Build AI Dev Agent Teams
Amidst the booming AI coding market, this challenge focuses on developing sophisticated AI agent teams to automate web application development and deployment tasks. Participants will leverage the LangChain framework to orchestrate a multi-agent system, simulating a collaborative development team capable of generating code, performing quality checks, and interacting with stakeholders via voice. The system will demonstrate how advanced generative AI can streamline the software development lifecycle, from initial concept to deployment readiness.
What you are building
The core problem, expected build, and operating context for this challenge.
Amidst the booming AI coding market, this challenge focuses on developing sophisticated AI agent teams to automate web application development and deployment tasks. Participants will leverage the LangChain framework to orchestrate a multi-agent system, simulating a collaborative development team capable of generating code, performing quality checks, and interacting with stakeholders via voice. The system will demonstrate how advanced generative AI can streamline the software development lifecycle, from initial concept to deployment readiness.
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, how much each dimension matters, and which criteria separate a passable run from a strong one.
Feature Files Present
All required files (HTML, CSS, JS, report) for the generated feature must exist and be accessible.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
Coval Trace Completeness
Coval logs must show a complete execution trace, including agent transitions and tool calls.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
Code Correctness Score
Automated linting and basic functionality tests on generated code (0-100). • target: 85 • range: 0-100
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
Security Vulnerability Count
Number of critical security vulnerabilities identified by StarCoder 2 (lower is better). • target: 0 • range: 0-5
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 LangChain's AgentExecutor and LangGraph for designing stateful, graph-based agent workflows that manage task decomposition and inter-agent communication.
Integrate Claude 4 Opus into LangChain agents to perform sophisticated code generation, architectural design, and problem-solving tasks for web applications.
Implement real-time, bidirectional voice communication for agent interfaces using LiveKit, enabling natural language interaction for task assignment and status updates.
Leverage StarCoder 2 within a LangChain tool-use agent for focused code quality analysis, vulnerability scanning, and automated refactoring suggestions.
Design an evaluation and observability pipeline using Coval to trace agent decisions, monitor performance metrics, and ensure adherence to development standards.
Build a 'Project Manager' agent that uses LangChain's planning capabilities to orchestrate 'Developer' and 'QA' agents for end-to-end web app feature delivery.
Develop custom tools for LangChain agents to interact with external systems, simulating version control and deployment platforms.
[ok] Wrote CHALLENGE.md
[ok] Wrote .versalist.json
[ok] Wrote eval/examples.json
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