Load Binary Ninja data to DuckDB
Build a Binary Ninja to DuckDB pipeline with your coding agent. One prompt scaffolds it with the dltHub AI harness, plus the Binary Ninja API base URL, auth, endpoints, and incremental loading.
Binary Ninja Collaboration REST API allows remote interaction with collaboration servers for binary analysis and project management. Everything needed to build a working Binary Ninja → DuckDB pipeline is on this page: the API's base URL, authentication, endpoints, pagination and incremental field — plus a prompt that hands the whole job to your coding agent.
Build your Binary Ninja to DuckDB pipeline
Paste this prompt into Claude, Codex, or Cursor. The agent does the rest.
PromptRunuvx dlthub-init@latestto build a pipeline from Binary Ninja to DuckDB and run it on dltHub
That scaffolds a dltHub workspace and installs the dltHub AI harness — the project rules, the secrets-management skill, and the dlt MCP server your agent needs to work safely. From there it reads the Binary Ninja API, proposes the endpoints to load, then writes, runs and validates the pipeline while you review rather than type. Credentials are inspected through MCP tools, so your agent never reads secrets.toml itself. How the LLM-native workflow works →
Prefer to write it yourself? Every fact the agent uses is below.
Binary Ninja API at a glance
| Base URL | The base URL for collaboration API requests is provided by the specific Remote instance configuration. |
| Example endpoint | GET functions?offset={offset}&limit={limit} |
| Authentication | all requests require an Authorization header with a token — sent in the request header |
| Also required | `` |
| Pagination | Offset-based via offset, page size via limit. Binary Ninja does not provide a native REST API. Community-built MCP (Model Context Protocol) servers exist that implement pagination using 'offset' and 'limit' query parameters. Responses include 'has_more' and 'next_offset' for navigating results. |
| Incremental field | offset |
| API reference | https://warp.binary.ninja/docs/api |
These values come from the Binary Ninja API reference — the authoritative source if anything here looks out of date.
How do I authenticate with the Binary Ninja API?
Authenticated requests require an 'Authorization: Token ' header.
1. Get your credentials
Log in to the Sidekick portal at sidekick.binary.ninja/account. Navigate to the API keys section to copy your valid API key. In the Binary Ninja client, go to Plugins > Sidekick > Configure API Key, or click the Sidekick status indicator in the status bar and select Configure API Key to paste and save the key.
2. Add them to .dlt/secrets.toml
[sources.binary_ninja_source] sidekick_service_api_key = "your_api_key_here"
dlt reads this file automatically at runtime. With the harness, the setup-secrets skill prompts you for the values and never handles the raw credential in chat. For production, see setting up credentials with dlt.
What Binary Ninja data can I load into DuckDB?
These are the Binary Ninja endpoints dlt can load into DuckDB:
| Resource | Endpoint | Method | Data selector | Description |
|---|---|---|---|---|
| functions | /functions | GET | List functions with offset and limit parameters | |
| types | /types | GET | List types with offset and limit parameters | |
| imports | /imports | GET | List imports with offset and limit parameters | |
| exports | /exports | GET | List exports with offset and limit parameters | |
| strings | /strings | GET | List strings with offset and limit parameters |
How do I load only new Binary Ninja records?
Binary Ninja exposes offset on functions?offset={offset}&limit={limit}, so dlt can request only the records that changed since the last run. Set it as the cursor_path and dlt tracks the high-water mark for you between runs.
{"name": "functions", "endpoint": { "path": "functions?offset={offset}&limit={limit}", "incremental": {"cursor_path": "offset", "initial_value": "2024-01-01T00:00:00Z"}, }}
On the first run dlt loads everything from initial_value; on every run after that it requests only what changed and appends with write_disposition="merge" if you set a primary key. See incremental loading.
What does the generated Binary Ninja pipeline look like?
A standard dlt REST API pipeline — the same code you would write by hand, loading sidekick and ollama from the Binary Ninja API into DuckDB:
import dlt from dlt.sources.rest_api import RESTAPIConfig, rest_api_resources @dlt.source def binary_ninja_source(token=dlt.secrets.value): config: RESTAPIConfig = { "client": { "base_url": "The base URL for collaboration API requests is provided by the specific Remote instance configuration.", "auth": {"type": "bearer", "token": token}, }, "resources": [ {"name": "functions", "endpoint": {"path": "functions?offset={offset}&limit={limit}"}}, {"name": "types", "endpoint": {"path": "types?offset={offset}&limit={limit}"}} ], } yield from rest_api_resources(config) def load_binary_ninja_to_duckdb() -> 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) if __name__ == "__main__": load_binary_ninja_to_duckdb()
Run it with python binary_ninja_pipeline.py. The agent iterates on this until it loads cleanly — you review and approve, rather than write it from scratch.
How do I query Binary Ninja data in DuckDB?
dlt creates one table per resource. Query the loaded data with Python or SQL — or ask your agent to, through the MCP server's execute_sql_query tool.
Python (pandas DataFrame):
import dlt data = dlt.pipeline("binary_ninja_pipeline").dataset() df = data.functions.df() print(df.head())
SQL:
SELECT * FROM binary_ninja_data.functions LIMIT 10;
See querying your data with dataset and exploring it in marimo notebooks.
How do I deploy the Binary Ninja to DuckDB pipeline in production?
The pipeline runs locally, which is ideal for prototyping and one-off analysis. When you need it on a schedule, monitored on every load, and shared with your team, deploy the same dlt code on the dltHub platform — no infrastructure to maintain. The prompt above already ends with "run it on dltHub", so your agent can take it there directly.
- Deploy & schedule — run the pipeline as a managed job with automatic retries.
- Monitor — observable job queues, alerting, and load metrics for every run.
- Transform — promote raw Binary Ninja loads into governed, documented models.
- Visualize & share — explore data in notebooks and publish live dashboards instead of static screenshots.
What other destinations can I load Binary Ninja data to?
dlt loads into any of these — only the destination argument changes:
| Destination | Example value |
|---|---|
| PostgreSQL | "postgres" |
| BigQuery | "bigquery" |
| Snowflake | "snowflake" |
| Redshift | "redshift" |
| Databricks | "databricks" |
| Filesystem (S3, GCS, Azure) | "filesystem" |
Set dlt.pipeline(destination="snowflake") and add credentials in .dlt/secrets.toml. On the dltHub platform the same pipeline runs against a managed Iceberg lakehouse. See the full destinations list.
Next steps
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