Collaborative Multi-Agent Workflow System for Legal Case Analysis

Added Jun 2025 3 design docs

Legal work is naturally multi-role: one person researches precedent, another reviews documents, another drafts, and a senior lawyer synthesizes it all into advice. Single-prompt AI tools flatten this into one shot and lose the rigor. The frontier of legal AI lies in systems where specialized agents mirror that division of labor, checking and building on each other's work, and engineering such a system is among the most demanding tasks in applied AI today. The intern implements this system using LangGraph to define the collaboration as a stateful graph: nodes for a research agent, a document-analysis agent, a drafting agent, and a reviewer agent, with edges controlling how work and intermediate conclusions flow between them. LangChain supplies each agent's tools and prompt infrastructure, OpenAI models provide the reasoning, and shared memory lets agents reference earlier findings so the workflow stays context-aware across steps. The whole system is exposed through a Python FastAPI service where a user submits a legal question or document set and receives the orchestrated result with each agent's contribution traceable. This hard-difficulty project demonstrates elite-tier skills: graph-based orchestration, inter-agent state management, role-specialized prompt engineering, and delivery of a complex agentic system as a dependable API.

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