Bugherd Python API Docs | dltHub

BugHerd is a visual bug‑tracking and feedback platform that offers a REST API for managing projects, tasks, users and related resources. The REST API base URL is https://www.bugherd.com/api_v2 and All requests use HTTP Basic authentication with the API key as the username and "x" as the password..

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 Bugherd data in under 10 minutes.

---

What data can I load from Bugherd?

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

---

How do I authenticate with the Bugherd API?

Send an Authorization header with the value "Basic <base64(api_key:x)>" on every request; the API key is obtained from the BugHerd UI.

1. Get your credentials

1. Log in to your BugHerd account.
2. Click your avatar → Settings.
3. Choose General Settings.
4. Locate the API Key section.
5. Click Generate new API key (or copy the existing one).
6. Copy the key; it will be used as the username in HTTP Basic auth.

2. Add them to .dlt/secrets.toml

Copy
[sources.bugherd_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.

---

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 Bugherd 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 bugherd_pipeline.py

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

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

Inspect your pipeline and data:

Copy
dlt pipeline bugherd_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 projects and tasks from the Bugherd 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 bugherd_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://www.bugherd.com/api_v2",
            "auth": {
                "type": "http_basic",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "projects", "endpoint": {"path": "projects.json", "data_selector": "projects"}},
            {"name": "tasks", "endpoint": {"path": "projects/{project_id}/tasks.json", "data_selector": "tasks"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="bugherd_pipeline",
        destination="duckdb",
        dataset_name="bugherd_data",
    )
    load_info = pipeline.run(bugherd_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("bugherd_pipeline").dataset()
sessions_df = data.projects.df()
print(sessions_df.head())

SQL (DuckDB example):

Copy
SELECT * FROM bugherd_data.projects LIMIT 10;

In a marimo or Jupyter notebook:

Copy
import dlt

data = dlt.pipeline("bugherd_pipeline").dataset()
data.projects.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 Bugherd 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 errors

• 401 Unauthorized or 403 Forbidden occurs when the API key is missing, malformed, or the password is not the required literal "x". Verify that the Authorization header contains a correct Base64‑encoded "api_key:x" string.

Rate limiting

• The API enforces a sliding‑window limit of 60 requests per minute with bursts of up to 10. Exceeding this returns 429 Too Many Requests. Implement retry‑after logic or throttle calls.

Pagination

• Collection endpoints return a top‑level meta.count indicating total records and accept a page query parameter. Each page returns up to 100 items. Continue requesting subsequent pages until the returned array length is less than 100 or the cumulative count matches meta.count.

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