Symphony Messaging Python API Docs | dltHub

The Agent API in Symphony Messaging allows bots to send and receive encrypted messages. It uses tokens for authentication and interacts with the Key Manager for encryption. For detailed API endpoints, refer to the official API reference documentation. The REST API base URL is https://{your-pod-subdomain}.symphony.com and Two-token model: Session token for Pod APIs and Key Manager token for Agent encryption APIs; both sent as Bearer tokens..

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 Symphony Messaging data in under 10 minutes.

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What data can I load from Symphony Messaging?

Here are some of the endpoints you can load from Symphony Messaging:

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How do I authenticate with the Symphony Messaging API?

Bots authenticate by calling the Session Authenticate endpoint to receive a Session Token and the Key Manager Authenticate endpoint to receive a Key Manager Token. Both tokens are sent as Bearer tokens in the Authorization header for subsequent requests.

1. Get your credentials

1. Register the bot's RSA key pair with Symphony according to your organization’s onboarding guide. 2. Call the Session Authenticate endpoint (Session Auth URL: YOUR-POD-SUBDOMAIN-api.symphony.com) to obtain a Session Token. 3. Call the Key Manager Authenticate endpoint (Key Auth URL: YOUR-POD-SUBDOMAIN-api.symphony.com) to obtain a Key Manager Token. 4. Use the Session Token in the Authorization: Bearer header for Pod/Agent calls and the Key Manager Token in the Authorization or X-Key-Manager-Authorization header for Agent‑specific calls.

2. Add them to .dlt/secrets.toml

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[sources.symphony_messaging_source]
session_token = "your_session_token_here"
key_manager_token = "your_key_manager_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 Symphony Messaging 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 symphony_messaging_pipeline.py

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

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Pipeline symphony_messaging_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset symphony_messaging_data
The duckdb destination used duckdb:/symphony_messaging.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 symphony_messaging_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 messages and datafeeds from the Symphony Messaging 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 symphony_messaging_source(session_token, key_manager_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://{your-pod-subdomain}.symphony.com",
            "auth": {
                "type": "bearer",
                "session_token (and key_manager_token for Agent encryption calls)": session_token, key_manager_token,
            },
        },
        "resources": [
            {"name": "messages", "endpoint": {"path": "v4/streaming/message", "data_selector": "messages"}},
            {"name": "datafeeds", "endpoint": {"path": "v1/datafeed/{id}/read", "data_selector": "events"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="symphony_messaging_pipeline",
        destination="duckdb",
        dataset_name="symphony_messaging_data",
    )
    load_info = pipeline.run(symphony_messaging_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("symphony_messaging_pipeline").dataset()
sessions_df = data.messages.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM symphony_messaging_data.messages LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("symphony_messaging_pipeline").dataset()
data.messages.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 Symphony Messaging 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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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