> ## Documentation Index
> Fetch the complete documentation index at: https://docs.reasonblocks.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Deploy your connected application

> Ship the generated helper, connection configuration and runtime secrets with the Python application that makes model calls.

Connect to the exact HTTPS source URL returned in **Data**. Your application
keeps using its OpenAI or Anthropic Python client library and provider credential.
See [Quickstart](/quickstart) to create the connection.

## Application files

Deploy the generated `reasonblocks_setup.py` and its adjacent
`.reasonblocks/config.json` with the application. Include the hidden config
directory explicitly in wheel package data or image copy rules. The generated
`.reasonblocks/SETUP.md` is optional at runtime.

Install `rbtrace==1.2.1` from PyPI into the application's Python environment
and record the version in its dependency manifest. That release contains the
CLI and the helper's automatic run labelling.

Use an importable helper location appropriate to your package. Verify the real
entrypoint from a working directory outside the repository. See
[helper placement](/agent-setup#choose-an-importable-helper-location).

## Runtime secrets

Supply `REASONBLOCKS_CAPTURE_KEY` through the deployment's secret environment.
The existing `RB_CAPTURE_KEY` alias is accepted; if both names are set, their
values must agree. Keep the provider credential in its normal secret location.
Do not put either key in generated files or the image.

The capture service receives model requests and the provider credential needed
to forward them. Review the workflow content you send. Capture keys expire after
seven days; rotating a key in **Data** invalidates the previous key, so update
connected applications as part of that change.

## Tasks and verification

Wire the helper into the process that makes the actual model calls. Create one
run ID per task and reuse its headers for every step, including in workers that
handle many tasks. See [Integrating your agent](/client-integration).

Run `doctor` in the deployed Python environment with the configured helper path.
It is a local check, not a remote credential or capture test. Confirm source
records after a real workflow before claiming the deployment is capturing correctly.
Normal provider charges apply to those calls.

## Training environment

Capturing calls does not require a sandbox. Full-agent training later needs a
resettable test copy of your tools and data plus an outcome evaluator. The adapter
can connect an existing test environment; this does not inherently require a
new customer-hosted server. Follow [Train your complete agent](/full-agent-training)
for the application-specific connection and release requirements.