examples: add Synap memory tools example

examples/tools/synap_memory.py

@@ -0,0 +1,73 @@
+"""Example: Synap memory tools.
+
+Demonstrates how to give an OpenAI Agent persistent, cross-session memory using
+Synap (https://maximem.ai) — a managed memory layer for AI agents.
+
+`synap-openai-agents` provides two tool factories:
+  - `create_search_tool` — semantic search over the user's stored memories
+  - `create_store_tool`  — persist explicit facts to the user's memory
+
+Both return async callables you can wrap with `@function_tool` or pass to
+`FunctionTool` directly.
+
+Install:
+    pip install synap-openai-agents maximem-synap openai-agents
+
+Set `SYNAP_API_KEY` in your environment. Get a free key at
+https://synap.maximem.ai.
+
+Open source: https://github.com/maximem-ai/maximem_synap_sdk
+"""
+
+import asyncio
+import os
+
+from agents import Agent, FunctionTool, Runner
+from maximem_synap import MaximemSynapSDK
+from synap_openai_agents import create_search_tool, create_store_tool
+
+
+async def main() -> None:
+    sdk = MaximemSynapSDK(api_key=os.environ["SYNAP_API_KEY"])
+    await sdk.initialize()
+
+    user_id = "demo-user-001"
+
+    search_fn = create_search_tool(sdk, user_id=user_id, customer_id="acme_corp")
+    store_fn = create_store_tool(sdk, user_id=user_id, customer_id="acme_corp")
+
+    agent = Agent(
+        name="memory-assistant",
+        instructions=(
+            "You are a helpful assistant with long-term memory. "
+            "Call search_memory to recall what you know about the user. "
+            "Call store_memory to save important new facts."
+        ),
+        tools=[
+            FunctionTool(
+                search_fn,
+                name="search_memory",
+                description="Search the user's long-term memory.",
+            ),
+            FunctionTool(
+                store_fn,
+                name="store_memory",
+                description="Store an explicit fact in the user's long-term memory.",
+            ),
+        ],
+    )
+
+    print("=== Turn 1: teach the agent something ===")
+    result = await Runner.run(
+        agent,
+        "I'm a software engineer who prefers concise answers and is allergic to peanuts.",
+    )
+    print(result.final_output)
+
+    print("\n=== Turn 2: agent recalls from Synap ===")
+    result = await Runner.run(agent, "What do you know about my dietary restrictions?")
+    print(result.final_output)
+
+
+if __name__ == "__main__":
+    asyncio.run(main())