TypeScript · Next.js · LangChain · RAG
Multi-Agent Research Synthesizer
Multi-source research synthesizer powered by agentic RAG that retrieves and synthesizes knowledge across heterogeneous sources.
A multi-agent system that decomposes complex research questions into parallel sub-queries, dispatches them to specialist retrieval agents across heterogeneous sources (vector stores, web search, internal docs), and synthesizes the results into a coherent, cited answer.
Problem
Single-agent RAG pipelines hit a ceiling when research questions span multiple domains or data sources. Latency compounds sequentially, failures cascade, and the single agent becomes a bottleneck.
Solution
A fan-out / fan-in orchestration pattern built with LangGraph. The orchestrator decomposes the query, spawns specialist agents in parallel, collects results, and passes them to a synthesis agent that produces a final answer with source citations.
Key Features
- Parallel retrieval across multiple sources for low latency
- Source citation enforced at the synthesis layer
- Typed message passing between agents for compile-time safety
- Graceful degradation when individual retrieval agents fail
- Full trace logging per query for observability