fix: Complete LangChain integration example in README

- Add missing ChatPromptTemplate and MessagesPlaceholder imports
- Include complete prompt definition with proper format
- Add example invocation to show usage
- Fix test.py to use dotenv and proper prompt format

The previous example was incomplete and would cause 'max iterations'
errors because the prompt format was wrong for tool-calling agents.

Author: abrookins

README.md

@@ -66,6 +66,7 @@ results = await client.search_long_term_memory(
 from agent_memory_client import create_memory_client
 from agent_memory_client.integrations.langchain import get_memory_tools
 from langchain.agents import create_tool_calling_agent, AgentExecutor
+from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
 from langchain_openai import ChatOpenAI
 
 # Get LangChain-compatible tools automatically
@@ -76,10 +77,19 @@ tools = get_memory_tools(
     user_id="alice"
 )
 
-# Use with LangChain agents - no manual @tool wrapping needed!
+# Create prompt and agent
+prompt = ChatPromptTemplate.from_messages([
+    ("system", "You are a helpful assistant with memory."),
+    ("human", "{input}"),
+    MessagesPlaceholder("agent_scratchpad"),
+])
+
 llm = ChatOpenAI(model="gpt-4o")
 agent = create_tool_calling_agent(llm, tools, prompt)
 executor = AgentExecutor(agent=agent, tools=tools)
+
+# Use the agent
+result = await executor.ainvoke({"input": "Remember that I love pizza"})
 ```
 
 > **Note**: While you can call client functions directly as shown above, using **MCP or SDK-provided tool calls** is recommended for AI agents as it provides better integration, automatic context management, and follows AI-native patterns. See **[Memory Integration Patterns](https://redis.github.io/agent-memory-server/memory-integration-patterns/)** for guidance on when to use each approach.

test.py

@@ -0,0 +1,53 @@
+import os
+
+from agent_memory_client import create_memory_client
+from agent_memory_client.integrations.langchain import get_memory_tools
+from dotenv import load_dotenv
+from langchain.agents import AgentExecutor, create_tool_calling_agent
+from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
+from langchain_openai import ChatOpenAI
+
+
+load_dotenv()
+
+
+async def main():
+    # Check for API key
+    if not os.getenv("OPENAI_API_KEY"):
+        print("❌ OPENAI_API_KEY environment variable is required")
+        print("Set it with: export OPENAI_API_KEY='your-key-here'")
+        return
+    # Initialize memory client
+    memory_client = await create_memory_client("http://localhost:8000")
+
+    # Get LangChain-compatible tools automatically
+    tools = get_memory_tools(
+        memory_client=memory_client, session_id="my_session", user_id="alice"
+    )
+
+    # Create the prompt with proper format for tool-calling agents
+    prompt = ChatPromptTemplate.from_messages(
+        [
+            ("system", "You are a helpful assistant with memory capabilities."),
+            ("human", "{input}"),
+            MessagesPlaceholder("agent_scratchpad"),
+        ]
+    )
+
+    # Use with LangChain agents - no manual @tool wrapping needed!
+    llm = ChatOpenAI(model="gpt-4o")
+    agent = create_tool_calling_agent(llm, tools, prompt)
+    executor = AgentExecutor(agent=agent, tools=tools, verbose=True)
+
+    result = await executor.ainvoke(
+        {"input": "What is the capital of France? Remember that I like France."}
+    )
+    print("\n" + "=" * 60)
+    print("RESULT:", result["output"])
+    print("=" * 60)
+
+
+if __name__ == "__main__":
+    import asyncio
+
+    asyncio.run(main())