context-labs/HALO

What it solves

HALO addresses the difficulty of optimizing AI agent harnesses, especially in production environments where execution traces are long and complex. General-purpose coding agents often overfit to isolated errors in a few traces rather than identifying systemic behavioral patterns. HALO provides a specialized methodology and engine to analyze these traces and generate actionable recommendations for harness improvement.

How it works

HALO implements a recursively self-improving loop using a specialized form of Recursive LLM (RLM) engine:

1. Trace Collection: It gathers OpenTelemetry-compatible execution traces from an agent harness.
2. Analysis: The HALO-RLM engine decomposes these traces to identify common failure modes and systemic issues across multiple executions.
3. Reporting: The engine produces a detailed report highlighting ranked failures and bottlenecks.
4. Implementation: This report is fed into a coding agent (such as Cursor or Claude Code) to apply fixes to the harness.
5. Iteration: The updated harness is redeployed to collect new traces, repeating the cycle.

Who it’s for

Developers building and deploying AI agents who need to move beyond manual trace review to systematically improve their agent's performance and reliability using production data.

Highlights

• RLM-based Analysis: Uses a specialized Recursive LLM approach to avoid overfitting and generalize findings across large datasets.
• Comprehensive Tooling: Includes a desktop application for local analysis, a Python package (halo-engine) for programmatic use, and a CLI.
• openzeppelin-compatible: Supports OpenTelemetry-compatible tracing for easy integration with existing observability tools.
• Proven Improvements: Demonstrated significant performance gains on the AppWorld benchmark for models like Gemini and Claude Sonnet.