Misty Robotics Python API Docs | dltHub

Misty Robotics API is a REST API that exposes control and sensor endpoints for the Misty II robot, allowing external applications to command movement, sensors, media assets, skills, and device/system information over HTTP to the robot's local web server. The REST API base URL is http://<robot-ip-address>/api and Local network access to the robot's HTTP API; no provider‑issued API key – requests are sent directly to the robot on the local network..

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 Misty Robotics data in under 10 minutes.

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What data can I load from Misty Robotics?

Here are some of the endpoints you can load from Misty Robotics:

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How do I authenticate with the Misty Robotics API?

Misty's HTTP API is accessed by sending HTTP requests to the robot's IP. Standard HTTP headers like Content-Type: application/json are used for request bodies; no bearer token or API key is required.

1. Get your credentials

1. Connect your computer or development machine to the same local network as the Misty II robot.
2. Locate the robot's IP address (shown on the robot's screen, via your router's DHCP client list, or by scanning the subnet).
3. Use that IP address in API calls; no dashboard or API key provisioning is required.

2. Add them to .dlt/secrets.toml

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[sources.misty_robotics_source]
robot_ip = "192.168.1.100"

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 Misty Robotics 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 misty_robotics_pipeline.py

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

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Pipeline misty_robotics_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset misty_robotics_data
The duckdb destination used duckdb:/misty_robotics.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 misty_robotics_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 images_list and device from the Misty Robotics 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 misty_robotics_source(robot_ip=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "http://<robot-ip-address>/api",
            "auth": {
                "type": "none_local",
                "": robot_ip,
            },
        },
        "resources": [
            {"name": "images_list", "endpoint": {"path": "images/list", "data_selector": "result"}},
            {"name": "device", "endpoint": {"path": "device", "data_selector": "result"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="misty_robotics_pipeline",
        destination="duckdb",
        dataset_name="misty_robotics_data",
    )
    load_info = pipeline.run(misty_robotics_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("misty_robotics_pipeline").dataset()
sessions_df = data.images_list.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM misty_robotics_data.images_list LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("misty_robotics_pipeline").dataset()
data.images_list.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 Misty Robotics 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 / Network access failures

If you cannot reach the robot, verify your host is on the same local network and you can reach http:///api via curl or a browser. The Misty API does not document cloud‑issued API keys – access is allowed by network access to the robot.

400 / 422 Parameter errors

Many commands return a JSON object with an error field and status: "Failed" when required parameters are missing (example: DriveHeading returns an error string listing missing parameters and status: "Failed"). Always validate required parameters in request body or query string.

404 Not Found / Asset not present

GET endpoints that request an asset by FileName return HTTP 404 or a failed status if the file does not exist. Use the list endpoints (e.g., /api/images/list or /api/videos/list) to enumerate available assets before requesting them.

Large media / log truncation and size limits

GET /api/logs returns up to ~3 MB of log data and only stores up to 14 days of logs; responses may be truncated. For media endpoints, use Base64=true to receive data inline or Base64=false to receive binary streamed responses.

Rate limiting and alpha/beta behaviour

The documentation notes some endpoints are Alpha/Beta and may behave unpredictably. No public rate limits are documented in the reference; implement retry/backoff on intermittent failures.

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