FreeAgent Python API Docs | dltHub

FreeAgent is an online accounting platform providing a REST API for programmatic access to company and accounting data (invoices, projects, contacts, bank transactions, timeslips, etc.). The REST API base URL is https://api.freeagent.com/v2 and All requests require OAuth 2.0 access tokens presented as a Bearer token in the Authorization 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 FreeAgent data in under 10 minutes.

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What data can I load from FreeAgent?

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

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How do I authenticate with the FreeAgent API?

FreeAgent uses OAuth 2.0 (authorization code and refresh token flows). Exchange the authorization code at https://api.freeagent.com/v2/token_endpoint using HTTP Basic auth (client_id as username and client_secret as password) to receive an access_token and refresh_token. Include the access token in requests as: Authorization: Bearer .

1. Get your credentials

1. Register an app at the FreeAgent Developer Dashboard to obtain a Client ID and Client Secret.
2. Direct users to the authorize URL https://api.freeagent.com/v2/approve_app?client_id=...&response_type=code&redirect_uri=... to obtain an authorization code.
3. Exchange the code at POST https://api.freeagent.com/v2/token_endpoint using HTTP Basic auth (client_id/client_secret) with grant_type=authorization_code and the code to receive access_token and refresh_token.
4. Use the refresh_token with grant_type=refresh_token at the same token endpoint to obtain new access tokens when required.

2. Add them to .dlt/secrets.toml

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[sources.free_agent_source]
client_id = "your_client_id_here"
client_secret = "your_client_secret_here"
refresh_token = "your_refresh_token_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.

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How do I set up and run the pipeline?

Set up a virtual environment and install dlt:

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

1. Install the dlt AI Workbench:

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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:

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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 FreeAgent 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:

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python free_agent_pipeline.py

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

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

Inspect your pipeline and data:

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dlt pipeline free_agent_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.

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Python pipeline example

This example loads projects and invoices from the FreeAgent API into DuckDB. It mirrors the endpoint and data selector configuration from the table above:

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

@dlt.source
def free_agent_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.freeagent.com/v2",
            "auth": {
                "type": "bearer",
                "access_token": access_token,
            },
        },
        "resources": [
            {"name": "projects", "endpoint": {"path": "v2/projects", "data_selector": "projects"}},
            {"name": "invoices", "endpoint": {"path": "v2/invoices", "data_selector": "invoices"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="free_agent_pipeline",
        destination="duckdb",
        dataset_name="free_agent_data",
    )
    load_info = pipeline.run(free_agent_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.

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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):

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import dlt

data = dlt.pipeline("free_agent_pipeline").dataset()
sessions_df = data.projects.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM free_agent_data.projects LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("free_agent_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.

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What destinations can I load FreeAgent 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.

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Troubleshooting

Authentication failures

If you receive 401 Unauthorized, verify the Authorization header: Authorization: Bearer <access_token>. Ensure the token is not expired; refresh using the refresh_token at the token endpoint. Also confirm your app's Client ID/Secret and that the redirect_uri matches the one registered.

Rate limits and pagination

Responses returning multiple items are paginated (default 25, up to per_page=100). Use page and per_page query params. The Link response header provides rel="next"/"prev"/"first"/"last" links; X-Total-Count contains the total number of entries. Handle pagination by following rel="next" until none remains.

Required headers and format

Include Accept: application/json and Content-Type: application/json for JSON responses. The API requires a User-Agent header that identifies your application; requests without a User-Agent may be rejected.

Common errors

• 400 Bad Request: invalid parameters or malformed payload.
• 401 Unauthorized: invalid/expired token or missing Authorization header.
• 403 Forbidden: insufficient access level for the resource.
• 404 Not Found: resource id does not exist.
• 429 Too Many Requests: rate limiting (respect Retry-After if present).
• 5xx Server errors: transient issues — implement retries with backoff.

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

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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.

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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