Tree Traversal Agent Architecture: Structured Exploration with Local LLMs

A Playground for Structured Thought

At its core, the script leverages a simple concept: thoughts as nodes in a traversable tree. Each thought maintains its lineage while branching into new directions, ensuring it maintains both the original context and an awareness for which thoughts are adjacent.

    initial_seed = generate_thoughts(
        context=PROBLEM_STATEMENT_CONTEXT,
        constraints=THINKING_CONSTRAINTS
    )
    
    thought_tree = explore_thought_tree(
        context=PROBLEM_STATEMENT_CONTEXT,
        seed=initial_seed,
        constraints=THINKING_CONSTRAINTS
    )
    
    branch_insights = process_thought_tree(thought_tree, PROCESSING_RULES)

This implementation offers three key advantages over a simple breadth first approach:

• Local Model Synergy: Leverages Deepseek R1 32B for reasoning and Qwen 2.5 32B for instruction following, trying to leverage the best of both worlds (divergent and structured thinking)
• Built-in Semantic Filtering: Automatically prunes redundant branches using similarity checks
• Configurable Depth/Similarity: Adjust exploration parameters to match your specific needs

Core Architecture: Tree Traversal for Focused Exploration

The exploration process follows a structured flow:

1. Initial Seed Generation: Create root thoughts based on the problem context
2. Branch Identification: Analyze each thought for potential exploration paths
3. Depth-controlled Exploration: Recursively explore branches while respecting depth limits
4. Semantic Similarity Pruning: Filter out redundant or too-similar thoughts

# Main execution flow
initial_seed = generate_thoughts(context, constraints)
thought_tree = explore_thought_tree(context, initial_seed, constraints)
process_thought_tree(thought_tree)

Configurability: Adapt to Your Workflow

The script's behavior can be fine-tuned through configuration:

MODEL_CONFIG = MODEL_CONFIG = {
    "reasoning_model": "deepseek-r1:32b",  
    "instruction_model": "qwen2.5:32b",   
    "ollama_endpoint": "http://localhost:11434",
}

GENERATION_CONFIG = {
    "temperature_reasoning": 0.8,    
    "temperature_instruction": 0.7,  
    "max_branch_depth": 5,          
    "similarity_threshold": 0.6      
}

These parameters let you control:

• Temperature settings to balance creativity and focus
• Branch depth for controlling exploration intensity
• Similarity thresholds for managing thought novelty

Experiment Yourself

To try the script, you'll need ollama and some Python dependencies:

ollama run deepseek-r1:32b
ollama run qwen2.5:32b
pip install -r requirements.txt

Here's a sample usage exploring game design concepts:

PROBLEM_STATEMENT_CONTEXT = """
I need a title for my new movie set in a cyberpunk dystopia
"""

THINKING_CONSTRAINTS = """## THINKING CONSTRAINTS
- Identify 2 complementary approaches
- Find 1 paradoxical element
- Suggest 3 concrete adjustments"""

Practical Applications

This structured approach to thought exploration proves particularly valuable for:

• Early-stage Problem Exploration: Map out solution spaces while maintaining clear paths back to core problems
• Overcoming Creative Blocks: Generate structured alternatives when stuck on a particular approach
• Systematic Knowledge Synthesis: Build connected understanding of complex topics
• Team Brainstorming Documentation: Create traceable thought evolution for collaborative projects

What Was Learned Since Last Time and What's Next

In my previous exploration of breadth-first agent architectures, I learned about their use for finding unknown unknowns. However, the outputs were still hard to bring together into a cohesive solution and it wasn't obvious which adjacenices should be connected. This tree traversal hopes to address these issues, but I need to do more testing.

At a minimum, this was a good exercise in vetting how capable Qwen 2.5 32B is at giving more consistency to the thoughts and outputs from Deepseek R1 32B. That's a huge win since I while the divergent outputs from Deepseek have been interesting to read they're not all that useful in an agent context without more structure.