Production RAG agents
Retrieval-augmented graphs that loop, branch, and re-query — without paying twice.
The problem
The same prompts get answered twice
RAG graphs retry, branch on relevance, and re-rank. Identical prompts hit the model repeatedly, so you pay for — and wait on — duplicate LLM calls.
Retrieved context bloats the checkpoint
Chunks, embeddings, and intermediate scores accumulate in graph state. By the time it’s a few hundred KB, Python deepcopy dominates every checkpoint.
How fast-langraph fixes it
@cached Content-addressed caching around the LLM call site collapses duplicate prompts to a sub-microsecond lookup — 9.78× throughput at a 90% hit rate, and a direct cut to API spend.
Guide →RustSQLiteCheckpointer Large retrieved-context state serializes up to 737× faster in Rust than Python deepcopy, so frequent checkpointing stops being the tax it was.
Guide →The outcome
Lower LLM bills and faster turns on the exact workload — high-hit-rate retrieval with heavy state — where fast-langraph’s gains are largest.
Questions
Does caching risk stale answers in a RAG agent? +
The @cached decorator is content-addressed by the call arguments, so a different prompt or context produces a different key and a fresh call. Cache hits only occur on genuinely identical calls, and eviction is configurable, so you control freshness.
Explore more use cases, the full feature set, or start with the quickstart.
Hit a LangGraph scaling wall?
We help production teams squeeze every bottleneck out of LangGraph — checkpoints, state, LLM costs, memory. Honest audits. Measurable fixes.