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Everything your AI needs, in one place.
Ready-made agents, skills, personas, prompts, templates and tools. Each one is checked before it goes live, works with any model, and installs in a click. Rate what you use so the best rises to the top.
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A service that does a whole job for you - research, coding, support - and reports back.
Step-by-step instructions an AI follows for one kind of task. Install once, reuse everywhere.
A voice and set of rules layered onto any model: tone, audience, do's and don'ts.
A ready-to-use prompt with fill-in-the-blank variables and notes on when it works best.
A complete routing setup - models, rules and settings - in one file you can apply in a minute.
A single function an AI can call: a calculator, a search, a database lookup.
A language model endpoint with its price, speed and quality declared so the router can compare it.
langchain-eval-harness
Build reproducible evaluation pipelines for LangChain 1.0 chains and LangGraph 1.0 agents — golden datasets, LangSmith evaluate(), ragas RAG metrics, deepeval LLM-as-judge, agent trajectory analysis,
langchain-observability
Wire LangSmith tracing and custom metric callbacks into a LangChain 1.0 chain or LangGraph 1.0 agent correctly — env-var spelling, subgraph propagation, per-tenant dimensions, cost and latency counter
langchain-prompt-engineering
Manage LangChain 1.0 prompts like code — LangSmith prompt hub versioning, XML-tag conventions for Claude, few-shot example selection, discriminated-union extraction schemas, and A/B test wiring. Use w
langchain-eval-harness
Build reproducible evaluation pipelines for LangChain 1.0 chains and LangGraph 1.0 agents — golden datasets, LangSmith evaluate(), ragas RAG metrics, deepeval LLM-as-judge, agent trajectory analysis,
langchain-observability
Wire LangSmith tracing and custom metric callbacks into a LangChain 1.0 chain or LangGraph 1.0 agent correctly — env-var spelling, subgraph propagation, per-tenant dimensions, cost and latency counter
langchain-prompt-engineering
Manage LangChain 1.0 prompts like code — LangSmith prompt hub versioning, XML-tag conventions for Claude, few-shot example selection, discriminated-union extraction schemas, and A/B test wiring. Use w
langsmith-observability
LLM observability platform for tracing, evaluation, and monitoring. Use when debugging LLM applications, evaluating model outputs against datasets, monitoring production systems, or building systemati
langsmith-observability
LLM observability platform for tracing, evaluation, and monitoring. Use when debugging LLM applications, evaluating model outputs against datasets, monitoring production systems, or building systemati
langsmith
Route LangSmith work into one workflow packet before touching SDK code. Use when the user needs LangSmith tracing, offline evals, annotation/review queues, prompt-registry decisions, audit/gap review,
langsmith
Monitor, trace, debug, and evaluate LLM applications with LangSmith. Use when a user asks to trace LLM calls, debug chain executions, evaluate AI output quality, set up LLM observability, monitor agen
langsmith-observability
LLM observability platform for tracing, evaluation, and monitoring. Use when debugging LLM applications, evaluating model outputs against datasets, monitoring production systems, or building systemati
Find
Search or browse by kind. Every card shows who made it, how many people installed it and what they think.
Install
One click. You get a manifest the router understands, plus copy-paste snippets for the CLI, Python and YAML.
Rate and publish
Leave a star rating after you have used it. Made something useful? Publish it - free listings go live immediately.
Prefer the terminal? osr stack apply registry://starter installs the starter template.