Bonfire Networks Python API Docs | dltHub

Bonfire Networks is an open-source social platform framework that exposes a primary GraphQL API for clients, provides Mastodon-compatible REST adapter endpoints (partial) and ActivityPub endpoints for federation. The REST API base URL is Instance-specific. Primary API served at https://<your-instance>/ with GraphQL reachable at https://<your-instance>/api/ (no single global base URL in docs). and GraphQL and web APIs use instance account authentication (sessions/cookies or app-provided tokens); Mastodon-compatible REST is a partial adapter and may use token-based auth where implemented..

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 Bonfire Networks data in under 10 minutes.

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What data can I load from Bonfire Networks?

Here are some of the endpoints you can load from Bonfire Networks:

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How do I authenticate with the Bonfire Networks API?

The GraphQL API (accessible at /api/) populates the current user from the request context via Bonfire.API.GraphQL.Plugs.GraphQLContext. Browser access via GraphiQL uses session/cookie authentication; programmatic auth depends on instance configuration and any token mechanisms implemented by an instance (no single documented global API token header in public docs). ActivityPub endpoints use HTTP signatures for S2S federation (HTTPSignature plugs). Rate limit middleware respects Retry-After headers.

1. Get your credentials

1. Host or sign up on your Bonfire instance. 2) Log in to the web UI and check account settings or developer/API section for API token support (if instance enabled). 3) If tokens are not exposed, use session-based auth (login credentials) or ask instance admin to enable an API token mechanism. 4) For ActivityPub federation, configure HTTP keypairs in instance settings for HTTPSignatures.

2. Add them to .dlt/secrets.toml

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[sources.bonfire_networks_source]
auth_token = "your_instance_token_here"

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 Bonfire Networks 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 bonfire_networks_pipeline.py

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

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Pipeline bonfire_networks_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset bonfire_networks_data
The duckdb destination used duckdb:/bonfire_networks.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 bonfire_networks_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 graphql and mastodon_statuses from the Bonfire Networks 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 bonfire_networks_source(auth_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "Instance-specific. Primary API served at https://<your-instance>/ with GraphQL reachable at https://<your-instance>/api/ (no single global base URL in docs).",
            "auth": {
                "type": "instance_auth_or_custom_token",
                "token": auth_token,
            },
        },
        "resources": [
            {"name": "graphql", "endpoint": {"path": "api/", "data_selector": "data"}},
            {"name": "mastodon_statuses", "endpoint": {"path": "api/v1/statuses"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="bonfire_networks_pipeline",
        destination="duckdb",
        dataset_name="bonfire_networks_data",
    )
    load_info = pipeline.run(bonfire_networks_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("bonfire_networks_pipeline").dataset()
sessions_df = data.graphql.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM bonfire_networks_data.graphql LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("bonfire_networks_pipeline").dataset()
data.graphql.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 Bonfire Networks 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 GraphiQL works in the browser but programmatic requests return 401/403, verify you are sending the same session cookie or token the instance expects. Some instances do not expose API tokens; contact the instance admin.

Rate limiting

Bonfire uses a Hammer-based rate limiter. When the limit is exceeded the instance may return 429 with a Retry-After header — respect Retry-After and back off.

Mastodon-compatibility caveats

The Mastodon-compatible REST API is a partial adapter implemented as a thin layer over GraphQL — not all Mastodon endpoints or fields are guaranteed. Some endpoints may be missing or return responses that differ from upstream Mastodon. Prefer the GraphQL API for reliable, complete access.

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