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Horizons

OSS agent execution engine

Details

External ID
46907651
Source
HN
Company
—
Product
Horizons
Website domain
github.com
Launched
Feb. 6, 2026
Cohort
—
Upvotes
39
Upvotes percentile
0.7809973045822103
Tags
—
Fetched at
Sept. 7, 2026, 9:26 p.m.
Updated at
Sept. 7, 2026, 9:26 p.m.

Description

I'm Josh, founder of Synth. We've been working on coding agent optimization with method like GEPA and MIPRO (the latter of which, I helped to originally develop), agent evaluation via methods like RLMs, and large scale deployment for training and inference. We've also worked on patterns for memory, processing live context, and managing agent actions, combining it all in a single stack called Horizons. With the release of OpenAI's Frontier and the consumer excitement around OpenClaw, we think the timing is right to release a v0.It integrates with our sdk for evaluation and optimization but also comes batteries-included with self-hosted implementations. We think Horizons will make building agent-based products a lot easier and help builders focus on their proprietary data, context, and algorithmsSome notes:- you can configure claude code, codex, opencode to run in the engine. on-demand or on a cron- we're striving to make it simple to integrate with existing backends via a 2-way event driven interface, but I'm 99.9% sure it'll change as there are a ton of unknown unknowns- support for mcp, and we are building with authentication (rbac) in mind, although it's a long-journey- all self-host able via dockerA very simplistic way to think about it - an OSS take on Frontier, or maybe OpenClaw for prod

Enrichment

Theme
AI agent frameworks and developer tools
Vertical
Horizontal
Function
Agent / copilot
Audience
Developer
AI stance
AI-native
Project type
Hobby / open-source project
Normalized one-liner
agent execution engine
Manually corrected
False

Could you build this?

No Horizons is an agent execution and optimization engine built around cutting-edge agent training algorithms (GEPA, MIPRO), RLMs, and large-scale deployment infrastructure requiring deep machine learning research background.

What it would actually take: Building an advanced agent optimization engine requires implementing complex prompt-optimization algorithms (like DSPy's MIPRO), reinforcement learning workflows, execution sandboxes, and distributed inference/evaluation pipelines. The stack involves Python, PyTorch/vLLM, Ray or Kubernetes for orchestration, and custom tracing systems. The core barrier is deep expertise in prompt-program synthesis, reinforcement learning from agent trajectories, and distributed systems engineering.

Discussion

8 comments analyzed.

Competitors mentioned: Horizons, Codex, OpenCode, Claude Code SDK

Concerns raised: Not truly open source - delayed release with 2-year lag behind current version, License restricts commercial competition, Unclear market size - when do teams actually need this vs. lighter alternatives, Requires significant scale to justify adoption

Competitors

Other products that read as similar to this one — 626 launches clear the similarity bar, closest 8 shown.

Attention rank: #148 of 627 (itself plus its competitors, highest first — normalized so YC and Product Hunt are compared fairly).

Launched 94 days after the earliest competitor.

Other launches for this product

Same idea, different domain

Nobody's really built a agent / copilot tool for Agriculture yet.