REPOGEO REPORT · LITE

spcl/graph-of-thoughts

Default branch main · commit 3d9d9dbd · scanned 5/10/2026, 3:47:20 PM

GitHub: 2,657 stars · 204 forks

AI VISIBILITY SCORE

40 /100

Critical

Category recall

0 / 2

Not recommended in any query

Rule findings

2 pass · 0 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 spcl/graph-of-thoughts, 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

Reposition the README's opening statement to emphasize its framework type

Why:

CURRENT

This is the official implementation of Graph of Thoughts: Solving Elaborate Problems with Large Language Models. This framework gives you the ability to solve complex problems by modeling them as a Graph of Operations (GoO), which is automatically executed with a Large Language Model (LLM) as the engine. This framework is designed to be flexible and extensible, allowing you to not only solve problems using the new GoT approach, but also to implement GoOs resembling previous approaches like CoT or ToT.

COPY-PASTE FIX

Graph of Thoughts (GoT) is a **Python framework for advanced LLM reasoning**, enabling large language models to solve elaborate problems by structuring their 'thoughts' as a Graph of Operations (GoO). This framework is designed to be flexible and extensible, allowing you to not only solve problems using the new GoT approach, but also to implement GoOs resembling previous approaches like Chain of Thought (CoT) or Tree of Thoughts (ToT).

hightopics#2

Add more specific topics to improve categorization

Why:

CURRENT

graph-of-thoughts, graph-structures, graphs, large-language-models, llm, prompt-engineering, prompting

COPY-PASTE FIX

graph-of-thoughts, graph-structures, graphs, large-language-models, llm, prompt-engineering, prompting, llm-reasoning, ai-framework, multi-step-reasoning, llm-orchestration

mediumreadme#3

Add a section clarifying the project's license

Why:

COPY-PASTE FIX

## License
This project is licensed under [describe the license(s) found in the LICENSE file, e.g., 'a custom license combining X and Y terms']. Please refer to the [LICENSE](LICENSE) file for full details.

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 spcl/graph-of-thoughts

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

langchain-ai/langchain

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

langchain-ai/langchain · recommended 1×
run-llama/llama_index · recommended 1×
deepset-ai/haystack · recommended 1×
PrefectHQ/marvin · recommended 1×
stanfordnlp/dspy · recommended 1×

CATEGORY QUERY
How to structure complex LLM reasoning tasks using a graph-based approach?
you: not recommended
AI recommended (in order):
1. LangChain (langchain-ai/langchain)
2. LlamaIndex (run-llama/llama_index)
3. Haystack (deepset-ai/haystack)
4. Marvin (PrefectHQ/marvin)
5. DSPy (stanfordnlp/dspy)
6. GraphRAG
7. NetworkX (networkx/networkx)
AI recommended 7 alternatives but never named spcl/graph-of-thoughts. This is the gap to close.
Show full AI answer
CATEGORY QUERY
Python framework for advanced LLM problem-solving beyond Chain of Thought or Tree of Thought?
you: not recommended
AI recommended (in order):
1. LangChain
2. LlamaIndex
3. AutoGen
4. CrewAI
5. DSPy
6. Haystack
AI recommended 6 alternatives but never named spcl/graph-of-thoughts. This is the gap to close.
Show full AI answer

Objective checks

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

Metadata completeness
pass
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 spcl/graph-of-thoughts?
pass
AI named spcl/graph-of-thoughts explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts spcl/graph-of-thoughts in production, what risks or prerequisites should they evaluate first?
pass
AI named spcl/graph-of-thoughts 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 spcl/graph-of-thoughts solve, and who is the primary audience?
pass
AI named spcl/graph-of-thoughts explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

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