Every Lab turn is fully traced. When you ask a question, cf0 captures the system prompt, the tools called, the model inputs and outputs, the tool execution results, and what was rendered. Traces surface inside the product via the in-app Trace panel and export with the audit trail.Documentation Index
Fetch the complete documentation index at: https://docs.cf0.ai/llms.txt
Use this file to discover all available pages before exploring further.
What’s traced, per turn
- System prompt — the framing cf0 gives the model
- Tool definitions — which tools were available
- Tool calls — which tools the model invoked, with arguments
- Tool results — what each call returned, with byte size for context-bloat detection
- Model inputs and outputs — every token in, every token out
- Render output — what was streamed to the chat UI
Per-agent metrics
cf0 tracks per-agent token usage, tool latency, and rate-limit events. Optimisation decisions are data-driven, not anecdotal.What a trace looks like
A single Lab turn captures the tools the model could see, the calls it made, and the bytes each call returned. Conceptually, for a DCF turn:| Captured | Example |
|---|---|
| Thread | nvda-q3-readthrough · turn 4 |
| Tools called | Filings (10-K, Item 7 MD&A) · Consensus (FY26–FY28 revenue) · DCF template (3-stage growth) |
| Output rendered | Bear / Base / Bull fair-value cards · Sensitivity tornado |
| Captured per call | Latency, byte size, input arguments, result summary |
| Audit chain | Every figure on screen walks back to one specific tool call |
What you can do with it
- As an analyst — open the Trace panel from any thread to see exactly what the model read before it answered.
- As an org admin — review per-member token usage and tool patterns on the Dashboard.
- In an audit — pair traces with the audit trail export for a turn-by-turn record of how an output was produced.