RRepoGEO

REPOGEO REPORT · LITE

langchain-ai/langgraph-supervisor-py

Default branch main · commit 74c87527 · scanned 6/18/2026, 12:41:48 PM

GitHub: 1,601 stars · 245 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE
30 /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
3 / 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

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

OVERALL DIRECTION
  • highreadme#1
    Rephrase the initial "Note" to clearly state the library's specific value proposition

    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**: While direct tool-calling is often recommended for new LangChain multi-agent systems, this library offers a structured, LangChain 1.0 compatible implementation of the supervisor pattern. It's particularly valuable for upgrading existing code or for scenarios where its explicit hierarchical orchestration simplifies complex multi-agent coordination beyond manual tool-calling.
  • highabout#2
    Add a concise repository description

    Why:

    COPY-PASTE FIX
    A Python library for creating hierarchical multi-agent systems using LangGraph, providing a supervisor agent to orchestrate specialized agents.
  • hightopics#3
    Add relevant topics to the repository

    Why:

    CURRENT
    (none)
    COPY-PASTE FIX
    langgraph, multi-agent, ai-agents, supervisor, orchestration, python, langchain

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. CrewAI · recommended 2×
  3. Haystack · recommended 2×
  4. AutoGen · recommended 1×
  5. DSPy · recommended 1×
  • CATEGORY QUERY
    How to build a system for coordinating multiple specialized AI agents effectively?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. AutoGen
    3. CrewAI
    4. Haystack
    5. DSPy
    6. OpenAI Assistants API
    7. Apache Kafka
    8. RabbitMQ

    AI recommended 8 alternatives but never named langchain-ai/langgraph-supervisor-py. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for a Python library to manage task delegation in a multi-agent AI setup.
    you: not recommended
    AI recommended (in order):
    1. CrewAI
    2. LangChain
    3. Haystack
    4. Autogen
    5. SPADE
    6. Pykka

    AI recommended 6 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 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?

  • 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 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?

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  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite