HALO
Hierarchal Agent Loop Optimizer
HALO (Hierarchal Agent Loop Optimizer) is an RLM-based AI agent that analyzes production execution traces to identify and fix systemic failures in agent deployments. It ingests OpenTelemetry-compatible traces from observability platforms like Langfuse or Arize, uses a specialized Reasoning Language Model to diagnose common failure patterns across executions, and generates actionable reports that can be fed into coding agents like Claude or Cursor to implement fixes. The system creates a self-improving loop where each iteration refines the agent harness based on real-world trace data, making it particularly effective for high-traffic production environments.
Key Features
Use Cases
- 01Debugging production agent deployments by analyzing traces to find hallucinated tool calls and refusal loops
- 02Optimizing agent harness performance by identifying latency bottlenecks and expensive API spans
- 03Improving agent benchmark scores through iterative prompt and tool refinement (demonstrated with AppWorld)
- 04Auditing multi-step agent conversations to understand branching behavior and retry patterns
- 05Building self-improving agent systems that evolve based on real production usage data
- 06Analyzing traces from high-traffic agent deployments to detect variance across diverse execution patterns
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HALO — FAQ
What is HALO and what does it do?+
HALO is an AI agent optimizer that uses Reasoning Language Models to analyze execution traces from your agent deployments. It identifies systemic failure patterns, generates diagnostic reports, and produces recommendations that can be implemented by coding agents to improve your agent harness iteratively.
How do I install the HALO desktop app?+
Run the installation script with curl -fsSL https://inference.net/halo/install.sh | sh, which downloads the latest release for your platform. Alternatively, install the Python engine via pip install halo-engine to use the CLI and SDK directly.
Which AI clients and platforms does HALO work with?+
HALO integrates with Claude, Cursor, and Codex for implementing fixes. It ingests traces from Langfuse, Arize, JSONL exports, or any OpenTelemetry-compatible source. The engine supports OpenAI-compatible API providers via OPENAI_BASE_URL.
Do I need an API key to use HALO?+
Yes, HALO requires an OpenAI API key (set via OPENAI_API_KEY) or credentials for an OpenAI-compatible provider. The hosted version at inference.net requires signing up for an INFERENCE_API_KEY for telemetry uploads.
Is HALO free and open source?+
Yes, HALO is released under the MIT license and the source code is available on GitHub. The desktop app and Python engine are free, though LLM provider API costs apply. A hosted plug-and-play version is available at inference.net.
What trace format does HALO accept?+
HALO accepts OpenTelemetry-compatible traces in JSONL format. You can import traces from observability platforms like Langfuse and Arize, export them as JSONL files, or instrument your agent directly using OpenTelemetry tracing.
How do I install HALO?+
Open the source repository on GitHub and follow its README. HALO is a agent — MCP Agents Market links you directly to the official repo.
Is HALO free?+
HALO is an open-source project hosted on GitHub. Check the repository for its license and any usage requirements.