Crowe Forest Architecture

The Crowe Forest Architecture organizes intelligent agents into domain-specific trees. Each agent is an expert node with a unique specialization, allowing the forest to dynamically assign tasks to the most relevant agent using keyword extraction and embedding-based similarity matching.

A forest is composed of multiple trees, each representing a knowledge domain (e.g., Finance, Legal, Research). Within each tree, agents are ranked by their relevance to a given task. Tasks are processed asynchronously, ensuring fast, scalable execution.

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Module Path

crowe.structs.tree_forest

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Core Components

Class: TreeAgent

Represents an individual expert agent inside a tree.

Attribute	Type	Description
system_prompt	str	Defines the agent’s domain expertise.
llm	callable	The language model used for task execution.
agent_name	str	Identifier for the agent.
system_prompt_embedding	tensor	Embedding vector for semantic matching.
relevant_keywords	list[str]	Keywords auto-extracted from the prompt.
distance	float?	Embedding similarity distance to another agent.

Key Methods

• calculate_distance(other_agent) → float Calculates cosine similarity between two agents.

• run_task(task) → Any Executes the assigned task and logs the I/O.

• is_relevant_for_task(task, threshold=0.7) → bool Determines if the agent should handle the task.

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Class: Tree

A container of agents within the same domain.

Attribute	Type	Description
tree_name	str	Domain name for the tree.
agents	list[TreeAgent]	Agent instances within the tree.

Key Methods

• calculate_agent_distances() Precomputes similarity scores between agents.

• find_relevant_agent(task) → TreeAgent? Selects the best-matched agent for the task.

• log_tree_execution(task, agent, result) Logs details of execution for auditing.

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Class: ForestCrowe

The top-level orchestrator managing multiple trees.

Attribute	Type	Description
trees	list[Tree]	All registered trees in the forest.

Key Methods

• find_relevant_tree(task) → Tree? Finds the most suitable tree for the task.

• run(task) → Any Selects the best agent from the best tree and runs the task.

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Example Code

from crowe.structs.tree_forest import TreeAgent, Tree, ForestCrowe

# Agents
finance_agents = [
    TreeAgent(system_prompt="Stock market analysis", agent_name="Stock Analyst"),
    TreeAgent(system_prompt="Financial planning & budgeting", agent_name="Planner"),
    TreeAgent(system_prompt="Retirement strategies", agent_name="Retirement Advisor")
]

tax_agents = [
    TreeAgent(system_prompt="Tax filing and compliance", agent_name="Tax Expert"),
    TreeAgent(system_prompt="Investment strategy", agent_name="Investment Strategist"),
    TreeAgent(system_prompt="ROTH IRA guidance", agent_name="ROTH Specialist")
]

# Trees
finance_tree = Tree(tree_name="Finance", agents=finance_agents)
tax_tree = Tree(tree_name="Tax & Investment", agents=tax_agents)

# Forest
crowe_forest = ForestCrowe(trees=[finance_tree, tax_tree])

# Task
task = "Our company is in Delaware, how do we file taxes for free?"
result = crowe_forest.run(task)
print(result)

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Workflow

1. Agent Creation Define each expert with a system_prompt.

2. Tree Formation Group agents into domain-specific trees.

3. Forest Execution Forest scans all trees, selects the best tree → best agent → executes task.

4. Result Delivery Output is returned, execution is logged.

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Architecture Advantages

• Domain Modularity — Add or remove entire trees without disrupting others.

• Precision Matching — Combines keyword & embedding similarity for accurate selection.

• Asynchronous Scaling — Handles many tasks in parallel.

• Audit-Friendly — Detailed execution logs per task.

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Mermaid Diagram

graph TD
    A[ForestCrowe] --> B[Finance Tree]
    A --> C[Tax & Investment Tree]
    B --> B1[Stock Analyst]
    B --> B2[Planner]
    B --> B3[Retirement Advisor]
    C --> C1[Tax Expert]
    C --> C2[Investment Strategist]
    C --> C3[ROTH Specialist]

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Summary

The Crowe Forest Architecture transforms agent orchestration into a search + match + execute pipeline. By structuring agents into trees and forests, the system achieves:

• Scalability for growing knowledge bases

• Relevance-driven routing for higher task accuracy

• Transparent logging for trust and compliance