Entire
A next-generation developer platform built for agent–human collaboration, capturing full agent context alongside code in Git.
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Product Overview
What is Entire?
Entire is a developer platform founded by former GitHub CEO Thomas Dohmke, backed by a $60M seed round, and purpose-built for the era of AI agents writing code. Rather than retrofitting agents into legacy development workflows, Entire reimagines the software development lifecycle from the ground up. Its first shipped product, Checkpoints, is an open-source CLI that automatically captures agent session data — prompts, reasoning, tool calls, files touched, and token usage — as versioned data in Git on every commit. The platform's long-term vision comprises three pillars: a Git-compatible database unifying code and intent, a semantic reasoning layer enabling multi-agent coordination via a context graph, and an AI-native UI for reviewing and shipping agent-generated changes at scale.
Key Features
Checkpoints CLI
Open-source CLI that hooks into Git and captures full agent session context — transcripts, prompts, tool calls, and reasoning — as versioned checkpoint objects stored on a separate orphan branch, keeping main history clean.
Git-Compatible Context Database
A version-controlled store that unifies code, developer intent, constraints, and agent reasoning in a single system, making every change traceable and auditable across the full development lifecycle.
Semantic Reasoning Layer
A universal context graph that enables coordination across multiple agents, allowing them to share, recall, and build on prior session data rather than starting from scratch each time.
Agent-Agnostic Integration
Works with any AI agent or model (Claude Code, Gemini CLI, and others), ensuring developers are not locked into a single provider or toolchain.
AI-Native Developer Interface
A purpose-built UI designed for reviewing, approving, and deploying hundreds of agent-generated changes per day — shifting developer workflow from writing code to expressing intent and validating outcomes.
Use Cases
- Agent Context Traceability : Engineering teams can audit exactly why and how AI-generated code was produced, reviewing the full reasoning trail behind any commit without digging through ephemeral chat logs.
- Multi-Agent Coordination : Teams running parallel agent workflows can use the context graph to coordinate multiple agents, reducing redundant token usage and preventing conflicting changes.
- Specification-Driven Development : Developers working in intent-first workflows can track how natural language specifications translate into agent actions and final code outputs across sessions.
- AI Code Review & Compliance : Organizations shipping AI-generated code at scale can use versioned checkpoints to satisfy audit, security, and compliance requirements with a full record of agent decisions.
- Developer Productivity at Scale : Individual developers and teams can manage and review large volumes of daily agent-generated changes through a streamlined, AI-native interface built for speed.
FAQs
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