I Built a Multi-Agent Legal AI That Actually Doesn't Hallucinate (Here's the Architecture)

The project described is an advanced legal research system that leverages multi-agent orchestration and anti-hallucination techniques to provide accurate, efficient, and cost-effective legal memos. Here’s a quick breakdown of the key components and metrics:

Key Components

1. Multi-Agent Orchestration:

   • CrewAI: Manages multiple agents (each responsible for specific tasks like case law verification or damage calculation).
   • LangChain: Abstracts interactions with large language models (LLMs) to facilitate task execution.
   • OpenRouter: Facilitates model switching and cost optimization.

2. Anti-Hallucination Techniques:

   • Persona Constraints: Agents are instructed that their internal memory is unreliable, ensuring they rely on external tools for verification.
   • Tool-Mandatory Verification: Agents must use specific APIs (e.g., CourtListener) to verify citations or data points.
   • Negative Instructions: Explicitly forbid agents from inventing case names, citations, or overconfident assessments.
   • Output Validation: Post-processing layer ensures PII redaction and proper citation formatting.

3. Tech Stack:

   • Core Frameworks: CrewAI, LangChain,

Read the full article at Towards AI - Medium

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