Telegram Python API Docs | dltHub

Telegram Bot API is an HTTP-based API that allows developers to create and control Telegram bots to send and receive messages and interact with Telegram users and chats. The REST API base URL is https://api.telegram.org/bot<TOKEN>/ and All requests require the bot token included in the request URL (token‑based 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 Telegram data in under 10 minutes.

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What data can I load from Telegram?

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

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How do I authenticate with the Telegram API?

Authentication is done with a bot token issued by BotFather; include the token in the base URL as https://api.telegram.org/bot/METHOD_NAME. No Authorization header is required for standard Bot API requests.

1. Get your credentials

1. Open Telegram and start a chat with @BotFather.
2. Send /newbot and follow prompts to name and username your bot.
3. BotFather will return a token string in the format 123456:ABC-DEF...; this is your bot token.
4. (Optional) Use /token to regenerate or /revoke to invalidate.

2. Add them to .dlt/secrets.toml

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[sources.telegram_source]
bot_token = "123456:ABC-DEF1234ghIkl-zyx57W2v1u123ew11"

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 Telegram 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 telegram_pipeline.py

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

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Pipeline telegram_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset telegram_data
The duckdb destination used duckdb:/telegram.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 telegram_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 getUpdates and getMe from the Telegram 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 telegram_source(bot_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.telegram.org/bot<TOKEN>/",
            "auth": {
                "type": "api_key",
                "bot_token": bot_token,
            },
        },
        "resources": [
            {"name": "updates", "endpoint": {"path": "getUpdates", "data_selector": "result"}},
            {"name": "get_me", "endpoint": {"path": "getMe", "data_selector": "result"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="telegram_pipeline",
        destination="duckdb",
        dataset_name="telegram_data",
    )
    load_info = pipeline.run(telegram_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("telegram_pipeline").dataset()
sessions_df = data.updates.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM telegram_data.updates LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("telegram_pipeline").dataset()
data.updates.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 Telegram 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 requests return {"ok":false, "error_code":401, "description":"Unauthorized"} or similar, verify the token was pasted exactly and that it is included in the URL as https://api.telegram.org/bot/METHOD. Regenerate token with @BotFather if needed.

Rate limiting and retries

Telegram may limit excessive requests; on failed deliveries for webhooks the server retries several times then gives up. Use long polling (getUpdates with timeout) or webhooks with max_connections to control load.

Pagination / getUpdates offsets

Use the update_id and the offset parameter when calling getUpdates to mark updates as confirmed. Set offset to last_update_id+1 to avoid reprocessing updates; negative offsets retrieve from the end.

Common API errors: responses always include 'ok' boolean; on error 'ok' is false and 'error_code' (int) and 'description' (string) explain the problem. Example errors include 401 Unauthorized (invalid token), 400 Bad Request (invalid parameters), 429 Too Many Requests (rate limiting). For webhook delivery errors, last_error_message and last_error_date appear in getWebhookInfo.

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