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.
What you are building
The core problem, expected build, and operating context for this challenge.
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.
Shared data for this challenge
Review public datasets and any private uploads tied to your build.
How submissions are scored
These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.
State Integrity
Verify state moves correctly through nodes
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
Latency
Time per agent step • target: 1500 • range: 0-5000
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
What you should walk away with
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
How this agent runs
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
- Verify state moves correctly through nodes
- Time per agent step • target: 1500 • range: 0-5000
- Latency target: 1500
- 1 public reference case
- JavaScript execution harness
View technical recipe
Configured tools
- Langchain · Required
- LangChain · Optional
- Langfuse · Optional
- Langfuse · Optional
- Langchain · Required
- LangChain · Optional
Evaluation contract
- State Integrity · Weight 1
- Latency · Weight 1
Recipe state
This is a preview. The configuration can change before the evaluation recipe is locked.
Run this agent on your dataset and AI stack
Bring your dataset, model providers, and success criteria. We will scope the right managed run for your team.
Scope a managed run[ok] Wrote CHALLENGE.md
[ok] Wrote .versalist.json
[ok] Wrote eval/examples.json
Requires VERSALIST_API_KEY. Works with any MCP-aware editor.
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