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    Introducing Catalyst: Train self-improving AI models

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    Optimize agents from real traces.

    HALO is an RLM-based open-source AI agent built to identify issues in other AI agents by analyzing OpenTelemetry data. Connect HALO to your favorite coding agent for a complete development flywheel.

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    HALO runs everywhere your agents run.

    Run development loops locally, build custom workflows using HALO libraries, and use hosted HALO on inference.net for production.

    HALO Desktop import traces screen

    Desktop

    Collect or import OTEL traces and run HALO analysis in the desktop app. Built for local development.

    from engine.agents.agent_config import AgentConfig
    from engine.engine_config import EngineConfig
    from engine.main import stream_engine_output_async
    from engine.model_config import ModelConfig
    from engine.model_provider_config import ModelProviderConfig
    from engine.models.engine_output import AgentOutputItem
    
    
    @dataclass(frozen=True)
    class EngineRunResult:
        """Everything the runtime entrypoint needs after the engine returns.
    
        ``items`` is the raw, typed pydantic list — callers serialise via
        ``model_dump`` when building the result JSON. Keeping the typed
        list (instead of a pre-serialized ``list[dict]``) means downstream
        code (review tooling, future result shape tweaks) gets full type
        info without re-parsing"

    PyPi Package

    Build HALO into existing workflows using HALO Engine. Free & Open Source. Works with every agent.

    Inference
    InferenceApp8:20 AM
    Run completed

    Bi-weekly HALO analysis

    inf-system / HALO Catalyst

    Duration

    20m 38s

    Schedule

    Bi-weekly

    Report

    ## Executive summary

    The agent completed most runs successfully, but traces show reliability and observability issues.

    - 3 traces dominate latency and cost. Tool usage is highly redundant...

    Hosted

    Use hosted HALO on inference.net with scheduled reports, Slack integration, and production trace storage.

    Add traces, run reports, and ship the fix.

    Use HALO to build a powerful development loop from production agent trace data. Run reports, investigate traces with timeline & session views, and send to Claude or Codex for final implementation.

    Import Trace Data

    Import agent trace data from Langfuse, Arize, JSONL, or stream directly from a local agent.

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    Import Trace Data

    Read complete sessions

    Move from trace metadata into the full agent conversation and tool-call context.

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    Read complete sessions

    Understand timing

    Use timeline views to spot expensive spans, retries, stalls, and branching behavior.

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    Understand timing

    Apply fixes with an agent

    Send HALO reports to your favorite coding agent to turn reports into concrete product improvements.

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    Apply fixes with an agent

    Hosted HALO on Catalyst

    Capture production traces, run HALO over real sessions, and turn every finding into the next eval, prompt change, model update, or tool fix.

    Deploy