LMQL
A programming language for LLMs that enables robust and modular prompting with types, constraints, and an optimizing runtime.

The version-controlled prompt registry for professional LLM orchestration.

LangChain Hub is the industry-standard repository for discovering, versioning, and sharing prompts, chains, and agents within the LangChain ecosystem. By 2026, it has solidified its position as a critical component of the LLMOps lifecycle, enabling teams to decouple prompt engineering from application code. This architecture allows domain experts to iterate on prompt logic independently of software deployment cycles. The platform provides a git-like versioning system for LLM instructions, ensuring that developers can pull specific 'shas' or tags (like 'prod' or 'latest') directly into their runtime environments using the LangChain SDK. Technical architects leverage the Hub to maintain a single source of truth for prompt templates, preventing 'prompt drift' and ensuring consistency across multi-modal applications. With its deep integration into LangSmith, the Hub facilitates a seamless workflow from prompt ideation in the playground to production-grade deployment and monitoring. Its 2026 feature set includes advanced support for multi-modal inputs, dynamic few-shot example selection, and automated prompt optimization based on performance telemetry.
LangChain Hub is the industry-standard repository for discovering, versioning, and sharing prompts, chains, and agents within the LangChain ecosystem.
Explore all tools that specialize in manage prompt versions. This domain focus ensures LangChain Hub delivers optimized results for this specific requirement.
Explore all tools that specialize in prompt engineering. This domain focus ensures LangChain Hub delivers optimized results for this specific requirement.
Git-like commit hashes and tags for every prompt iteration, allowing rollbacks and stable production pointers.
A browser-based execution environment to test prompts against OpenAI, Anthropic, and Google models simultaneously.
Direct programmatic retrieval of prompts via `hub.pull()`, which caches local versions for performance.
Management of message lists containing text, image URLs, and base64 encoded data for GPT-4o and Claude 3.5 Sonnet.
Ability to clone community-standard prompts (like RAG or ReAct) and customize them for specific data schemas.
Partitioned environments for different departments with granular permissioning.
Schema enforcement for input variables to ensure the application sends required data to the LLM.
Create a LangSmith/LangChain account.
Install the LangChain SDK via pip or npm.
Generate a LangChain API Key from the settings dashboard.
Set environment variables for LANGCHAIN_API_KEY.
Navigate to the Hub UI to browse public prompts or create a new repository.
Write your prompt template using LangChain PromptTemplate syntax.
Commit the prompt to a public or private repository with a version tag.
Use `hub.pull('owner/repo:tag')` in your application code.
Test variables dynamically in the Hub's integrated Playground.
Set up webhooks for automated deployment when prompt tags are updated.
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"Highly praised for its ability to bridge the gap between prompt engineering and software engineering, though some users find the UI slightly complex."
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A programming language for LLMs that enables robust and modular prompting with types, constraints, and an optimizing runtime.

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