# Entire Git Forge: How Trails and Continuous Memory Work

Entire brings agent reviews and project memory into source control, but the useful question is what evidence a team needs before merging.

By Paras Madan | 2026-10-28


Entire launched its Git forge for agent-human collaboration on October 26. The platform combines Git hosting, a review workflow called Trails and persistent project memory for coding agents. It is available to developers now, with an option to keep GitHub as the source of truth rather than migrate immediately.

For a team already using coding agents, the interesting problem is familiar. Producing another branch is easy. Understanding why it exists, whether its tests matter and what still needs a human decision takes longer.

## What changed with Entire's launch

The [launch announcement](https://entire.com/blog/the-git-forge-for-agent-human-collaboration) describes a platform built around that review problem. Teams can host repositories natively or mirror GitHub repositories, and launch partners Buildkite and Depot supply CI integrations. Native hosting is available in the US, EU, Australia and India.

The distinction between hosting and mirroring matters. Mirroring keeps GitHub authoritative, so pushes still route through GitHub. It offers a way to evaluate the workflow without treating a new review interface as a reason to move every repository and integration at once.

Entire also advertises faster Git pushes. That is a vendor benchmark, not evidence that an application builds faster or that a team's total delivery time falls by the same amount. Repository traffic and software review are different bottlenecks.

## How Entire Trails differ from pull requests

A [Trail](https://entire.com/features/trails) connects the goal, related changes, agent sessions and reviews in one place. It can bring work across multiple repositories together, rather than leaving a reviewer to reconstruct the relationship between separate changes.

Automated reviews surface findings and scores, while gates make required checks, approvals and unresolved findings visible before a merge. The interface is meant to show what still needs attention, not just accumulate another timeline of comments.

Consider a billing change that touches an API and a dashboard. A reviewer needs to know whether both sides agree on the data contract, whether retries can duplicate charges and whether the tests exercise those cases. Grouping the changes helps establish context. It does not answer those questions by itself.

**A review score is a signal, not a proof of correctness.** A useful gate combines the agent's reasoning with deterministic tests and human judgment where a change affects product behavior. Repeatedly asking agents to review one another can still preserve a shared wrong assumption.

## Continuous Memory has two different jobs

Entire separates code structure from remembered context. The graph tool maps relationships in the repository; the brain tool adds earlier decisions, sessions, documents and project memories. That distinction is useful because knowing which function calls another is different from knowing why the team chose that design.

The [graph setup documentation](https://docs.entire.com/guides/graph/set-up-graph) requires Git 2.36 or later and an agent that reads repository instructions. Setup writes a generated guide and managed blocks in `AGENTS.md` and `CLAUDE.md`, preserving content outside those blocks.

For a team adopting it, those instruction changes deserve ordinary code review. A tool that helps agents find context also changes the instructions they receive. Keeping the generated and team-owned parts separate makes later upgrades easier to inspect.

The [brain documentation](https://docs.entire.com/guides/brain/overview) adds an important qualification to the local-first story. Memories stay on the machine by default, and hosted synchronization requires an explicit connection. However, extracting memories from past sessions can call a coding agent, send session content to its model provider and incur token charges.

Local storage therefore does not mean every operation is offline. Before enabling historical extraction, a team should decide which sessions are appropriate to process and which model-provider rules apply to them.

## What to evaluate before adopting Entire

The most useful pilot is a real change with enough history to test both review and memory. Can the next agent find the previous decision? Can a reviewer trace a finding to code and a test? Does an obsolete decision remain distinguishable from a current rule?

Those are more informative questions than how much context the system can retain. Persistent memory can save repeated investigation, but it can also make an old mistake easier to retrieve. Teams still need ownership of the decisions being carried forward.

Entire's opportunity is to make the evidence behind an agent-written change easier to inspect. If that reduces the work required to approve a change responsibly, the new interface earns its place. More automated commentary alone would simply move the existing review burden somewhere else.

I'm Paras. I work at the intersection of AI, engineering and distribution.

I help technical founders turn product launches and engineering updates into clear stories that developers and founders want to read and share.

[Book a 30-minute call](https://cal.com/paras-madan/30min).