Branch Python API Docs | dltHub

Branch is a mobile growth platform that provides REST APIs for creating and managing deep links, quick links, querying analytics, and managing app configuration. The REST API base URL is https://api2.branch.io and Requests use Branch Key/Branch Secret for app-level auth; some endpoints require an Access-Token header for org-level permissions..

dlt is an open-source Python library that handles authentication, pagination, and schema evolution automatically. dlthub provides AI context files that enable code assistants to generate production-ready pipelines. Install with uv pip install "dlt[workspace]" and start loading Branch data in under 10 minutes.

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What data can I load from Branch?

Here are some of the endpoints you can load from Branch:

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How do I authenticate with the Branch API?

Most Branch APIs require your Branch Key and Branch Secret passed either in the JSON body or query string. Organization-level operations and some sensitive methods require an Access-Token sent in the Access-Token request header. Accept and Content-Type must be application/json.

1. Get your credentials

1. Log in to the Branch Dashboard; 2) For app credentials navigate to Settings → App (or Configuration → Security & Access → Credentials in the new UI) and copy the Branch Key and Branch Secret for the app; 3) For organization-level Access Token go to Configuration → Security & Access → Credentials (or Account → User tab in legacy UI) and Generate token; copy the Access-Token.

2. Add them to .dlt/secrets.toml

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[sources.branch_source]
branch_key = "key_live_XXXXXXXXXXXXX"
branch_secret = "secret_live_XXXXXXXXXXXXX"
access_token = "org_access_token_XXXXXXXX"

dlt reads this automatically at runtime — never hardcode tokens in your pipeline script. For production environments, see setting up credentials with dlt for environment variable and vault-based options.

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How do I set up and run the pipeline?

Set up a virtual environment and install dlt:

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uv venv && source .venv/bin/activate
uv pip install "dlt[workspace]"

1. Install the dlt AI Workbench:

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dlt ai init --agent <your-agent> # <agent>: claude | cursor | codex

This installs project rules, a secrets management skill, appropriate ignore files, and configures the dlt MCP server for your agent. Learn more →

2. Install the rest-api-pipeline toolkit:

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dlt ai toolkit rest-api-pipeline install

This loads the skills and context about dlt the agent uses to build the pipeline iteratively, efficiently, and safely. The agent uses MCP tools to inspect credentials — it never needs to read your secrets.toml directly. Learn more →

3. Start LLM-assisted coding:

Use /find-source to load data from the Branch API into DuckDB.

The rest-api-pipeline toolkit takes over from here — it reads relevant API documentation, presents you with options for which endpoints to load, and follows a structured workflow to scaffold, debug, and validate the pipeline step by step.

4. Run the pipeline:

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python branch_pipeline.py

If everything is configured correctly, you'll see output like this:

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Pipeline branch_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset branch_data
The duckdb destination used duckdb:/branch.duckdb location to store data
Load package 1749667187.541553 is LOADED and contains no failed jobs

Inspect your pipeline and data:

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dlt pipeline branch_pipeline show

This opens the Pipeline Dashboard where you can verify pipeline state, load metrics, schema (tables, columns, types), and query the loaded data directly.

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Python pipeline example

This example loads deep_links and app_config from the Branch API into DuckDB. It mirrors the endpoint and data selector configuration from the table above:

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import dlt
from dlt.sources.rest_api import RESTAPIConfig, rest_api_resources

@dlt.source
def branch_source(branch_key, branch_secret, access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api2.branch.io",
            "auth": {
                "type": "api_key",
                "branch_secret or access_token": branch_key, branch_secret, access_token,
            },
        },
        "resources": [
            {"name": "deep_links", "endpoint": {"path": "v1/url", "data_selector": "url (single object) or top-level array for bulk responses"}},
            {"name": "app_config", "endpoint": {"path": "v1/app/{branch_key}", "data_selector": "(response is an object; top-level fields for app config)"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="branch_pipeline",
        destination="duckdb",
        dataset_name="branch_data",
    )
    load_info = pipeline.run(branch_source())
    print(load_info)

To add more endpoints, append entries from the resource table to the "resources" list using the same name, path, and data_selector pattern.

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How do I query the loaded data?

Once the pipeline runs, dlt creates one table per resource. You can query with Python or SQL.

Python (pandas DataFrame):

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import dlt

data = dlt.pipeline("branch_pipeline").dataset()
sessions_df = data.deep_links.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM branch_data.deep_links LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("branch_pipeline").dataset()
data.deep_links.df().head()

See how to explore your data in marimo Notebooks and how to query your data in Python with dataset.

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What destinations can I load Branch data to?

dlt supports loading into any of these destinations — only the destination parameter changes:

Change the destination in dlt.pipeline(destination="snowflake") and add credentials in .dlt/secrets.toml. See the full destinations list.

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Troubleshooting

Authentication failures

If you receive HTTP 400/401 with message "Authentication failed" ensure you are using the correct credential level: app-level calls require the app's branch_key and branch_secret (branch_secret in body or query as required); organization-level endpoints require an Access-Token header. Using an app access token in place of an org access token can cause failures.

Conflicts and 409 errors

POSTing a link with an alias that conflicts with an existing link (without matching params) returns HTTP 409. To avoid, ensure aliases are unique or supply matching parameters for idempotent returns.

Rate limiting and large exports

Branch APIs may impose rate limits on high-volume calls and exports. For Query API exports use the provided analytics export endpoints (POST /v1/query/analytics) with limits and use pagination or limit parameters to control result size. For very large datasets use the bulk export/async mechanisms documented in the Query API.

Pagination and response shapes

Many endpoints return object responses rather than a top-level array. The Query API returns analytics results according to the requested dimensions — inspect the response keys in examples; bulk endpoints return a top-level array where each element is either a {"url":...} or {"error":...}.

Ensure that the API key is valid to avoid 401 Unauthorized errors. Also, verify endpoint paths and parameters to avoid 404 Not Found errors.

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Next steps

Continue your data engineering journey with the other toolkits of the dltHub AI Workbench:

• data-exploration — Build custom notebooks, charts, and dashboards for deeper analysis with marimo notebooks.
• dlthub-runtime — Deploy, schedule, and monitor your pipeline in production.

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dlt ai toolkit data-exploration install
dlt ai toolkit dlthub-runtime install

• How to deploy a pipeline
• How to explore your data in marimo Notebooks
• How to query your data in Python with dataset
• Join the dlt community on Slack