RRepoGEO

REPOGEO REPORT · LITE

NousResearch/Hermes-Function-Calling

Default branch main · commit ea3c4723 · scanned 6/29/2026, 6:32:56 AM

GitHub: 1,410 stars · 202 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
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 NousResearch/Hermes-Function-Calling, 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 repository description

    Why:

    COPY-PASTE FIX
    An open-source framework for enabling Nous Hermes Pro LLMs to perform reliable function calling and structured JSON output, integrating with external tools for data retrieval and more.
  • highreadme#2
    Reposition the README's opening to clearly state its purpose and differentiation

    Why:

    CURRENT
    # Hermes-Function-Calling

This repository contains code for the Hermes Pro Large Language Model to perform function calling based on the provided schema. It allows users to query the model and retrieve information related to stock prices, company fundamentals, financial statements, and more.
    COPY-PASTE FIX
    # Hermes-Function-Calling: Open-Source Function Calling for Nous Hermes Pro LLMs

This repository provides an open-source framework and code for enabling Nous Hermes Pro Large Language Models to perform reliable function calling and generate structured JSON output based on provided schemas. It allows users to integrate LLMs with external tools for tasks like retrieving stock prices, company fundamentals, financial statements, and more, offering a self-hosted alternative to proprietary function-calling APIs.

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 NousResearch/Hermes-Function-Calling
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 Function Calling · recommended 1×
  4. Microsoft Semantic Kernel · recommended 1×
  5. Haystack · recommended 1×
  • CATEGORY QUERY
    How to enable an LLM to call external tools based on user requests?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. OpenAI Function Calling
    4. Microsoft Semantic Kernel
    5. Haystack
    6. CrewAI
    7. AutoGPT
    8. BabyAGI

    AI recommended 8 alternatives but never named NousResearch/Hermes-Function-Calling. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What's the best way to get structured JSON output from a large language model?
    you: not recommended
    AI recommended (in order):
    1. OpenAI's Function Calling
    2. Instructor (jxnl/instructor)
    3. JSON Schema
    4. Guidance (microsoft/guidance)
    5. LangChain (langchain-ai/langchain)
    6. LlamaIndex (run-llama/llama_index)

    AI recommended 6 alternatives but never named NousResearch/Hermes-Function-Calling. 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 NousResearch/Hermes-Function-Calling?
    pass
    AI did not name NousResearch/Hermes-Function-Calling — 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 NousResearch/Hermes-Function-Calling in production, what risks or prerequisites should they evaluate first?
    pass
    AI named NousResearch/Hermes-Function-Calling 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 NousResearch/Hermes-Function-Calling solve, and who is the primary audience?
    pass
    AI did not name NousResearch/Hermes-Function-Calling — 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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NousResearch/Hermes-Function-Calling — 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