How Pydantic Merges 91% of Their Pull Requests Without a Human
Before Macroscope, Pydantic’s coding agents were opening PRs faster than its engineers could review them. Today, Macroscope’s Approvability approves roughly 91% of Pydantic’s PRs, escalating the rest to human reviewers.
Why Pydantic Chose Macroscope
Pydantic evaluated every code review tool it could find. What set Macroscope apart from the other code review tools Pydantic tried was the specificity of its feedback: it found actionable bugs, pinpointed the problematic lines of code, and explained how to fix them. The team could confidently turn it on across their repositories without needing to tune it for each repo or engineer.
Macroscope has transformed the speed at which the Pydantic team ships code. Before Macroscope, Pydantic was averaging 100-150 PRs a week. Now, that number has increased 4X to ~500 PRs a week.
Auto-Approving Safe PRs
Pydantic adopted Approvability to split their PR queue into two camps: changes that Macroscope deems safe to merge without human review, and changes that warrant human attention.
Approvability now approves ~91% of Pydantic’s PRs and escalates the rest to human reviewers, so the engineering team can spend their review time on changes that actually require human judgment.
Encoding Pydantic’s Review Standards
Getting to a 91% approval rate took more than bug detection. It meant codifying everything a human reviewer would check and automating the conventions Pydantic’s engineers had been enforcing by hand.
The Pydantic team had agents read a year’s worth of human-written PR comments and turned the feedback their senior and principal engineers gave most often into Macroscope Check Run Agents. Pydantic now runs a large suite of Check Run Agents across the codebase, enforcing their review standards across style, security, performance and safety.
Keeping Product and Engineering in Sync
Pydantic also uses Macroscope Status to see where engineering effort is going and how that effort connects to their product goals.
Bill Easton, Head of Product at Pydantic, described it as especially valuable through his first weeks at the company. Seeing what the team was working on and shipping gave him a picture of the product that would otherwise have taken months of conversations and research to assemble.
Shipping a Full Epic in an Afternoon with Murmur
With code review covered, the constraint moved upstream to writing the code. Pydantic became an early customer of Murmur, Macroscope’s agent orchestration platform.
Each week the Pydantic team plans an epic in GitHub and spawns dozens of Murmur agents against the parallelizable work, each in its own cloud VM running Pydantic’s full development stack. The agents verify their changes, open pull requests, and implement Macroscope’s review comments until the work is clear to merge.
With Murmur, work that used to take the team 15 hours now takes two.
Watch the full conversation with Bill Easton, Head of Product at Pydantic, to hear how his team puts it all into practice.
Pydantic: The Most Depended-On Library in Python
Pydantic is the type-safety layer most of Python runs on. Founded by Samuel Colvin in 2017, today nearly every Python agent framework is built on Pydantic. Pydantic has 16.9 billion downloads; more than 900,000 GitHub repositories depend on it. Pydantic Validation is the layer underneath the OpenAI SDK, Anthropic SDK, Google ADK, LangChain and LlamaIndex: most Python code that calls a frontier model runs through Pydantic.
The company now builds Pydantic Validation, the open-source library that started it; Pydantic AI, a framework for building agents in Python; and Logfire, an observability platform for monitoring agents at scale in production. Nearly every Python agent framework in use today is built on Pydantic.
