RLM-based local debugger for AI agent traces
Details
- External ID
- 48649137
- Source
- HN
- Company
- —
- Product
- RLM-based local debugger for AI agent traces
- Website domain
- github.com
- Launched
- June 23, 2026
- Cohort
- —
- Upvotes
- 27
- Upvotes percentile
- 0.7814207650273224
- Tags
- —
- Fetched at
- Sept. 7, 2026, 9:26 p.m.
- Updated at
- Sept. 7, 2026, 9:26 p.m.
Description
We built HALO (Hierarchal Agent Loop Optimizer), an open-source tool for debugging and optimizing AI agents using their execution traces.It’s a loop. Run your agent, feed the traces to HALO, get the report, apply the fixes, then re-run your agent.HALO takes in OTEL compliant traces from AI agents using tracing frameworks such as Langfuse, Arize/OpenInference, or even just plain JSONL. It uses an RLM (Recursive Language Model) to more efficiently break trace analysis into smaller subproblems in order to find recurring patterns across large amounts of data and fix systemic issues that regular LLMs might typically miss.You can also optionally provide a path to where your agent code lives to give the engine more context so it can more concretely provide useful insights.The repo also includes a desktop app that you can run locally without having to sign up for anything or configure anything complex.Check out the readme in the repo for more in depth information on what HALO is and how you can use it to your benefit :)
Enrichment
- Theme
- AI agent frameworks and developer tools
- Vertical
- Horizontal
- Function
- Observability & eval
- Audience
- Developer
- AI stance
- AI feature
- Project type
- Commercial product
- Normalized one-liner
- debugger for ai agent traces
- Manually corrected
- False
Could you build this?
Partial Parsing OpenTelemetry traces and displaying them in a UI is straightforward, but automated algorithmic optimization and root-cause analysis of complex agent loops requires non-trivial trace evaluation algorithms.
What it would actually take: The product needs an OpenTelemetry trace ingestion pipeline (OTel collector, Jaeger/ClickHouse), an execution DAG parser, and an analytical engine to identify hallucinations, infinite tool loops, and prompt regressions. Building the evaluator requires specialized knowledge of distributed tracing, AI eval frameworks, and deterministic execution modeling to automatically suggest verifiable prompt or agent topology fixes.
Discussion
10 comments analyzed.
Competitors mentioned: Claude Code for trace analysis, Codex for failure pattern identification
Concerns raised: Unclear what specific common failure modes are discovered, Recursive depth beyond 1 may not provide meaningful uplift with current models, Lacks public benchmarks beyond AppWorld repo, Questions about whether recursion is necessary vs. direct optimization of token efficiency
Feature requests: Provide concrete examples of systematic issues teams uncover, Public benchmarks demonstrating performance improvements, Extended context/searchable index for follow-up questions on full data
Competitors
Other products that read as similar to this one — 297 launches clear the similarity bar, closest 8 shown.
Attention rank: #66 of 298 (itself plus its competitors, highest first — normalized so YC and Product Hunt are compared fairly).
Launched 232 days after the earliest competitor.
- Halo · hn · 2026-07-07 · 37 upvotes · similarity 0.57
- Dbg · hn · 2026-04-13 · 7 upvotes · similarity 0.48
- Spec27 · hn · 2026-04-30 · 13 upvotes · similarity 0.45
- LLM agents that write Python to analyze execution traces at scale · hn · 2026-03-07 · 5 upvotes · similarity 0.45
- Raindrop Workshop · ph · 2026-05-14 · 189 upvotes · similarity 0.44
- Tuneloop · hn · 2026-07-30 · 5 upvotes · similarity 0.44
- Autofix Bot · hn · 2025-12-11 · 37 upvotes · similarity 0.44
- Oodle.ai · hn · 2026-07-14 · 31 upvotes · similarity 0.43
Other launches for this product
- No other launches for this product.
Same idea, different domain
Nobody's really built a observability & eval tool for Media & entertainment yet.