Nebo

Nebo is a modern logging SDK that lets you track experiments containing multi-modal data (text, images, audio) and inspect metrics with function-level granularity.

pip install nebo

Get started: https://docs.graphbook.ai/nebo

Why Nebo?

Nebo offers a wide range of logging capabilities for data processing applications.

• You can log metrics, images, audio, and text
• Every log event can be tracked at specified time ranges or steps
• Nebo offers function-level capturing data at the granularity of individual functions, so you can monitor inputs, outputs, and execution flow of your code.

These features enable observability for applications such as:

• DAG-structured data-processing pipelines
• Model chaining applications
• ML training

Following the Tensorboard model, Nebo is local-first, so you don’t need to start another separate service, or worse, create an account to log data. Each run is stored in one .nebo file, a self-contained file format for simplicity, so that managing them is easy. When you're ready, you can deploy Nebo as a remote service, visit the web UI from your mobile device, and watch live metrics away from your desk since the UI is mobile-friendly.

Agent skills are released in the package preparing coding agents to not only write nebo-integrated code but also to monitor, analyze, and deliver derived metrics directly to the UI. This allows the full development lifecycle to progress inside the user's favorite coding agent application.

Features

• Captured log types: text, metrics, images, audio, progress
• Automatically infers a DAG from your call graph
• CLI, MCP and agent skill for AI agent query support
• MCP write tools so external agents can push metrics, images, audio, and text into a run
• Fully self-contained log file per run
• Mobile-first web UI
• Notebook embedding via nb.show() (Jupyter-renderable iframe of any slice of a run)
• One-command deploy to a Hugging Face Space (nebo deploy) with public/private read+write modes
• Organize runs into a tree with groups

Architecture

graph LR
    A[Your Python Pipeline] --> B[Nebo SDK<br>@fn, log_text, track, ...]
    B --> C[Daemon Server<br>FastAPI, port 7861]
    B --> D[Terminal Dashboard<br>Rich]
    C --> E[CLI<br>nebo]
    C --> F[MCP Tools<br>Claude]
    C --> G[Web UI]

Two execution modes:

• Local mode (default): In-process only. No daemon needed.
• Server mode: Events stream to a persistent daemon via HTTP. Use nebo serve to start the daemon.

The daemon can run on your laptop, in CI, or on a Hugging Face Space (nebo deploy). The same SDK code works against any of them — set NEBO_URL and NEBO_API_TOKEN to point at the target. When the daemon enforces auth, every API request must carry the token via the X-Nebo-Token header (HTTP) or the ?token=… query param (browsers / WebSocket).