Pathfix Python API Docs | dltHub

Pathfix is a service that manages OAuth/token handling and provides pass‑through connectivity to Instagram Graph API. The REST API base URL is https://labs.pathfix.com/integrate/command and OAuth‑based end‑user access tokens managed by Pathfix; client integrations use Pathfix keys..

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 Pathfix data in under 10 minutes.

---

What data can I load from Pathfix?

Here are some of the endpoints you can load from Pathfix:

---

How do I authenticate with the Pathfix API?

End‑user Instagram Graph API access tokens (Bearer) are managed via Pathfix; client code includes "Authorization: Bearer <access_token>" and may also send Pathfix public key and x‑partner‑key headers as required.

1. Get your credentials

1. Log in to your Pathfix account at https://app.pathfix.com.
2. Navigate to Integrations → Instagram Graph API.
3. Click Add Application (or select an existing one).
4. Enter the Facebook/Instagram App ID and App Secret obtained from the Facebook Developer portal.
5. Save the configuration.
6. In the left navigation bar select Keys and copy the Pathfix Public Key and x‑Partner‑Key.
7. Use the generated user access token (available after connecting an Instagram account) as the Bearer token for API calls.

2. Add them to .dlt/secrets.toml

Copy
[sources.pathfix_instagram_graph_api_source]
access_token = "your_user_access_token_here"
pathfix_public_key = "your_pathfix_public_key"
x_partner_key = "your_x_partner_key"

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.

---

How do I set up and run the pipeline?

Set up a virtual environment and install dlt:

Copy
uv venv && source .venv/bin/activate
uv pip install "dlt[workspace]"

1. Install the dlt AI Workbench:

Copy
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:

Copy
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 Pathfix 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:

Copy
python pathfix_instagram_graph_api_pipeline.py

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

Copy
Pipeline pathfix_instagram_graph_api_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset pathfix_instagram_graph_api_data
The duckdb destination used duckdb:/pathfix_instagram_graph_api.duckdb location to store data
Load package 1749667187.541553 is LOADED and contains no failed jobs

Inspect your pipeline and data:

Copy
dlt pipeline pathfix_instagram_graph_api_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.

---

Python pipeline example

This example loads media and comments from the Pathfix API into DuckDB. It mirrors the endpoint and data selector configuration from the table above:

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

@dlt.source
def pathfix_instagram_graph_api_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://labs.pathfix.com/integrate/command",
            "auth": {
                "type": "bearer",
                "access_token": access_token,
            },
        },
        "resources": [
            {"name": "media", "endpoint": {"path": "v15.0/{ig-user-id}/media", "data_selector": "data"}},
            {"name": "comments", "endpoint": {"path": "v15.0/{media-id}/comments", "data_selector": "data"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="pathfix_instagram_graph_api_pipeline",
        destination="duckdb",
        dataset_name="pathfix_instagram_graph_api_data",
    )
    load_info = pipeline.run(pathfix_instagram_graph_api_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.

---

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):

Copy
import dlt

data = dlt.pipeline("pathfix_instagram_graph_api_pipeline").dataset()
sessions_df = data.media.df()
print(sessions_df.head())

SQL (DuckDB example):

Copy
SELECT * FROM pathfix_instagram_graph_api_data.media LIMIT 10;

In a marimo or Jupyter notebook:

Copy
import dlt

data = dlt.pipeline("pathfix_instagram_graph_api_pipeline").dataset()
data.media.df().head()

See how to explore your data in marimo Notebooks and how to query your data in Python with dataset.

---

What destinations can I load Pathfix 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.

---

Troubleshooting

Authentication failures

• Invalid or expired token – The API returns 400 with error code OAuthAccessTokenException. Refresh the user access token via Pathfix or re‑authenticate.
• Missing Pathfix keys – Ensure pathfix_public_key and x_partner_key are correctly set in secrets.toml.

Rate limits

• Instagram Graph API enforces a limit of 200 calls per hour per user. Exceeding this returns 429 Too Many Requests. Implement back‑off and retry after the Retry-After header.

Pagination

• List endpoints return a paging object with cursors (before, after). Use the after cursor as the after query parameter to retrieve the next page. Continue until paging.next is absent.

Ensure that the API key is valid to avoid 401 Unauthorized errors. Also, verify endpoint paths and parameters to avoid 404 Not Found errors.

---

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.

Copy
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