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Tree of Thoughts
Tree of Thoughts is a framework component that enables language model problem-solving by using search algorithms to explore, score, and prune multiple lines of reasoning.
Tree of Thoughts implements deliberate problem-solving for large language models within the Swarms framework. It grows a tree of partial solutions by proposing candidate next steps, evaluating them with custom scoring, and searching through the tree to find optimal paths. Users can configure the search strategy to be breadth-first or depth-first, incorporating backtracking to prune ineffective branches. Every model output is validated against a Pydantic schema to prevent free-form prose. The framework supports any model accessible via LiteLLM that allows for function calling, enabling developers to integrate various LLM providers.
Tree of Thoughts at a glance
Key features
- Breadth-first search with configurable beam width
- Depth-first search with backtracking and node pruning
- Evaluation strategies using value scoring or comparative voting
- Pydantic schema validation for all model outputs
- Support for any LiteLLM-compatible model
- Configurable generation, evaluation, and search parameters
- Concurrency support for model calls at the same tree level
Use it for
- Solving complex logic grids and deductive puzzles
- Mathematical problem solving involving multi-step proofs
- Physics simulations requiring iterative calculation
- Planning tasks involving sequence optimization
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