Harvest Python API Docs | dltHub

Harvest is a REST API for time tracking, projects, expenses and billing allowing programmatic access to Harvest account data. The REST API base URL is https://api.harvestapp.com/v2 and all requests require a Bearer access token and the Harvest account id 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 Harvest data in under 10 minutes.

---

What data can I load from Harvest?

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

---

How do I authenticate with the Harvest API?

Authentication uses either a Personal Access Token (or OAuth2 access token). Include the token in the Authorization header (Bearer ) and include the Harvest-Account-Id header (Harvest-Account-Id: <ACCOUNT_ID>) on requests. A User-Agent header identifying your app is also required.

1. Get your credentials

1. Sign in to Harvest and open Harvest ID (https://id.getharvest.com/developers). 2) In Developers create a Personal Access Token. 3) Copy the returned access token and note the listed account ID(s). 4) Use the token in Authorization: Bearer and Harvest-Account-Id: <account_id>.

2. Add them to .dlt/secrets.toml

Copy
[sources.harvest_time_tracking_source]
access_token = "your_personal_access_token_here"
account_id = "your_account_id_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 Harvest 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 harvest_time_tracking_pipeline.py

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

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

Inspect your pipeline and data:

Copy
dlt pipeline harvest_time_tracking_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 time_entries from the Harvest 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 harvest_time_tracking_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.harvestapp.com/v2",
            "auth": {
                "type": "bearer",
                "token": access_token,
            },
        },
        "resources": [
            {"name": "projects", "endpoint": {"path": "projects", "data_selector": "projects"}},
            {"name": "time_entries", "endpoint": {"path": "time_entries", "data_selector": "time_entries"}}
        ],
    }
    yield from rest_api_resources(config)


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

SQL (DuckDB example):

Copy
SELECT * FROM harvest_time_tracking_data.projects LIMIT 10;

In a marimo or Jupyter notebook:

Copy
import dlt

data = dlt.pipeline("harvest_time_tracking_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 Harvest 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

If Authorization or Harvest-Account-Id are missing or invalid you will receive 401/403 responses. Ensure Authorization: Bearer and Harvest-Account-Id: <ACCOUNT_ID> headers are present and that the Personal Access Token has not been revoked.

Rate limiting

Harvest enforces rate limits (100 requests per 15 seconds for general API; 100 requests per 15 minutes for Reports). When throttled the API returns HTTP 429 and includes a Retry-After header indicating how many seconds to wait.

Pagination notes

Most list endpoints use page and per_page query parameters. Responses are paginated; request additional pages with ?page=N&per_page=M. Use response Link/headers or the documented pagination to navigate pages.

Common API errors: 401 Unauthorized, 403 Forbidden, 404 Not Found, 422 Unprocessable Entity (validation errors), 429 Too Many Requests (rate limit), 500 Server Error.

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