chore: add more robust test for runtime injection w/ explicit args_schema (#34051)

libs/langchain_v1/tests/unit_tests/agents/test_injected_runtime_create_agent.py

@@ -21,11 +21,14 @@ from typing import Any
 import pytest
 from langchain_core.messages import AIMessage, HumanMessage, ToolMessage
 from langchain_core.tools import tool
+from langgraph.prebuilt import InjectedStore
+from langgraph.store.base import BaseStore
 from langgraph.store.memory import InMemoryStore
+from typing_extensions import Annotated
 
 from langchain.agents import create_agent
 from langchain.agents.middleware.types import AgentState
-from langchain.tools import ToolRuntime
+from langchain.tools import InjectedState, ToolRuntime
 
 from .model import FakeToolCallingModel
 
@@ -589,3 +592,243 @@ def test_tool_runtime_name_based_injection() -> None:
     assert injected_data["tool_call_id"] == "name_call_123"
     assert injected_data["state"] is not None
     assert "messages" in injected_data["state"]
+
+
+def test_combined_injected_state_runtime_store() -> None:
+    """Test that all injection mechanisms work together in create_agent.
+
+    This test verifies that a tool can receive injected state, tool runtime,
+    and injected store simultaneously when specified in the function signature
+    but not in the explicit args schema. This is modeled after the pattern
+    from mre.py where multiple injection types are combined.
+    """
+    # Track what was injected
+    injected_data = {}
+
+    # Custom state schema with additional fields
+    class CustomState(AgentState):
+        user_id: str
+        session_id: str
+
+    # Define explicit args schema that only includes LLM-controlled parameters
+    weather_schema = {
+        "type": "object",
+        "properties": {
+            "location": {"type": "string", "description": "The location to get weather for"},
+        },
+        "required": ["location"],
+    }
+
+    @tool(args_schema=weather_schema)
+    def multi_injection_tool(
+        location: str,
+        state: Annotated[Any, InjectedState],
+        runtime: ToolRuntime,
+        store: Annotated[Any, InjectedStore()],
+    ) -> str:
+        """Tool that uses injected state, runtime, and store together.
+
+        Args:
+            location: The location to get weather for (LLM-controlled).
+            state: The graph state (injected).
+            runtime: The tool runtime context (injected).
+            store: The persistent store (injected).
+        """
+        # Capture all injected parameters
+        injected_data["state"] = state
+        injected_data["user_id"] = state.get("user_id", "unknown")
+        injected_data["session_id"] = state.get("session_id", "unknown")
+        injected_data["runtime"] = runtime
+        injected_data["tool_call_id"] = runtime.tool_call_id
+        injected_data["store"] = store
+        injected_data["store_is_none"] = store is None
+
+        # Verify runtime.state matches the state parameter
+        injected_data["runtime_state_matches"] = runtime.state == state
+
+        return f"Weather info for {location}"
+
+    # Create model that calls the tool
+    model = FakeToolCallingModel(
+        tool_calls=[
+            [
+                {
+                    "name": "multi_injection_tool",
+                    "args": {"location": "San Francisco"},  # Only LLM-controlled arg
+                    "id": "call_weather_123",
+                }
+            ],
+            [],  # End the loop
+        ]
+    )
+
+    # Create agent with custom state and store
+    agent = create_agent(
+        model=model,
+        tools=[multi_injection_tool],
+        state_schema=CustomState,
+        store=InMemoryStore(),
+    )
+
+    # Verify the tool's args schema only includes LLM-controlled parameters
+    tool_args_schema = multi_injection_tool.args_schema
+    assert "location" in tool_args_schema["properties"]
+    assert "state" not in tool_args_schema["properties"]
+    assert "runtime" not in tool_args_schema["properties"]
+    assert "store" not in tool_args_schema["properties"]
+
+    # Invoke with custom state fields
+    result = agent.invoke(
+        {
+            "messages": [HumanMessage("What's the weather like?")],
+            "user_id": "user_42",
+            "session_id": "session_abc123",
+        }
+    )
+
+    # Verify tool executed successfully
+    tool_messages = [msg for msg in result["messages"] if isinstance(msg, ToolMessage)]
+    assert len(tool_messages) == 1
+    tool_message = tool_messages[0]
+    assert tool_message.content == "Weather info for San Francisco"
+    assert tool_message.tool_call_id == "call_weather_123"
+
+    # Verify all injections worked correctly
+    assert injected_data["state"] is not None
+    assert "messages" in injected_data["state"]
+
+    # Verify custom state fields were accessible
+    assert injected_data["user_id"] == "user_42"
+    assert injected_data["session_id"] == "session_abc123"
+
+    # Verify runtime was injected
+    assert injected_data["runtime"] is not None
+    assert injected_data["tool_call_id"] == "call_weather_123"
+
+    # Verify store was injected
+    assert injected_data["store_is_none"] is False
+    assert injected_data["store"] is not None
+
+    # Verify runtime.state matches the injected state
+    assert injected_data["runtime_state_matches"] is True
+
+
+async def test_combined_injected_state_runtime_store_async() -> None:
+    """Test that all injection mechanisms work together in async execution.
+
+    This async version verifies that injected state, tool runtime, and injected
+    store all work correctly with async tools in create_agent.
+    """
+    # Track what was injected
+    injected_data = {}
+
+    # Custom state schema
+    class CustomState(AgentState):
+        api_key: str
+        request_id: str
+
+    # Define explicit args schema that only includes LLM-controlled parameters
+    # Note: state, runtime, and store are NOT in this schema
+    search_schema = {
+        "type": "object",
+        "properties": {
+            "query": {"type": "string", "description": "The search query"},
+            "max_results": {"type": "integer", "description": "Maximum number of results"},
+        },
+        "required": ["query", "max_results"],
+    }
+
+    @tool(args_schema=search_schema)
+    async def async_multi_injection_tool(
+        query: str,
+        max_results: int,
+        state: Annotated[Any, InjectedState],
+        runtime: ToolRuntime,
+        store: Annotated[Any, InjectedStore()],
+    ) -> str:
+        """Async tool with multiple injection types.
+
+        Args:
+            query: The search query (LLM-controlled).
+            max_results: Maximum number of results (LLM-controlled).
+            state: The graph state (injected).
+            runtime: The tool runtime context (injected).
+            store: The persistent store (injected).
+        """
+        # Capture all injected parameters
+        injected_data["state"] = state
+        injected_data["api_key"] = state.get("api_key", "unknown")
+        injected_data["request_id"] = state.get("request_id", "unknown")
+        injected_data["runtime"] = runtime
+        injected_data["tool_call_id"] = runtime.tool_call_id
+        injected_data["config"] = runtime.config
+        injected_data["store"] = store
+
+        # Verify we can write to the store
+        if store is not None:
+            await store.aput(("test", "namespace"), "test_key", {"query": query})
+            # Read back to verify it worked
+            item = await store.aget(("test", "namespace"), "test_key")
+            injected_data["store_write_success"] = item is not None
+
+        return f"Found {max_results} results for '{query}'"
+
+    # Create model that calls the async tool
+    model = FakeToolCallingModel(
+        tool_calls=[
+            [
+                {
+                    "name": "async_multi_injection_tool",
+                    "args": {"query": "test search", "max_results": 10},
+                    "id": "call_search_456",
+                }
+            ],
+            [],
+        ]
+    )
+
+    # Create agent with custom state and store
+    agent = create_agent(
+        model=model,
+        tools=[async_multi_injection_tool],
+        state_schema=CustomState,
+        store=InMemoryStore(),
+    )
+
+    # Verify the tool's args schema only includes LLM-controlled parameters
+    tool_args_schema = async_multi_injection_tool.args_schema
+    assert "query" in tool_args_schema["properties"]
+    assert "max_results" in tool_args_schema["properties"]
+    assert "state" not in tool_args_schema["properties"]
+    assert "runtime" not in tool_args_schema["properties"]
+    assert "store" not in tool_args_schema["properties"]
+
+    # Invoke async
+    result = await agent.ainvoke(
+        {
+            "messages": [HumanMessage("Search for something")],
+            "api_key": "sk-test-key-xyz",
+            "request_id": "req_999",
+        }
+    )
+
+    # Verify tool executed successfully
+    tool_messages = [msg for msg in result["messages"] if isinstance(msg, ToolMessage)]
+    assert len(tool_messages) == 1
+    tool_message = tool_messages[0]
+    assert tool_message.content == "Found 10 results for 'test search'"
+    assert tool_message.tool_call_id == "call_search_456"
+
+    # Verify all injections worked correctly
+    assert injected_data["state"] is not None
+    assert injected_data["api_key"] == "sk-test-key-xyz"
+    assert injected_data["request_id"] == "req_999"
+
+    # Verify runtime was injected
+    assert injected_data["runtime"] is not None
+    assert injected_data["tool_call_id"] == "call_search_456"
+    assert injected_data["config"] is not None
+
+    # Verify store was injected and writable
+    assert injected_data["store"] is not None
+    assert injected_data["store_write_success"] is True

libs/langchain_v1/uv.lock

@@ -2174,7 +2174,7 @@ wheels = [
 
 [[package]]
 name = "langchain-core"
-version = "1.0.6"
+version = "1.0.7"
 source = { editable = "../core" }
 dependencies = [
     { name = "jsonpatch" },
@@ -2626,15 +2626,15 @@ wheels = [
 
 [[package]]
 name = "langgraph-prebuilt"
-version = "1.0.4"
+version = "1.0.5"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "langchain-core" },
     { name = "langgraph-checkpoint" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/84/08/45857c7c65f696307834af13946a72293e6cc49141de887f0957c2eb2c46/langgraph_prebuilt-1.0.4.tar.gz", hash = "sha256:7b4f9e97a146d2d625695c3549bdb432974b80817165139ec2ec869721e72c0f", size = 142470, upload-time = "2025-11-13T19:02:14.807Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/46/f9/54f8891b32159e4542236817aea2ee83de0de18bce28e9bdba08c7f93001/langgraph_prebuilt-1.0.5.tar.gz", hash = "sha256:85802675ad778cc7240fd02d47db1e0b59c0c86d8369447d77ce47623845db2d", size = 144453, upload-time = "2025-11-20T16:47:39.23Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/69/14/a83e50129f66df783a68acb89e7b3e9c39b5c128a8748e961bc2b187f003/langgraph_prebuilt-1.0.4-py3-none-any.whl", hash = "sha256:50b1aa2b434783b6da30785568cf7155136b484750cc2ec695c0d4255db08262", size = 34414, upload-time = "2025-11-13T19:02:13.416Z" },
+    { url = "https://files.pythonhosted.org/packages/87/5e/aeba4a5b39fe6e874e0dd003a82da71c7153e671312671a8dacc5cb7c1af/langgraph_prebuilt-1.0.5-py3-none-any.whl", hash = "sha256:22369563e1848862ace53fbc11b027c28dd04a9ac39314633bb95f2a7e258496", size = 35072, upload-time = "2025-11-20T16:47:38.187Z" },
 ]
 
 [[package]]