Meta-agent: self-improving agent harnesses from live traces
Details
- External ID
- 47665630
- Source
- HN
- Company
- —
- Product
- Meta-agent: self-improving agent harnesses from live traces
- Website domain
- github.com
- Launched
- April 6, 2026
- Cohort
- —
- Upvotes
- 14
- Upvotes percentile
- 0.6876606683804627
- Tags
- —
- Fetched at
- Sept. 7, 2026, 9:26 p.m.
- Updated at
- Sept. 7, 2026, 9:26 p.m.
Description
We built meta-agent: an open-source library that automatically and continuously improves agent harnesses from production traces.Point it at an existing agent, a stream of unlabeled production traces, and a small labeled holdout set.An LLM judge scores unlabeled production traces as they stream.A proposer reads failed traces and writes one targeted harness update at a time, such as changes to prompts, hooks, tools, or subagents. The update is kept only if it improves holdout accuracy.On tau-bench v3 airline, meta-agent improved holdout accuracy from 67% to 87%.We open-sourced meta-agent. It currently supports Claude Agent SDK, with more frameworks coming soon.Try it here: https://github.com/canvas-org/meta-agent
Enrichment
- Theme
- AI agent frameworks and developer tools
- Vertical
- Horizontal
- Function
- Agent / copilot
- Audience
- Developer
- AI stance
- AI-native
- Project type
- Commercial product
- Normalized one-liner
- self-improving ai agent framework
- Manually corrected
- False
Could you build this?
Partial The basic framework of tracing prompts and feeding outputs to an LLM judge is approachable, but building an automated optimizer that continuously refines agent harnesses without regressing requires complex evaluation pipelines and prompt optimization theory.
What it would actually take: The system requires a distributed tracing and observability pipeline (OpenTelemetry, ClickHouse/PostgreSQL), an automated LLM-as-a-judge scoring harness, and an iterative optimization engine using algorithms like DSPy or GEPO (Genetic Prompt Optimization). The hard problem is preventing reward hacking, managing prompt drift, and maintaining statistical rigor across agent harness mutations without human-in-the-loop validation, requiring specialized AI evaluation engineering.
Discussion
No comments on this launch.
Competitors
Other products that read as similar to this one — 698 launches clear the similarity bar, closest 8 shown.
Attention rank: #215 of 699 (itself plus its competitors, highest first — normalized so YC and Product Hunt are compared fairly).
Launched 159 days after the earliest competitor.
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- Moda: The Continual Learning Layer for AI Agents · yc · 2026-03-04 · 122 upvotes · similarity 0.49
- octop-harness · github · 2026-09-24 · 17 upvotes · similarity 0.49
- agent-flow · github · 2026-09-26 · 10 upvotes · similarity 0.48
- agent-harness · ph · 2026-09-10 · 3 upvotes · similarity 0.48
- Coordination Layer for Coding Agents · hn · 2026-08-28 · 9 upvotes · similarity 0.47
- Gambit, an open-source agent harness for building reliable AI agents · hn · 2026-01-16 · 91 upvotes · similarity 0.47
- Progress AI Observability · ph · 2026-08-07 · 168 upvotes · similarity 0.47
Other launches for this product
- No other launches for this product.
Same idea, different domain
Nobody's really built a agent / copilot tool for Agriculture yet.