Metadata-Version: 2.1
Name: llm-recorder
Version: 0.1.0
Summary: Record and replay LLM interactions
Author-Email: Your Name <your.email@example.com>
License: MIT
Project-URL: Homepage, https://github.com/zby/llm_recorder
Requires-Python: >=3.9
Requires-Dist: litellm>=1.30.7
Provides-Extra: openai
Requires-Dist: openai>=1.0.0; extra == "openai"
Provides-Extra: anthropic
Requires-Dist: anthropic; extra == "anthropic"
Provides-Extra: google
Requires-Dist: google-generativeai; extra == "google"
Description-Content-Type: text/markdown

# llm_recorder

`llm_recorder` is a basic observability and debugging tool for language model apps.
It stores interactions with LLMs in a local directory and provides a way to replay them.
It is particularly useful for debugging chained LLM calls.

With `llm_recorder`, you can:

- Save every request/response pair to a directory.
- Replay part of previously recorded interactions instead of making live LLM calls.

You can use it to:
- Inspect LLM responses and requests that lead to them
- Record an execution path that leads to a specific response and then replay it and see how the application behaves downstream
- Modify the recorded responses before replaying them

## Installation

```bash
pip install llm_recorder
```

## Getting Started

For a quick start use `enable_replay_mode`; it monkey patches `litellm` to record and replay responses.

`llm_recorder` records any calls to `litellm.completion` and their responses and stores them in the directory specified by `store_path`.

Specify `replay_count` to replay previously recorded interactions (by default no interactions are replayed).
Once the replayed interactions are exhausted, `llm_recorder` falls back to live LLM calls.

The `store_path` is cleaned up at the start of each run, but interactions from the previous run are read before that cleanup.


### Basic Example

```python
from llm_recorder import enable_replay_mode

import litellm


enable_replay_mode(store_path="saves")

print("After:", litellm.completion.__name__)

# Now just use litellm as usual:

response = litellm.completion(
    model="openai/gpt-4o",
    messages=[{ "content": "Hello, how are you?","role": "user"}]
)
```
Then have a look at the 'saves' directory to see the recorded interactions.

### Chained Calls Example
See [examples/chained_calls.py](examples/chained_calls.py) for an example of how to use `llm_recorder` to record and replay chained LLM calls.
I saved the first interaction in [examples/saves/chained_calls/](examples/saves/chained_calls/) and the example
replays it before making live calls.

## Advanced Usage

For more fine-grained control instantiate and use the LiteLLMRecorder class directly.
See [examples/direct_recorder.py](examples/direct_recorder.py).

## Limitations

- Direct support for SDKs (not through litellm) and the recording HTTP client is experimental.
- Only supports synchronous calls.

## Contributing

Contributions are welcome! Please open issues or pull requests on GitHub.

## License

This project is licensed under the MIT License.


