Bagel 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.

🥯 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.
• Dockerized environments: No local dependencies required.
• Extensible capabilities: Bagel can learn new tricks.
• Wide format coverage: Missing your data format? Open a ticket.

⚡️ Quickstart

[!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).

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:

[!TIP] To give Bagel access to your local files, edit compose.yaml before starting Docker: uncomment and update the volumes section under your chosen service.

Wait for this output:

INFO:     Uvicorn running on http://0.0.0.0:8000 (Press CTRL+C to quit)

2. Connect Claude Code

In a new terminal:

claude mcp add --transport sse bagel http://localhost:8000/sse

[!NOTE] The MCP endpoint is bound to localhost only (not exposed to the LAN) for security. To share it with other machines, drop the 127.0.0.1 prefix in compose.yaml and put an authenticated proxy in front: see SECURITY.md.

3. Prompt

claude

Summarize the metadata of the ROS2 bag "./data/sample/ros2/mcap".

That’s it: you’re chatting with your data.

🔒 Prefer fully offline?

Swap step 2 for a local model: your data and your LLM stay on the machine:

brew install ollama && ollama serve &                                  # or ollama.com
ollama pull qwen3:8b
uvx ollmcp --mcp-server-url http://localhost:8000/sse --model qwen3:8b

Model picks, expectations, and troubleshooting: Local LLMs guide.

🔌 Agent plugins (Claude Code and Codex)

Bagel ships an agent plugin: four skills that teach the agent when and how to drive the server (log triage, pipeline authoring, live sinks, visualization export) plus the MCP connection, wired automatically. The same plugin/ directory serves both Claude Code and OpenAI Codex.

/plugin marketplace add Extelligence-ai/bagel
/plugin install bagel@bagel

Codex and ChatGPT users: install bagel from the OpenAI Plugins Directory (one click), or clone the repo and add it as a plugin marketplace (the repo carries .agents/plugins/marketplace.json). Directory installs bundle the skills only, so also connect the server once in ~/.codex/config.toml:

[mcp_servers.bagel]
url = "http://localhost:8000/mcp"

Repo-marketplace and Claude Code installs wire this connection automatically.

Then start the container for your data format (see Quickstart): the plugin connects to http://localhost:8000/mcp by default. Any other MCP client can discover the same workflows server-side via the list_agent_capabilities tool.

Keep what matters, drop the rest

A robot records more data than you can afford to move. Bagel turns a question into a detector, runs it where the data is recorded, and ships only the windows around real events.

Here it is in one conversation:

The session above: a 20-minute (1,200 s) recording and the prompt "keep 10 seconds before and after every deceleration harder than −10 m/s²". The preview detects 7 events, merges them into 4 windows, and keeps 92 s of the 1,200 (7.6%); the run writes a 2.1 GB bag down to 161 MB. These figures are illustrative demo output, not a measured benchmark: the ratio is event-window duration over total duration, so it depends entirely on your workload.

✅ Supported Data Formats

🆚 Bagel vs. the Tools You Already Use

You already have ros2 *, PlotJuggler, and grep. Bagel doesn't replace them: it answers the questions they make you work for, then hands off to them:

Read the full README on GitHub →