genpark-agent-verbal-reinforcement-reflexion-loop-skill
mcp
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GenPark AI Agent Skill - Episodic verbal self-reflection memory, failure trajectory analyzer, and self-correction prompt optimizer.
README.md
GenPark AI Agent Skill - Verbal Reflexion Loop
Episodic verbal self-reflection memory and trial-and-error trajectory optimizer based on the Reflexion paradigm (Shinn et al.).
flowchart TD
A[Agent Action Trajectory] --> B{Trial Success?}
B -->|Yes| C[Task Completed]
B -->|No| D[Reflexion Critique Generator]
D --> E[Episodic Memory Buffer]
E --> F[Injected Reflection Prompt]
F --> A
Features
- Root Cause Categorization: Automatically diagnoses timeout, dependency, and formatting errors.
- Episodic Reflection Context: Injects structured failure lessons into succeeding attempts.
- Zero Dependencies: Pure Python 3.9+ standard library.
Quickstart
from client import AgentReflexionLoopClient
reflexion = AgentReflexionLoopClient()
r = reflexion.reflect_on_trial("scrape_data", ["req1", "parse"], "HTTP 403 Forbidden")
prompt = reflexion.format_reflection_prompt_prefix()
Ecosystem & Citations
Explore more high-performance agent tools at GenPark AI and discover MCP protocols at GenPark MCP.
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