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h5i
Next-Gen AI-Aware Git. Sandbox, Token Saving, Agent Radio
h5i is an open-source Git sidecar for AI-era development, achieving auditable sandbox, prompt-aware commits, 95% less token waste, 3.5x richer PR brief, 1.8x faster multi-agent real-time conversation. Git records what changed; h5i records the rest: the prompt, model, files read, reasoning, tests, audit signals, sandbox events, and agent-to-agent handoffs behind every change.
Git records what changed. h5i records the rest: who, why, what the agent knew, whether it was safe, and how the next agent picks up where the last left off.
Use h5i if you want your AI agents to stop leaving their work in thin air.
Want to know which model, prompt, and reasoning led to a commit?
Want the next agent to inherit the full context of the last one?
Want Claude and Codex to talk in real time, with the conversation stored in Git?
Want to reduce token usage by shrinking noisy tool output while keeping the raw evidence?
Want to run a risky AI-generated code in a confined sandbox, then review it before it touches your tree?
Want to catch leaked secrets, blind edits, and risky AI changes before review?
Feature Example - 1: Agent Radio — agents that talk over Git
Your agents can talk to each other through it: h5i offers a cross-agent message channel stored in Git, built for typed operational handoffs (ASK · REVIEW_REQUEST · RISK · DONE · ACK). Claude can ask, Codex can review, risks can be flagged and resolved, and the whole log survives clones, machines, and branches.
Feature Example - 2: Agent Sandbox — a confined, fully auditable environment
`h5i env` gives you a disposable, confined environment — a git worktree plus a policy that limits what the code inside can read, write, and reach over the network — so you can run a refactor, a dependency upgrade, or an untrusted build (yourself or via an agent) without it touching your main tree. Your loop is four commands:
$ h5i env create env-name --profile agent-claude # make a confined box (picks the strongest isolation the host supports)
# h5i env create env-name --profile agent-codex
$ h5i env shell env-name # work inside it — or hand the box to an agent
$ h5i env propose env-name # mediated commit + review brief
$ h5i env apply fix-auth # merge it into your branch (only when you choose to)
Feature Example - 3: Token Reduction with Unified Form
CLI output from tools like pytest and cargo run by AI agents often becomes long and consumes a large number of tokens. h5i solves this by structuring and compressing tool output while keeping the raw logs recoverable through Git/LFS/S3, achieving both token reduction (up to 95%) and auditability.
Feature Example - 4: Context DAG
The context DAG shows how the work unfolded: the goal, every milestone, and the OBSERVE / THINK / ACT trace behind each change, captured automatically as the agent works. Because it is snapshotted on every commit, you can replay exactly what an agent knew and why it acted at any point in history.
Feature Example - 5: Pull Request Integration
When a branch is ready for review, h5i surfaces all of it where reviewers already work — on the pull request.
h5i was submitted on Product Hunt and earned 0 upvotes and 1 comments, placing #54 on the daily leaderboard. h5i is an open-source Git sidecar for AI-era development, achieving auditable sandbox, prompt-aware commits, 95% less token waste, 3.5x richer PR brief, 1.8x faster multi-agent real-time conversation. Git records what changed; h5i records the rest: the prompt, model, files read, reasoning, tests, audit signals, sandbox events, and agent-to-agent handoffs behind every change.
h5i was featured in Developer Tools (514.5k followers), Artificial Intelligence (471.7k followers) and GitHub (41.3k followers) on Product Hunt. Together, these topics include over 198.5k products, making this a competitive space to launch in.
Who hunted h5i?
h5i was hunted by Koukyosyumei. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
Want to see how h5i stacked up against nearby launches in real time? Check out the live launch dashboard for upvote speed charts, proximity comparisons, and more analytics.
Koukyosyumei
Git records what changed. h5i records the rest: who, why, what the agent knew, whether it was safe, and how the next agent picks up where the last left off.
Use h5i if you want your AI agents to stop leaving their work in thin air.
Want to know which model, prompt, and reasoning led to a commit?
Want the next agent to inherit the full context of the last one?
Want Claude and Codex to talk in real time, with the conversation stored in Git?
Want to reduce token usage by shrinking noisy tool output while keeping the raw evidence?
Want to run a risky AI-generated code in a confined sandbox, then review it before it touches your tree?
Want to catch leaked secrets, blind edits, and risky AI changes before review?
Feature Example - 1: Agent Radio — agents that talk over Git
Your agents can talk to each other through it: h5i offers a cross-agent message channel stored in Git, built for typed operational handoffs (ASK · REVIEW_REQUEST · RISK · DONE · ACK). Claude can ask, Codex can review, risks can be flagged and resolved, and the whole log survives clones, machines, and branches.
Feature Example - 2: Agent Sandbox — a confined, fully auditable environment
`h5i env` gives you a disposable, confined environment — a git worktree plus a policy that limits what the code inside can read, write, and reach over the network — so you can run a refactor, a dependency upgrade, or an untrusted build (yourself or via an agent) without it touching your main tree. Your loop is four commands:
Feature Example - 3: Token Reduction with Unified Form
CLI output from tools like pytest and cargo run by AI agents often becomes long and consumes a large number of tokens. h5i solves this by structuring and compressing tool output while keeping the raw logs recoverable through Git/LFS/S3, achieving both token reduction (up to 95%) and auditability.
Feature Example - 4: Context DAG
The context DAG shows how the work unfolded: the goal, every milestone, and the OBSERVE / THINK / ACT trace behind each change, captured automatically as the agent works. Because it is snapshotted on every commit, you can replay exactly what an agent knew and why it acted at any point in history.
Feature Example - 5: Pull Request Integration
When a branch is ready for review, h5i surfaces all of it where reviewers already work — on the pull request.