Build an Autonomous Software Engineering Team with LangGraph

In LangGraph, the 'State' is the shared memory that all agents read from and write to. Start by defining a TypedDict that includes fields for the original requirements, technical tasks, generated code, and review feedback. This ensures type safety and predictable data flow throughout the graph.

Create a file named state.py and define your schema:

from typing import TypedDict, List, Annotated
import operator

class AgentState(TypedDict):
    requirements: str
    plan: List[str]
    backend_code: str
    frontend_code: str
    review_comments: str
    iterations: int
    is_approved: bool

Watch out: Avoid using deeply nested dictionaries in your state; LangGraph's reducer functions work best with flat structures or specific 'Annotated' merge strategies like operator.add for lists.

The Orchestrator acts as the 'brain' that analyzes requirements and breaks them down into a technical plan. This node uses a high-reasoning model like gpt-4o to decide which specialist agents need to be called. Configure the orchestrator function to return the plan field to the state.

Watch out: If the orchestrator's plan is too vague, the specialist agents will struggle. Use structured output to force the LLM to provide concrete technical specifications.

This node implements the server-side logic and API endpoints defined in the plan. It focuses exclusively on the backend_code field, ensuring modularity. Implementation detail: Filter plan for backend tasks and generate code using a Senior Backend persona.

Watch out: Large backend files can exceed context windows. Instruct the agent to provide clean, modular snippets rather than one massive file.

The Frontend Specialist takes the UI requirements and the backend API specs to build an interactive interface. It writes to the frontend_code state field. By knowing the backend schema, it generates accurate types and fetch logic.

Watch out: Without a shared context of 'style guidelines', the frontend agent might default to generic CSS. Provide a system prompt that specifies a framework like Tailwind CSS.

The Reviewer node acts as the final gatekeeper. It analyzes both backend and frontend code for bugs and security flaws. It updates review_comments and sets is_approved to true or false, triggering a loop if needed.

Watch out: If the reviewer is too strict, you might enter an infinite loop. Implement a 'max_iterations' check in your graph edges to prevent this.

Connect the nodes using StateGraph. Use add_conditional_edges after the Reviewer to route back to specialists for fixes or to the END if approved. This creates the 'agentic loop' that defines LangGraph.

Watch out: Forgetting to handle the 'Not Approved' case in your conditional edges will cause the graph to terminate prematurely with incomplete code.