RLM-based local debugger for AI agent traces
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…
RLM-based local debugger for AI agent traces launched on June 23, 2026 with 27 votes, #764 of 22,647 launches that month and more than 95% of that year's launches.
In plain words
HALO is an open-source debugging tool for AI agents that analyzes execution traces to identify and fix systemic issues. It accepts traces from frameworks like Langfuse and Arize in OTEL-compliant formats, then uses a Recursive Language Model to break down trace analysis into smaller problems and find recurring patterns across large datasets. Users run their agent, feed traces to HALO, review the report, apply fixes, and re-run—creating an optimization loop. The tool includes a local desktop application requiring no signup, and optionally accepts agent code paths for additional context.
written from the facts on this page · September 2026
From the sources
Hierarchal Agent Loop Optimizer. Contribute to context-labs/HALO development by creating an account on GitHub.
In the maker’s words, at launch
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…
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