Slack Python API Docs | dltHub

Slack Web API is an HTTP RPC-style API for querying and acting on Slack workspaces (messages, conversations, users, files, views, etc.). The REST API base URL is https://slack.com/api/ and all requests require a Bearer token (OAuth access token) for authentication.

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 Slack data in under 10 minutes.

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What data can I load from Slack?

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

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How do I authenticate with the Slack API?

Authenticate by providing an OAuth bearer token (xoxb-, xoxp-, xoxa-, etc.) in the Authorization header: Authorization: Bearer xoxb-... . Tokens may alternatively be provided as a POST body parameter named token, but not as a query parameter. Use TLS/HTTPS.

1. Get your credentials

1. Create/register an app at https://api.slack.com/apps. 2) Configure app scopes (bot or user scopes) under OAuth & Permissions. 3) Install the app to a workspace via the OAuth install flow (or the app management UI). 4) After installation obtain the access token (bot or user) shown in the app's OAuth & Permissions page or returned by oauth.v2.access.

2. Add them to .dlt/secrets.toml

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[sources.slack_block_kit_source]
slack_token = "xoxb-your_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 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:

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python slack_block_kit_pipeline.py

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

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Pipeline slack_block_kit_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset slack_block_kit_data
The duckdb destination used duckdb:/slack_block_kit.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 slack_block_kit_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 users from the Slack 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 slack_block_kit_source(slack_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://slack.com/api/",
            "auth": {
                "type": "bearer",
                "token": slack_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_block_kit_pipeline",
        destination="duckdb",
        dataset_name="slack_block_kit_data",
    )
    load_info = pipeline.run(slack_block_kit_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("slack_block_kit_pipeline").dataset()
sessions_df = data.conversations.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM slack_block_kit_data.conversations LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("slack_block_kit_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 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.

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Troubleshooting

Authentication failures

If you receive ok: false with error "invalid_auth" or HTTP 401/403, your token is missing, expired, revoked, or lacks required scopes. Verify the token used, ensure Authorization: Bearer header is sent, and re-install the app or grant required scopes.

Rate limits and 429 responses

Exceeding method-level rate limits returns HTTP 429 Too Many Requests and a Retry-After header. Respect Retry-After and implement exponential backoff with jitter; many methods are cursor-paginated and have specific per-method tiers (see method reference).

Pagination

Many list methods are cursor-paginated. Responses include response_metadata.next_cursor (or next_cursor) — pass cursor parameter on subsequent calls. When next_cursor is empty or absent, pagination is complete.

Common API errors

Slack returns JSON with ok: false and an error string (e.g., invalid_auth, missing_scope, not_in_channel, channel_not_found, rate_limited, invalid_arguments). For JSON syntax problems you may see invalid_json or json_not_object. Server-side transient errors are 5xx; use 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