Fireflies-ai Python API Docs | dltHub

Fireflies.ai is an AI-powered meeting transcription and search platform that offers a GraphQL API for accessing meeting data. The REST API base URL is https://api.fireflies.ai/graphql and All requests require a Bearer token passed in the Authorization header..

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 Fireflies-ai data in under 10 minutes.

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What data can I load from Fireflies-ai?

Here are some of the endpoints you can load from Fireflies-ai:

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How do I authenticate with the Fireflies-ai API?

Authentication is performed via an HTTP Authorization header containing a Bearer token. Include the header: Authorization: Bearer <your_api_key>.

1. Get your credentials

1. Log in to https://app.fireflies.ai. 2. Click your profile avatar and select Settings. 3. In the left menu choose Integrations. 4. Find the Fireflies API section and click Generate New API Key. 5. Copy the generated key; it will be used as the Bearer token in API requests.

2. Add them to .dlt/secrets.toml

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[sources.fireflies_ai_source]
api_key = "your_api_key_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 Fireflies-ai 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 fireflies_ai_pipeline.py

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

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Pipeline fireflies_ai_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset fireflies_ai_data
The duckdb destination used duckdb:/fireflies_ai.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 fireflies_ai_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 users and transcripts from the Fireflies-ai 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 fireflies_ai_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.fireflies.ai/graphql",
            "auth": {
                "type": "bearer",
                "token": api_key,
            },
        },
        "resources": [
            {"name": "users", "endpoint": {"path": "", "data_selector": "users"}},
            {"name": "transcripts", "endpoint": {"path": "", "data_selector": "transcripts"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="fireflies_ai_pipeline",
        destination="duckdb",
        dataset_name="fireflies_ai_data",
    )
    load_info = pipeline.run(fireflies_ai_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("fireflies_ai_pipeline").dataset()
sessions_df = data.users.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM fireflies_ai_data.users LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("fireflies_ai_pipeline").dataset()
data.users.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 Fireflies-ai 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

• Symptom: API returns HTTP 401 Unauthorized.
• Cause: Missing or invalid Bearer token.
• Resolution: Verify that the Authorization: Bearer <api_key> header is present and the key is correct.

Rate limiting

• Symptom: HTTP 429 Too Many Requests response.
• Cause: Exceeded the allowed number of requests per time window.
• Resolution: Respect the Retry-After header (if present) and implement exponential back‑off.

GraphQL errors

• Symptom: HTTP 200 response but the JSON contains an errors array.
• Cause: Malformed query, invalid arguments, or permission issues.
• Resolution: Inspect the errors messages, correct the query syntax, and ensure the token has required scopes.

Pagination quirks

• Symptom: Incomplete result sets when fetching large collections.
• Cause: Queries use cursor‑based pagination (after, first) or classic limit/offset parameters.
• Resolution: Use the cursor value returned in the previous response to request the next page, or increase limit while respecting rate limits.

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