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FAQ

Why not LangGraph?

LangGraph is the right tool for a lot of agent work, and for some projects it's a better fit than looplet. Here's the honest comparison so you can pick deliberately instead of by vibes.

Use LangGraph when

  • Your agent is a multi-node graph, not a loop. A triage node hands off to a research branch and a coding branch, each branch has its own state, and they join again at a review node. That's the shape LangGraph is designed around. Trying to express it as a single for step in loop(...) with hooks is fighting the tool.
  • You want durable checkpointing as a first-class feature. LangGraph has built-in checkpointer backends (SQLite, Postgres, Redis) with interrupt and resume semantics tied directly to node boundaries. looplet lets you build that — ProvenanceSink dumps every step and a hook can persist state — but LangGraph gives it to you out of the box.
  • You're already in the LangChain ecosystem. Your prompts are ChatPromptTemplates, your retrievers are LangChain retrievers, your streaming consumers expect LangChain events. Bridging all of that through looplet adds friction.
  • You need the graph visualiser. LangGraph's Studio / .get_graph() view is genuinely useful for explaining a multi-agent system to someone who doesn't want to read Python.

Use looplet when

  • Your agent really is a loop. One LLM calling tools until it's done. That's ~80% of real agent work, and a graph for it is overkill.
  • You want to see every prompt the LLM saw, in order, without a debugger. step.pretty() and ProvenanceSink are the whole point.
  • You want to shape behaviour without forking. A 10-line hook that redacts PII, injects docs, or rewrites tool args composes with everything else — no class hierarchy, no node refactor.
  • Cold-import time and dependency footprint matter. Core looplet pulls in zero third-party packages; see Benchmarks.
  • You want your debug trace and your eval harness to be the same artifact. The pytest-style eval_* helpers read ProvenanceSink output directly.

If you're building a research swarm or a multi-stage ETL with a literal DAG, use LangGraph. If you're building an agent that reasons and calls tools until it's done, the loop is the product and looplet stays out of your way.