Add an example of the augmented LLM

Author: RussellLuo

README.md

@@ -167,6 +167,36 @@ coagent translator -H type:ChatMessage --chat -d '{"role": "user", "content": "
 
 ## Patterns
 
+(The following patterns are mainly inspired by [Anthropic's Building effective agents][4] and [OpenAI's Orchestrating Agents][5].)
+
+### Basic: Augmented LLM
+
+**Augmented LLM** is an LLM enhanced with augmentations such as retrieval, tools, and memory. Our current models can actively use these capabilities—generating their own search queries, selecting appropriate tools, and determining what information to retain.
+
+<p align="center">
+<img src="assets/patterns-augmented-llm.png">
+</p>
+
+**Example** (see [examples/patterns/augmented_llm.py](examples/patterns/augmented_llm.py) for a runnable example):
+
+```python
+from coagent.agents import ChatAgent, ModelClient, tool
+from coagent.core import AgentSpec, new
+
+
+class Assistant(ChatAgent):
+    system = """You are an agent who can use tools."""
+    client = ModelClient(...)
+
+    @tool
+    async def query_weather(self, city: str) -> str:
+        """Query the weather in the given city."""
+        return f"The weather in {city} is sunny."
+
+
+assistant = AgentSpec("assistant", new(Assistant))
+```
+
 ### Workflow: Chaining
 
 **Chaining** decomposes a task into a sequence of steps, where each agent processes the output of the previous one.
@@ -423,3 +453,5 @@ triage = AgentSpec(
 [1]: https://docs.nats.io/nats-concepts/jetstream
 [2]: https://modelcontextprotocol.io/introduction
 [3]: https://docs.nats.io/running-a-nats-service/nats_docker/nats-docker-tutorial
+[4]: https://www.anthropic.com/research/building-effective-agents
+[5]: https://cookbook.openai.com/examples/orchestrating_agents

assets/patterns-augmented-llm.png

@@ -0,0 +1,46 @@
+import asyncio
+import os
+
+from coagent.agents import ChatAgent, ModelClient, tool
+from coagent.agents.messages import ChatMessage
+from coagent.core import AgentSpec, new, set_stderr_logger
+from coagent.runtimes import LocalRuntime
+
+
+class Assistant(ChatAgent):
+    system = """You are an agent who can use tools."""
+    client = ModelClient(
+        model=os.getenv("MODEL_NAME"),
+        api_base=os.getenv("MODEL_API_BASE"),
+        api_version=os.getenv("MODEL_API_VERSION"),
+        api_key=os.getenv("MODEL_API_KEY"),
+    )
+
+    @tool
+    async def query_weather(self, city: str) -> str:
+        """Query the weather in the given city."""
+        return f"The weather in {city} is sunny."
+
+
+assistant = AgentSpec("assistant", new(Assistant))
+
+
+async def main():
+    async with LocalRuntime() as runtime:
+        await runtime.register(assistant)
+
+        result = await assistant.run(
+            ChatMessage(
+                role="user",
+                content="What's the weather like in Beijing?",
+            ).encode(),
+            stream=True,
+        )
+        async for chunk in result:
+            msg = ChatMessage.decode(chunk)
+            print(msg.content, end="", flush=True)
+
+
+if __name__ == "__main__":
+    set_stderr_logger("TRACE")
+    asyncio.run(main())