Show HN: RLM-based local debugger for AI agent traces
Not a pilot. Developers just got a local debugger that uses recursive language models to fix AI agent failures at scale.
Why it matters
HALO addresses a real operational pain point for AI agent builders—debugging complex execution traces and finding systemic issues that standard LLMs miss. Open-source + local-first tooling could accelerate adoption of agents in production.
The key facts
13 to knowOpen-source tool: HALO (Hierarchal Agent Loop Optimizer)
Uses RLM (Recursive Language Model) for trace analysis
Integrates with OTEL-compliant tracing: Langfuse, Arize/OpenInference, JSONL
Desktop app included—no signup, no complex config required
Designed for loop: run agent → feed traces → get report → fix → re-run
Can ingest agent source code for enhanced context and insights
Published on Hacker News (Show HN), 7 points, 0 comments as of publish
HALO: open-source Hierarchal Agent Loop Optimizer for AI agent debugging
Uses RLM (Recursive Language Model) for trace analysis—breaks problems into subproblems to find systemic patterns
Accepts OTEL-compliant traces from Langfuse, Arize/OpenInference, or JSONL
Desktop app for local execution—no sign-up or complex setup required
Can integrate with source code for contextual debugging insights
Published on Hacker News (Show HN) with 7 points, 0 comments—early-stage community interest
Go to the source
Hacker Newsgithub.com
Publisher excerpt: 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…