Sgai
Goal-driven multi-agent software dev (GOAL.md → working code)
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Hey HN,We built Sgai to experiment with a different model of AI-assisted development.Instead of prompting step-by-step, you define an outcome in GOAL.md (what should be built, not how), and Sgai runs a coordinated set of AI agents to execute it.- It decomposes the goal into a DAG of roles (developer → reviewer → safety analyst, etc.) - It asks clarifying questions when needed - It writes code, runs tests, and iterates - Completion gates (e.g. make test) determine when it's actually doneEverything runs locally in your repo. There’s a web dashboard showing real-time execution of the agent graph. Nothing auto-pushes to GitHub.We’ve used it internally for prototyping small apps and internal tooling. It’s still early and rough in places, but functional enough to share.Demo (4 min): https://youtu.be/NYmjhwLUg8Q GitHub: https://github.com/sandgardenhq/sgaiOpen source (Go). Works with Anthropic, OpenAI, or local models via opencode.Curious what people think about DAG-based multi-agent workflows for coding. Has anyone here experimented with similar approaches?
HN · launched Feb 25, 2026 · 36 upvotes (top 23%) · 0 views
Niche: ai coding agents · 470 launches, 22 in the last 7 days ▼ -48% · Explore this niche →
Category and tags
- Horizontal
- Agent / copilot
- Developer
- AI-native
- Commercial product
Visit website ↗ Source listing ↗
More like this: all 470 launches in ai coding agents →
Could you build this?
Yes Sgai is an LLM agent orchestrator that decomposes a Markdown goal into tasks and executes bash/file operations using standard LLM API loops and DAG scheduling.
Discussion
20 comments analyzed.
Competitors mentioned: Claude Code (Anthropic), Gas Town (Yegge's concept)
Concerns raised: Custom license may not hold up in court without legal review, UX friction for multi-repository setup requires extra steps, Unclear how DAG decomposition handles multi-service goals, Limited examples of large-scale app generation in practice, One-shot generation capability uncertain for large applications
Feature requests: GOAL.md examples and guidelines for writing effective prompts, Native multi-repository support without manual directory setup, Parallel agent execution for improved output speed, Better documentation on cross-repository file change coordination
Competitors
Other products that read as similar to this one : 1190 launches clear the similarity bar, closest 8 shown.
Attention rank: #341 of 1191 (itself plus its competitors, highest first, normalized so YC and Product Hunt are compared fairly).
Launched 119 days after the earliest competitor.
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