Agentic Design

Patterns
🌲

Forest-of-Thoughts(FoT)

Generates multiple reasoning trees to enhance diversity and robustness

Complexity: highReasoning Techniques

🎯 30-Second Overview

Pattern: Generate multiple independent reasoning trees to enhance diversity and robustness

Why: Provides error tolerance, solution diversity, and cross-validation for complex problems

Key Insight: Multiple trees → Different strategies → Cross-validate → Robust consensus or best path

⚡ Quick Implementation

1Generate Trees:Create multiple independent reasoning trees
2Diversify:Ensure different starting points & strategies
3Evaluate:Score each tree for quality & validity
4Cross-Validate:Compare solutions across trees
5Select Best:Choose optimal path or consensus
Example: Problem → [Tree1: Strategy A, Tree2: Strategy B, Tree3: Strategy C] → Evaluate → Best Solution

📋 Do's & Don'ts

✅Generate trees with different starting assumptions
✅Use diverse reasoning strategies per tree
✅Implement robust cross-tree validation
✅Set limits on forest size (typically 3-7 trees)
✅Cache tree results to avoid redundant computation
❌Generate too many similar trees (redundancy)
❌Skip diversity checks between trees
❌Allow unlimited tree generation (cost explosion)
❌Use same evaluation criteria for all trees
❌Ignore computational overhead vs single ToT

🚦 When to Use

Use When

  • • High-stakes decisions requiring robustness
  • • Problems with multiple valid approaches
  • • When single tree might miss solutions
  • • Complex creative problem-solving
  • • Risk-sensitive applications

Avoid When

  • • Simple problems with clear solutions
  • • Tight computational budgets
  • • Real-time/low-latency requirements
  • • When Tree-of-Thought suffices
  • • Highly constrained solution spaces

📊 Key Metrics

Solution Diversity
Unique approaches across trees
Consensus Rate
Agreement between tree solutions
Error Tolerance
Robustness to individual tree failures
Computational Efficiency
Quality improvement per additional tree
Coverage Completeness
Solution space exploration breadth
Best Path Selection
Accuracy of optimal tree identification

💡 Top Use Cases

Investment Analysis: Tree1(Technical), Tree2(Fundamental), Tree3(Sentiment) → Consensus decision
Medical Diagnosis: Multiple specialist perspectives → Cross-validate symptoms → Robust conclusion
Strategic Planning: Different scenario trees → Stress-test strategies → Optimal approach
Research Validation: Approach problem from multiple angles → Verify findings → Reliable results
Creative Design: Generate diverse concept trees → Evaluate aesthetics/function → Best design

References & Further Reading

Deepen your understanding with these curated resources

Academic Papers

Tree of Thoughts: Deliberate Problem Solving with Large Language Models (Yao et al., 2023)

Graph of Thoughts: Solving Elaborate Problems with Large Language Models (Besta et al., 2023)

Self-Consistency Improves Chain of Thought Reasoning (Wang et al., 2022)

Constitutional AI: Harmlessness from AI Feedback (Bai et al., 2022)

Implementation Guides

LangGraph: Multi-Agent Reasoning Workflows

DSPy: Programming with Language Models

OpenAI Function Calling for Multi-Path Reasoning

Anthropic Constitutional AI Implementation

Tools & Libraries

LangChain Multi-Agent Systems

AutoGen: Multi-Agent Conversation Framework

CrewAI: Multi-Agent Orchestration

Guidance: Structured Generation Library

Community & Discussions

LangChain Discord - Multi-Agent Development

OpenAI Developer Forum - Advanced Reasoning

r/MachineLearning - Forest Reasoning Discussions

Anthropic Discord - Constitutional AI

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