bagel

Bagel by Extelligence lets you ask questions about robotics, drone, and IoT data in plain English. Every calculation over your message data is DuckDB SQL, not model guesswork, and Bagel shows you the query so you can audit it.

Is my IMU sensor overheating?

Bagel also has an intelligent edge data reduction pipeline: describe an event and Bagel runs the detection on the robot, keeping the windows that matter and dropping the rest. An MCP server puts all of it in your LLM's hands: Claude Code, Gemini, Cursor, or a fully local model.

Bagel was the first MCP server to ship a real analysis toolkit for robotics data, and it keeps the LLM where it belongs: in front of your logs, never in your robot's control loop.

📦 Install

Bagel is on PyPI as bagel-mcp. No Docker and no ROS install needed for recorded data.

Claude Code, one line (needs uv):

claude mcp add bagel -- uvx --from "bagel-mcp[ros]" bagel-mcp --transport stdio

pip, into any Python 3.10 to 3.12 environment:

pip install "bagel-mcp[ros]"

Then point your MCP client at the bagel-mcp command it installed (setup for Claude Desktop, Cursor and Codex is in the Quickstart). Open your agent and ask:

Summarize the bag "~/logs/run_42.mcap".

The ros extra reads ROS 1 and ROS 2 bags. Flight logs, CAN / MDF4, live MQTT and the rest are extras too. Live ROS robots (rosbridge) and fleet/edge pipelines run in Docker.

🥯 Key Features

• Ask in plain language: No deep domain expertise needed.
• Transparent calculations: Deterministic SQL queries. No black-box LLM math.
• Natural-language pipelines: "Keep 10s around every hard brake, drop the rest": one sentence becomes an auditable pipeline: previewed before a byte is written, then run once, across a fleet, or standing at the edge.
• Broad LLM support: Claude Code, Gemini, Cursor, Codex, and more.
• Installs your way: pip / uvx for recorded data, Docker images for live robots and the edge.
• Extensible capabilities: Bagel can learn new tricks.
• Wide format coverage: Missing your data format? Open a ticket.

🥯 Try it in 60 seconds

No MCP client, no LLM, no config: run the same deterministic checks against a bundled sample log and get a robot-health report card straight to your terminal.

docker run -it --rm ghcr.io/extelligence-ai/bagel/px4:latest demo

sample.ulg - 41.5s, 2018 messages, 77 topics

Power      ⚠️  min 21.07V, largest drop 2.37V at ~t=+4.8s, end 23.45V (battery_status_0)
IMU        ✅  accel_z stddev 1.6x the log baseline at ~t=+36.8s (sensor_combined_0)
GPS        —  skipped: no GPS topic
Data gaps  ✅  no gap > 1.05x median interval (checked battery_status_0, sensor_combined_0)
...

The ROS2 images (ros2-kilted, ros2-jazzy, ros2-iron, ros2-humble) run demo the same way, against a lighter bundled sample (px4 is the one that ships with a flight log rich enough to show every check). Point it at your own log with demo /path/to/log (mount it with -v first), or keep reading for the full MCP setup below.

⚡️ Quickstart

Two ways to run Bagel. Pick by data:

You have	Run Bagel with
Recorded data: ROS 1 .bag and ROS 2 .db3 / .mcap bags, flight logs (PX4, ArduPilot, Betaflight), CAN / MDF4, CSV / JSON / Parquet, ROS text logs; live MQTT robots	uvx or pip, below: no Docker, no ROS install
Live ROS robots (rosbridge), fleet and edge deployments	Docker, further down: the images carry the ROS stacks and the standing-pipeline runtime

🐍 Install with uvx or pip (no Docker)

Your MCP client launches the server itself over stdio, so there is nothing to start or keep running. Pick uvx (nothing to manage: it fetches and caches Bagel on first use) or pip (a normal install you control).

With uvx. Install uv (curl -LsSf https://astral.sh/uv/install.sh | sh on macOS and Linux), then:

Claude Code:

claude mcp add bagel -- uvx --from "bagel-mcp[ros]" bagel-mcp --transport stdio

Any client that takes a JSON MCP config (Claude Desktop, Cursor, Codex, ...):

{
  "mcpServers": {
    "bagel": {
      "command": "uvx",
      "args": ["--from", "bagel-mcp[ros]", "bagel-mcp", "--transport", "stdio"]
    }
  }
}

With pip. Python 3.10 to 3.12; a virtual environment keeps it tidy:

python3 -m venv ~/.bagel-venv
~/.bagel-venv/bin/pip install "bagel-mcp[ros]"
claude mcp add bagel -- ~/.bagel-venv/bin/bagel-mcp --transport stdio

For a JSON config, use the full path as the command: "command": "/Users/you/.bagel-venv/bin/bagel-mcp", "args": ["--transport", "stdio"].

Apple Silicon Macs: use an ARM Python, not an Intel Python running through Rosetta. Check with python3 -c "import platform; print(platform.machine())"; it should print arm64. If it prints x86_64, create a separate native environment:

uv venv --seed --python cpython-3.12-macos-aarch64-none ~/.bagel-arm-venv
~/.bagel-arm-venv/bin/python -m pip install "bagel-mcp[ros]"
claude mcp add bagel -- ~/.bagel-arm-venv/bin/bagel-mcp --transport stdio

This avoids the cryptography source build: upstream removed Intel macOS support in version 49. Fresh installs currently select MCP 1.30.0, whose dependency chain is bagel-mcp → mcp → pyjwt[crypto] → cryptography. This applies to the base package and bagel-mcp[ros], even without the upload extra. On an Intel Mac, if installation fails while building cryptography, use the Docker path below. Do not downgrade cryptography to restore an old wheel; its older releases miss security fixes.

Format support comes as extras, so that a PX4 user never downloads the automotive parsers. List the ones you need, comma-separated: uvx --from "bagel-mcp[ros,px4,automotive]" bagel-mcp --transport stdio, or pip install "bagel-mcp[ros,px4,automotive]". If you ask about a format whose extra is missing, Bagel's error names the exact command to add it.

Extra	Adds
ros	ROS 1 .bag and ROS 2 .db3 bags (reading, and the reduce / snippet writers), in pure Python
px4	PX4 .ulg
ardupilot	ArduPilot .bin
betaflight	Betaflight .bbl / .bfl
automotive	CAN captures (.blf, .asc + DBC) and ASAM MDF4
iot	Live MQTT (incl. Sparkplug B) and InfluxDB sources
viz	Rerun export
upload	GCS and Azure Blob upload tasks
cloudini	Cloudini point-cloud tasks

MCAP, CSV / JSON / Parquet, ROS text logs, PlotJuggler / Lichtblick / LeRobot exports and S3 upload need no extra.

PX4 log parsing does not require Git. Git and network access are used only for optional field descriptions; pass download_description: false in the tool's args to skip fetching those descriptions.

To upgrade, repeat the extras you installed with, so their dependencies update too. uvx picks up new releases on its own (force it with uvx --refresh --from "bagel-mcp[ros,px4]" bagel-mcp --help); with pip, run pip install -U "bagel-mcp[ros,px4]", using your own list of extras.

Then prompt, pointing at your own file:

Summarize the metadata of the MCAP bag "~/logs/run_42.mcap".

[!NOTE] No ROS installation is needed for bag files: the ros extra reads and writes them with the pure-Python rosbags library (sqlite3 and MCAP storage, zstd / bz2 / lz4 compression, message types from the bag itself or from the bundled interface definitions of every distro from Humble on). Live ROS topics (rosbridge) and the fleet/edge runtime stay with Docker.

🐳 Run with Docker (live robots, fleet and edge, distro-specific ROS)

[!TIP] Already have Claude Code? Just paste the link to this repo and tell Claude what environment you want:

Set up https://github.com/Extelligence-ai/bagel for ROS2 Kilted.

Claude will clone the repo, start Docker, and wire up the MCP connection for you.

📋 Prerequisites

Install Docker Desktop and Claude Code (or another MCP-enabled LLM).

[!NOTE] arm64 hosts (Apple Silicon, Raspberry Pi, Jetson, Graviton): ros2-kilted — the default service, and the one server.json pins — ships as a multi-arch image, so Docker pulls a native arm64 build. No extra setup.

The other services are published for amd64 only. Docker Desktop emulates them automatically, so they run on Apple Silicon (slower, but they work). On arm64 Linux with plain Docker Engine there is no emulation by default and they fail immediately with exec format error — install QEMU/binfmt first:

docker run --privileged --rm tonistiigi/binfmt --install amd64

1. Clone and start Bagel

git clone https://github.com/Extelligence-ai/bagel.git && cd bagel
docker compose run --service-ports ros2-kilted

[!TIP] Port 8000 already in use? Set MCP_SERVER_PORT to something else, for example MCP_SERVER_PORT=8100 docker compose run --service-ports ros2-kilted, and use that port in step 2.

Pick the service that matches your environment:

Service	Use case
ros2-kilted	ROS2 Kilted (latest)
ros2-jazzy	ROS2 Jazzy
ros2-jazzy-jev	ROS2 Jazzy + on-robot decision model (GPU, beta)
ros2-iron	ROS2 Iron
ros2-humble	ROS2 Humble
ros1-noetic	ROS1 Noetic
ros1-noetic-cv	ROS1 Noetic + CV
px4	PX4 flight logs
ardupilot	ArduPilot flight logs