RRepoGEO

REPOGEO REPORT · LITE

langchain-ai/agent-protocol

Default branch main · commit 8246ac0b · scanned 6/2/2026, 3:06:43 AM

GitHub: 598 stars · 52 forks

AI VISIBILITY SCORE
35 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 1 warn · 0 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/agent-protocol, 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
  • highabout#1
    Add a concise 'About' description for the repository

    Why:

    COPY-PASTE FIX
    A framework-agnostic, open protocol specification for building and deploying interoperable Large Language Model (LLM) agents in production.
  • hightopics#2
    Add specific topics to improve categorization

    Why:

    COPY-PASTE FIX
    llm-agents, agent-protocol, api-specification, open-standard, interoperability, large-language-models, ai-agents
  • highreadme#3
    Reposition README opening to clarify its nature as a protocol, not a framework

    Why:

    CURRENT
    # Agent Protocol

Agent Protocol is our attempt at codifying the framework-agnostic APIs that are needed to serve LLM agents in production. This document explains the purpose of the protocol and makes the case for each of the endpoints in the spec. We finish by listing some roadmap items for the future.
    COPY-PASTE FIX
    # Agent Protocol: An Open Standard for LLM Agent Interoperability

Agent Protocol is an open, framework-agnostic specification for the APIs needed to serve and interact with Large Language Model (LLM) agents in production. Unlike agent frameworks or platforms, Agent Protocol provides the common language for agents to communicate, enabling true interoperability across different implementations. This document explains the purpose of the protocol and makes the case for each of the endpoints in the spec. We finish by listing some roadmap items for the future.

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/agent-protocol
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
LangChain
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. LangChain · recommended 1×
  2. LlamaIndex · recommended 1×
  3. OpenAI Assistants API · recommended 1×
  4. Microsoft Semantic Kernel · recommended 1×
  5. Haystack · recommended 1×
  • CATEGORY QUERY
    Seeking a standardized interface for deploying and interacting with various AI agents.
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. OpenAI Assistants API
    4. Microsoft Semantic Kernel
    5. Haystack
    6. AgentVerse

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

    Show full AI answer
  • CATEGORY QUERY
    What are open specifications for building interoperable large language model agent systems?
    you: not recommended
    AI recommended (in order):
    1. OpenAPI Specification
    2. JSON Schema
    3. W3C DID
    4. Verifiable Credentials (VCs)
    5. ActivityPub
    6. Schema.org
    7. JSON-LD

    AI recommended 7 alternatives but never named langchain-ai/agent-protocol. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    warn

    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/agent-protocol?
    pass
    AI named langchain-ai/agent-protocol 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/agent-protocol in production, what risks or prerequisites should they evaluate first?
    pass
    AI named langchain-ai/agent-protocol 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/agent-protocol solve, and who is the primary audience?
    pass
    AI named langchain-ai/agent-protocol 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/agent-protocol — 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