Lár v1.2.0 Release Notes

Lár v1.2.0 Release Notes

Observability & Debugging Update

This release focuses on "Glass Box" visibility, incorporating feedback from power users who need granular cost tracking and cleaner debug logs.

New Features

1. Cost Attribution per Model

The execution summary now includes a tokens_by_model breakdown. This allows you to audit costs across different providers (e.g., separating Ollama usage from OpenAI usage).

Example Output:

"summary": {
  "total_tokens": 1500,
  "tokens_by_model": {
    "ollama/phi4": 500,
    "gemini-pro": 1000
  }
}

2. Console Noise Reduction

Large data structures (like 50kb state dumps or long prompts) are now automatically truncated in the console output to keep your terminal readable.

• Console: Shows ... [truncated, total len: 50000 chars]
• JSON Log: Preserves the full, untruncated data for auditing.

3. Granular Node Logging

Every LLMNode now explicitly logs prompt_tokens, output_tokens, and model in the step metadata, enabling precise per-step cost analysis.

Changes

• utils.py: Added truncate_for_log utility.
• executor.py: Updated GraphExecutor to aggregate token usage by model.
• node.py: Updated ToolNode, LLMNode, and AddValueNode to use truncation.

Upgrading

pip install lar-engine==1.2.0