Entire GitHub Alternative 🚀: Agent-First Dev Platform (AI Review)
Discover Entire, an agent-first development platform and Entire GitHub alternative—Trail-based PRs, AI code review, and agent memory. Learn what to test!
Entire Wants to Rebuild Git for Coding Agents. Here's How It Works
AI coding tools are changing how software gets built. If an agent can crank out a big patch quickly, the "write the code" step stops being the bottleneck. Teams still have to figure out what changed, whether it still meets requirements, and whether it's safe to merge. That puts real pressure on workflows that rely on humans reading diffs one line at a time.
Entire is a developer platform aimed at that shift. Backed by former GitHub CEO Thomas Dohmke, it combines Git hosting with a review workflow called Trails, agent-run evaluations, and a feature called Continuous Memory that keeps session context alongside code. The company argues this setup reduces review friction and makes agent-produced changes easier to assess.
It's an interesting direction, but the ambition isn't the same thing as a guarantee. A platform can claim it helps, and still be a poor fit for how a specific team works - or for the risks they care about. Below, we'll break down how Entire works, what its claims mean in practice, where it might genuinely help, and what to test before moving a repository.
What is Entire, and why is it built for agents?
Entire is a developer platform that combines Git hosting with tools meant to support coding agents. The core premise is pretty simple: if agents generate more code, faster, the rest of the pipeline - testing, reviewing, and merging - has to keep up.
Traditional Git hosting and pull-request workflows were built for people. Someone submits a change, reviewers inspect the diff, automated checks run, and an authorised person approves the merge. That still works. But if agents start producing frequent or large changes, the reviewer step can become the bottleneck. "Faster code generation" doesn't automatically fix that.
Entire's approach is to treat more than the final diff as part of the change. Instead of only showing what an agent wrote, the platform also keeps the task or intent, the agent's activity, evaluation results, and the resulting code. The idea is to give reviewers (and agents) a clearer picture of how a change was produced and whether it matches expectations.
The company says its decentralized Git network (announced earlier) is meant to support higher volumes of agent-driven coding activity. It also says teams can create, migrate, and host private repositories across regions including the US, India, Australia, and the EU. That could matter for distributed teams, but it doesn't tell you enough about data residency, security, or compliance for a specific organisation. You'll still need to verify those details directly.
The key point is that Entire isn't only offering a faster place to host repos. It's proposing a different operating model for development: agents handle more of the repetitive implementation and checking, while humans set expectations, review edge cases, and stay accountable for what actually ships. Whether that model works will come down less to the "agent-first" label and more to the quality of evaluations and the controls around merging.
The problem Entire is trying to solve
If agents create more pull requests than a team can review responsibly, you can't just "approve faster" and call it a day. You need a better way to identify which changes meet requirements, which ones need human attention, and which ones should be rejected. That's the workflow gap Entire is aiming at.
How do Trails change code review?
Entire replaces the usual pull-request model with a unit of work called a Trail. The company describes a Trail as a review experience that bundles the code change, the agent's trace, and evaluations run on each code push. Instead of asking reviewers to start with a raw diff, the interface is meant to show whether the change has met the configured checks.
The analogy Entire uses is a pilot's heads-up display: surface relevant status and warnings so the operator can decide without reconstructing every detail from scratch. In practice, an agent can make a change, run the configured evaluations, report findings, and revise its work. The CLI and web interface show the results, and then a human developer validates progress and decides whether it's ready to merge.
This could be helpful for tasks where you can clearly define acceptance criteria and test them reliably. For example, a team might set up evaluations for a routine dependency update: confirm the version change, run the test suite, check formatting, and ensure edits stay within the intended files. When checks fail, an agent can iterate again and again, while the human focuses on real failures, unusual diffs, or changes with higher risk.
But passing an evaluation isn't the same thing as proving the code is correct. Tests only cover the behaviours they exercise. An agent's "green" result can also reflect the assumptions - or blind spots - of the agent that wrote the code. Security, privacy, product behaviour, and architecture still involve judgement that a simple status badge can't fully capture.
Entire also says agents can be customised and that Trails show how agents use tokens. That visibility could help teams understand the cost and effort behind a change. The company further suggests that developers may be able to merge without inspecting every line once evaluations are satisfied. It's worth treating that as a possible workflow, not a default safety rule. For low-risk, reversible changes, strong evaluations might justify lighter review. For authentication, payments, data handling, or infrastructure work, human review should remain a key control.
A practical review policy
Use Trails to make review more selective, not to make accountability optional. One workable approach is to split changes into three buckets:
- Routine and reversible: allow a well-tested agent loop, with sampling or lightweight review.
- Behaviour-changing or difficult to reverse: require a named human reviewer and a clear explanation of what changed.
- Security-sensitive or high impact: require specialist review, even if automated evaluations pass.
The point is to make review effort track risk, instead of treating every green check as if it means the same thing.
What do hosting and Continuous Memory add?
Entire's platform includes Git hosting plus its review workflow. The company claims its hosting is up to 89 times faster than GitHub and six times faster than Cursor origin. Those numbers are vendor claims, not something you should assume will hold everywhere. The available information doesn't spell out benchmark conditions - things like repository size, network locations, or what operations were measured. If you're considering migration, don't let a headline speed comparison do the heavy lifting. Test the operations your team actually runs.
In practical terms, that means measuring clone and fetch times for representative repos, push latency, large-file behaviour, CI checkout performance, and reliability across where developers work. Also test day-to-day collaboration: permissions, branch protections, integrations, audit logs, backups, and recovery. And test what happens during incidents. Fast Git operations help, but they don't compensate for missing controls or a broken delivery pipeline.
The other major feature is Continuous Memory. Entire says it keeps developer session logs alongside code so agents can search prior work for context. The company describes it as combining a codebase graph with an "agentic brain" that uses previous sessions and prompts as lessons. Its claim is that this can reduce token use by up to 71% by avoiding repeated context gathering.
The appeal is straightforward. A coding agent often needs to rediscover why a module behaves a certain way, what an earlier attempt changed, or what constraints the team already discussed. Better session context can reduce duplicated investigation and help an agent avoid repeating a known mistake. It can also make it easier for another developer to follow the reasoning behind a change.
The trade-off is that memory has to be managed. Old sessions can be misleading when architecture or requirements change. Logs can also include sensitive material - credentials, customer data, or internal details - if teams don't control what gets recorded. Before enabling persistent memory across the board, check what's stored, who can access it, how long it's retained, how it's deleted, and whether it ends up in backups or exports. Context only helps when it's accurate and governed.
What to verify in a pilot
Run the same tasks with and without Continuous Memory. Compare token consumption, time to a correct result, and how often the agent repeats the same errors - not just whether it looks faster. Review stored records for stale or sensitive content, and set an owner for pruning or correcting memory. This is how you turn a vendor "efficiency" claim into a measurable team outcome.
Should your team try Entire instead of GitHub?
Entire makes the most sense for teams already using coding agents at a meaningful scale, especially when coordinating agent output is starting to cost real time and attention. If agents generate frequent changes, repeatedly need repository context, or leave reviewers with a growing queue, a Trails-and-evaluations workflow is worth testing. Teams with mature automation may also find value in treating agent traces and task outcomes as review artifacts rather than keeping that information scattered across separate tools.
If your biggest problems are unclear requirements, unreliable tests, or unclear ownership, an "agent-first" interface won't magically fix them. In fact, faster code generation can increase the volume of bad or ambiguous changes unless you improve specifications and checks alongside the tooling.
Migration also has costs. Hosting affects permissions, CI, developer tooling, security processes, and incident response. A simple feature comparison won't show those dependencies. Before moving a critical project, check what integrations are available, how access is handled, what audit and recovery features exist, and what your organisation's legal or compliance review requires. The source information doesn't establish how Entire handles every relevant area, so treat these as questions you need to verify directly.
A low-risk evaluation plan
Start with a small pilot instead of trying to migrate everything at once. Pick a non-critical repository that really uses agents and a team willing to compare results. Build a baseline from the current workflow, then evaluate Entire against it over a defined period.
Track a few operational measures:
- Time from task assignment to a reviewed, mergeable change.
- How often evaluations catch defects that would otherwise reach review.
- Human review time per change, broken down by risk level.
- Agent cost, including tokens and repeated failed attempts.
- Reliability of Git operations and CI integrations.
- Developer confidence in understanding why a change passed or failed.
Agree on exit criteria before you start. For example, continue only if the pilot reduces review effort without increasing escaped defects, preserves required controls, and works reliably with your existing tooling. If the numbers come back mixed, it may make sense to keep the workflow ideas and delay a hosting migration. You don't have to adopt a whole platform to improve your review process.
Conclusion
Entire is built around a real shift: as coding agents produce more changes, teams need a better way to evaluate and manage that output. Trails aim to put agent activity and configured checks directly in the review surface, while Continuous Memory aims to reduce repeated context gathering. The platform also includes its own Git hosting, along with performance claims you should test under your own workloads.
The sensible response is not to dismiss the idea, and not to treat agent evaluations as a substitute for engineering judgement. Pilot the workflow on low-risk work, measure review effort and defect rates, and verify hosting, security, and compliance requirements before migrating. Adopt Entire only if it improves the delivery system as a whole - not just because it makes code appear faster.