Binary Ninja Python API Docs | dltHub

Binary Ninja is a reverse‑engineering platform exposing rich programmatic APIs (Python/C++/Rust) and headless automation for binary analysis and plugin development; it does not provide a hosted HTTP REST API. The REST API base URL is https://api.binary.ninja/ (documentation site — no REST API base URL) and No HTTP API; authentication is via Binary Ninja license activation and local application licensing — not a Bearer/API key for REST..

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 Binary Ninja data in under 10 minutes.

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What data can I load from Binary Ninja?

Here are some of the endpoints you can load from Binary Ninja:

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How do I authenticate with the Binary Ninja API?

Binary Ninja uses local license activation; plugins and headless automation run under the licensed Binary Ninja installation. There are no HTTP auth headers for a REST API because none exists.

1. Get your credentials

1. Purchase or download Binary Ninja from https://binary.ninja/.
2. Install Binary Ninja and log in or enter your license key in the application license dialog.
3. For enterprise / headless environments, follow Binary Ninja's license/server activation docs in the user/developer guide to configure licenses on the host running headless automation.

2. Add them to .dlt/secrets.toml

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[sources.binary_ninja_source]
# Binary Ninja has no REST API credentials to store for HTTP access
# License credentials are managed in the application, not in secrets.toml

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 Binary Ninja 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 binary_ninja_pipeline.py

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

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Pipeline binary_ninja_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset binary_ninja_data
The duckdb destination used duckdb:/binary_ninja.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 binary_ninja_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 binaryview and pluginmanager from the Binary Ninja 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 binary_ninja_source(license_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.binary.ninja/ (documentation site — no REST API base URL)",
            "auth": {
                "type": "application_license",
                "license_key": license_key,
            },
        },
        "resources": [
            {"name": "binaryview", "endpoint": {"path": "(python API: binaryninja.BinaryView)"}},
            {"name": "pluginmanager", "endpoint": {"path": "(python API: binaryninja.plugin)"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="binary_ninja_pipeline",
        destination="duckdb",
        dataset_name="binary_ninja_data",
    )
    load_info = pipeline.run(binary_ninja_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("binary_ninja_pipeline").dataset()
sessions_df = data.binaryview.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM binary_ninja_data.binaryview LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("binary_ninja_pipeline").dataset()
data.binaryview.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 Binary Ninja 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 / Licensing failures

Binary Ninja is licensed per installation; errors when running headless automation typically indicate an unactivated license or missing license file. Ensure the Binary Ninja installation on the host is activated with a valid license or an enterprise license server is reachable. Check application logs for activation errors.

Headless / Batch Automation quirk

Headless mode exposes the same Python API semantics but runs without a GUI. Some UI‑only plugins or APIs will raise UIPluginInHeadlessError. Use documented headless‑compatible APIs and run long‑running analysis under headless‑friendly settings (disable UI‑dependent calls).

No REST endpoints / integration approach

There is no hosted HTTP REST API. Integrations must use the Python/C++/Rust SDK or run Binary Ninja headless and script it locally; if you need remote access, build a thin HTTP service around a headless Binary Ninja instance that serializes Python objects to JSON according to your contract.

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