDKs & MCP ExtensibilityAgentic AI Index Expansion Evidence Report | Full |
| Testing, Debugging & OptimizationAgentic AI Index Expansion Evidence Report | Partial |
| Browser & Computer UseAgentic AI Index Expansion Evidence Report | Partial |
The Agentic Index coverage score grades every vendor Full, Partial or Unable to verify against the same 14 buyer facing capabilities, from public evidence only. Each capability links to how all vendors in the index score on it. How this evidence is graded
Recent platform changes
Agno released version 2.8.0, introducing the agno.scorer module to quantitatively evaluate and test agent runs. The update adds CodeScorer for typed field comparison, JudgeScorer for LLM-as-a-judge evaluations, and ToolCallScorer for deterministically verifying tool executions, with support for synchronous and asynchronous execution.
Bears on: Observability / auditability
View sourceAgno released version 2.7.4, adding toolkits for Plivo, The Context Company, and Superserve, which provides a Firecracker-based sandbox for secure agent code execution. The release also updates the CLI with new project deployment starters for Azure, Helm, Modal, and Render.
Bears on: Integrations
View sourceEleven Agno releases (v2.6.10 to 2.6.20) added tool-batch checkpointing and run forking, full CRUD on agent learnings, a StudioTool for dynamic agent composition, ClickHouse trace backends, custom scoped MCP tools, and five new model providers including Cloudflare AI Gateway.
Bears on: Agent capability
View sourcePricing
From $150/mo · free tier
flat
Included quota
OSS framework (free, Apache 2.0, self-host): build agents/teams/workflows in ~20 lines, memory/knowledge/tools, model-agnostic (OpenAI/Anthropic/Gemini/open-source via MCP), AgentOS runtime, local control plane. Free tier: open-source + control plane for LOCAL AgentOS. Pro ($150/mo): control plane for LIVE/production AgentOS - 1 live connection, 4 seats, unlimited usage/monitoring/retention/knowledge/memories/chats (add-ons $30/seat, $95/live connection). Enterprise (custom): custom SSO/RBAC, self-hosted control plane, dedicated Slack + technical lead, support SLA.
What is public
Agno (agno.com, formerly Phidata - open-source Python framework + AgentOS runtime + control plane for building/running/managing multi-agent systems) is free as open-source (Apache 2.0, self-host). The managed control plane is published as tiers: Free (open-source, local control plane), Pro at $150/mo (control plane for live/production AgentOS - 1 live connection + 4 seats included; add-ons $30/mo per seat, $95/mo per live connection), and custom Enterprise (custom SSO/RBAC, self-hosted control plane, dedicated support). Crucially, AgentOS runs in YOUR cloud (BYOC - AWS/GCP/Railway or airgapped), so there are no per-event fees, no egress costs, and 'infinite retention' - your data never leaves your environment.
Billing mechanics
Agno's three layers - the Python SDK (build), AgentOS (a stateless FastAPI runtime: tracing, scheduling, JWT-based RBAC, per-session isolation, HITL/approval), and the control-plane UI (monitor/manage) - all run in your own infrastructure. Because deployment is BYOC, Agno charges a flat platform/subscription fee for the control plane/management layer (Free / Pro $150/mo + per-seat & per-live-connection add-ons / Enterprise), NOT per-run or per-event usage; you separately pay your own cloud infra and LLM provider costs. No data, logs, or traces leave your environment.
Cost watchouts
LLM provider costs and your own cloud infra are separate (BYOC) - Agno's fee covers the control plane/management layer, not compute or model calls; self-hosting means you operate AgentOS yourself; Pro add-ons ($30/seat, $95/live connection) raise the effective bill above the $150 base; Python-only framework
Variable cost rationale
Agno's own fee is a flat platform subscription with no per-event/egress charges (BYOC); the variable cost lives entirely in your own cloud infra + LLM provider usage, which scale with agent runs but are billed by those providers, not Agno
Additional watchouts
Python-only; BYOC means YOU run/scale the infrastructure (Agno manages the framework/runtime layer, not your servers); pricing transparency was limited earlier in 2026 (beta/early-access); LLM + infra costs are entirely yours; Pro add-ons ($30/seat, $95/live connection) can raise the effective bill beyond the $150 base
Overage / add-ons
No Agno per-event/egress fees by design (BYOC - you run AgentOS in your own cloud with infinite retention); scaling moves you up a platform tier (Free -> Pro -> Enterprise) or adds per-seat/per-live-connection add-ons on Pro ($30/seat, $95/live connection); your own infra and LLM provider usage are billed separately by those providers.
Sales call required
Mixed (some tiers require a call)
Free / trial
Free tier
Lowest paid plan
Pro $150/mo
Commercial notes
Rebranded from Phidata (late 2024); ~40k GitHub stars, 424+ contributors; production-first design (sessions/memory/knowledge/traces in YOUR database); AgentOS turns agents into production APIs in ~20 lines (stateless, horizontally scalable, multi-tenant); 'own your agent stack' positioning for security-conscious enterprises; competes with LangGraph, CrewAI, Mastra, AutoGen
Key ambiguities
Pricing visibility evolved in 2026 (Cloud was beta/early-access in April 2026; tiers now published: Free / Pro $150/mo / Enterprise); exact per-tier limits beyond the 1 live connection + 4 seats on Pro aren't fully detailed; the OSS license is Apache 2.0 (confirmed on the agno-agi GitHub repos; earlier 'MIT' citations were name-collisions with unrelated products)
Cancellation / refund
OSS is free (self-host, no contract); managed Free tier is free; Pro is self-serve at $150/mo; Enterprise is custom-contracted via sales; BYOC means no vendor data lock-in (everything stays in your environment)
Support SLA / resale
Community support (OSS); Enterprise adds governance/compliance support; AgentOS deploys as a Docker container in your cloud/data center (AWS/GCP/Railway/airgapped); doubles as an MCP server; certified implementation-partner network; single-line integrations to LLMs/tools via MCP
Missing data
Exact per-tier limits beyond Pro's 1 live connection + 4 seats aren't fully published. OSS license confirmed Apache 2.0 on GitHub (earlier MIT citations were name-collisions). Seed 'Free tier; Pro $150/mo; OSS self-hosted free' aligns with the published tiers; note the BYOC model (no per-event/egress fees; add-ons $30/seat + $95/live connection; LLM + infra costs are separate).
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Alternatives to Agno
The closest documented capability profiles to Agno among agent infrastructure platforms tracked by Agentic Index, ordered by similarity on the same 14 point evidence the rankings use. No vendor pays for placement.
- Tray.ai11.5 / 14A lighter documented profile than Agno
- Kestra12.0 / 14Fuller documented coverage on Prebuilt Agents, Templates & Packs
- LangChain11.5 / 14Fuller documented coverage on Testing, Debugging & OptimizationAgno vs LangChain →
- Paragon11.5 / 14Fuller documented coverage on Prebuilt Agents, Templates & Packs
- Trigger.dev10.5 / 14A lighter documented profile than Agno
- xpander.ai10.5 / 14A lighter documented profile than Agno
Similarity is computed from each vendor's Agentic Index coverage score evidence, axis by axis, not from the totals. How this evidence is graded