Add LangGraph examples and CLI templates (sync + async)

- Add sync LangGraph example at examples/tutorials/00_sync/030_langgraph
- Add async LangGraph example at examples/tutorials/10_async/00_base/100_langgraph
- Add sync-langgraph and default-langgraph CLI templates with graph.py, tools.py, and LangGraph deps
- Update init.py with new TemplateType entries and sub-menu options for LangGraph

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

Author: danielmillerp

examples/tutorials/00_sync/030_langgraph/.dockerignore

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+# Python
+__pycache__/
+*.py[cod]
+*$py.class
+*.so
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+
+# Environments
+.env**
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# IDE
+.idea/
+.vscode/
+*.swp
+*.swo
+
+# Git
+.git
+.gitignore
+
+# Misc
+.DS_Store

examples/tutorials/00_sync/030_langgraph/Dockerfile

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+# syntax=docker/dockerfile:1.3
+FROM python:3.12-slim
+COPY --from=ghcr.io/astral-sh/uv:0.6.4 /uv /uvx /bin/
+
+# Install system dependencies
+RUN apt-get update && apt-get install -y \
+    htop \
+    vim \
+    curl \
+    tar \
+    python3-dev \
+    postgresql-client \
+    build-essential \
+    libpq-dev \
+    gcc \
+    cmake \
+    netcat-openbsd \
+    && apt-get clean \
+    && rm -rf /var/lib/apt/lists/*
+
+RUN uv pip install --system --upgrade pip setuptools wheel
+
+ENV UV_HTTP_TIMEOUT=1000
+
+# Copy pyproject.toml and README.md to install dependencies
+COPY 00_sync/030_langgraph/pyproject.toml /app/030_langgraph/pyproject.toml
+COPY 00_sync/030_langgraph/README.md /app/030_langgraph/README.md
+
+WORKDIR /app/030_langgraph
+
+# Copy the project code
+COPY 00_sync/030_langgraph/project /app/030_langgraph/project
+
+# Copy the test files
+COPY 00_sync/030_langgraph/tests /app/030_langgraph/tests
+
+# Copy shared test utilities
+COPY test_utils /app/test_utils
+
+# Install the required Python packages with dev dependencies
+RUN uv pip install --system .[dev]
+
+# Set environment variables
+ENV PYTHONPATH=/app
+
+# Set test environment variables
+ENV AGENT_NAME=s030-langgraph
+
+# Run the agent using uvicorn
+CMD ["uvicorn", "project.acp:acp", "--host", "0.0.0.0", "--port", "8000"]

examples/tutorials/00_sync/030_langgraph/README.md

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+# Tutorial 030: Sync LangGraph Agent
+
+This tutorial demonstrates how to build a **synchronous** LangGraph agent on AgentEx with:
+- Tool calling (ReAct pattern)
+- Streaming token output
+- Multi-turn conversation memory via AgentEx checkpointer
+- Tracing integration
+
+## Graph Structure
+
+![Graph](graph.png)
+
+## Key Concepts
+
+### Sync ACP
+The sync ACP model uses HTTP request/response for communication. The `@acp.on_message_send` handler receives a message and yields streaming events back to the client.
+
+### LangGraph Integration
+- **StateGraph**: Defines the agent's state machine with `AgentState` (message history)
+- **ToolNode**: Automatically executes tool calls from the LLM
+- **tools_condition**: Routes between tool execution and final response
+- **Checkpointer**: Uses AgentEx's HTTP checkpointer for cross-request memory
+
+### Streaming
+The agent streams tokens as they're generated using `convert_langgraph_to_agentex_events()`, which converts LangGraph's stream events into AgentEx `TaskMessageUpdate` events.
+
+## Files
+
+| File | Description |
+|------|-------------|
+| `project/acp.py` | ACP server and message handler |
+| `project/graph.py` | LangGraph state graph definition |
+| `project/tools.py` | Tool definitions (weather example) |
+| `tests/test_agent.py` | Integration tests |
+| `manifest.yaml` | Agent configuration |
+
+## Running Locally
+
+```bash
+# From this directory
+agentex agents run
+```
+
+## Running Tests
+
+```bash
+pytest tests/test_agent.py -v
+```

examples/tutorials/00_sync/030_langgraph/graph.png

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+build:
+  context:
+    root: ../../
+    include_paths:
+      - 00_sync/030_langgraph
+      - test_utils
+    dockerfile: 00_sync/030_langgraph/Dockerfile
+    dockerignore: 00_sync/030_langgraph/.dockerignore
+
+local_development:
+  agent:
+    port: 8000
+    host_address: host.docker.internal
+  paths:
+    acp: project/acp.py
+
+agent:
+  acp_type: sync
+  name: s030-langgraph
+  description: A sync LangGraph agent with tool calling and streaming
+
+  temporal:
+    enabled: false
+
+  credentials:
+    - env_var_name: OPENAI_API_KEY
+      secret_name: openai-api-key
+      secret_key: api-key
+    - env_var_name: REDIS_URL
+      secret_name: redis-url-secret
+      secret_key: url
+    - env_var_name: SGP_API_KEY
+      secret_name: sgp-api-key
+      secret_key: api-key
+    - env_var_name: SGP_ACCOUNT_ID
+      secret_name: sgp-account-id
+      secret_key: account-id
+    - env_var_name: SGP_CLIENT_BASE_URL
+      secret_name: sgp-client-base-url
+      secret_key: url
+
+deployment:
+  image:
+    repository: ""
+    tag: "latest"
+
+  global:
+    agent:
+      name: "s030-langgraph"
+      description: "A sync LangGraph agent with tool calling and streaming"
+    replicaCount: 1
+    resources:
+      requests:
+        cpu: "500m"
+        memory: "1Gi"
+      limits:
+        cpu: "1000m"
+        memory: "2Gi"

examples/tutorials/00_sync/030_langgraph/manifest.yaml

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+"""
+ACP (Agent Communication Protocol) handler for Agentex.
+
+This is the API layer — it manages the graph lifecycle and streams
+tokens and tool calls from the LangGraph graph to the Agentex frontend.
+"""
+
+from typing import AsyncGenerator
+
+import agentex.lib.adk as adk
+from agentex.lib.adk import create_langgraph_tracing_handler, convert_langgraph_to_agentex_events
+from agentex.lib.core.tracing.tracing_processor_manager import add_tracing_processor_config
+from agentex.lib.sdk.fastacp.fastacp import FastACP
+from agentex.lib.types.acp import SendMessageParams
+from agentex.lib.types.tracing import SGPTracingProcessorConfig
+from agentex.lib.utils.logging import make_logger
+from agentex.types.task_message_content import TaskMessageContent
+from agentex.types.task_message_delta import TextDelta
+from agentex.types.task_message_update import TaskMessageUpdate
+from dotenv import load_dotenv
+
+load_dotenv()
+import os
+
+from project.graph import create_graph
+
+logger = make_logger(__name__)
+
+# Register the Agentex tracing processor so spans are shipped to the backend
+add_tracing_processor_config(
+    SGPTracingProcessorConfig(
+        sgp_api_key=os.environ.get("SGP_API_KEY", ""),
+        sgp_account_id=os.environ.get("SGP_ACCOUNT_ID", ""),
+        sgp_base_url=os.environ.get("SGP_CLIENT_BASE_URL", ""),
+    ))
+# Create ACP server
+acp = FastACP.create(acp_type="sync")
+
+# Compiled graph (lazy-initialized on first request)
+_graph = None
+
+
+async def get_graph():
+    """Get or create the compiled graph instance."""
+    global _graph
+    if _graph is None:
+        _graph = await create_graph()
+    return _graph
+
+
+@acp.on_message_send
+async def handle_message_send(
+    params: SendMessageParams,
+) -> TaskMessageContent | list[TaskMessageContent] | AsyncGenerator[TaskMessageUpdate, None]:
+    """Handle incoming messages from Agentex, streaming tokens and tool calls."""
+    graph = await get_graph()
+
+    thread_id = params.task.id
+    user_message = params.content.content
+
+    logger.info(f"Processing message for thread {thread_id}")
+
+    async with adk.tracing.span(
+        trace_id=thread_id,
+        name="message",
+        input={"message": user_message},
+        data={"__span_type__": "AGENT_WORKFLOW"},
+    ) as turn_span:
+        callback = create_langgraph_tracing_handler(
+            trace_id=thread_id,
+            parent_span_id=turn_span.id if turn_span else None,
+        )
+
+        stream = graph.astream(
+            {"messages": [{"role": "user", "content": user_message}]},
+            config={
+                "configurable": {"thread_id": thread_id},
+                "callbacks": [callback],
+            },
+            stream_mode=["messages", "updates"],
+        )
+
+        final_text = ""
+        async for event in convert_langgraph_to_agentex_events(stream):
+            # Accumulate text deltas for span output
+            delta = getattr(event, "delta", None)
+            if isinstance(delta, TextDelta) and delta.text_delta:
+                final_text += delta.text_delta
+            yield event
+
+        if turn_span:
+            turn_span.output = {"final_output": final_text}

examples/tutorials/00_sync/030_langgraph/project/__init__.py

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+"""
+LangGraph graph definition.
+
+Defines the state, nodes, edges, and compiles the graph.
+The compiled graph is the boundary between this module and the API layer.
+"""
+
+from datetime import datetime
+from typing import Annotated, Any
+
+from agentex.lib.adk import create_checkpointer
+from langchain_core.messages import SystemMessage
+from langchain_openai import ChatOpenAI
+from langgraph.graph import START, StateGraph
+from langgraph.graph.message import add_messages
+from langgraph.prebuilt import ToolNode, tools_condition
+from typing_extensions import TypedDict
+
+from project.tools import TOOLS
+
+MODEL_NAME = "gpt-5"
+SYSTEM_PROMPT = """You are a helpful AI assistant with access to tools.
+
+Current date and time: {timestamp}
+
+Guidelines:
+- Be concise and helpful
+- Use tools when they would help answer the user's question
+- If you're unsure, ask clarifying questions
+- Always provide accurate information
+"""
+
+
+class AgentState(TypedDict):
+    """State schema for the agent graph."""
+    messages: Annotated[list[Any], add_messages]
+
+
+async def create_graph():
+    """Create and compile the agent graph with checkpointer.
+
+    Returns:
+        A compiled LangGraph StateGraph ready for invocation.
+    """
+    llm = ChatOpenAI(
+        model=MODEL_NAME,
+        reasoning={"effort": "high", "summary": "auto"},
+    )
+    llm_with_tools = llm.bind_tools(TOOLS)
+
+    checkpointer = await create_checkpointer()
+
+    def agent_node(state: AgentState) -> dict[str, Any]:
+        """Process the current state and generate a response."""
+        messages = state["messages"]
+        if not messages or not isinstance(messages[0], SystemMessage):
+            system_content = SYSTEM_PROMPT.format(
+                timestamp=datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+            )
+            messages = [SystemMessage(content=system_content)] + messages
+        response = llm_with_tools.invoke(messages)
+        return {"messages": [response]}
+
+    builder = StateGraph(AgentState)
+    builder.add_node("agent", agent_node)
+    builder.add_node("tools", ToolNode(tools=TOOLS))
+    builder.add_edge(START, "agent")
+    builder.add_conditional_edges("agent", tools_condition, "tools")
+    builder.add_edge("tools", "agent")
+
+    return builder.compile(checkpointer=checkpointer)