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For AI agent creators

Build the agent. Let Claix handle how it reads your database.

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 any SQL 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.

Try it in the sandboxRead the docs

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

Your time

Your time is the infrastructure budget

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

Illustrative estimate

  • Estimated initial work

    40 hrs

  • Estimated monthly maintenance

    6 hrs

  • 3-month opportunity cost at your rate

    €4,350

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

Spend your time on

Spend your time on

  • Agent behavior
  • Tool calls
  • Product UX
  • Demos and feedback
  • Shipping features

Not on

  • Hand-written query tools
  • Permission checks for every table
  • Turning prose answers into values
  • Logging what the agent asked
  • Fixing it when a column is renamed

The builder problem

The agent is ready. The data access isn't.

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

  • Wrong-table risk

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

  • Text answers are hard to use

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

  • You can't fix what you can't see

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

  • Every new agent repeats the work

    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

Homegrown data access vs Claix

Homegrown data access

  • Write query tools for each question type
  • Give the model your schema and hope it behaves
  • Add checks so it only reads
  • Parse the model's output into fields
  • Add logging
  • Maintain all of it alone

With Claix

  • Connect your database
  • Review the Data Map
  • Choose tables and columns and add a short guide
  • Create an agent and call its endpoint
  • Get typed values and a trace
  • Ship the feature

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

One person, one integration

Start with the smallest integration that proves the feature

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.

  1. 1.Connect one database.
  2. 2.Create one route.
  3. 3.Create one agent.
  4. 4.Ask a question and read the typed answer.
  5. 5.Test with real data.
  6. 6.Add routes, agents and your own LLM key as the project grows.

What you delegate and what you keep

Claix takes the data path. You keep the agent.

Delegate

  • Connecting to the database with a read-only role
  • Describing tables and relationships
  • Scoping what each agent can read
  • Turning questions into validated queries
  • Returning typed answers
  • Tracing every request

Keep

  • Agent decisions
  • Tool definitions
  • Business logic
  • User interface
  • Your database
  • Authentication
  • Your deployment

Agents you can ship

Builders put Claix under real products

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

  • A support agent that checks live data

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

  • An internal assistant

    Ask about customers, bookings or stock in plain language.

  • A workflow gate

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

  • A reporting helper

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

  • A multi-agent setup

    One agent hands data questions to another over A2A.

In your agent

From need to typed fact

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

  1. 1

    The agent needs a fact

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

  2. 2

    You call the endpoint

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

  3. 3

    Claix runs it

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

  4. 4

    You get typed data

    Booleans, numbers, strings, lists or objects.

  5. 5

    Your agent acts

    It uses the value in its next step.

  6. 6

    You check the trace

    If something is off, you see what ran.

Try it in the sandbox

What the call gives your agent

Predictable inputs for your tools

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

  • Predictable inputs for your tools

    Typed values instead of text to interpret.

  • Several questions in one call

    Answers come back in order.

  • Live data

    No copy to keep in sync.

  • A boundary

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

  • A trace

    Every request is logged so you can debug it.

  • Your runtime

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

Use the runtime you already have

REST, MCP, A2A, n8n and Make

  • REST API from a Python or TypeScript agent
  • MCP, so an IDE or agent host can use the database as a tool
  • A2A, so other agents can delegate data questions
  • n8n and Make, when the product is a workflow

Minimal call

One request in, typed answers out

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 any SQL database with typed answers, read-only scoping per route, and a trace of every request—while the agent loop stays yours.

Questions builders ask

FAQ

Do I need to know SQL or RAG?

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

Does Claix replace LangChain?

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

Can I use Claix for an MVP?

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

What if I want to build this myself later?

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

Can the agent trust every answer?

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.

Can I use my own LLM key?

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.

Next step

You already built the agent. Now give it your data.

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

Try it in the sandboxRead the docs
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