The AI kit for Prompt Engineers

Tightens a working prompt and cuts tokens, like turning a 400-word support reply prompt into 120 words with the same accuracy.

Builds a new prompt from your source material and tests each edit, like a policy-summary prompt checked against five real documents.

Runs eval sets, measures RAG retrieval, checks agent tools, and designs structured output, like scoring 50 answers against a pass mark.

Reviews prompts and agents for jailbreaks, tool abuse, and poisoning, like mapping a chatbot attack path to MITRE ATLAS techniques.

Like "Draft Your First Prompt Template"

Like "Simplify Wordy Prompts For Users"

Like "Generate Prompt Variation Sets"

Like "Simulate Model Responses Before Live Testing"

Find the ways your prompt can be attacked and fix them without hurting normal use

Keep the test set current so real failures become permanent tests

Choose a model with evidence on quality, cost and speed for the real task

Leave a smaller library where each purpose has one tested, owned prompt

Lets your AI look up and fill in your Langfuse prompts right inside the chat.

Your AI can save, version and improve your prompts, and switch models and settings for each one.

Lets your AI run chat, text embeddings and image generation through Cloudflare's AI service.

Ask Claude to send a question to OpenAI models like gpt-4o or o1 and bring the answer back.

Langfuse Prompt Experiments enables teams to test, evaluate, and optimize LLM prompts with detailed tracing, metrics, and dataset testing. Improve your LLM appl

TokenCounter accurately counts tokens and estimates costs across AI models, helping developers and researchers optimize prompts, control expenses, and improve e

Prompt Engineering Studio enables teams to build, test, and deploy AI prompts at scale with collaborative editing, version control, and integration across 1600+

Laminar is an open-source platform for tracing LLM apps, running evaluations, labeling production data, and refining prompts. Built with Rust, it delivers fast,

Gives your team one reviewable record of every prompt change, so revisions stop living in chat threads and stray docs.

Cuts prompt regression risk by keeping versions and test results together, so a wording tweak cannot quietly break a live flow.

Keeps prompts, context, and data under your control with sources linked, replacing a pile of disconnected tools.

Reduce prompt release risk while keeping evaluation evidence and production traces in one owned workspace.

Transform weak prompts into precision-engineered instructions for any LLM.

Design, test, and refine prompts for reliable LLM outputs.

Design and run systematic LLM evaluations with metrics, human review, and A/B testing.

Curates project context to maximize agent output quality and reduce hallucination.