Freshchat Python API Docs | dltHub

Freshchat is a cloud messaging platform for customer engagement and support, exposing REST APIs to manage conversations, messages, users, agents, channels, groups and reports. The REST API base URL is https://{account_subdomain}.freshchat.com/v2 and all requests require an OAuth2 / API key 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 Freshchat data in under 10 minutes.

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What data can I load from Freshchat?

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

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How do I authenticate with the Freshchat API?

Freshchat uses OAuth2‑style API tokens (referred to as API Key). Include header: Authorization: Bearer <API_KEY>. Also set Accept: application/json and Content-Type for write requests.

1. Get your credentials

1. Sign in to your Freshchat account as an admin.
2. Navigate to Admin > Settings > Website Tracking and APIs (or Admin > Configure > API Tokens) in the Freshchat UI.
3. In API DETAILS FOR CHAT, copy the displayed "Your API Key" or click Generate token to create one.
4. Note your chat account URL shown (the {account_subdomain}.freshchat.com) and use it as the API base domain.

2. Add them to .dlt/secrets.toml

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[sources.freshchat_source]
api_key = "your_freshchat_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.

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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 Freshchat 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 freshchat_pipeline.py

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

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Pipeline freshchat_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset freshchat_data
The duckdb destination used duckdb:/freshchat.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 freshchat_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 conversations and messages from the Freshchat 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 freshchat_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://{account_subdomain}.freshchat.com/v2",
            "auth": {
                "type": "bearer",
                "token": api_key,
            },
        },
        "resources": [
            {"name": "conversations", "endpoint": {"path": "conversations", "data_selector": "conversation"}},
            {"name": "messages", "endpoint": {"path": "conversations/{conversation_id}/messages"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="freshchat_pipeline",
        destination="duckdb",
        dataset_name="freshchat_data",
    )
    load_info = pipeline.run(freshchat_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("freshchat_pipeline").dataset()
sessions_df = data.conversations.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM freshchat_data.conversations LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("freshchat_pipeline").dataset()
data.conversations.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 Freshchat 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 the Authorization header is missing or the token is expired you receive 401 Unauthenticated Request. Regenerate or retrieve a valid API key from Admin > API Tokens and retry.

Rate limits and 429

Freshchat uses standard HTTP codes; heavy queries may be throttled. When receiving 429, implement exponential backoff and respect page/items_per_page limits (messages max items_per_page=50).

Pagination and list endpoints

List endpoints use page and items_per_page query params. Messages API defaults: page=1, items_per_page default 20, max 50. Some list endpoints return an array at top level (e.g., messages, conversations/fields) while others wrap results with resource keys (e.g., users -> "users", agents -> "agents"). Always inspect the response for the data selector shown in docs.

Common server errors

200, 202 indicate success/accepted. 401 unauthorized, 403 forbidden for insufficient scopes, 404 not found for invalid ids, 500/503 server errors — retry 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