956501819

956501819 / deep-research-agent

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Agentic RAG 驱动的深度研究 Agent — Agent 自主拆解问题、选择检索策略、评估质量、自我纠正，最终生成带引用溯源的结构化研究报告。

36
3
85% credibility
Found May 31, 2026 at 36 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

Deep Research Agent is an intelligent research assistant that takes complex questions, automatically breaks them into smaller parts, searches through your documents and the web, evaluates result quality, retries when needed, and generates a structured report with citations.

How It Works

1
🔬 Discover the research assistant

You hear about a smart research tool that can dive deep into complex topics and write reports for you.

2
📁 Add your documents

You upload your own PDFs, Word files, and notes so the assistant can search through your knowledge.

3
❓ Ask a big question

You type in a complex research question like 'What are the differences between AI in healthcare imaging versus drug discovery?'

4
🤖 Watch the agent work

The assistant breaks your question into smaller parts, searches for answers, and checks if each answer is good enough.

5
Results need improvement?
✅
Quality is good

The assistant moves forward with confident results

🔁
Quality needs work

The assistant rewrites the query and searches again, up to three times

📊 Get your research report

You receive a complete, structured report with clear sections and citations showing where each fact came from.

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Star Growth

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AI-Generated Review

What is deep-research-agent?

Deep-research-agent is a Python-based research system that autonomously breaks down complex questions, retrieves information across multiple strategies, evaluates quality, corrects itself when results fall short, and synthesizes structured reports with cited sources. It combines a FastAPI backend with a Vue 3 frontend, using LangGraph to orchestrate the agent's decision loop. The system supports hybrid retrieval mixing semantic vector search with keyword-based BM25, optional two-stage reranking via SiliconFlow, and can ingest documents (PDF, Word, Markdown, TXT) into a vector store for grounded answers.

Why is it gaining traction?

The project addresses a real pain point in RAG systems: one-shot retrieval that either works or fails silently. This agent loop evaluates each retrieval pass against a critique threshold and rewrites queries when quality is insufficient, retrying up to three times before moving on. Streaming SSE updates keep users informed as the agent decomposes their question, executes searches, and generates the final report in real time. The Vue frontend visualizes the entire process, making the agent's reasoning observable rather than opaque.

Who should use this?

Product teams building knowledge-intensive applications who need traceable, multi-step research rather than simple Q&A will find this most useful. Researchers comparing AI outputs against source documents benefit from the inline citation system. Teams already invested in SiliconFlow or Qwen models get the most immediate value, though OpenAI-compatible endpoints are supported. Organizations with stricter data requirements may prefer the local Chroma backend over cloud vector options.

Verdict

At 36 stars with recent activity and a credibility score of 0.8500000238418579%, this is a functioning but early-stage project with solid architecture underneath. The documentation is comprehensive and the two frontend options (Vue 3 and Streamlit) cover different preferences well. Test coverage exists but is modest, so production deployments should include additional validation. Worth exploring for agentic RAG use cases, though teams should evaluate maintenance responsiveness before committing to a dependency.

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