AI Agent Builders can configure their retrieval in 5 minutes

You've got the agent loop, the tools and the interface. Then the agent needs to read real data, and you're writing query tools, worrying about what happens if the model asks for the wrong table, turning text answers into values, and wondering why it answered the way it did.

Claix replaces that with one endpoint. Connect a Postgres or Supabase database, choose the tables your agent can use, and call it from your LangChain app, MCP client, n8n flow or custom backend. You get typed answers and a trace of every request. The agent logic stays yours.

Read the docs

For independent developers, indie hackers and small teams shipping agents that need to read data.

Estimate the data-access work you'd otherwise build, debug and maintain yourself. This is illustrative, not a fixed saving.

Actual effort depends on your schema, the quality you need and how many agents you build.

A model that can query is also a model that can query the wrong thing.

Deciding what the agent may read, and making sure it can only read, shouldn't take over your repository.

A paragraph is difficult to validate or pass to the next tool. A boolean, a number or a list is not.

When an answer looks wrong, you need to know which tables the agent used and what it ran.

A second agent on the same database shouldn't need a second set of query tools.

From a weekend of plumbing to an agent feature

One integration doesn't replace your agent. It removes the infrastructure between your agent and your data.

Your first agent may live in n8n, move to a TypeScript backend, use MCP during development and later run as a production service. Claix gives you the same endpoint across all of them.

Support, internal assistants, workflow gates, reporting helpers and multi-agent setups.

It looks up an order or an account before it replies, instead of guessing.

Ask about customers, bookings or stock in plain language.

A yes or no from your data decides whether an automation continues.

Counts and lists from your tables, delivered as values your code can use.

One agent hands data questions to another over A2A.

Your agent stays in charge. Claix answers the data step.

An order status, a booking, a flag, a count.

One request, one or several questions, each with the type you expect.

Validated read-only queries on the tables that agent can use.

Booleans, numbers, strings, lists or objects.

Typed values, multi-question calls, live data, boundaries and traces.

Typed values instead of text to interpret.

The agent only reaches the tables and columns of the routes you attach.

Every request is logged so you can debug it.

Call it from Python, TypeScript, LangChain, MCP, n8n, Make or any backend that can send an HTTP request.

Adjust the agent name and questions to your project. Endpoint: POST /retrieve/{agent_name}.

import requests

response = requests.post( "https://www.claix.dev/retrieve/support-bot", headers={"x-api-key": CLAIX_API_KEY}, json={ "questions": [ { "question": "Has the customer Javier Pérez accepted marketing consent?", "format": "boolean", }, { "question": "What is Marta's job title?", "format": "string", }, ], }, )

result = response.json()

What is Claix for AI agent creators?

Claix gives agent builders one endpoint to read a Postgres or Supabase database with typed answers, read-only scoping per route, and a trace of every request—while the agent loop stays yours.

No. You describe which tables an agent can use and write a short guide. Claix handles the queries.

No. LangChain, your own loop or any framework still runs the agent. Claix is a tool the agent calls to read from your database.

Yes. Start with one route and one agent, test with real data and add more as the project proves itself.

You can. Your database, backend and agent logic stay in your control, so the decision can wait until you have usage and clearer requirements.

No system is automatically correct. A model can write a query that is allowed but wrong. Good table descriptions, a clear guide and the traces help you catch it. Treat high-impact actions with care.

Yes. Connect your own key and choose your model, or use Claix Managed AI. With your own key, results depend on the model you pick, so test it first.

Connect a database, create your first agent and see the typed answers your next tool can use.