---

name: control-loop-extraction description: Extract and analyze agent reasoning loops, step functions, and termination conditions. Use when needing to (1) understand how an agent framework implements reasoning (ReAct, Plan-and-Solve, Reflection, etc.), (2) locate the core decision-making logic, (3) analyze loop mechanics and termination conditions, (4) document the step-by-step execution flow of an agent, or (5) compare reasoning patterns across frameworks.

Control Loop Extraction

Extracts and documents the core agent reasoning loop from framework source code.

Process

1. Locate the loop - Find the main agent execution loop
2. Classify the pattern - Identify ReAct, Plan-and-Solve, Reflection, or Tree-of-Thoughts
3. Extract the step function - Document the LLM → Parse → Decide flow
4. Map termination - Catalog all loop exit conditions

Reasoning Pattern Identification

Pattern Signatures

ReAct (Reason + Act)

Copy# Signature: Thought → Action → Observation cycle
while not done:
    thought = llm.generate(prompt)      # Reasoning
    action = parse_action(thought)       # Action selection
    observation = execute(action)        # Environment feedback
    prompt = update_prompt(observation)  # Loop continuation

Plan-and-Solve

Copy# Signature: Upfront planning, then execution
plan = llm.generate("Create a plan for...")
for step in plan.steps:
    result = execute_step(step)
    if needs_replan(result):
        plan = replan(...)

Reflection

Copy# Signature: Act → Self-critique → Adjust
while not done:
    action = llm.generate(prompt)
    result = execute(action)
    critique = llm.generate(f"Evaluate: {result}")
    if critique.needs_adjustment:
        prompt = adjust_approach(critique)

Tree-of-Thoughts

Copy# Signature: Branch → Evaluate → Select
thoughts = [generate_thought() for _ in range(n)]
scores = [evaluate(t) for t in thoughts]
best = select_best(thoughts, scores)

Step Function Analysis

The "step function" is the atomic unit of agent execution. Extract:

1. Input Assembly - How context is constructed for the LLM
2. LLM Invocation - The actual model call
3. Output Parsing - How raw output becomes structured actions
4. Action Dispatch - Tool execution vs. final response routing

Key Code Patterns

Copy# Common step function structure
def step(self, state):
    # 1. Assemble input
    messages = self._build_messages(state)
    
    # 2. Call LLM
    response = self.llm.invoke(messages)
    
    # 3. Parse output
    parsed = self._parse_response(response)
    
    # 4. Dispatch
    if parsed.is_tool_call:
        return self._execute_tool(parsed.tool, parsed.args)
    else:
        return AgentFinish(parsed.final_answer)

Termination Condition Catalog

Common Termination Patterns

Anti-Pattern: No Termination Guard

Copy# DANGEROUS: No exit condition
while True:
    result = agent.step()
    if result.is_done:  # What if LLM never outputs done?
        break

Fix: Always include a step counter:

Copyfor step in range(max_steps):
    result = agent.step()
    if result.is_done:
        break
else:
    logger.warning("Hit max steps limit")

Output Template

Copy## Control Loop Analysis: [Framework Name]

### Reasoning Topology
- **Pattern**: [ReAct | Plan-and-Solve | Reflection | Tree-of-Thoughts | Hybrid]
- **Location**: `path/to/agent.py:L45-L120`

### Step Function
- **Input Assembly**: [Description of context building]
- **LLM Call**: [Method and parameters]
- **Parser**: [How output is structured]
- **Dispatch Logic**: [Tool vs Finish decision]

### Termination Conditions
1. [Condition 1 with code reference]
2. [Condition 2 with code reference]
3. ...

### Loop Detection
- **Method**: [Heuristic | State hash | None]
- **Implementation**: [Code reference or N/A]

Integration Points

• Prerequisite: codebase-mapping to identify agent files
• Feeds into: comparative-matrix for pattern comparison
• Feeds into: architecture-synthesis for new loop design