Audit Log & Transcripts

Every conversation, every tool call, every token in and out of every LLM — recorded, timestamped, and queryable. LIT doesn't summarize what happened; it keeps the full record.

Full LLM Transcripts

Every message sent to an LLM and every response received is stored verbatim. Not a summary, not a digest — the raw transcript, with:

• Timestamps on every message (millisecond precision)
• Model identity — which model was used for each response
• Token counts — prompt tokens, completion tokens, total
• Measured response time — wall-clock latency for every LLM call
• Tool call records — every tool invoked, with inputs and outputs

This creates a complete audit trail of every AI decision in your system.

Why This Matters

Most AI tools are black boxes. You see inputs and outputs, but not the reasoning, the tool calls, or the intermediate steps. LIT is the opposite.

When a model makes a surprising recommendation, you can trace exactly what context it had, what tools it called, what it saw, and what it decided. Debugging AI behavior is the same as debugging software — you look at the logs.

For regulated industries (finance, healthcare, legal), full transcripts provide the audit trail required for AI-assisted decisions.

Querying Transcripts

Channel message history is queryable via the Python SDK:

from lit import channels

# Get a channel
ch = channels.get("volatility-model")

# Iterate all messages
for msg in ch.messages():
    print(msg.timestamp, msg.direction, msg.from_id, msg.content)

# Filter by date range
for msg in ch.messages(start="2025-12-01", end="2025-12-31"):
    print(msg.timestamp, msg.content)

# Export a full transcript
ch.messages().export("transcript-dec-2025.json")

Safe Mode Audit

When agents run in safe mode, every confirmation request and human decision is logged alongside the agent action. You have a complete record of what the agent wanted to do and what the human approved.