feat: enable remote SDK and resilient device orchestration

artemis/llm/fake_model.py

@@ -0,0 +1,179 @@
+# Copyright 2026 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Fake chat model for infrastructure testing (ARTEMIS_FAKE_LLM=1).
+
+Completes any task on the first LLM invocation so that queueing, entry
+points, device locking, and status propagation can be exercised without
+real model calls:
+
+- Flash (report_task_status bound): immediately reports status=completed.
+- Pro planner (save_note bound): saves a task plan whose only subgoal is
+  already checked, so convergence_gate ends the graph after planning.
+- Anything else: returns a plain "FAKE_LLM_OK" text message.
+
+ARTEMIS_FAKE_LLM_DELAY_S adds an artificial per-call delay so tasks stay
+alive long enough to observe queue/concurrency behavior.
+"""
+
+import asyncio
+import json
+import time
+from typing import Any, Iterator, AsyncIterator
+import uuid
+
+from langchain_core.callbacks import (
+    AsyncCallbackManagerForLLMRun,
+    CallbackManagerForLLMRun,
+)
+from langchain_core.language_models.chat_models import BaseChatModel
+from langchain_core.messages import AIMessage, AIMessageChunk, BaseMessage
+from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult
+
+FAKE_DONE_TEXT = "FAKE_LLM: task auto-completed for infrastructure testing."
+
+
+def _tool_name(tool: Any) -> str | None:
+    name = getattr(tool, "name", None)
+    if isinstance(name, str):
+        return name
+    if isinstance(tool, dict):
+        if isinstance(tool.get("name"), str):
+            return tool["name"]
+        fn = tool.get("function")
+        if isinstance(fn, dict) and isinstance(fn.get("name"), str):
+            return fn["name"]
+    return None
+
+
+class FakeChatModel(BaseChatModel):
+    """Deterministic fake model that finishes every task on the first call."""
+
+    bound_tool_names: list[str] = []
+    delay_s: float = 0.0
+
+    @property
+    def _llm_type(self) -> str:
+        return "artemis-fake"
+
+    def bind_tools(self, tools: Any, **kwargs: Any) -> "FakeChatModel":
+        names = [n for n in (_tool_name(t) for t in tools) if n]
+        return FakeChatModel(bound_tool_names=names, delay_s=self.delay_s)
+
+    def with_structured_output(self, schema: Any, **kwargs: Any) -> "FakeChatModel":
+        return self
+
+    def _decide(self, messages: list[BaseMessage]) -> AIMessage:
+        call_id = f"fake-{uuid.uuid4().hex[:12]}"
+        usage = {"input_tokens": 1, "output_tokens": 1, "total_tokens": 2}
+
+        if "report_task_status" in self.bound_tool_names:
+            return AIMessage(
+                content="Task verified complete on first observation.",
+                tool_calls=[
+                    {
+                        "name": "report_task_status",
+                        "args": {"status": "completed", "explanation": FAKE_DONE_TEXT},
+                        "id": call_id,
+                    }
+                ],
+                usage_metadata=usage,
+            )
+
+        if "save_note" in self.bound_tool_names:
+            already_planned = any(
+                tc.get("name") == "save_note"
+                for m in messages
+                for tc in (getattr(m, "tool_calls", None) or [])
+            )
+            if already_planned:
+                return AIMessage(content="Plan saved. " + FAKE_DONE_TEXT, usage_metadata=usage)
+            return AIMessage(
+                content="Saving completed plan.",
+                tool_calls=[
+                    {
+                        "name": "save_note",
+                        "args": {
+                            "key": "task_plan",
+                            "content": "- [x] " + FAKE_DONE_TEXT,
+                        },
+                        "id": call_id,
+                    }
+                ],
+                usage_metadata=usage,
+            )
+
+        return AIMessage(content=FAKE_DONE_TEXT, usage_metadata=usage)
+
+    def _to_chunk(self, msg: AIMessage) -> ChatGenerationChunk:
+        tool_call_chunks = [
+            {
+                "name": tc["name"],
+                "args": json.dumps(tc["args"]),
+                "id": tc["id"],
+                "index": i,
+                "type": "tool_call_chunk",
+            }
+            for i, tc in enumerate(msg.tool_calls or [])
+        ]
+        chunk = AIMessageChunk(
+            content=msg.content,
+            tool_call_chunks=tool_call_chunks,
+            usage_metadata=msg.usage_metadata,
+        )
+        return ChatGenerationChunk(message=chunk)
+
+    def _generate(
+        self,
+        messages: list[BaseMessage],
+        stop: list[str] | None = None,
+        run_manager: CallbackManagerForLLMRun | None = None,
+        **kwargs: Any,
+    ) -> ChatResult:
+        if self.delay_s > 0:
+            time.sleep(self.delay_s)
+        return ChatResult(generations=[ChatGeneration(message=self._decide(messages))])
+
+    async def _agenerate(
+        self,
+        messages: list[BaseMessage],
+        stop: list[str] | None = None,
+        run_manager: AsyncCallbackManagerForLLMRun | None = None,
+        **kwargs: Any,
+    ) -> ChatResult:
+        if self.delay_s > 0:
+            await asyncio.sleep(self.delay_s)
+        return ChatResult(generations=[ChatGeneration(message=self._decide(messages))])
+
+    def _stream(
+        self,
+        messages: list[BaseMessage],
+        stop: list[str] | None = None,
+        run_manager: CallbackManagerForLLMRun | None = None,
+        **kwargs: Any,
+    ) -> Iterator[ChatGenerationChunk]:
+        if self.delay_s > 0:
+            time.sleep(self.delay_s)
+        yield self._to_chunk(self._decide(messages))
+
+    async def _astream(
+        self,
+        messages: list[BaseMessage],
+        stop: list[str] | None = None,
+        run_manager: AsyncCallbackManagerForLLMRun | None = None,
+        **kwargs: Any,
+    ) -> AsyncIterator[ChatGenerationChunk]:
+        if self.delay_s > 0:
+            await asyncio.sleep(self.delay_s)
+        yield self._to_chunk(self._decide(messages))

artemis/llm/router.py

@@ -188,6 +188,16 @@ class ModelFactory:
 
     @classmethod
     def create_model(cls, endpoint: ModelEndpoint) -> BaseChatModel:
+        if os.environ.get("ARTEMIS_FAKE_LLM") == "1":
+            from artemis.llm.fake_model import FakeChatModel
+
+            delay = float(os.environ.get("ARTEMIS_FAKE_LLM_DELAY_S", "0") or 0)
+            logger.warning(
+                f"ARTEMIS_FAKE_LLM=1 — returning FakeChatModel (delay={delay}s) "
+                f"instead of {endpoint.provider}/{endpoint.model_name}"
+            )
+            return FakeChatModel(delay_s=delay)
+
         provider = ModelProvider.from_string(endpoint.provider)
 
         if provider == ModelProvider.GOOGLE: