RRepoGEO

REPOGEO REPORT · LITE

langchain-ai/langgraph-supervisor-py

Default branch main · commit 5d341ac3 · scanned 5/8/2026, 8:21:54 PM

GitHub: 1,574 stars · 239 forks

AI VISIBILITY SCORE
17 /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
1 / 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/langgraph-supervisor-py, 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

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

OVERALL DIRECTION
  • highabout#1
    Add a concise 'About' description

    Why:

    COPY-PASTE FIX
    A Python library for building hierarchical multi-agent systems with a central supervisor using LangGraph, primarily for upgrading existing code or advanced use cases where the direct tool-calling pattern is insufficient.
  • mediumreadme#2
    Rephrase the prominent 'Note' in the README

    Why:

    CURRENT
    > **Note**: We now recommend using the **supervisor pattern directly via tools** rather than this library for most use cases. The tool-calling approach gives you more control over context engineering and is the recommended pattern in the LangChain multi-agent guide. See our supervisor tutorial for a step-by-step guide. We're making this library compatible with LangChain 1.0 to help users upgrade their existing code. If you find this library solves a problem that can't be easily addressed with the manual supervisor pattern, we'd love to hear about your use case!
    COPY-PASTE FIX
    > **Note**: For new projects, we generally recommend implementing the supervisor pattern directly via tools, as detailed in the LangChain multi-agent guide. This library remains valuable for upgrading existing LangGraph multi-agent systems to LangChain 1.0, or for specific advanced use cases where its structured approach simplifies complex orchestration challenges. We welcome feedback on such use cases!

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/langgraph-supervisor-py
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. Haystack · recommended 2×
  3. CrewAI · recommended 2×
  4. SPADE · recommended 1×
  5. Mesa · recommended 1×
  • CATEGORY QUERY
    How to build a multi-agent system with a central orchestrator in Python?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. Haystack
    3. CrewAI
    4. SPADE
    5. Mesa
    6. PyTorch
    7. TensorFlow

    AI recommended 7 alternatives but never named langchain-ai/langgraph-supervisor-py. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What framework helps manage communication and task delegation between AI agents?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. AutoGen
    3. CrewAI
    4. Haystack
    5. LlamaIndex

    AI recommended 5 alternatives but never named langchain-ai/langgraph-supervisor-py. 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/langgraph-supervisor-py?
    pass
    AI did not name langchain-ai/langgraph-supervisor-py — 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/langgraph-supervisor-py in production, what risks or prerequisites should they evaluate first?
    pass
    AI named langchain-ai/langgraph-supervisor-py 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/langgraph-supervisor-py solve, and who is the primary audience?
    pass
    AI did not name langchain-ai/langgraph-supervisor-py — 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?

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langchain-ai/langgraph-supervisor-py — 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