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Choose by what you are building. The graph runtime fits when you are building the agent itself and want a graph of your own nodes with state that survives a crash. Pick Obversa when the agents already exist as tools on your machine and what you need is the team around them: named roles, a review that sends the work back, a person who decides, and a record of every step. LangGraph’s own words: “A low-level orchestration framework and runtime for building, managing, and deploying long-running, stateful agents.” A node is a function over shared state, and edges say what runs next. A review that sends work back is something you build from a conditional edge or a jump to an earlier node. A person is an interrupt that a client resumes with a value. The checkpointer saves the graph’s state at every step, in Postgres or SQLite, so a run resumes after a crash. Here is the shape Obversa is for, as one file:

Side by side

Where LangGraph wins

  • You are writing the agent’s inner loop, not driving an existing one.
  • You need crash resume for any graph today, with a database behind it.
  • Your team writes Python.

Where Obversa wins

  • The agents are Claude Code, Codex and their kin, and the work is to give them a process with a review that sends work back and a person at the end.
  • You want that process readable as one file, with the record beside it.
  • You want a second opinion from another provider without writing a client.

Where to go

A feature team, as a file for the file above with its recorded run; what is a meta-harness for the layer Obversa is.