v0.3 Add EnvGenAgent for agentic env gen

.github/workflows/ci.yml

@@ -100,6 +100,9 @@ jobs:
     runs-on: [self-hosted, gpu-arena]
     timeout-minutes: 80
     needs: [pre_commit]
+    env:
+      # NV_API_KEY is consumed by the live-endpoint tests for agentic env gen.
+      NV_API_KEY: ${{ secrets.ARENA_NV_API_KEY }}
 
     container:
       image: nvcr.io/nvstaging/isaac-amr/isaaclab_arena:latest

docker/run_docker.sh

@@ -200,6 +200,12 @@ else
         fi
     fi
 
+    # pass through API keys used by the agentic env-gen prototype; values are
+    # inherited from the host shell so the key never lives in the repo.
+    if [ -n "$NV_API_KEY" ]; then
+        DOCKER_RUN_ARGS+=("--env" "NV_API_KEY")
+    fi
+
     # if gr00t is installed, mount the gr00t directory in case anything needs to change there
     if [ "$INSTALL_GROOT" = "true" ]; then
         DOCKER_RUN_ARGS+=("-v" "./submodules/Isaac-GR00T:${WORKDIR}/submodules/Isaac-GR00T")

isaaclab_arena/agentic_environment_generation/__init__.py

@@ -0,0 +1,4 @@
+# Copyright (c) 2026, The Isaac Lab Arena Project Developers (https://github.com/isaac-sim/IsaacLab-Arena/blob/main/CONTRIBUTORS.md).
+# All rights reserved.
+#
+# SPDX-License-Identifier: Apache-2.0

isaaclab_arena/agentic_environment_generation/agent_utils.py

@@ -0,0 +1,84 @@
+# Copyright (c) 2026, The Isaac Lab Arena Project Developers (https://github.com/isaac-sim/IsaacLab-Arena/blob/main/CONTRIBUTORS.md).
+# All rights reserved.
+#
+# SPDX-License-Identifier: Apache-2.0
+
+"""Utilities for the environment-generation agent (OpenAI-compatible API)."""
+
+from __future__ import annotations
+
+import copy
+from typing import Any
+
+from pydantic import BaseModel
+
+
+def ping(client: Any, model: str) -> str:
+    """Smoke-test the endpoint + API key + model with a minimal request.
+
+    Args:
+        client: An OpenAI-compatible client (typically
+            ``openai.OpenAI`` or a compatible mock).
+        model: Model identifier forwarded to
+            ``client.chat.completions.create(model=...)``.
+
+    Returns:
+        The model's response text.
+    """
+    # TODO(qianl): wrap with transient-error retry.
+    resp = client.chat.completions.create(
+        model=model,
+        messages=[{"role": "user", "content": "Respond with exactly: OK"}],
+        temperature=0,
+        max_tokens=8,
+    )
+    choices = getattr(resp, "choices", None) or []
+    assert choices, (
+        f"ping to model {model!r} returned HTTP 200 with no choices "
+        "(content filter / guardrail / rate-limit response with empty body)."
+    )
+    return choices[0].message.content or ""
+
+
+def build_strict_schema(model_cls: type[BaseModel]) -> dict[str, Any]:
+    """Return ``model_cls``'s JSON schema munged for OpenAI strict mode."""
+    schema = copy.deepcopy(model_cls.model_json_schema())
+    apply_strict_constraints(schema)
+    return schema
+
+
+def apply_strict_constraints(node: Any) -> None:
+    """Recursively apply OpenAI strict-mode constraints to a JSON-schema node."""
+    if isinstance(node, dict):
+        if node.get("type") == "object" and "properties" in node:
+            node["additionalProperties"] = False
+            node["required"] = list(node["properties"].keys())
+        # Strict mode forbids ``default`` keys (every field is required, so
+        # defaults can never apply). Drop them defensively at every level.
+        node.pop("default", None)
+        for v in node.values():
+            apply_strict_constraints(v)
+    elif isinstance(node, list):
+        for v in node:
+            apply_strict_constraints(v)
+
+
+def extract_response_text(message: Any) -> tuple[str, str]:
+    """Pull the agent's structured-output text from the chat-completion message.
+
+    Returns ``(text, route)`` where ``route`` is one of:
+
+      * ``"content"`` — the standard OpenAI-compatible channel.
+      * ``"reasoning_content"`` — NVIDIA DeepSeek's provider-specific
+        channel; the model emits structured outputs here instead of
+        ``content``. We treat it as equivalent.
+      * ``"empty"`` — both channels were empty / missing; the caller
+        should surface a clear error.
+    """
+    content = getattr(message, "content", None)
+    if content:
+        return content, "content"
+    reasoning = getattr(message, "reasoning_content", None)
+    if reasoning:
+        return reasoning, "reasoning_content"
+    return "", "empty"