pydantic-ai-trace 0.1.1

Fast local web viewer for pydantic-ai traces (list[ModelMessage] JSON dumps)

pydantic-ai-trace

Use pydantic-ai-trace when you need to inspect an agent run from a JSON dump. Give it a pydantic-ai list[ModelMessage] dump and it opens the run in a browser view.

It is a lightweight local tool. Point it at a trace or a directory of traces and it reads the files from disk, reloads them when they change, and exports individual traces as self-contained HTML files. Your traces stay on disk, with no hosted service or account.

What you can inspect

• The full request and response sequence, including prompts, text, thinking, tool calls, tool results, and unknown parts
• Tool calls paired with their results, including results in later messages
• Model, provider, timing, and token usage
• A searchable directory tree for .json and .jsonl traces
• Collapsible large values, rendered Markdown, and keyboard navigation

Run from a checkout

This project uses pixi. Build the bundled frontend once, then run paitrace with a trace file or directory.

pixi run build-frontend
pixi run paitrace trace.json

Usage

# View one trace
pixi run paitrace trace.json

# Browse a directory tree of traces
pixi run paitrace ./my-traces/

# Write one trace to a standalone HTML file
pixi run paitrace export trace.json -o trace.html

# Choose a trace line when exporting a multi-trace JSONL file
pixi run paitrace export runs.jsonl --line 2

The viewer binds to 127.0.0.1:1205 and opens your browser. Pass --port, --host, or --no-open to change that behavior.

Trace files

The viewer reads the JSON emitted by ModelMessagesTypeAdapter.dump_json(messages):

• .json: one bare JSON array of ModelMessage objects
• .jsonl: one such array per line

When you open a directory, each line in a multi-trace .jsonl file is available as a separate trace.

Development

Run the API and frontend in separate terminals while working on the viewer.

pixi run dev-api ./trace.json  # API with reload on port 1205
pixi run dev-web               # Vite with HMR, proxying /api

pixi run test        # pytest and vitest
pixi run lint        # ruff, prettier, and eslint
pixi run typecheck   # pyright and TypeScript
pixi run build       # bundled frontend plus sdist and wheel

Releasing

Push a vX.Y.Z tag. The package version is derived from that tag, so no source file needs a version bump. GitHub Actions runs the full check suite, builds the frontend into the wheel and source distribution, publishes both to PyPI using trusted publishing, and creates a GitHub release. Configure a pypi environment in GitHub and add this repository as a trusted publisher for the pydantic-ai-trace project on PyPI before the first release.