Building Multi-Agent Systems with LangGraph

Design Your Agent Architecture

Before writing code, decide on the coordination pattern that matches your workflow. The supervisor pattern uses a central agent that receives all requests, decides which specialist should handle each one, and coordinates the overall flow. This works well when tasks can be clearly categorized and routed. The scatter-gather pattern sends the same request to multiple agents in parallel and combines their results. This suits research tasks, multi-perspective analysis, and redundancy-based validation. Pipeline parallelism chains agents sequentially, with each agent handling a different processing stage.

The choice depends on your specific workflow. If tasks are independent and categorizable, use supervisor. If you need multiple perspectives on the same input, use scatter-gather. If processing has distinct sequential stages, use pipeline. For complex organizations, combine patterns using hierarchical subgraphs.

Define Individual Agent Subgraphs

Build each specialized agent as a standalone subgraph. Each subgraph has its own state schema that includes the fields specific to its domain, its own nodes for LLM calls and tool execution, and its own internal edges for managing its workflow. The subgraph is a complete, testable unit that can be developed and debugged independently.

For example, a research agent subgraph might have a state with search queries, retrieved documents, and a draft summary. A data analysis agent might have a state with SQL queries, result tables, and analysis conclusions. Each agent's state schema reflects its specialized domain.

Keep agent prompts focused and specific. A specialized agent with a clear, narrow role prompt performs better than a generalist agent trying to handle multiple domains. The narrower prompt reduces LLM confusion and improves output quality.

Create the Orchestrator Graph

Build a parent graph that coordinates the agent subgraphs. The parent graph's nodes include the supervisor logic (which decides which agent to invoke), the agent subgraph invocations, and any aggregation or synthesis logic. Conditional edges route execution to the appropriate agent based on the supervisor's decisions.

In the supervisor pattern, the orchestrator node receives the current task, uses an LLM to classify it and select the appropriate agent, and routes to that agent's subgraph. After the agent completes, execution returns to the supervisor, which decides whether the task is complete or another agent needs to contribute.

For scatter-gather, the parent graph fans out to multiple agent subgraphs simultaneously and includes a merge node that consolidates their outputs. The merge logic can be as simple as concatenating results or as complex as using another LLM to synthesize diverse perspectives into a coherent summary.

Implement State Communication

Define clear interfaces between the parent graph and child subgraphs. The parent passes relevant context to each subgraph through input state fields, and the subgraph returns its results through output state fields. This boundary keeps internal agent state isolated from the shared workflow state.

Use the parent graph's state to accumulate results from multiple agents. A results field with a list reducer can collect outputs from sequential agent invocations without overwriting previous contributions. A messages field with an append reducer maintains the full interaction history across all agent turns.

Avoid putting large data objects in the shared state. Pass references or summaries between agents rather than full documents or datasets. This keeps checkpoints manageable and reduces serialization overhead.

Add Error Handling and Fallbacks

Production multi-agent systems need robust error handling. Add conditional edges that detect agent failures (timeouts, empty responses, malformed outputs) and route to fallback behavior. Fallbacks might include retrying the same agent with a modified prompt, trying a different agent, escalating to human review, or returning a partial result with an explanation of what failed.

Set maximum iteration limits on supervisor loops to prevent infinite cycling when agents cannot resolve a task. Implement timeout logic for individual agent calls so that one slow agent does not block the entire workflow. Use LangGraph's checkpointing to ensure that partial progress is preserved even when errors occur, allowing the system to resume from the last successful step after the issue is resolved.