Metadata-Version: 2.1
Name: langgraph_log_parser
Version: 0.1.7
Summary: Parser for logs from LangGraph
Author: Tomasz Serafiński
Author-email: tomasz@serafinski.net
Requires-Python: >=3.11,<4.0
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Requires-Dist: langchain (>=0.3.7,<0.4.0)
Requires-Dist: langchain-community (>=0.3.5,<0.4.0)
Requires-Dist: langchain-core (>=0.3.15,<0.4.0)
Requires-Dist: langchain-experimental (>=0.3.3,<0.4.0)
Requires-Dist: langchain-openai (>=0.2.6,<0.3.0)
Requires-Dist: langgraph (>=0.2.45,<0.3.0)
Requires-Dist: langgraph-checkpoint-sqlite (>=2.0.1,<3.0.0)
Requires-Dist: langsmith (>=0.1.142,<0.2.0)
Requires-Dist: matplotlib (>=3.9.2,<4.0.0)
Requires-Dist: msgpack (>=1.1.0,<2.0.0)
Requires-Dist: numpy (<2.0.0)
Requires-Dist: pandas (>=2.2.3,<3.0.0)
Requires-Dist: pm4py (>=2.7.12.1,<3.0.0.0)
Requires-Dist: prefixspan (>=0.5.2,<0.6.0)
Requires-Dist: pydantic (>=2.9.2,<3.0.0)
Requires-Dist: python-dotenv (>=1.0.1,<2.0.0)
Requires-Dist: pyvis (>=0.3.2,<0.4.0)
Requires-Dist: tavily-python (>=0.5.0,<0.6.0)
Description-Content-Type: text/markdown

# LangGraph Log Parser
[![wakatime](https://wakatime.com/badge/user/c88d1b82-ebdd-4842-ad45-93f471842103/project/b6d31f0d-340f-42d5-aa2d-bf3e2e6a0370.svg)](https://wakatime.com/badge/user/c88d1b82-ebdd-4842-ad45-93f471842103/project/b6d31f0d-340f-42d5-aa2d-bf3e2e6a0370)

### Download package
```dotenv
pip install langgraph_log_parser
```

### Documentation
Documentation is available at: https://serafinski.github.io/LangGraph-Log-Parser/

# Purpose
This Python package facilitates the parsing of run logs generated by [LangGraph](https://langchain-ai.github.io/langgraph/). During execution, logs are stored in an SQLite database in an encoded format _(using msgpack)_. These logs are then decoded and exported to a `json` format. Subsequently, the `json` files are transformed into `csv` files for further analysis.

Once in `csv` format, the data can be analyzed using methods from the [py4pm](https://processintelligence.solutions/static/api/2.7.11/index.html) library. These methods calculate specific statistics related to the multi-agent infrastructure's performance and enable visualizations of the process behavior and execution flow.

This pipeline provides a streamlined approach for extracting, transforming, and analyzing logs, offering valuable insights into multi-agent systems.
