spcl/graph-of-thoughts

Health v2 · maintenance only

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2833 stars · 217 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Official Python implementation of the Graph of Thoughts (GoT) framework from ETH Zurich, which models complex problem-solving as a Graph of Operations executed with an LLM as the reasoning engine. It is flexible and extensible, also supporting prior prompting paradigms like Chain-of-Thought and Tree-of-Thoughts.

Use cases

• solve elaborate reasoning problems with LLMs using graph-structured prompting
• implement chain-of-thought or tree-of-thoughts prompting pipelines
• model multi-step LLM reasoning as a graph of operations
• sort or decompose complex tasks with LLM feedback loops
• experiment with advanced LLM prompting strategies in Python
• score and refine LLM-generated candidate solutions iteratively

When to choose

• you want graph-structured, non-linear LLM reasoning beyond simple CoT/ToT
• you need a programmable Graph of Operations with scoring and refinement steps
• you are doing research on prompting paradigms and want the reference implementation

When to avoid

• you just need simple single-prompt LLM calls without structured reasoning
• you want a production chatbot or agent product rather than a research framework
• you need a framework with no LLM API access or offline models

Facets

framework · maturity active

llm-inference prompt-engineering agent-framework rag large-language-models artificial-intelligence python cross-platform graph-of-thoughts chain-of-thought tree-of-thoughts reasoning llm-framework research natural-language-processing

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markdown · JSON · MCP: product_card(name="spcl/graph-of-thoughts")