Agno

Agno is a Python framework for building multi-modal, multi-agent systems with a focus on raw performance and production-ready deployment. It grew out of Phidata, a framework the same team shipped earlier, and the rewrite addressed the API friction that accumulated in Phidata's first generation. The result is a framework that starts agents faster, ships them to production as a full FastAPI service, and handles multi-modal inputs without plugin wiring. Performance is treated as a first-class feature, not an afterthought, and the benchmarks the team published at launch made that point loudly.

Agno is what happens when a team builds a framework, ships it to production, watches where it breaks, and then rebuilds it from scratch with those failures in mind. The first generation was Phidata, a framework with real users and real adoption. Agno is the rewrite, and the difference in API clarity is the kind of gap you only achieve by throwing out the accumulated decisions of the first attempt.

The framework reached 40,000 GitHub stars and 188 releases by May 2026. Those are not vanity numbers. The star count reflects genuine developer interest in the performance narrative. The release cadence reflects an active team shipping real changes rather than managing a legacy codebase.

From Phidata to Agno

The Phidata team spent years building agents for customers. The framework grew to support that work, and like any codebase that grows under real-world pressure, it accumulated abstraction layers that made sense individually but added friction collectively. Tool definitions were verbose. Multi-modal support required separate wiring. Deploying to production meant writing your own server layer on top of the SDK.

Agno started from a clean slate with a specific question: if we knew everything we know now, what would this look like? The answers shaped the framework's three distinguishing choices.

First, performance would be measured, not assumed. The team benchmarked agent instantiation time and API overhead from the start and treated regressions as bugs. Second, multi-modal would mean first-class, not plugin-based. Text, images, audio, and video would all pass through the same agent interface without preprocessing ceremony. Third, the SDK would ship with a production server, not expect users to build one.

That last point is worth dwelling on. Most frameworks stop at the agent abstraction and leave deployment as an exercise for the user. Agno's Runtime layer turns any agent definition into a FastAPI application with over 50 endpoints, streaming via SSE and WebSockets, session persistence, and JWT-based RBAC. You write the agent, Agno handles the server. The gap between a working prototype and a production service collapses from weeks to an afternoon.

The performance claims, examined honestly

The headline benchmark from Agno's launch said the framework was 10,000 times faster than LangGraph. That number generated attention, and some skepticism, which is warranted.

The benchmark measured agent instantiation time in isolation: how long it takes to construct an agent object before any inference runs. In that specific scenario, Agno is genuinely and significantly faster. LangGraph's graph execution engine carries overhead that shows up clearly in a cold-start measurement. Agno's lighter runtime does not.

The problem is that agent instantiation is not the bottleneck in real workloads. When your agent is waiting for a 200ms model response, a 2ms versus 200ms instantiation difference disappears into the noise. The 10,000x claim holds in the benchmark and fails as a practical performance guide for most applications.

Where Agno's performance advantage is real and meaningful: high-throughput scenarios where you are spinning up many short-lived agents to process items in parallel, or serverless environments where cold starts matter. If you are building a document processing pipeline that needs to instantiate an agent per document across thousands of concurrent requests, Agno's lighter runtime has genuine value. If you are building a single long-running assistant, the performance gap is not the reason to choose it.

The honest read is that Agno is measurably faster to instantiate, the framework is lighter-weight than LangGraph as a system, and the API is cleaner. Those are real advantages. The 10,000x headline is a benchmark that proves a specific technical point but should not drive a framework decision on its own.

Performance-first architecture

Agno achieves its instantiation speed through a deliberate design decision: the agent object is as thin as possible. There is no graph compilation step at construction time, no schema validation pass across all registered tools, and no lazy import chain that pulls in the full dependency tree on first use. The framework separates what must happen at definition time from what can happen at runtime, and it errs toward deferring work.

The practical consequence is that Agno agents start fast even in environments where Python module initialization is expensive. That matters in Lambda functions, in containerized services that scale to zero, and in test suites that spin up hundreds of agent instances.

Multi-modal agents

Multi-modal support in Agno means you pass images, audio, or video alongside your message and the framework routes it correctly without requiring you to handle encoding or provider-specific formatting. The agent definition does not change based on the modality you expect to receive; you declare the model and Agno handles the conversion layer.

This is a meaningful quality-of-life improvement over frameworks where multi-modal is a separate feature with its own abstraction. A document analysis agent that needs to handle both PDFs (text) and scanned receipts (images) uses one agent definition, not two.

The constraint is that multi-modal capability is bounded by what the underlying model supports. If you are using a text-only model, Agno will not magically add vision. What Agno removes is the boilerplate of converting between modalities at the framework level.

Teams and workflows

Single agents handle single tasks. Agno's team primitives handle the case where a task needs multiple specialists working together, which is the real workload shape for most production agent systems.

Agno exposes three team patterns: route, coordinate, and collaborate. In route mode, a lead agent classifies the input and dispatches it to the right specialist. In coordinate mode, a lead agent breaks a task into subtasks and assigns them to specialists, then assembles the results. In collaborate mode, agents share a context and contribute to the response together.

These patterns cover most real-world multi-agent requirements without forcing you to build a custom orchestration layer. Teams at the same company can coordinate across divisions where each division has its own specialist agent, all orchestrated by a lead agent that understands task routing. Research pipelines can use a coordinator agent to parallelize web searches across multiple searcher agents and then synthesize results. The API for all three patterns is consistent.

Comparing this to CrewAI, which also uses role-based team concepts, Agno's team patterns feel slightly more explicit about the data flow between agents. CrewAI's role abstractions are easier to prototype quickly. Agno's team patterns scale more cleanly when you need to understand exactly what a team member agent received and what it returned.

Built-in memory and knowledge

Agno treats memory as a first-class system rather than something you bolt on with a vector store integration. The framework distinguishes between three memory types: session memory (what happened in this conversation), user memory (what you know about this user across sessions), and agent memory (what the agent has learned and stored for its own use).

Knowledge bases work alongside memory. An agent can be connected to a vector store containing documents, and Agno handles the retrieval step before inference. The built-in storage adapters support PostgreSQL for sessions and ClickHouse for trace analytics. There is no requirement to use these; you can bring your own persistence layer. But the defaults remove the configuration overhead for teams that do not want to think about storage architecture on day one.

This compares favorably to LangGraph, where memory and knowledge retrieval require more manual wiring. LangGraph gives you more control over exactly how state flows through a graph, which is valuable for complex stateful workflows. Agno gives you working memory with less setup, which is valuable for teams who want to ship a useful agent before designing a state machine.

FastAPI server for production

The built-in Runtime is the most distinctive feature of Agno relative to other Python agent frameworks. When you wrap an agent or team in Agno's Runtime, you get a FastAPI application that handles the entire production API surface without writing a server yourself.

The endpoints cover session management, streaming inference via Server-Sent Events and WebSockets, tool call approval for human-in-the-loop workflows, cron scheduling for background agent jobs, and RBAC using JWT tokens. Multi-tenant isolation means different users see different sessions without additional code. OpenTelemetry tracing is available for every request.

The result in practice: a coding agent that can hold a conversation, remember what it discussed in previous sessions, await human approval before running shell commands, and run scheduled maintenance tasks can be shipped as a production API in roughly 30 lines of Python plus the FastAPI application setup. For teams building internal tooling or customer-facing agent products, that is a meaningful reduction in the work between "this agent works locally" and "this agent is in production."

Where Agno falls short

Python-only is a hard constraint for polyglot teams. If your frontend is TypeScript and your team wants to share agent logic between services, Agno offers nothing. For that need, Mastra handles the TypeScript side with similar production deployment values.

The community is younger than LangChain's or even LangGraph's. Stack Overflow coverage is thinner, third-party tutorials are sparse, and when you hit an edge case, you are more likely to need to read source code than find an existing answer. The GitHub issue tracker and Discord are active, but the accumulated knowledge base has not had the years to grow.

The AgentOS control plane is a commercial product. The SDK and Runtime are genuinely open-source under MPL-2.0, and most teams will never need the control plane. But teams that want the full observability UI, the hosted AgentOS dashboard, and ClickHouse-backed analytics will need to budget for it. This is not a criticism of the business model, just a fact to factor into evaluation.

The 3-layer architecture (SDK, Runtime, Control Plane) is well-designed but requires you to understand which layer you need. Teams new to Agno sometimes assume all features require the cloud control plane when the Runtime layer already covers most production needs without any cloud account.

Agno vs LangGraph: the real comparison

The 10,000x headline sets up a false binary. Agno and LangGraph are solving different problems.

LangGraph's value proposition is explicit, auditable control flow for complex stateful workflows. When you need to model a workflow as a state machine with precise branching, human approval at specific nodes, retry logic with backoff, and persistent checkpoints that survive process restarts, LangGraph's graph primitives give you that. The overhead is real but it is the cost of that expressiveness.

Agno's value proposition is faster development and lighter runtime for multi-agent task delegation patterns. When your workflow is "classify this, route it to the right specialist, collect the results," Agno's team primitives get there with less code and less startup overhead than building the same thing in LangGraph.

The choice is not performance. It is the shape of your workflow. Graph-native state machines with complex branching belong in LangGraph. Multi-modal agent teams with task routing belong in Agno. Simple single-agent APIs could go either way, and at that scope, developer preference is the deciding factor.

Getting started

Install is a pip command with an optional extras syntax for specific model providers or tool integrations. There is no mandatory cloud account, no configuration file, and no required service dependency. You can have a single-agent API running locally within ten minutes of starting.

The documentation is well-structured and the examples cover the common patterns without excessive ceremony. The multi-agent team examples are particularly clear and map directly to the route and coordinate patterns described above. For teams evaluating whether Agno fits their workload, the examples are the fastest way to find out.

For teams currently on Phidata, the migration path exists and the team published guidance. The core concepts carry over but the API surface changed enough that a mechanical find-and-replace will not work. Budget for a real migration review rather than an automated conversion.

Verdict

Agno is a well-designed framework with a genuine performance advantage in the scenarios where instantiation overhead matters and a cleaner API than its predecessor. The 10,000x benchmark is a marketing number that proves a narrow technical point; the real reasons to choose Agno are the built-in FastAPI server, the multi-modal first-class support, and the team orchestration primitives that cover real workload shapes.

Teams building multi-modal document pipelines, high-throughput agent APIs, or internal tools that need to deploy quickly without writing server boilerplate will find Agno a strong fit. Teams that need explicit state graph control, complex human-in-the-loop workflows, or TypeScript compatibility will need LangGraph or something else.

For coding agent use cases, Agno's built-in server and multi-modal support make it worth evaluating if you are building a custom coding assistant that needs to handle code, diagrams, and screenshots in the same pipeline. The team orchestration layer also works well for review-and-fix loops where one agent writes code and another checks it.

The framework is young enough that the ecosystem gaps are real but active enough that they are closing. At 40,000 stars and a high release cadence, Agno has moved past the point where choosing it is a bet on an unknown. It is a bet on a team that has shipped production agent systems before and built this framework to address what they learned doing it.

Key features

• Performance-first architecture with minimal agent instantiation overhead
• Multi-modal agents that handle text, images, audio, and video natively
• Team orchestration with route, coordinate, and collaborate patterns
• Built-in memory (session, user, agent) and vector knowledge bases
• Native FastAPI server with 50+ endpoints, SSE, WebSockets, and RBAC
• 100+ pre-built tool integrations including MCP and custom toolkits
• OpenTelemetry tracing and ClickHouse-backed audit logs
• Cron-based scheduling without external infrastructure
• Human-in-the-loop pause and tool approval workflows
• Multi-tenant isolation with JWT-based access control