High-throughput agent services
APIs invoking LangGraph thousands of times a minute, where per-invoke overhead is the bill.
The problem
Per-invoke fixed cost dominates at volume
On short graphs, up to 58% of wall-clock is executor setup — pure overhead that scales linearly with request volume.
Tight state-update loops hit the Python ceiling
High-frequency state merges in inner loops are exactly where Python’s object model is slowest.
How fast-langraph fixes it
shim.patch_langgraph() Executor caching removes the biggest fixed per-invocation cost across every request the service handles.
Guide →langgraph_state_update Rust-backed state merging runs at ~1.4 µs/call — 45.9× faster than the Python path on tight 1,000-step loops.
Guide →The outcome
Fixed overhead drops on every request, so throughput rises and tail latency tightens without touching your graph logic.
Questions
Does the shim add per-request overhead? +
The patch runs once at import time, not per request. After that, patched paths simply call into cached executors and Rust implementations, which is why the invoke path gets faster rather than slower.
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Hit a LangGraph scaling wall?
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