Open Source Libraries
Discover 309 open source Library repositories from GitHub, each with an analysis of what it does, key features, use cases and alternatives. Library projects here are most often combined with Python, Developer Tools and TypeScript. Last updated October 4, 2026.
309 repositories · updated October 4, 2026
RL4LMs: Fine-Tune Language Models with Reinforcement Learning
RL4LMs is a Python library for training language models against custom reward functions using on-policy reinforcement learning. It suits NLP researchers and developers who need configurable training components for text-generation tasks.
torchtune: Fine-Tune and Post-Train Large Language Models
torchtune is a PyTorch library for configuring and running LLM post-training workflows, from supervised fine-tuning to preference optimization. It is aimed at developers who want editable recipes and model-specific configs, but is no longer actively maintained.
simpleaichat: Build Python ChatGPT Apps with Minimal Code
simpleaichat is a Python library for building chat applications and workflows with OpenAI chat models. It offers conversation management, streaming, asynchronous calls, structured data, and custom tools through a compact interface.
rerankers: Use Diverse Reranking Models Through One Python API
rerankers provides a shared Python interface for reranking documents with cross-encoders, LLM-based methods, and hosted APIs. It suits developers building retrieval systems who want to compare or switch rerankers without adapting their application to each model's interface.
llm-compressor: Compress Models for vLLM Inference
LLM Compressor applies post-training quantization and related model transformations to prepare Hugging Face models for vLLM deployment. It suits teams seeking smaller or more inference-efficient checkpoints, with support for multiple formats and large-model workflows.
TensorRT-LLM: Optimize LLM Inference on NVIDIA GPUs
TensorRT-LLM is a Python framework and runtime for efficient LLM and visual-generation inference on NVIDIA GPUs. It suits teams deploying models on NVIDIA hardware that need optimized kernels and configurable single- or distributed-GPU execution.
docling: Convert Documents into Structured Content
Docling converts PDFs and many other document formats into structured representations and exports such as Markdown and JSON. It is suited to developers building document ingestion workflows for search, analytics, and generative AI, including local processing of sensitive files.
DataDreamer: Generate Synthetic Data and Train LLMs
DataDreamer is a Python library for building LLM workflows, generating synthetic datasets, and training or aligning models. It suits researchers and developers who want reproducible, resumable workflows across open-source and API-based models.
EasyInstruct: Generate, Select, and Prompt LLM Instructions
EasyInstruct is a Python framework for preparing instruction data and prompts for large language model research. It combines instruction generation and dataset selection tools with prompt and local-model execution modules.
promptbench: Evaluate LLMs and Test Prompt Robustness
PromptBench is a Python library for evaluating language and multimodal models across datasets, prompting methods, and adversarial attacks. It suits researchers and developers comparing model behavior or studying robustness and dynamic evaluation.
agentevals: Evaluate AI Agent Execution Trajectories
AgentEvals provides Python and TypeScript evaluators for checking the steps AI agents take, including tool calls and graph paths. Use it to compare runs with references or have an LLM judge trajectory quality.
observers: Track and Store AI API Interactions
Observers wraps generative AI clients to capture interactions and sync them to storage backends. It suits Python teams that need lightweight observability across supported LLM providers, with storage options ranging from DuckDB to OpenTelemetry-compatible services.