Conversation Tracking

Waxell automatically tracks conversation flow in interactive agents — chat bots, REPLs, copilots, and any agent that exchanges messages with users through an LLM.

What's Auto-Captured

When you use any auto-instrumented provider, waxell extracts conversation data from every LLM call:

Data	Source	How
User messages	messages array (role=user)	Last user message, deduplicated
Agent responses	LLM response content	Only final responses (finish_reason=stop), not tool calls
Message count	messages array length	Total messages in context window
Turn count	role=user count in messages	Number of user turns
Context utilization	prompt_tokens / model limit	Percentage of context window used
System prompt hash	role=system or system param	Detects system prompt changes

Supported Providers

Auto-capture works with all providers: OpenAI, Anthropic, Groq, Mistral, Gemini, Cohere, Bedrock, Ollama, Together, Azure AI, AI21, and more.

Context Window Monitoring

Each LLM call span includes context state attributes:

• waxell.context.message_count — messages in the context window
• waxell.context.user_turns — user turn count
• waxell.context.tokens_used — tokens consumed in context
• waxell.context.utilization_pct — context utilization percentage

Access these programmatically via WaxellContext properties:

async with WaxellContext(agent_name="my-agent") as ctx:
    # After LLM calls, these are auto-populated:
    print(ctx.conversation_turns)    # e.g. 5
    print(ctx.context_utilization)   # e.g. 42.3
    print(ctx.message_count)         # e.g. 23

Manual Recording

For custom LLM clients or non-instrumented providers:

import waxell

# Record user input
waxell.user_message("Clean up inactive users")

# Record agent output
waxell.agent_response("I've cleaned up 14,872 inactive users.")

Or on the context directly:

ctx.record_user_message("What's the weather?")
ctx.record_agent_response("It's sunny in Paris today.")

Viewing Conversation Data

Context Tab

The execution detail page includes a Context tab with:

• Conversation metrics — user turns, message count, context utilization
• Conversation timeline — visual thread of user messages, LLM calls, tool calls, and agent responses
• Context window gauge — utilization growth across LLM calls

Raw Data

Conversation state is stored in the run's context.conversation field:

{
  "user_turns": 5,
  "message_count": 23,
  "assistant_turns": 5,
  "tool_results": 8,
  "tokens_in_context": 4200,
  "context_utilization_pct": 3.3,
  "system_prompt_hash": "a1b2c3d4e5f6"
}

Governance

Use the context management policy to set limits on conversation length, context window usage, and session duration.