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Corporate Compliance Multi-Agent System with LangChain

Build a multi-agent system using LangChain and LangGraph to manage regulatory filing workflows, inspired by the recent Paramount and WBD antitrust developments. The system will feature a set of specialized agents—researcher, legal validator, and report generator—that collaborate to track ongoing merger freezes and antitrust hearings. By utilizing Agents.js, the system will handle state transitions across complex legal milestones while maintaining a clear audit trail using Langfuse for observability.

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What you are building

Design a multi-agent orchestration layer with LangChain and LangGraph to manage complex corporate compliance workflows.

How work is evaluated

Evaluation

Trace agent decisions through the legal milestone workflow.

Datasets

Public datasets, plus the private datasets you have access to.

How submissions are scored

Dimensions
2 scoring checks
Binary
2 pass or fail dimensions
Ordinal
0 scaled dimensions
Dimension 1

State Integrity

Verify state moves correctly through nodes

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Dimension 2

Latency

Time per agent step • target: 1500 • range: 0-5000

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Learning goals

  • Master LangGraph state management for complex decision paths

  • Implement multi-agent cooperation using LangChain agent runners

  • Design custom state trackers for monitoring multi-year legal timelines

  • Integrate Langfuse for real-time observability and latency monitoring

  • Build Coplay AI interface for natural language query of filing statuses

  • Leverage Windsurf for rapid agent workflow testing and code refactoring

Resources

Dataset notes

Sample data for 1 tasks

Challenge setup

Trace agent decisions through the legal milestone workflow.

Challenge input

Event data

Langchain

Building applications with LLMs

Langfuse

Open-source LLM observability and evals.

Evaluated output

State transition logs

Evaluation checks
  • Verify state moves correctly through nodes
  • Time per agent step • target: 1500 • range: 0-5000
Configured evaluation evidence
  • Latency target: 1500
  • 1 public reference case
Configured runtime
  • JavaScript execution harness

Shows the first task, up to three tools, and up to three evidence items.

View technical recipe

Configured tools

Action Space
  • Langchain · Required
  • LangChain · Optional
Observation
  • Langfuse · Optional
Reward / Eval
  • Langfuse · Optional
Orchestration
  • Langchain · Required
  • LangChain · Optional

Evaluation contract

  • State Integrity · Weight 1
  • Latency · Weight 1

Metrics

  • Latency · Target 1500

Reference cases

  • 1 public reference case

Workflow tasks

  1. Task 1

    Input: Event data

    Output: State transition logs

    Execution harness: JavaScript

Recipe freeze marker

No recipe freeze time is recorded.

Frequently Asked Questions about Corporate Compliance Multi-Agent System with LangChain