diff --git a/litellm/eval_utils.py b/litellm/eval_utils.py
new file mode 100644
index 00000000000..b3c16f251c9
--- /dev/null
+++ b/litellm/eval_utils.py
@@ -0,0 +1,181 @@
+"""
+SDK primitive for LLM-judge evaluation.
+
+Usage:
+    result = await litellm.aevaluate(
+        response=model_response,
+        criteria=[{"name": "Accuracy", "weight": 100, "description": "Is the answer correct?"}],
+        judge_model="claude-sonnet-4-6",
+    )
+    result.check_thresholds(overall_min=80)
+"""
+
+import asyncio
+import json
+from datetime import datetime, timezone
+from typing import Any, Dict, List, Optional, Union
+
+from litellm.types.utils import EvalCriterion, EvalResult, EvalVerdict
+
+
+_JUDGE_SYSTEM_PROMPT = (
+    "You are an expert evaluator. Grade the agent response against each criterion "
+    "and return ONLY valid JSON — no markdown, no backticks, no extra text.\n\n"
+    "Response format:\n"
+    '{"scores": [{"criterion": "criterion name", "score": 0-100, "comment": "brief reason"}], '
+    '"overall": 0-100, "verdict": "2-3 sentence summary", '
+    '"strongest": "criterion name", "weakest": "criterion name"}'
+)
+
+
+def _extract_response_text(response: Any) -> str:
+    """Extract plain text from a ModelResponse or raw string."""
+    if isinstance(response, str):
+        return response
+    try:
+        return response.choices[0].message.content or ""
+    except (AttributeError, IndexError, TypeError):
+        try:
+            return str(response)
+        except Exception:
+            return ""
+
+
+def _build_judge_messages(
+    response_text: str,
+    criteria: List[EvalCriterion],
+    system_prompt: Optional[str] = None,
+) -> List[Dict]:
+    """Build the judge messages (system + user) for the LLM-judge call."""
+    criteria_lines = "\n".join(
+        f"{i+1}. {c.get('name', f'Criterion {i+1}')} ({c.get('weight', 0)}%): {c.get('description', '')}"
+        for i, c in enumerate(criteria)
+    )
+    user_content = (
+        f"Grade the following agent response against the rubric.\n\n"
+        f"AGENT RESPONSE:\n{response_text}\n\n"
+        f"RUBRIC (score each criterion 0-100):\n{criteria_lines}\n\n"
+        f"Compute overall score as the weighted average: "
+        f"sum(score * weight / 100) for each criterion."
+    )
+    return [
+        {"role": "system", "content": system_prompt or _JUDGE_SYSTEM_PROMPT},
+        {"role": "user", "content": user_content},
+    ]
+
+
+async def aevaluate(
+    response: Any,
+    criteria: List[EvalCriterion],
+    judge_model: str,
+    system_prompt: Optional[str] = None,
+    iteration: int = 0,
+    litellm_kwargs: Optional[Dict] = None,
+) -> EvalResult:
+    """
+    Async SDK primitive: run an LLM-judge eval on a response.
+
+    Args:
+        response: ModelResponse or plain string to evaluate.
+        criteria: List of EvalCriterion dicts (name, weight, description).
+        judge_model: Model name to use as judge (e.g. "claude-sonnet-4-6").
+        system_prompt: Override the default judge system prompt.
+        iteration: Which retry iteration this is (0-indexed). Used for logging.
+        litellm_kwargs: Extra kwargs forwarded to litellm.acompletion (e.g. api_base, api_key).
+
+    Returns:
+        EvalResult — always returns (never raises). Check .eval_error for judge failures.
+    """
+    import litellm
+
+    start_time = datetime.now(timezone.utc)
+    response_text = _extract_response_text(response)
+    messages = _build_judge_messages(response_text, criteria, system_prompt)
+
+    try:
+        judge_response = await litellm.acompletion(
+            model=judge_model,
+            messages=messages,
+            response_format={"type": "json_object"},
+            **(litellm_kwargs or {}),
+        )
+        raw_content = judge_response.choices[0].message.content or "{}"  # type: ignore[union-attr]
+        raw_json: Dict = json.loads(raw_content)
+    except Exception as e:
+        # Fail open: judge error never blocks the response
+        return EvalResult(
+            overall_score=100.0,
+            passed=True,
+            judge_model=judge_model,
+            iteration=iteration,
+            eval_error=f"Judge call failed: {str(e)}",
+        )
+
+    try:
+        scores_raw = raw_json.get("scores", [])
+        verdicts: List[EvalVerdict] = []
+
+        # Build a weight map for computing overall score
+        weight_map: Dict[str, int] = {
+            c.get("name", ""): c.get("weight", 0) for c in criteria
+        }
+        total_weight = sum(weight_map.values()) or 1
+
+        weighted_sum = 0.0
+        for s in scores_raw:
+            cname = s.get("criterion", "")
+            score = float(s.get("score", 0))
+            weight = weight_map.get(cname, 0)
+            weighted_sum += score * (weight / total_weight)
+            verdicts.append(
+                EvalVerdict(
+                    criterion_name=cname,
+                    score=score,
+                    reasoning=s.get("comment", ""),
+                    passed=True,  # per-criterion pass/fail evaluated by caller if threshold set
+                )
+            )
+
+        overall_score = min(max(weighted_sum, 0.0), 100.0)
+
+        end_time = datetime.now(timezone.utc)
+        duration = (end_time - start_time).total_seconds()
+
+        return EvalResult(
+            overall_score=overall_score,
+            verdicts=verdicts,
+            passed=False,  # caller sets this after threshold check
+            judge_model=judge_model,
+            iteration=iteration,
+            raw_judge_response=raw_json,
+        )
+
+    except Exception as e:
+        return EvalResult(
+            overall_score=100.0,
+            passed=True,
+            judge_model=judge_model,
+            iteration=iteration,
+            eval_error=f"Verdict parsing failed: {str(e)}",
+        )
+
+
+def evaluate(
+    response: Any,
+    criteria: List[EvalCriterion],
+    judge_model: str,
+    system_prompt: Optional[str] = None,
+    iteration: int = 0,
+    litellm_kwargs: Optional[Dict] = None,
+) -> EvalResult:
+    """Sync wrapper for aevaluate(). See aevaluate() for full docs."""
+    return asyncio.run(
+        aevaluate(
+            response=response,
+            criteria=criteria,
+            judge_model=judge_model,
+            system_prompt=system_prompt,
+            iteration=iteration,
+            litellm_kwargs=litellm_kwargs,
+        )
+    )