Load Logfire data to DuckDB

Logfire is an observability tool providing a REST API for managing organizational resources, querying data, and accessing audit logs. Everything needed to build a working Logfire → 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.

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Build your Logfire to DuckDB pipeline

Paste this prompt into Claude, Codex, or Cursor. The agent does the rest.

PromptCopy prompt
Run uvx dlthub-init@latest to build a pipeline from Logfire 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 Logfire 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.

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Logfire API at a glance

These values come from the Logfire API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the Logfire API?

All API requests require an Authorization header with the value 'Bearer ', where the token is either a read token, write token, or API key depending on the specific endpoint being accessed.

1. Get your credentials

To obtain an API key for Logfire, navigate to the Logfire web console. Depending on your needs, you can generate keys at the organization or project level: 1. Log in to your Logfire account at logfire.pydantic.dev. 2. To generate an organization-level key, click your organization name in the bottom-left sidebar, then select 'Org settings', and navigate to 'API Keys'. To generate a project-level key, open the specific project, go to 'Settings', and navigate to 'API Keys'. 3. Click '+ New API key'. 4. Provide a name and select the appropriate scopes for your use case (e.g., read, write). 5. Click 'Create'. The API key is displayed only once; copy it immediately and store it securely, as it will not be shown again.

2. Add them to .dlt/secrets.toml

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[sources.logfire_source]
# Inside .dlt/secrets.toml
read_token = "pylf_v2_..."

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.

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What Logfire data can I load into DuckDB?

These are the Logfire endpoints dlt can load into DuckDB:

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How do I load only new Logfire records?

Logfire exposes start_timestamp on v2/query, 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.

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{"name": "query", "endpoint": {
    "path": "v2/query",
    "data_selector": "rows",
    "incremental": {"cursor_path": "start_timestamp", "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.

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What does the generated Logfire pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading records and metrics from the Logfire API into DuckDB:

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

@dlt.source
def logfire_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api-us.pydantic.dev/api or https://api-eu.pydantic.dev/api (or specific regional endpoints for query services)",
            "auth": {"type": "bearer", "token": api_key},
        },
        "resources": [
            {"name": "query", "endpoint": {"path": "v2/query", "data_selector": "rows"}},
            {"name": "audit_logs", "endpoint": {"path": "v1/audit-logs/", "data_selector": "logs"}}
        ],
    }
    yield from rest_api_resources(config)


def load_logfire_to_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="logfire_pipeline",
        destination="duckdb",
        dataset_name="logfire_data",
    )
    load_info = pipeline.run(logfire_source())
    print(load_info)


if __name__ == "__main__":
    load_logfire_to_duckdb()

Run it with python logfire_pipeline.py. The agent iterates on this until it loads cleanly — you review and approve, rather than write it from scratch.

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How do I query Logfire 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):

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import dlt

data = dlt.pipeline("logfire_pipeline").dataset()
df = data.query.df()
print(df.head())

SQL:

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SELECT * FROM logfire_data.query LIMIT 10;

See querying your data with dataset and exploring it in marimo notebooks.

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How do I deploy the Logfire 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 Logfire loads into governed, documented models.
• Visualize & share — explore data in notebooks and publish live dashboards instead of static screenshots.

Book a demo →

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What other destinations can I load Logfire data to?

dlt loads into any of these — only the destination argument changes:

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.

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Next steps

• Ready for production? Book a demo of the dltHub platform →
• dlt LLM-native workflow docs
• REST API source reference
• Join the dlt community on Slack