Agentic Tools

Qdrant provides tools and resources that help AI agents work with Qdrant more effectively. Use them to give your agent task-specific guidance, troubleshoot existing deployments, design and evaluate search systems, and apply Qdrant best practices to your specific use case.

Start Building

If you’re new to Qdrant, the Start building on Qdrant prompt takes your coding assistant from an empty project to a working search query. It stands up a local instance, installs the client with local embeddings, adds the Qdrant Advisor skill, and then builds against your own use case.

Help me get started building on Qdrant.

1. Start Qdrant locally with Docker. No signup and no API key needed:
   docker run -d -p 6333:6333 -p 6334:6334 -v "$(pwd)/qdrant_storage:/qdrant/storage:z" qdrant/qdrant
   Confirm it's running at http://localhost:6333/dashboard

2. Ask me which language I want to build in, then install the Qdrant client for it. If I have no preference, use Python with local embeddings so I don't need an embedding provider key either:
   pip install "qdrant-client[fastembed]"

3. Install the Qdrant Advisor agent skill:
   npx skills add qdrant/skills/meta/qdrant-advisor

4. Then ask me what I'm building and what I'll search over before you write any code. Once you know, build the integration against my local instance, and get the collection right the first time:
   - Derive the vector size from the embedding model itself. Never hardcode a dimension.
   - Choose the distance metric the model was trained for, and say which one you picked and why.
   - Create a payload index for every field I'll filter on, before I load data rather than after.
   Then load a small sample, run a real query, and show me the results.

Qdrant runs in Docker and FastEmbed generates embeddings on your machine, so you can go from nothing to a working search query without signing up for anything.

Collection configuration is important. A mismatched distance metric or a missing payload index can result in slower or lower quality search. The prompt is written to make your assistant ask what you are building, then commit to those choices out loud before it writes any code.

Agent Skills

Agent skills encode solutions architect knowledge as structured files your agent loads on demand. They’re organized around symptoms, so they answer “why is this happening, and what should I check first?” Reach for skills when something is already running and behaving unexpectedly: slow search, growing memory, a stuck optimizer, poor relevance.

Prompts in the Docs

Short, ready-to-use prompts appear directly on the documentation pages where they are relevant, so you can use the prompts when you need them instead of looking for them elsewhere. Each prompt points to the agent skill your assistant should load. The prompt gets the task started; the skill provides the deeper Qdrant-specific guidance. The prompt index lists all available prompts.