What problem does it solve?
This Skill addresses the complexity of inter-service communication in agentic AI pipelines and home networks. It eliminates manual event handling, ensures data integrity, and provides comprehensive traceability, preventing lost context and simplifying debugging in distributed systems.
Core Features & Use Cases
- Type-safe Eventing: Leverage Pydantic models for strict type validation, ensuring reliable data exchange between services and AI agents.
- Robust Messaging: Built on RabbitMQ for durable, topic-based routing, guaranteeing events are delivered even through system restarts.
- Automatic Correlation Tracking: Redis-backed correlation IDs automatically link related events, providing a complete audit trail of causation chains for every AI action.
- Deterministic Event IDs: Generate idempotent UUIDs for events, enabling consumers to easily deduplicate messages and prevent reprocessing.
- Standardized Error Events: Implement consistent error reporting with
.failed and .error event patterns, facilitating automated retry logic and alerting.
- Use Case: Imagine an AI agent that processes meeting transcripts. This Skill allows the agent to publish a
fireflies.transcript.upload event, automatically track its journey through transcription and RAG ingestion, and handle any fireflies.transcript.failed events, ensuring data integrity and a complete audit trail without manual intervention.
Quick Start
1. Start the HTTP server (if not already running)
uvicorn event_producers.http:app --reload --port 8682
2. Publish an LLM prompt event via HTTP
curl -X POST http://localhost:8682/events/llm/prompt
-H "Content-Type: application/json"
-d '{
"provider":"anthropic",
"model":"claude-sonnet-4",
"prompt":"Summarize this document."
}'
