Forge
Self-hosted workflow engine for all favor agents
Forge launched on May 18, 2026 with 6 votes, #1,742 of 20,538 launches that month and more than 75% of that year's launches.
What it does
Forge is a self-hosted workflow engine that lets coding agents hand off cleanly — one writes the code, another reviews it. Tasks run in isolated git worktrees, hit your CI gate, then merge. Adapters for Claude Code, Codex, Gemini, and opencode. MIT-licensed.
Forge is a self-hosted workflow engine that lets you pick the best coding agent for each task. Tasks run in isolated git worktrees with CI checks and a review and merge step. MIT-licensed, with REST, MCP, CLI, and an optional web UI.
Does a similar job
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Forge – Guardrails take an 8B model from 53% to 99% on agentic tasksMay 2026 · github.com · ▲687Hi HN, I'm Antoine Zambelli, AI Director at Texas Instruments. I built Forge, an open-source reliability layer for self-hosted LLM tool-calling. What it does: - Adds domain-and-tool-agnostic guardrails (retry nudges, step enforcement, error recovery, VRAM-aware context management) to local models running on consumer hardware - Takes an 8B model from ~53% to ~99% on multi-step agentic workflows without changing the model - just the system around it - Ships with an eval harness and interactive dashboard so you can reproduce every number I wanted to run a handful of always-on agentic systems…
Parallel CodeMar 2026 · parallelcode.app · ▲159I kept running into a structural problem with AI coding tools. You try one agent. It makes changes. You try another. Now you're manually diffing branches and cleaning up experimental commits. The tools are powerful, but the workflow isn’t designed for comparison. So I built Parallel Code. It runs Claude Code, Codex, and Gemini CLI natively (no API wrapper, no feature abstraction), but isolates each agent in: its own terminal its own Git worktree its own feature branch The workflow becomes: Spawn N worktrees Assign one AI agent per worktree Let them implement independently Compare diffs Merge…
Boxes.devJun 2026 · boxes.dev · ▲112Hi HN, we’re Nick and Drew, and we’re building boxes.dev – the first cloud-only agentic dev environment (ADE) that gives every Codex and Claude Code agent its own cloud computer. We’re two engineers who previously built Gem (co-founder/CTO and first hire), and we spent the last year coding almost exclusively using Codex and Claude Code. It’s been a huge change to how we code, and it’s been exhilarating seeing the models keep getting better – but we eventually realized that developing on localhost was holding us back: - Git worktrees are clunky to set up and use for parallelizing work -…
Laravel Forge2025 · laravel.com · ▲133Laravel is a framework for building modern web apps and AI agents. Expressive syntax, built-in tools, and everything you need to ship fast.
- 1D143.dev – we open-sourced our internal coding-agent infrastructureJun 2026 · ▲13
We just open-sourced the internal system we built at Assembled for running coding agents as a team. Coding agents worked well for individual engineers, but the surrounding workflow was a bit of a mess. We generally found that many engineers had different MCP connections and context for their agents, personal automations running that other people couldn’t access, and very little introspection for what a human’s input into the coding agent looked like. So we built an internal system that converted coding agents into shared team infrastructure. The system runs Codex, Claude Code, OpenCode, and…

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Source: Product Hunt launch ↗
Launched alongside, May 2026
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Parallel agents, diff reviewer, and multi-model comparisons
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Hey HN, Henry here from Cactus. We open-sourced Needle, a 26M parameter function-calling (tool use) model. It runs at 6000 tok/s prefill and 1200 tok/s decode on consumer devices. We were always frustrated by the little effort made towards building agentic models that run on budget phones, so we conducted investigations that led to an observation: agentic experiences are built upon tool calling, and massive models are overkill for it. Tool calling is fundamentally retrieval-and-assembly (match query to tool name, extract argument values, emit JSON), not reasoning. Cross-attention…
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