Slack Python API Docs | dltHub

Slack Web API is an interface for querying information from and enacting change in a Slack workspace. The REST API base URL is https://slack.com/api/ and requests require a Bearer token in the Authorization header or as a POST parameter.

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 add "dlt[hub]" and start loading Slack data in under 10 minutes.

---

What data can I load from Slack?

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

---

How do I authenticate with the Slack API?

Slack uses OAuth tokens that should be passed in the Authorization HTTP header with the Bearer scheme (e.g., 'Authorization: Bearer xoxb-...' or 'Authorization: Bearer xoxp-...'). Alternatively, tokens can be sent as a 'token' parameter in an 'application/x-www-form-urlencoded' POST body.

1. Get your credentials

To obtain credentials for the Slack API: 1. Sign in to your Slack account and navigate to the Slack API dashboard at https://api.slack.com/apps. 2. Click 'Create New App' and choose 'From scratch' or 'From an app manifest'. 3. Once the app is created, navigate to the 'OAuth & Permissions' section in the left sidebar. 4. Scroll to the 'Scopes' section and add the necessary Bot Token Scopes (e.g., channels

, users). 5. Scroll up to the 'OAuth Tokens for Your Workspace' section and click 'Install to Workspace' to authorize the app. 6. After installation, copy the 'Bot User OAuth Token' (which starts with xoxb-) displayed in the 'OAuth Tokens' section. This token acts as your credential.

2. Add them to .dlt/secrets.toml

Copy
[sources.slack_source]
access_token = "xoxb-your-slack-bot-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.

---

How do I set up and run the pipeline?

Set up a virtual environment and install dlt:

Copy
uv init
uv add "dlt[hub]"

1. Install the dlt AI harness:

Copy
uv run dlthub 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
uv run dlthub ai toolkit install rest-api-pipeline

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 Slack 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
uv run python slack_pipeline.py

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

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

Inspect your pipeline and data:

Copy
uv run dlthub 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 oauth.access and chat.postMessage from the Slack 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 slack_source(token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://slack.com/api/",
            "auth": {"type": "bearer", "token": token},
        },
        "resources": [
            {"name": "conversations", "endpoint": {"path": "conversations.list", "data_selector": "channels"}},
            {"name": "users", "endpoint": {"path": "users.list", "data_selector": "members"}}
        ],
    }
    yield from rest_api_resources(config)


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

SQL (DuckDB example):

Copy
SELECT * FROM slack_data.conversations LIMIT 10;

In a marimo or Jupyter notebook:

Copy
import dlt

data = dlt.pipeline("slack_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.

---

What destinations can I load Slack 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.

---

Next steps

Continue your data engineering journey with the other toolkits of the dltHub AI harness:

• data-exploration — Build custom notebooks, charts, and dashboards for deeper analysis with marimo notebooks.
• dlthub-platform — Deploy, schedule, and monitor your pipeline in production.

Copy
uv run dlthub ai toolkit install data-exploration
uv run dlthub ai toolkit install dlthub-platform

• 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