Agno vs LangGraph
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.
Facts checked against the LangGraph docs on 2026-08-10
Side by side
| 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. |
What LangGraph does well
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
Every claim on this page was checked against the documentation linked below. The performance figures come from our public harness, and the benchmarks page shows how to run it. If something here has gone stale, tell us and we will correct it.