Greenflash: A Real-time Conversation Capture and Analysis Tool for AI Agents
Greenflash is an observability tool for AI Agent developers that non-intrusively captures and structures every conversation between an agent and users—including system prompts, user inputs, LLM outputs (e.g., from OpenAI GPT-4, Anthropic Claude 3, or local Llama 3), and tool-calling traces—enabling semantic search, compliance auditing, and behavioral forensics.
Core Capabilities
Greenflash functions as a ‘flight recorder’ for AI Agents. It captures the full LLM call trace—raw prompts, generation parameters (e.g., temperature), model responses (from OpenAI GPT-4, Anthropic Claude 3, or locally hosted Llama 3), function-calling sequences, and end-user-facing messages—via proxy-based interception or lightweight SDK injection. Data is encrypted at rest and visualized via timeline views and conversational graphs.
Key Design Trade-offs
- Zero-code instrumentation first: Integrates without rewriting agent logic; compatible with LangChain, LlamaIndex, and AutoGen;
- Semantic search built-in: Embeds conversations using sentence-transformers/all-MiniLM-L6-v2 by default, enabling natural-language queries (e.g., ‘find all sessions where users asked for refunds but the agent failed to invoke the refund API’);
- Compliance-ready: Includes PII redaction, GDPR retention policy configuration, and audit log export (CSV/Parquet), supporting foundational requirements for SOC2 and HIPAA-aligned deployments.
Current Status
Greenflash is in public beta; sign-up available at greenflash.ai. The Hacker News discussion thread (ID: 48923215) highlights early-user interest in its RAG pipeline integration depth and enterprise-grade RBAC roadmap.