Open Source LLM Projects
Discover 273 open source LLM repositories from GitHub, each with an analysis of what it does, key features, use cases and alternatives. LLM projects here are most often combined with Python, AI Agents and AI. Last updated October 4, 2026.
273 repositories · updated October 4, 2026
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.
langtest: Test Language Models for Safety and Quality
LangTest is a Python library for generating and running tests that assess language-model quality, including robustness, bias, fairness, and accuracy. It helps NLP and AI teams identify issues and, for select models, augment training data based on results.
evalplus: Rigorously Evaluate LLM-Generated Code
EvalPlus evaluates code generated by language models with expanded correctness tests for HumanEval and MBPP, plus efficiency checks through EvalPerf. It is for researchers and developers comparing models or validating generated code more rigorously.
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.
evidently: Evaluate and Monitor ML and LLM Systems
Evidently is a Python framework for evaluating, testing, and monitoring machine-learning and LLM systems, from data quality to generated text. Use it to build offline reports and regression checks or track metrics over time in a monitoring dashboard.
phoenix: Observe and Evaluate AI Applications
Arize Phoenix is a self-hosted platform for tracing, evaluating, and troubleshooting LLM applications. It helps AI engineers inspect runtime behavior and test changes to prompts, models, and retrieval.
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.
xgrammar: Constrain Language Model Output to Structured Formats
XGrammar is a library for constrained decoding that helps language models produce outputs matching JSON, regular expressions, or context-free grammars. It suits teams integrating structured output into LLM inference and applications that need reliable machine-readable responses.
freellmapi: Route LLM Requests Through One API
FreeLLMAPI is a self-hosted TypeScript router that combines free LLM provider accounts and custom OpenAI-compatible endpoints behind one API. It selects models, tracks quotas, and retries across providers, mainly for personal experimentation and development.
jsonformer: Generate Schema-Conforming JSON with Language Models
Jsonformer guides Hugging Face language models to produce JSON that matches a supplied schema by generating variable content while inserting predictable structure itself. It suits developers who need structured model output and can work within its supported JSON Schema subset.
JailbreakEval: Compare LLM Jailbreak Evaluators
JailbreakEval brings together automated methods for assessing whether language-model responses comply with jailbreak attempts. Researchers can compare evaluators across datasets, while developers can build and benchmark new evaluation methods.
EasyJailbreak: Build and Evaluate LLM Jailbreak Attacks
EasyJailbreak is a Python framework for assembling and testing jailbreak methods against language models. It suits researchers and developers who need reusable attack components and a structured way to evaluate model responses.