This repository provides a framework for benchmarking Large Language Model (LLM) tasks, particularly those involving LangChain. It targets developers and researchers aiming to evaluate and compare the performance of LLM applications across various use cases, offering transparency in dataset collection and evaluation methodologies.

How It Works

The benchmarks are structured around end-to-end use cases and heavily leverage LangSmith for data storage, evaluation, and debugging. This approach allows for reproducible benchmarking by detailing dataset collection and evaluation methods, encouraging community contributions and comparisons.

Quick Start & Requirements

• Primary install / run command: pip install -U langchain-benchmarks
• Prerequisites: LangSmith account and API key (export LANGCHAIN_API_KEY=ls-...).
• Links: LangSmith Docs, LangChain Python Docs, LangChain JS Docs

Highlighted Details

• Benchmarks include Agent Tool Use, Query Analysis, RAG on Tables, and Q&A over CSV data.
• Supports detailed tracing of agent interactions on LangSmith, including multi-tool usage scenarios.
• Offers archived benchmarks for tasks like CSV Question Answering and Extraction.

Maintenance & Community

The project is part of the LangChain ecosystem, benefiting from its community and development efforts. Further information on related tools and cookbooks can be found in the LangSmith documentation.

Licensing & Compatibility

The repository's licensing is not explicitly stated in the provided README. Users should verify compatibility for commercial or closed-source use.

Limitations & Caveats

The README mentions that some directories are legacy and may be moved, suggesting potential for ongoing structural changes. Archived benchmarks require cloning the repository to run.