Comparisons / Agno vs LangGraph
Agno vs LangGraph: Which Agent Framework to Use?
Agno (formerly Phidata) is a lightweight Python framework for building agents. LangGraph is LangChain's stateful workflow framework — a graph of nodes (functions) connected by edges with shared state. Here is how they compare — paradigm, ecosystem, and the use cases each one is actually built for.
By the numbers
Agno
39.2k
5.2k
Python
Apache-2.0
2022-05-04
Agno (formerly Phidata)
LangGraph
18.9k
3.4k
Python
MIT
2024-01-17
LangChain Inc (Harrison Chase)
Sequoia Capital, Benchmark
Part of LangChain Inc — $50M raised across A and B
8.2M
LangGraph Platform (hosted), LangSmith (observability)
Yes
Used by: Replit, Klarna, Elastic
github.com/langchain-ai/langgraph→GitHub stats as of April 2026. Stars indicate community interest, not necessarily quality or fit for your use case.
| Concept | Agno | LangGraph |
|---|---|---|
| Agent | `Agent(model=OpenAIChat(), instructions=[...])` class with `run()` method | A `StateGraph` with nodes, edges, and a typed `State` channel |
| Tools | Function tools via `@tool` decorator or built-in toolkits (web search, SQL, etc.) | `ToolNode(tools)` paired with a conditional edge for routing |
| Agent Loop | `Agent.run()` handles tool dispatch internally, configurable via `show_tool_calls` | — |
| Memory / Knowledge | Knowledge bases (PDF, URL, vector DB) injected via `knowledge` param + built-in memory | — |
| Multi-Agent (Teams) | `Team` class with `agents` list, `mode` (sequential, parallel, coordinate), and shared memory | — |
| Storage | `SqlAgentStorage`, `PostgresAgentStorage` for persisting sessions and state | — |
| Loop | — | `add_conditional_edges` from a node back to itself until a `END` condition |
| State | — | Typed `State` channels with reducers (`Annotated[list, add_messages]`) |
| Checkpointing | — | `MemorySaver` / `PostgresSaver` persists state per `thread_id` |
| Human-in-loop | — | `interrupt_before` / `interrupt_after` pauses execution for review |
| Parallel fanout | — | Multiple edges from one node + reducers merge results |
Agno vs LangGraph, head to head
Agno Agno (formerly Phidata) is a lightweight Python framework for building agents.
LangGraph LangGraph is LangChain's stateful workflow framework — a graph of nodes (functions) connected by edges with shared state.
Both wrap the same underlying agent pattern — an LLM call, a tool dispatch, a loop — in different abstractions. The choice between them is mostly about which mental model and ecosystem fits the team you have, not which one is technically more capable.
Pick Agno if
Pick Agno if agno adds value when you want a batteries-included agent with minimal boilerplate — especially for multi-modal agents or team orchestration. But each of its abstractions maps to a small piece of plain Python. If your agent is straightforward, writing it directly gives you full control with zero framework overhead. Agno is the right fit when the tradeoffs in its intro line up with how your team actually wants to work day-to-day; LangGraph would force you to translate.
Pick LangGraph if
Pick LangGraph if langGraph earns its weight when your agent is a workflow — explicit branches, checkpoints, parallel branches, or a human approval gate. For a single-agent loop, the graph machinery is overkill and a plain while loop is faster to write, debug, and ship. LangGraph is the right fit when the tradeoffs in its intro line up with how your team actually wants to work day-to-day; Agno would force you to translate.
What both add
Both Agno and LangGraph pull in a class hierarchy and a dependency tree to wrap what is, at the core, an HTTP POST in a while loop. If your use case is straightforward — one provider, a handful of tools, a single agent — the framework cost may exceed the framework benefit. The lesson below shows the same pattern in ~60 lines without either dependency.
Or build your own in 60 lines
Both Agno and LangGraph implement the same 8 patterns. An agent is a function. Tools are a dict. The loop is a while loop. The whole thing composes in ~60 lines of Python.
No framework. No dependencies. No opinions. Just the code.
Build it from scratch →