Reasoning Patterns

Prompt the LLM to show its intermediate reasoning steps before arriving at a final answer, dramatically improving accuracy on complex tasks.

Interleave reasoning (thinking about what to do) with acting (using tools) in a Thought → Action → Observation loop.

Explore multiple reasoning paths in a tree structure, evaluate them, and backtrack to find the best solution.

The agent reflects on its past failures, stores verbal self-critiques in memory, and uses them to improve future attempts.

Generate multiple independent reasoning paths for the same problem and select the most consistent answer through majority voting.