semvec/pypi-packages huggingface dataset:
plomp 0.1.4
Plomp Python Module
License: MIT
Requires Python: >=3.10

# Plomp

A small python module for managing and debugging agent contexts.

Just `plomp` it in and *prompt* away and get progress tracking and visibility into what your agents.


# Overview

**Plomp** is a tiny library designed to easily integrate into python programs which invoke an LLM over multiple 
steps and provide some useful benefits such as visualizing progress and making context retrival easier.

Plomp has three main goals:
1. Provide simple observability into execution multistep LLM prompts ("agents" if you must)
2. Track and manage context retrieval
3. Integrate easily into existing code in an unopinionated way


# Example Usage

For a detailed example look I have a sample project which simulates the game survivor which uses `plomp`, the rendered progress can be seen here **[https://michaelgiba.github.io/survivor/static/plomp.html](https://michaelgiba.github.io/survivor/static/plomp.html)** with the repo here [https://github.com/michaelgiba/survivor](https://github.com/michaelgiba/survivor)


A simpler example below:

```python
import plomp

@plomp.wrap_prompt_fn()
def prompt_llm(prompt: str) -> str:
    return "<EXAMPLE LLM RESPONSE>"

llm_response = prompt_llm("What's the weather today?")

for i in range(4):
    plomp.record_event(
        {
            "plomp_display_event_type": "weather_data_accessed",
            "plomp_display_text": f"accessed weather data from API: {i + 1}/10",
            "value": random.random(),
        },
        tags={"tool": "weather_api"},
    )

past_weather_events = plomp.buffer().filter(tags_filter={"tool": ["weather_api"]}).last(3)
past_weather_events.record(tags={"type": "recent_weather_queries"})

llm_response = prompt_llm(
    f"How has the temperature changed over the last three samples?: "
    + str(past_weather_events.to_dict())
)

plomp.write_html(plomp.buffer(), "progress.html")
```

<p align="center">
    <img src="img/example-recording.gif" alt="Plomp UI Example">
</p>

This would produce a self-contained HTML page which looks like

# Structure

Plomp revolves around a centralized buffer which stores three different types of sequential records:

1. "Events": Discrete events which happened that could useful future context for decision making
2. "Queries": An expression which matches previous events based on user provided logic
3. "Prompts": A record of a prompt to an LLM and the response recieved



# Installation

Plomp only requires Python3.10+ and its only dependency is `typeguard`

```bash
pip install plomp
```

# Developing
To experiment locally with the UI you can run `cd frontend && npm run dev`. 

Note the project is pretty new so if you see a bug please feel free to file an issue or make a PR.

# Contributions

All contributions are welcome. There are a number of things that need to be added:
1. Concurrency support
2. More frontend features
3. Better API documentation 
4. Optional live progress reloading instead of only static HTML files for realtime playback
5. Or anything else valuable