2026 Flagship ยท Autonomous Multi-Agent Systems

DeepResearch AI

Autonomous multi-agent research engine orchestrating parallel web discovery, human-in-the-loop review, and streamed progress.

Year: 2026 | Role: Creator and Lead Engineer. Designed graph topology, built the FastAPI SSE backend, integrated search/LLM APIs, and deployed the production system.

View source on GitHub | Open Live Demo | Read Technical Case Study

Project Overview

DeepResearch AI replaces brittle single-prompt retrieval with an autonomous, graph-based multi-agent architecture. It executes parallel web search fan-outs across up to 25 workers, supports human plan approval, and streams progress in real time.

Problem Statement

Standard LLM web search queries produce superficial, hallucination-prone summaries without source grounding, depth verification, or user control over research direction.

Solution Architecture

Constructed a dual-mode research graph using LangGraph: a fast Web DeepSearch mode for rapid multi-query synthesis, and an Evidence-First Research Mode with checkpointed human review gates and an academic document generator.

  • Orchestrator Graph: Decomposes broad research briefs into targeted topic vectors.
  • Parallel Worker Fan-Out: Dynamically spawns up to 25 worker nodes using LangGraph Send() API for concurrent source gathering.
  • Human-in-the-Loop Gate: Interrupts execution via interrupt() to allow human review and plan adjustment before synthesis.
  • Backend & Streaming: FastAPI service streaming execution steps via Server-Sent Events (SSE) to a responsive React frontend.

Tech Stack & Engineering

  • LangGraph
  • FastAPI
  • Server-Sent Events (SSE)
  • Tavily API
  • DeepSeek API
  • Python
  • Docker
  • Vercel

Key Implementation Features

  • Parallel worker fan-out with up to 25 concurrent research agents.
  • Checkpointed Human-in-the-Loop review gates with MemorySaver persistence.
  • Low-latency Server-Sent Events streaming real-time agent thoughts and findings.
  • Evidence-first citation tracking with verifiable source metadata.

Technical Challenges & Solutions

  • Managing state explosion during parallel fan-out: Resolved using isolated worker sub-states merged into the parent graph via structured reducers.
  • Ensuring low latency over HTTP connections: Replaced polling with SSE connection pooling and chunked stream serialization.

Verified Results

  • Demonstrated structured multi-agent coordination with real-time streaming and human review checkpoints.
  • Open-source repository with active community adoption and full documentation.