fast-langraph vs. vanilla LangGraph
fast-langraph is not an alternative to LangGraph — it’s a Rust performance layer that drops into the LangGraph you already run. Same API, same graphs; the serialization, executor, and caching hot paths move to native code for up to 737× faster checkpointing and ~2.8× faster end-to-end execution.
Verdict: Stay on vanilla LangGraph while prototyping or at small scale; add fast-langraph the moment checkpointing, memory, or LLM cost becomes a production bottleneck.
Choose fast-langraph when
- → State grows large (messages, tool outputs, scratchpads)
- → You checkpoint frequently between super-steps
- → Graphs re-issue the same LLM prompts on retries/branches
- → You’re running LangGraph at real production volume
Stay on vanilla LangGraph when
- → Graphs are tiny and run once per request
- → State is small and simple
- → You’re still prototyping the graph shape
- → You want the smallest possible dependency set
Feature comparison
| Capability | fast-langraph | Vanilla LangGraph |
|---|---|---|
| Public LangGraph API | Identical | Baseline |
| Checkpoint serialization (235 KB state) | 0.28 ms (Rust) | 206 ms (deepcopy) |
| End-to-end graph execution | ~2.8× faster | Baseline |
| Executor reuse across invocations | Cached | Rebuilt each call |
| LLM response caching | Built-in @cached | DIY |
| Bottleneck profiler | GraphProfiler | None built-in |
| Upstream test compatibility | 85/88, 0 fail | n/a (is upstream) |
| Install / enable | pip + one import | pip |
| Reversible | Remove the import | n/a |
Numbers from the fast-langraph benchmark report (Python 3.12, Linux x86_64). Vanilla LangGraph is the measured baseline.
Common questions
Is fast-langraph a fork of LangGraph? +
No. fast-langraph is an add-on layer that patches a handful of LangGraph’s internal hot paths with Rust implementations via PyO3. It depends on LangGraph and tracks it — it does not replace or fork the framework. Remove the import and you are back to vanilla LangGraph.
Will my existing LangGraph code need changes? +
In automatic (shim) mode, no. You add one import that patches LangGraph at load time and keep writing StateGraph, nodes, edges, and tools exactly as before. Manual mode lets you wire individual Rust components in explicitly if you prefer control over transparency.
When is vanilla LangGraph the right choice? +
When your graphs are small, run once per request, and keep little state, Python overhead is not your bottleneck and vanilla LangGraph is simpler. fast-langraph’s gains scale with state size and invocation volume, so adopt it when you hit production latency, memory, or LLM-cost walls.
See the full benchmarks, learn how it works, or read the benchmark deep-dive.
Hit a LangGraph scaling wall?
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