HALO
HALO is a methodology and toolkit for recursively self-improving AI agent harnesses using RLMs and production traces.
HALO
What is HALO?
HALO vs Similar AI Tools
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How to use HALO?
- 1Install HALO via desktop app (curl -fsSL https://inference.net/halo/install.sh | sh) or pip install halo-engine.
- 2Integrate OpenTelemetry-compatible tracing into your agent harness.
- 3Collect traces by running your agent in production or on benchmarks.
- 4Run the HALO engine with a prompt: halo path_to_traces.jsonl -p "Diagnose errors and suggest fixes".
- 5Review the generated report and apply suggested changes to your harness.
- 6Repeat the cycle for continuous improvement.
HALO Key Features
- Collects execution traces via OpenTelemetry
- RL-based trace analysis to identify systemic failure modes
- Generates actionable harness improvement suggestions
- CLI and Python API for integration
- Supports OpenAI-compatible providers
- Telemetry for monitoring HALO's own LLM activity
HALO Use Cases
- Improving production agent deployments by finding hidden failure patterns
- Optimizing agent benchmark performance (e.g., AppWorld)
- Debugging hallucinated tool calls and refusal loops
- Automating agent harness iteration and maintenance
HALO Pricing & Free Credits
HALO currently operates on a Free model.
This tool is completely free to use
HALO Pros & Cons
Pros
- Open-source and free to use
- Simple loop that yields measurable improvements
- Proven effectiveness on benchmarks like AppWorld
- Supports multiple model providers via OpenAI-compatible API
- Integrates with existing tracing infrastructure
Cons
- Requires setting up OpenTelemetry tracing in agent harness
- Requires an API key (e.g., OpenAI) for the analysis engine
- Analysis quality depends on prompt engineering
- Limited documentation for custom integration scenarios
What is HALO best for?
- AI agent developers seeking to automate harness improvement
- Teams running production agent systems with high traffic
- Researchers optimizing agent benchmarks
HALO FAQ
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