RRepoGEO

REPOGEO REPORT · LITE

langchain-ai/deep_research_from_scratch

Default branch main · commit f7325d55 · scanned 5/29/2026, 1:47:50 AM

GitHub: 720 stars · 236 forks

AI VISIBILITY SCORE
23 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 0 warn · 1 fail
Objective metadata checks
AI knows your name
2 / 3
Direct prompts that named your repo
HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface langchain-ai/deep_research_from_scratch, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highlicense#1
    Add a LICENSE file to the repository

    Why:

    CURRENT
    (no LICENSE file detected — the repo has no recognizable license)
    COPY-PASTE FIX
    Add a LICENSE file (e.g., MIT, Apache-2.0) to the root of the repository.
  • hightopics#2
    Add relevant topics to the repository

    Why:

    CURRENT
    (none)
    COPY-PASTE FIX
    ["llm-agents", "deep-research", "langchain", "ai-agents", "generative-ai", "from-scratch", "tutorial", "report-generation", "agent-framework"]
  • highabout#3
    Add a concise About description

    Why:

    CURRENT
    (none)
    COPY-PASTE FIX
    A step-by-step guide and implementation for building a configurable deep research AI agent from scratch using LangChain, allowing users to integrate custom models and tools for comprehensive report generation.

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall
0 / 2
0% of queries surface langchain-ai/deep_research_from_scratch
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
LangChain
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. LangChain · recommended 2×
  2. OpenAI API · recommended 1×
  3. SerpApi · recommended 1×
  4. Google Search API · recommended 1×
  5. Pinecone · recommended 1×
  • CATEGORY QUERY
    How to develop an AI agent for deep research and comprehensive report generation?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. OpenAI API
    3. SerpApi
    4. Google Search API
    5. Pinecone
    6. Weaviate
    7. Chroma
    8. Beautiful Soup
    9. Playwright
    10. Selenium
    11. Pandas
    12. ReportLab
    13. FPDF

    AI recommended 13 alternatives but never named langchain-ai/deep_research_from_scratch. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking an open-source framework to build custom AI-powered research agents.
    you: not recommended
    AI recommended (in order):
    1. LlamaIndex
    2. LangChain
    3. Haystack
    4. AutoGen
    5. DSPy
    6. CrewAI

    AI recommended 6 alternatives but never named langchain-ai/deep_research_from_scratch. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    fail

    Suggestion:

  • README presence
    pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

  • Compared to common alternatives in this category, what is the core differentiator of langchain-ai/deep_research_from_scratch?
    pass
    AI did not name langchain-ai/deep_research_from_scratch — likely talking about a different project

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts langchain-ai/deep_research_from_scratch in production, what risks or prerequisites should they evaluate first?
    pass
    AI named langchain-ai/deep_research_from_scratch explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • In one sentence, what problem does the repo langchain-ai/deep_research_from_scratch solve, and who is the primary audience?
    pass
    AI named langchain-ai/deep_research_from_scratch explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

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langchain-ai/deep_research_from_scratch — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite