Omni workflow

Omni is the plugin’s default orchestration layer. It runs the standalone gepa==0.1.4 PyPI reflective engine beside the plugin-native AutoResearch and Meta-Harness engines, picks the strongest Phase 1 candidate, and starts a fresh Phase 2 continuation. The plugin-native best_of_n engine is the independent comparison baseline for standalone runs. omni is a preflight target and workflow mode, not a public PyPI engine= value.

Phases

seed + evaluator
       |
       +--> Phase 1: gepa (PyPI reflective engine)
       +--> Phase 1: autoresearch (plugin-native)
       +--> Phase 1: meta_harness (plugin-native)
                    |
             best Phase 1 candidate
                    |
       Phase 2: fresh continuation (gepa by default)
                    |
             candidate + scores + held-out report

All Phase 1 branches share the same seed, objective, evaluator, dataset, and selection valset. test_set is removed from every Phase 1 task and used only for final held-out scoring by the continuation or wrapper. Phase 2 starts a new external run_dir and output_dir rather than resuming a Phase 1 branch.

Launch

from omni_pipeline import run_omni

result = run_omni(
    seed_candidate,
    task=task,
    max_evals=40,
    max_token_cost=20.0,
    run_dir="/tmp/omni-run",
    output_dir="/tmp/omni-output",
    continuation_engine="gepa",
)

Budget partitioning

The total evaluation and token budgets are divided into three exploration slices and one continuation slice. Evaluation remainders go to the continuation.

An explicit positive max_evals and/or max_token_cost is required.
When max_evals is the only bound, at least four evaluations are needed so every phase receives a positive slice.
Use a standalone engine when the budget cannot support four phases.

Continuation choices

The default continuation is gepa, which uses the PyPI reflective engine and the read-only Chat Completions proposer. Set continuation_engine="autoresearch" or continuation_engine="meta_harness" to use a plugin-native agent continuation. Standalone run_optimization(..., engine=...) bypasses Omni and receives the full supplied budget.

Backend and model selection

export OPENAI_BASE_URL="https://api.openai.com/v1"
export OPENAI_MODEL="your-model"
export OPENAI_API_KEY="your-api-key"

agent_backend remains a compatibility label for codex, pi, or claude. OPENAI_MODEL is authoritative for every branch. There is no provider-specific model or CLI login resolution. The wrapper also preserves codex_command, pi_command, codex_timeout_seconds, codex_input_cost_per_million, codex_output_cost_per_million, max_concurrency, gepa_parallel_proposals, and stop_at_score. Codex input and output rates are required together when a Codex token cap is configured.

Runtime prerequisites

Run the local preflight before a real Omni run:

python3 skills/gepa-omni-skill/scripts/preflight.py --engine omni
python3 skills/gepa-omni-skill/scripts/preflight.py \
  --engine omni --agent-backend pi

Preflight verifies the published GEPA API, plugin-native runtime primitives, and the three OPENAI_* variables. sandbox=False is rejected at the wrapper boundary, and the Chat Completions model receives no local tools. Keep every run_dir and output_dir absolute and outside the checkout. Native evaluation artifacts include per-evaluation JSON, eval_trace.jsonl, progress, and a serializable result.json.

Fleet-RLM

Fleet Pi

GEPA Omni

Qredence Skills

Fleet Reasoner

Qredence Plugins

Phases

Launch

Budget partitioning

Continuation choices

Backend and model selection

Runtime prerequisites

​Phases

​Launch

​Budget partitioning

​Continuation choices

​Backend and model selection

​Runtime prerequisites

Phases

Launch

Budget partitioning

Continuation choices

Backend and model selection

Runtime prerequisites