Add examples of agent using Tavily server with key

agents/agentframework_learn.py

@@ -1,5 +1,3 @@
-from __future__ import annotations
-
 import asyncio
 import logging
 import os
@@ -14,17 +12,14 @@
 
 # Configure logging
 logging.basicConfig(level=logging.WARNING, format="%(message)s", datefmt="[%X]", handlers=[RichHandler()])
-logger = logging.getLogger("learn_mcp_lang")
+logger = logging.getLogger("agentframework_learn")
+logger.setLevel(logging.INFO)
 
 # Load environment variables
 load_dotenv(override=True)
 
-# Constants
-LEARN_MCP_URL = "https://learn.microsoft.com/api/mcp"
-
 # Configure chat client based on API_HOST
 API_HOST = os.getenv("API_HOST", "github")
-
 if API_HOST == "azure":
     client = AzureOpenAIChatClient(
         credential=DefaultAzureCredential(),
@@ -50,15 +45,13 @@
     )
 
 
-async def http_mcp_example() -> None:
+async def http_mcp_example():
     """
-    Demonstrate MCP integration with Microsoft Learn documentation.
-
     Creates an agent that can answer questions about Microsoft documentation
     using the Microsoft Learn MCP server.
     """
     async with (
-        MCPStreamableHTTPTool(name="Microsoft Learn MCP", url=LEARN_MCP_URL) as mcp_server,
+        MCPStreamableHTTPTool(name="Microsoft Learn MCP", url="https://learn.microsoft.com/api/mcp") as mcp_server,
         ChatAgent(
             chat_client=client,
             name="DocsAgent",

agents/agentframework_tavily.py

@@ -0,0 +1,69 @@
+import asyncio
+import logging
+import os
+
+from agent_framework import ChatAgent, MCPStreamableHTTPTool
+from agent_framework.azure import AzureOpenAIChatClient
+from agent_framework.openai import OpenAIChatClient
+from azure.identity import DefaultAzureCredential
+from dotenv import load_dotenv
+from rich import print
+from rich.logging import RichHandler
+
+# Configure logging
+logging.basicConfig(level=logging.WARNING, format="%(message)s", datefmt="[%X]", handlers=[RichHandler()])
+logger = logging.getLogger("agentframework_tavily")
+logger.setLevel(logging.INFO)
+
+# Load environment variables
+load_dotenv(override=True)
+
+# Configure chat client based on API_HOST
+API_HOST = os.getenv("API_HOST", "github")
+if API_HOST == "azure":
+    client = AzureOpenAIChatClient(
+        credential=DefaultAzureCredential(),
+        deployment_name=os.environ.get("AZURE_OPENAI_CHAT_DEPLOYMENT"),
+        endpoint=os.environ.get("AZURE_OPENAI_ENDPOINT"),
+        api_version=os.environ.get("AZURE_OPENAI_VERSION"),
+    )
+elif API_HOST == "github":
+    client = OpenAIChatClient(
+        base_url="https://models.github.ai/inference",
+        api_key=os.environ["GITHUB_TOKEN"],
+        model_id=os.getenv("GITHUB_MODEL", "openai/gpt-4o"),
+    )
+elif API_HOST == "ollama":
+    client = OpenAIChatClient(
+        base_url=os.environ.get("OLLAMA_ENDPOINT", "http://localhost:11434/v1"),
+        api_key="none",
+        model_id=os.environ.get("OLLAMA_MODEL", "llama3.1:latest"),
+    )
+else:
+    client = OpenAIChatClient(
+        api_key=os.environ.get("OPENAI_API_KEY"), model_id=os.environ.get("OPENAI_MODEL", "gpt-4o")
+    )
+
+
+async def http_mcp_example():
+    """
+    Creates an agent that can search the web using the Tavily MCP server.
+    """
+
+    tavily_key = os.environ["TAVILY_API_KEY"]
+    headers = {"Authorization": f"Bearer {tavily_key}"}
+    async with (
+        MCPStreamableHTTPTool(name="Tavily MCP", url="https://mcp.tavily.com/mcp/", headers=headers) as mcp_server,
+        ChatAgent(
+            chat_client=client,
+            name="WebSearchAgent",
+            instructions="You search the web with Tavily and provide concise answers with links.",
+        ) as agent,
+    ):
+        query = "What's new in Python 3.14? Include relevant links."
+        result = await agent.run(query, tools=mcp_server)
+        print(result)
+
+
+if __name__ == "__main__":
+    asyncio.run(http_mcp_example())

agents/langchainv1_http.py

@@ -14,7 +14,8 @@
 
 # Configure logging
 logging.basicConfig(level=logging.WARNING, format="%(message)s", datefmt="[%X]", handlers=[RichHandler()])
-logger = logging.getLogger("itinerario_lang")
+logger = logging.getLogger("langchainv1_http")
+logger.setLevel(logging.INFO)
 
 # Load environment variables
 load_dotenv(override=True)
@@ -88,5 +89,4 @@ def main() -> None:
 
 
 if __name__ == "__main__":
-    logger.setLevel(logging.INFO)
     main()

agents/langchainv1_tavily.py

@@ -0,0 +1,84 @@
+"""LangChain + Tavily MCP Example
+
+Creates a simple research agent that uses the Tavily MCP server
+to search the web and answer questions with relevant links.
+"""
+
+import asyncio
+import logging
+import os
+
+import azure.identity
+from dotenv import load_dotenv
+from langchain.agents import create_agent
+from langchain_core.messages import HumanMessage
+from langchain_mcp_adapters.client import MultiServerMCPClient
+from langchain_openai import ChatOpenAI
+from pydantic import SecretStr
+from rich.logging import RichHandler
+
+# Configure logging
+logging.basicConfig(level=logging.WARNING, format="%(message)s", datefmt="[%X]", handlers=[RichHandler()])
+logger = logging.getLogger("langchainv1_tavily")
+logger.setLevel(logging.INFO)
+
+# Load environment variables
+load_dotenv(override=True)
+
+api_host = os.getenv("API_HOST", "github")
+
+if api_host == "azure":
+    token_provider = azure.identity.get_bearer_token_provider(
+        azure.identity.DefaultAzureCredential(), "https://cognitiveservices.azure.com/.default"
+    )
+    model = ChatOpenAI(
+        model=os.environ.get("AZURE_OPENAI_CHAT_DEPLOYMENT"),
+        base_url=os.environ["AZURE_OPENAI_ENDPOINT"] + "/openai/v1/",
+        api_key=token_provider,
+    )
+elif api_host == "github":
+    model = ChatOpenAI(
+        model=os.getenv("GITHUB_MODEL", "gpt-4o"),
+        base_url="https://models.inference.ai.azure.com",
+        api_key=SecretStr(os.environ["GITHUB_TOKEN"]),
+    )
+elif api_host == "ollama":
+    model = ChatOpenAI(
+        model=os.environ.get("OLLAMA_MODEL", "llama3.1"),
+        base_url=os.environ.get("OLLAMA_ENDPOINT", "http://localhost:11434/v1"),
+        api_key=SecretStr(os.environ.get("OLLAMA_API_KEY", "none")),
+    )
+else:
+    model = ChatOpenAI(model=os.getenv("OPENAI_MODEL", "gpt-4o-mini"))
+
+
+async def run_agent() -> None:
+    """Run a Tavily-backed research agent via MCP tools."""
+    tavily_key = os.environ["TAVILY_API_KEY"]
+    client = MultiServerMCPClient(
+        {
+            "tavily": {
+                "url": "https://mcp.tavily.com/mcp/",
+                "transport": "streamable_http",
+                "headers": {"Authorization": f"Bearer {tavily_key}"},
+            }
+        }
+    )
+
+    # Fetch available tools and create the agent
+    tools = await client.get_tools()
+    agent = create_agent(model, tools, prompt="You search the web and include relevant links in answers.")
+
+    query = "What's new in Python 3.14? Include relevant links."
+    response = await agent.ainvoke({"messages": [HumanMessage(content=query)]})
+
+    final_response = response["messages"][-1].content
+    print(final_response)
+
+
+def main() -> None:
+    asyncio.run(run_agent())
+
+
+if __name__ == "__main__":
+    main()