LangGraph
LangGraph is an orchestration framework for stateful workflows and agents expressed through nodes, transitions and shared state. It lets a practitioner combine deterministic steps with model decisions, persist progress and interrupt execution for external input, making the control structure of a long-running task explicit.
What it is
A LangGraph application defines work units and how execution moves between them. Nodes read and update state; edges or commands select the next work. Cycles support repeated model-tool interaction, while persistence and interrupts enable recovery and human decisions. The framework is concerned with execution structure rather than prescribing a particular agent personality or prompting method. It can implement a single agent, multiple cooperating agents or a workflow with little autonomous behavior. This distinguishes it from a higher-level agent constructor. Understanding state updates and replay behavior is essential because durable continuation can revisit code around a saved transition.
What the work involves
The practitioner defines a state schema and control graph, chooses a checkpoint backend and makes termination conditions explicit. Parallel branches require careful handling of shared state and merge rules. Side-effecting steps should be idempotent or reconciled during recovery. Useful artifacts include the graph, transition traces and tests for interrupted runs. The developer evaluates the paths created by tool errors and missing evidence, not only the intended success path, and keeps operational status separate from a model's textual account of progress.
Illustrative example
A contract-review workflow extracts clauses, checks them against a policy and pauses on disputed findings. A LangGraph node stores the evidence and another node receives the reviewer decision before producing the final report. The graph records which documents and policy version were used. Recovery testing restarts the worker during the review pause and confirms that the decision resumes the same review without duplicating an external issue or losing previously completed checks.
Limits and common mistakes
A graph can make transitions visible while still containing unreliable model judgments. Checkpointing does not automatically provide transactional guarantees for external tools. Poorly designed cycles can continue indefinitely, and conflicting state updates can corrupt parallel work. Quality requires clear state semantics, bounded paths and tested recovery. LangGraph is useful when explicit control and durable state solve a real requirement; a simple single-call task may not benefit from the added orchestration machinery.
Prerequisites
- hardAI Agent Design
Multi-agent systems orchestrate multiple single agents — you must understand one agent before you can coordinate many
- mediumAgent State Management
Multi-agent coordination requires state tracking across agents — state machines formalize the handoffs
Related skills
- → is an instance of: Multi-Agent Orchestration
Sources and further reading
- LangGraph overview
Describes low-level orchestration, durable execution, state, streaming and human-in-the-loop capabilities.
Last updated: 2026-10-10