Load FalkorDB data to DuckDB

FalkorDB provides a REST API via the FalkorDB Browser interface for managing graphs, users, and database configurations. Everything needed to build a working FalkorDB → 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 FalkorDB 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 FalkorDB 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 FalkorDB 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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FalkorDB API at a glance

These values come from the FalkorDB API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the FalkorDB API?

Authentication requires a JWT bearer token passed in the Authorization header (e.g., 'Authorization: Bearer '). Tokens can be obtained by authenticating against /api/auth/tokens/credentials.

1. Get your credentials

To obtain API credentials, you must generate a JWT bearer token. You can do this via the Browser UI in the account settings or programmatically by sending a POST request to the /api/auth/tokens/credentials endpoint. Your request payload should include your database username, password, host, and port. The server will return a token which must be included in the Authorization: Bearer header for all subsequent API requests. Note that your server must have an ENCRYPTION_KEY set in your .env.local file (generated via openssl rand -hex 32) for the authentication system to function.

2. Add them to .dlt/secrets.toml

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[sources.falkordb_source]
falkordb_url = "http://localhost:6379"
falkordb_api_token = "your_jwt_token_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.

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What FalkorDB data can I load into DuckDB?

These are the FalkorDB endpoints dlt can load into DuckDB:

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How do I load only new FalkorDB records?

FalkorDB exposes updated_at on api/runs, 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": "etl_runs", "endpoint": {
    "path": "api/runs",
    "incremental": {"cursor_path": "updated_at", "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 FalkorDB pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading /api/auth/tokens/credentials and /graphs from the FalkorDB API into DuckDB:

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

@dlt.source
def falkordb_source(api_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "The base URL corresponds to the specific FalkorDB Browser instance URL (e.g., http://localhost:5000 or your hosted server address).",
            "auth": {"type": "bearer", "token": api_token},
        },
        "resources": [
            {"name": "etl_runs", "endpoint": {"path": "api/runs"}},
            {"name": "etl_configs", "endpoint": {"path": "api/configs"}}
        ],
    }
    yield from rest_api_resources(config)


def load_falkordb_to_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="falkordb_pipeline",
        destination="duckdb",
        dataset_name="falkordb_data",
    )
    load_info = pipeline.run(falkordb_source())
    print(load_info)


if __name__ == "__main__":
    load_falkordb_to_duckdb()

Run it with python falkordb_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 FalkorDB 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("falkordb_pipeline").dataset()
df = data.graphs.df()
print(df.head())

SQL:

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SELECT * FROM falkordb_data.graphs LIMIT 10;

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

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How do I deploy the FalkorDB 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 FalkorDB 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 FalkorDB 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