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LangGraph
An MIT-licensed low-level orchestration framework for long-running, stateful agents, with durable execution, memory, and human-in-the-loop controls; developers can compose custom multi-agent workflows as graphs.
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Where it fits
LangGraph provides low-level primitives for long-running stateful agent workflows rather than prescribing a ready-made team. The README describes durable execution, human-in-the-loop intervention, and memory.
It belongs in Multi-Agent Platforms And Builders because official LangChain multi-agent documentation directs custom multi-agent workflows to LangGraph. Developers can mix deterministic and agentic behavior and embed other coordination patterns as graph nodes; the framework's broader single-agent scope should not be mistaken for an automatically configured multi-agent product.
Builder web analytics
Measure projects built with LangGraph
A LangGraph-based application can optionally use separately configured product analytics to assess outcomes beyond graph execution.
This is an optional application-level workflow, not a native LangGraph integration. Implement and authenticate any Agent Analytics tool or API access separately; product analytics does not replace graph state, checkpoints, or LangSmith traces.
First loop to measure
- developers implement and test a graph-based application
- the deployed product is separately instrumented with meaningful events
- an explicitly implemented tool or external agent queries outcomes using separately configured access
- developers use measured behavior to guide the next application change
Copyable prompt
Use Agent Analytics for this project. If event reporting is missing, add the tracker and report events for this project surface, including application task completion, signup, activation event, or funnel step. Verify events are arriving. Then fetch the last 7 days and compare them with the prior 7 days. Tell me which application task completion, signup, activation event, or funnel step moved users toward value, where users dropped off, which sources mattered, and what my agent workflow should improve next.