Agent Reflection Engine

Enables AI agents to self-audit decision steps, identify reasoning bottlenecks, and generate improvement patches via chain-of-thought critique.

A lightweight, pluggable reflection engine that enables AI agents to self-audit their decision traces, identify reasoning bottlenecks, and generate improvement patches using chain-of-thought critique—ideal for developers tuning autonomous agents.

Usage

bash

# Run reflection on an agent trace
python agent_reflection_engine.py traces/demo_trace.json -o reports/reflection.json --verbose

# Example trace format (demo_trace.json):
# [
#   {
#     "step_id": 1,
#     "thoughts": "I should search for the nearest coffee shop.",
#     "action": "search_web",
#     "value": "coffee shop near me",
#     "observation": "Found 'Brew Haven' 0.3 miles away."
#   }
# ]

Integrate into agent loops by logging each step and running periodic reflection to generate improvement heuristics.

Price

$4.99