collab-evals

You can capture manifest evidence for multi-agent evaluation workflows by orchestrating tests that export a manifest.json file and runner.ndjson logs. This approach records run traceability and documents findings for auditability.

Yes, you can pause and resume long-running symbolic RLM evaluations using checkpointing support. This handles multi-hour refactors and context-rot regression scenarios spanning 24 to 72 hours with multiple resumes.

You can run repeatable large-context interaction tests by orchestrating collab-driven evaluation pipelines. This framework reduces ad-hoc experimentation by standardizing scenario execution and generating manifest-backed outputs.

You need to export MCP_RUNNER_TASK_ID with a valid task identifier before starting an evaluation pipeline. This task context ensures run traceability when you execute the codex-orchestrator start command in JSON format.

Yes, multi-agent evaluation supports traceability for multi-day initiatives by generating manifest-backed evidence. It updates documentation with findings and exports manifest.json and runner.ndjson files for long-running experiments.

The best way to document findings from large-context symbolic RLM evaluations is using a framework that automatically updates documentation. It exports manifest.json and runner.ndjson files to preserve evidence and ensure reproducibility.