Traces
Audit trails and chain-of-thought traceability for agent operations
Traces provide end-to-end observability into your agents' data operations. Meko collects traces for the complete chain of thought, giving you the explainability that production AI applications require.
What's traced
Meko captures:
- Data operations. Which memory, knowledge, and database operations each agent performed.
- Latencies. Timing at each stage of the pipeline (embedding, search, retrieval, etc.).
- Token costs. Per-interaction token usage for LLM calls.
- LLM reasoning. The chain of thought and reasoning traces from agent interactions.
- Execution logs. Detailed logs of MCP tool invocations and their results.
Chain-of-thought traceability
One of Meko's four pillars is Full Chain-of-Thought Traceability. This means you can:
- Debug performance issues. See exactly where time is spent in the data pipeline.
- Optimize costs. Identify which operations consume the most tokens.
- Explain agent behavior. Trace back from an agent's output to the data operations and reasoning that produced it.
- Audit compliance. Maintain a complete record of what data agents accessed and how they used it.
Integration with LangFuse
Meko integrates with LangFuse for trace collection and visualization.