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LLM Observability & Tracing System

backendAdvanced365d access
199onwards

Log every LLM call with latency, token usage, and model output. Build a query layer surfacing slow calls, expensive prompts, error rates, and cost trends over time.

  • Instrument every LLM call with latency, token usage, and cost tracking
  • Group related LLM calls into traces for end-to-end session visibility
  • Write analytical SQL queries for performance monitoring (slow calls, cost-by-model)
  • Build a live observability dashboard using Chart.js

Multi-Agent Orchestration Backend

backendAdvanced365d access
199onwards

Coordinate multiple specialized AI agents — planner, researcher, writer — passing context and managing state between them. Return a streamed unified result to the client.

  • Design a multi-agent architecture with clearly defined agent roles
  • Implement a stateful agent graph using LangGraph
  • Stream intermediate agent progress to clients using WebSockets
  • Implement per-agent timeouts and graceful fallback strategies
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