Fork and merge Pydantic AI agent runs with Pydantic Deep Agents
Run a self-hosted terminal agent or custom Pydantic AI harness that can branch a coding or research run, test isolated alternatives, and merge the winning branch.
npx skills add agentskillexchange/skills --skill fork-and-merge-pydantic-ai-agent-runs-with-pydantic-deep-agents
Use Pydantic Deep Agents when an operator needs a self-hosted coding or research agent that can branch one run into isolated alternatives, run checks on each branch, and merge or select the best result. The workflow is: install the pydantic-deep package or CLI, point it at an approved model provider, enable live run forking for high-uncertainty coding or research tasks, steer branches independently, compare diffs and test results, then merge the chosen path back into the parent run. Invoke this instead of using a hosted coding assistant normally when the task needs auditable branch exploration, self-hosted model choice, Pydantic AI typed tooling, or a repeatable custom-agent harness rather than a single interactive chat. The scope boundary is live-run forking and custom Pydantic AI deep-agent orchestration; it is not a generic Claude Code alternative listing, a broad agent framework card, or a plain Python SDK entry.
What this skill actually does
Inputs and prerequisites: Python 3.10+, pydantic-deep CLI or Python package, approved model provider key, optional sandbox support for isolated execution.
Setup notes: Install from PyPI with pip install pydantic-deep , then run the terminal assistant or create a custom harness with create_deep_agent(…) . Configure an approved model provider key, enable live run forking for branch-and-merge workflows, and compare branch results with a project check command such as pytest -q before merging.
Source and verification boundary: use https://vstorm-co.github.io/pydantic-deepagents/ as the canonical reference before running the workflow; keep commands, API calls, CLI usage, and generated outputs reviewable against that upstream source.
Framework fit: publish this as a Custom Agents workflow only when the operator can invoke the documented toolchain directly, rather than treating the upstream project as a generic product listing.