feat: add a subagent frame

2026-04-03 22:15:53 +08:00
parent ce580d1f8b
commit 185361f2c3
72 changed files with 13062 additions and 0 deletions
--- a/agentlite/examples/llm_client_example.py
+++ b/agentlite/examples/llm_client_example.py
@@ -0,0 +1,124 @@
+"""Example demonstrating LLMClient usage.
+
+This example shows how to use LLMClient for simple LLM calls
+without the overhead of an Agent.
+"""
+
+import asyncio
+
+from agentlite import LLMClient, llm_complete, llm_stream
+from agentlite.config import AgentConfig, ProviderConfig, ModelConfig
+
+
+async def main():
+    """Run LLM client examples."""
+
+    # Example 1: Using simple function interface
+    print("=== Example 1: Simple Function ===")
+    print("Using llm_complete() function:")
+
+    # Note: This requires a valid API key
+    # response = await llm_complete(
+    #     user_prompt="What is Python?",
+    #     api_key="your-api-key",
+    #     model="gpt-4",
+    # )
+    # print(response)
+
+    print("(Requires API key - uncomment to run)")
+
+    # Example 2: Using configuration-based client
+    print("\n=== Example 2: Configuration-Based Client ===")
+
+    config = AgentConfig(
+        name="simple_llm",
+        system_prompt="You are a helpful coding assistant.",
+        providers={
+            "openai": ProviderConfig(
+                type="openai",
+                api_key="your-api-key",  # Replace with actual key
+            )
+        },
+        models={
+            "gpt4": ModelConfig(
+                provider="openai",
+                model="gpt-4",
+                temperature=0.7,
+            ),
+            "gpt35": ModelConfig(
+                provider="openai",
+                model="gpt-3.5-turbo",
+                temperature=0.5,
+            ),
+        },
+        default_model="gpt4",
+    )
+
+    # Create client
+    client = LLMClient(config)
+
+    # Make a call
+    # response = await client.complete(
+    #     user_prompt="Explain async/await in Python",
+    # )
+    # print(f"Response: {response.content}")
+    # print(f"Model: {response.model}")
+    # if response.usage:
+    #     print(f"Tokens: {response.usage.total}")
+
+    print("(Requires API key - uncomment to run)")
+
+    # Example 3: Streaming
+    print("\n=== Example 3: Streaming ===")
+    print("Using llm_stream() function:")
+
+    # async for chunk in llm_stream(
+    #     user_prompt="Write a haiku about programming",
+    #     api_key="your-api-key",
+    # ):
+    #     print(chunk, end="")
+
+    print("\n(Requires API key - uncomment to run)")
+
+    # Example 4: Direct provider usage
+    print("\n=== Example 4: Direct Provider ===")
+
+    from agentlite import OpenAIProvider
+
+    provider = OpenAIProvider(
+        api_key="your-api-key",
+        model="gpt-4",
+        temperature=0.8,
+    )
+
+    client = LLMClient(provider=provider)
+
+    # response = await client.complete(
+    #     user_prompt="What are the benefits of type hints?",
+    #     system_prompt="You are a Python expert.",
+    # )
+    # print(response.content)
+
+    print("(Requires API key - uncomment to run)")
+
+    # Example 5: Model switching
+    print("\n=== Example 5: Model Switching ===")
+
+    # Use default model (gpt4)
+    # response1 = await client.complete(user_prompt="Hello!")
+
+    # Switch to different model
+    # client_gpt35 = LLMClient(config, model="gpt35")
+    # response2 = await client_gpt35.complete(user_prompt="Hello!")
+
+    print("(Requires API key - uncomment to run)")
+
+    print("\n=== Examples Complete ===")
+    print("To run these examples:")
+    print("1. Set your OpenAI API key")
+    print("2. Uncomment the example code")
+    print("3. Run: python examples/llm_client_example.py")
+
+
+if __name__ == "__main__":
+    asyncio.run(main())