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)

# 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

# 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

# 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

# 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

# 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

if step_count >= max_steps

if total_tokens >= max_tokens

if action.type == "finish"

if elapsed > timeout

if state in seen_states

if error_count >= max_errors

Anti-Pattern: No Termination Guard

# 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:

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

Output Template

## 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