fix(optimization): allow train-only GEPA runs

CHANGELOG.md

@@ -4,6 +4,12 @@ All notable changes to `@tangle-network/agent-eval` and its sibling `agent-eval-
 
 ---
 
+## [0.126.4] - 2026-07-24 - train-only GEPA optimization
+
+### Fixed
+
+- Official GEPA optimization accepts an empty selection set, reuses the non-empty training set for candidate comparison, and labels that comparison as a training-set fallback.
+
 ## [0.126.3] - 2026-07-24 - execution-bound optimizer resume
 
 ### Added

clients/python/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "hatchling.build"
 
 [project]
 name = "agent-eval-rpc"
-version = "0.126.3"
+version = "0.126.4"
 description = "Python RPC client, official optimizer bridge, and DSPy metric adapter for @tangle-network/agent-eval."
 readme = "README.md"
 requires-python = ">=3.10"

clients/python/src/agent_eval_rpc/__init__.py

@@ -53,7 +53,7 @@
 try:
     __version__ = version("agent-eval-rpc")
 except PackageNotFoundError:
-    __version__ = "0.126.3"
+    __version__ = "0.126.4"
 
 __all__ = [
     "Client",

clients/python/src/agent_eval_rpc/gepa_bridge.py

@@ -121,14 +121,17 @@ def evaluate(
                 raise ValueError("GEPA requested an invalid example")
             return evaluate_one(candidate, example)
 
-        comparison_set = input_value["selectionSet"] or input_value["trainSet"]
+        selection_set = input_value["selectionSet"]
+        comparison_set = selection_set or input_value["trainSet"]
         if not comparison_set:
             raise ValueError("GEPA selection re-score requires at least one example")
         rows = [evaluate_one(candidate, item) for item in comparison_set]
         return (
             sum(score for score, _ in rows) / len(rows),
             {
-                "comparison": "selection-set",
+                "comparison": (
+                    "selection-set" if selection_set else "train-set-fallback"
+                ),
                 "examples": [
                     {"id": item["id"], "score": score, "info": info}
                     for item, (score, info) in zip(comparison_set, rows, strict=True)

clients/python/src/agent_eval_rpc/gepa_bridge_contract.py

@@ -77,8 +77,8 @@ def _validate_input(value: dict[str, Any]) -> None:
         value.get("selectionSet"), list
     ):
         raise ValueError("GEPA bridge input requires trainSet and selectionSet arrays")
-    if not value["trainSet"] or not value["selectionSet"]:
-        raise ValueError("GEPA bridge requires non-empty trainSet and selectionSet")
+    if not value["trainSet"]:
+        raise ValueError("GEPA bridge requires a non-empty trainSet")
     validate_json_size(value["objective"], value["maxEvidenceChars"], "GEPA objective")
     validate_json_size(
         value.get("background", ""),

clients/python/tests/test_gepa_bridge.py

@@ -102,7 +102,7 @@ def test_bridge_calls_gepa_and_writes_a_cost_report(
                 "objective": "Return a better candidate.",
                 "seedCandidate": "baseline",
                 "trainSet": [{"id": "train", "data": {"prompt": "visible"}}],
-                "selectionSet": [{"id": "selection", "data": {"prompt": "also-visible"}}],
+                "selectionSet": [],
                 "maxCandidateChars": 100,
                 "maxEvidenceChars": 1_000,
                 "outputDir": str(tmp_path / "external"),
@@ -119,9 +119,18 @@ def __init__(self, **kwargs: Any) -> None:
     def fake_optimize(seed_candidate: str, **kwargs: Any) -> SimpleNamespace:
         calls["seedCandidate"] = seed_candidate
         calls["arguments"] = kwargs
-        score, info = kwargs["evaluator"]("better", kwargs["dataset"][0])
+        score, info = kwargs["evaluator"]("better")
         assert score == 0.75
-        assert info == {"source": "agent-eval"}
+        assert info == {
+            "comparison": "train-set-fallback",
+            "examples": [
+                {
+                    "id": "train",
+                    "score": 0.75,
+                    "info": {"source": "agent-eval"},
+                }
+            ],
+        }
         return SimpleNamespace(
             best_candidate="better",
             best_score=score,
@@ -156,7 +165,7 @@ def fake_post(*args: Any, **kwargs: Any) -> httpx.Response:
 
     assert calls["seedCandidate"] == "baseline"
     assert calls["arguments"]["dataset"] == [{"id": "train", "data": {"prompt": "visible"}}]
-    assert calls["arguments"]["valset"] == [{"id": "selection", "data": {"prompt": "also-visible"}}]
+    assert calls["arguments"]["valset"] == []
     config = calls["config"]
     output_dir = config.pop("output_dir")
     state_dir = config.pop("run_dir")
@@ -506,6 +515,18 @@ def test_input_requires_evaluation_identity(tmp_path: Path) -> None:
         gepa_bridge._validate_input(input_value)
 
 
+def test_input_allows_train_only_optimization_but_requires_training_data(
+    tmp_path: Path,
+) -> None:
+    input_value = _valid_input(tmp_path)
+    input_value["selectionSet"] = []
+    gepa_bridge._validate_input(input_value)
+
+    input_value["trainSet"] = []
+    with pytest.raises(ValueError, match="non-empty trainSet"):
+        gepa_bridge._validate_input(input_value)
+
+
 def test_proxied_gepa_accepts_official_retry_configuration(tmp_path: Path) -> None:
     input_value = _valid_input(tmp_path)
     input_value["recipe"] = {

package.json

@@ -1,6 +1,6 @@
 {
   "name": "@tangle-network/agent-eval",
-  "version": "0.126.3",
+  "version": "0.126.4",
   "description": "Evaluate and improve AI agents from runs, traces, judges, and feedback. Compare candidates, cluster failures, measure lift, and gate releases.",
   "homepage": "https://github.com/tangle-network/agent-eval#readme",
   "repository": {