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LMQL

Appears in 4 awesome lists

LMQL is a Python-based programming language for large language models, allowing seamless integration of LLMs into code with advanced features like conditional logic, constraints, and multi-model support github | website

Open github.cometh-sri/lmql

Found in these lists

awesome-ChatGPT-repositories

Section: NLP · A language for constraint-guided and efficient LLM programming.

FreshScore 87

Awesome GPT Prompt Engineering

Section: Prompt Generators · Query language for programming large language models.

SlowScore 65

Awesome LangChain

Section: Other LLM Frameworks · A programming language for large language models.

FreshScore 90

Awesome AI Agents: Tools, Resources, and Projects

Section: Repositories · LMQL is a Python-based programming language for large language models, allowing seamless integration of LLMs into code with advanced features like conditional logic, constraints, and multi-model support github | website

SlowScore 68

Opik

Comet's open-source AI observability and evaluation platform: deep tracing of LLM calls, conversation logging, and agent activity, plus built-in eval metrics, prompt versioning, guardrails, and the Opik Agent Optimizer. Worth including because it unifies observability, verification, and…

In 16 listsDetails

transformers

(formerly known as pytorch-transformers and pytorch-pretrained-bert) provides state-of-the-art general-purpose architectures (BERT, GPT-2, RoBERTa, XLM, DistilBert, XLNet, CTRL...) for Natural Language Understanding (NLU) and Natural Language Generation (NLG) with over 32+ pretrained models in…

In 14 listsDetails

LlamaIndex

(MIT) provides modules for structured outputs at different levels of abstraction, including output parsers for text completion endpoints, Pydantic programs for mapping prompts to structured outputs using function calling or output parsing, and pre-defined Pydantic programs for specific output types.

In 14 listsDetails

Mem0

Mem0 is an intelligent memory layer for Large Language Models that enhances personalized AI experiences by retaining and utilizing contextual information across various applications. github | website | docs | discord | twitter | github profile | linkedin

In 13 listsDetails

Haystack

Open-source AI orchestration framework for building context-engineered, production-ready LLM applications. Design modular pipelines and agent workflows with explicit control over retrieval, routing, memory, and generation. Built for scalable agents, RAG, multimodal applications, semantic search,…

In 13 listsDetails

AutoGen

Microsoft's multi-agent conversation framework with a complete AgentChat layer covering agent loop, tool integration, termination conditions, and human-in-the-loop. The most comprehensive open-source reference for large-scale multi-agent harness design.

In 14 listsDetails

DSPy

(MIT) is a framework for algorithmically optimizing LM prompts and weights. DSPy introduced typed predictor and signatures to leverage Pydantic for enforcing type constraints on inputs and outputs, improving upon string-based fields.

In 11 listsDetails

promptfoo

Test your prompts, models, RAGs. Evaluate and compare LLM outputs, catch regressions, and improve prompt quality. LLM evals for OpenAI/Azure GPT, Anthropic Claude, VertexAI Gemini, Ollama, Local & private models like Mistral/Mixtral/Llama with CI/CD

In 11 listsDetails