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Guides, technical articles, and LangGraph production pain points — all in one place. Filter by type, sorted newest first.
Cache LLM calls with @cached for a 10x speedup
LangGraph graphs re-issue the same LLM prompts constantly. The fast-langraph @cached decorator drops onto your LLM call sites and eliminates redundant API spend.
Find LangGraph bottlenecks with GraphProfiler
Before you adopt fast-langraph or any other optimization, measure. The GraphProfiler adds ~1.6 μs of overhead per operation and tells you exactly where your wall clock goes.
Quickstart: enable fast-langraph in under a minute
Install fast-langraph, flip the shim on, and measure your first speedup. No code changes to your existing LangGraph application.
Swap in RustSQLiteCheckpointer for 5–6x faster checkpointing
Replace LangGraph's built-in SQLite checkpointer with the Rust-backed drop-in. Same API, 5–6x faster per operation, and up to 737x faster on the serialization step for large state.
Automatic shim mode: zero-code-change acceleration
fast_langgraph.shim.patch_langgraph() monkey-patches LangGraph at import time, swapping in faster executors and Rust-backed channel updates. No code changes anywhere else.
Manual acceleration mode: direct Rust component usage
When the shim isn't enough, explicit Rust components deliver the headline speedups: 737x on checkpoint serialization, 46x on sustained state updates, and 10x on LLM caching.
Migrating from vanilla LangGraph to fast-langraph
A step-by-step migration checklist: profile, adopt the shim, swap the checkpointer, add caching, validate. Each step is reversible and measured.
Why Python's deepcopy kills LangGraph at scale
The 737x speedup on checkpoint serialization isn't magic. It's the direct consequence of what Python's deepcopy actually does — and doesn't do — to your agent state.
Scaling LangGraph in production: the three real bottlenecks
Production LangGraph workloads hit three predictable bottlenecks: checkpointing, executor churn, and LLM redundancy. An architect-level look at the cost math at each stage.
Executor churn: the 58% problem in LangGraph invocations
Most LangGraph invocation overhead isn't in your nodes or your channels. It's in ThreadPoolExecutor construction on every single call — 58% of wall clock on short graphs.
LangGraph vs fast-langraph: side-by-side benchmarks
An apples-to-apples comparison across checkpoint serialization, state updates, end-to-end graph execution, and LLM caching — every number reproducible from the public benchmark scripts.
When not to use fast-langraph
An honest guide to when fast-langraph won't help — small graphs, simple state, LLM-bound workloads. Optimization isn't free, and the wrong tool at the wrong time is just noise.
LangGraph checkpoint serialization overhead on large state
Production teams running LangGraph with non-trivial agent state hit a wall where every super-step pays 100+ ms in Python deepcopy overhead. This is the single largest source of p95 latency growth.
LangGraph memory footprint grows unbounded on long-running graphs
Long-running LangGraph workloads see peak memory grow well beyond the logical state size. The culprit isn't a leak — it's deepcopy allocating parallel object graphs on every checkpoint.
LangGraph retry and branching loops re-issue identical LLM calls
LangGraph graphs with retries, branches, or reflection passes frequently send the same prompt to the LLM multiple times per invocation — inflating both latency and API spend.