# Agno vs LangGraph

URL: https://agno-com-nine.vercel.app/compare/agno-vs-langgraph

LangGraph facts checked against their own docs on 2026-08-10.

Both frameworks produce durable, resumable agents; a feature checklist settles little. The difference is one of kind. LangGraph is a formalism: you express the system as a graph and compile it. Agno is infrastructure: plain Python above, with a runtime, approvals with real access control and a control plane beneath. For teams serving many users, that is the deciding line.

| Aspect | LangGraph | Agno |
|---|---|---|
| What you write | A graph: state, nodes, edges. You compile it, then invoke it. | Agents, teams and workflows in plain Python. No graph to compile. |
| Crash recovery | Checkpointers save state each super-step. You re-invoke with the thread id and it resumes from the last checkpoint. | Run checkpointing persists each tool batch. continue_run resumes the same run in place, and the cookbook proves it with a SIGKILL test. |
| Long-running work | Checkpoint replay covers crashes when you re-invoke. The hosted platform adds a managed task queue. | A durable job queue ships in the runtime. Jobs survive restarts and deploys, any replica can claim them, and failures land in a dead letter queue. |
| Streaming | Stream reconnection is a feature of the hosted platform. | Streams are resumable in the open source runtime. Reconnect with the last event index and catch up, on any replica with Redis. |
| Time travel | Walk checkpoint history and fork from any checkpoint. The snapshot restores full graph state. | Continue from any message of any past run. Forks become sibling runs in the same session, so history forms a tree. |
| Human in the loop | interrupt() pauses anywhere in a node and waits for days. On resume the whole node runs again, so code before the pause must be idempotent. | Pauses at tool boundaries and waits in your database. Resume picks up exactly where it stopped. Nothing runs twice. |
| Approvals | You build the approval flow on top of interrupt(): the inbox, the approver identity, the access rules. | Included by default. Pending approvals persist, admins resolve them by scope, and the requester cannot approve their own run. |
| Serving many users | You key threads by id in your own code. Per-user auth ships with the licensed Agent Server, outside the MIT library. | Every request gets a fresh copy of the agent, on by default. JWT scopes and user isolation come with AgentOS. |
| Runtime | The Agent Server is a licensed product: managed cloud, hybrid or self-hosted on the Enterprise plan. | AgentOS is open source. One command serves your agents in your cloud, on your database. |
| Creating one agent | 1.1 ms and 145 KiB, measured with the public harness. | 3.2 µs and 5.2 KiB, same harness, same day. |
| License | MIT for the library. The server verifies a license key at startup. | Apache 2.0 for the SDK and AgentOS. |

## Where LangGraph is strong

- interrupt() can pause anywhere inside a node, mid-function. Agno pauses at tool and step boundaries.
- Checkpoints snapshot full graph state every super-step once a checkpointer is enabled. Agno forks restore the conversation transcript.
- The low-level graph API gives you precise, explicit control over every edge.
- LangSmith tracing and evaluation are mature and deeply integrated.

## Choose LangGraph when

- You want your control flow drawn as an explicit graph you can inspect edge by edge.
- You need to pause in the middle of arbitrary code, where tool boundaries do not suffice.
- Your team already lives in LangSmith and wants its deployment story.

## Choose Agno when

- You serve many users and want isolation, approvals and access control from the start.
- You want resume semantics that never run the same code twice.
- You want the runtime on infrastructure you own, no license key required.

## Sources

- LangGraph docs: persistence: https://docs.langchain.com/oss/python/langgraph/persistence
- LangGraph docs: interrupts: https://docs.langchain.com/oss/python/langgraph/interrupts
- Agno docs: checkpointing examples: https://docs.agno.com/examples/agents/checkpointing/crash-recovery
- Agno docs: human in the loop: https://docs.agno.com/hitl/overview
- The benchmark harness: https://github.com/agno-agi/agno/tree/main/cookbook/09_evals/performance/comparison