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RLM provides built-in logging capabilities to save execution trajectories, enabling you to analyze how the LM decomposes tasks, executes code, and makes recursive calls.

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Setting Up the Logger

To log RLM execution trajectories, initialize an RLMLogger and pass it to the RLM constructor: 
from rlm import RLM
from rlm.logger import RLMLogger

logger = RLMLogger(log_dir="./logs")

rlm = RLM(
    backend="openai",
    backend_kwargs={"model_name": "gpt-5-mini"},
    logger=logger,
    verbose=False,
)

result = rlm.completion("Your prompt here")

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Accessing Logged Trajectories

Trajectories are saved as JSON-lines files in the specified log directory. Each line contains a complete snapshot of one RLM iteration, including:
  • LM prompts and responses
  • Generated code
  • Code execution results
  • Sub-LM calls
  • Metadata
import json

with open("./logs/trajectory_20250101_123456.jsonl", "r") as f:
    for line in f:
        iteration = json.loads(line)
        print(f"Iteration {iteration['iteration']}")
        print(f"Prompt: {iteration['prompt'][:100]}...")
        print(f"Code: {iteration.get('code', 'N/A')}")
        print(f"Result: {iteration.get('result', {}).get('stdout', 'N/A')}")
        print("---")

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Visualization Example

The logged trajectories can be visualized to understand the RLM’s decision-making process. The logger captures all this information, enabling detailed analysis of the RLM’s reasoning process.

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Log File Structure

Each log file contains one JSON object per line (JSON-lines format). The structure includes: 
{
  "iteration": 0,
  "timestamp": "2025-01-01T12:34:56.789Z",
  "prompt": "...",
  "response": "...",
  "code": "...",
  "result": {
    "stdout": "...",
    "stderr": "",
    "return_value": null
  },
  "sub_calls": [
    {
      "prompt": "...",
      "response": "...",
      "model": "gpt-5-mini"
    }
  ],
  "usage": {
    "input_tokens": 150,
    "output_tokens": 75
  },
  "execution_time": 1.23
}