LangGraph performance, answered

LangGraph checkpoints your graph state between super-steps, and Python serializes that state on every checkpoint. As state grows — accumulated messages, tool outputs, intermediate scratchpads — serialization through Python's deepcopy becomes a real bottleneck. Storage grows with it: LangGraph issue #7714 documents roughly 85% storage bloat and 37.8% token overhead in checkpointing, with no built-in opt-out. To fix the serialization side, fast-langraph's RustSQLiteCheckpointer serializes the same state up to 737× faster than Python deepcopy on large state (measured at 0.28 ms vs 206 ms on ~235 KB of state). The gain scales with state size, so the bigger your checkpoints, the more you save.

Yes. fast-langraph ships RustSQLiteCheckpointer, a drop-in replacement for LangGraph's SQLite checkpointer that moves the hot serialization path into Rust. You can adopt it directly, or enable automatic mode with a single import — fast_langgraph.shim.patch_langgraph() transparently patches LangGraph at import time, so your graph code, tools, and prompts stay unchanged. On realistic workloads with checkpointing, this yields roughly a 2.8× end-to-end speedup while keeping full LangGraph API compatibility.

Graphs re-issue identical prompts constantly through retries, branching, and tool routing, so you pay for the same LLM call more than once. fast-langraph's @cached decorator is a one-line wrapper around your LLM call site: it is content-addressed by the call arguments, does sub-microsecond lookups, and supports configurable eviction. On a RAG-style workload at a 90% cache hit rate we measure about a 9.8× improvement in LLM call throughput, which translates directly into lower latency and lower API spend on the duplicated calls.

It depends on what you need from durability and deployment, not just speed. MemorySaver keeps checkpoints in process memory: it is the simplest option and fine for tests or short-lived runs, but it is not durable — state is lost when the process exits. Postgres and Redis savers are durable, networked backends: choose them when you need shared, centralized checkpoint storage across services or instances, and you accept a network round-trip per checkpoint. RustSQLiteCheckpointer is a fast, local, durable saver backed by SQLite with a Rust serialization path; it suits single-node or per-instance workloads that want on-disk durability without standing up separate database infrastructure. Pick MemorySaver for throwaway runs, a networked saver when durable shared storage is the requirement, and RustSQLiteCheckpointer when you want durable local checkpoints with a fast serialization path.