CrewAgent API

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  Dashboard Overview

Inspiration We wanted to build an automated ecosystem that minimizes human effort in conducting deep web research and structuring technical engineering reports. Current AI systems lack sequential team-based execution, which inspired us to develop CrewAgent API.

What it does CrewAgent API is an advanced multi-agent AI orchestration platform. It leverages multiple specialized AI agents (Senior Web Researcher, Professional AI Writer, AI Task Manager) that work collaboratively. Backed by Vector DB RAG, the platform allows users to explain a flow, automatically crawl relevant knowledge sources, and output structured, domain-specific engineering reports.

How we built it - Orchestration: Built using CrewAI Flow architecture for structured agent execution.

• Backend & APIs: Developed robust server endpoints utilizing Python, Node.js, and Express.
• Database & Storage: Implemented PostgreSQL (pgvector) and SQLite for vector database management and persistent storage, with Redis for optimized caching.
• Validation: Enforced secure payload parsing using Zod middleware schemas.

Challenges we ran into - Synchronizing multi-agent states and handling data exchange seamlessly during parallel execution.

• Mitigating rate limits and integrating Vector DB workflows without data loss.

Accomplishments that we're proud of Successfully engineered a working multi-agent production platform that reliably generates complete engineering reports without crashing or losing vector context.

What we learned - Deep insights into multi-agent workflow architecture and the practical application of RAG (Retrieval-Augmented Generation).

• Implementing structured server logging and strict CORS scoping for production-ready security.

What's next for CrewAgent API

Built With

• and-express.-database-&-storage:-implemented-postgresql-(pgvector)-and-sqlite-for-vector-database-management-and-persistent-storage
• crewai
• express.js
• node.js
• postgresql
• python
• redis
• zod