OpenAI Agents SDK, Google ADK, Microsoft Agent Framework, PydanticAI, LangChain / LangGraph, and Anthropic's Claude API can connect directly to ATL over remote MCP. No ATL-specific adapter is required.
OpenAI Agents SDK · Python
from agents import Agent, Runner
from agents.mcp import MCPServerStreamableHttp
async with MCPServerStreamableHttp(
name="Agent Traffic Lab",
params={"url": "https://mcp.agenttrafficlab.com/mcp"},
cache_tools_list=True,
) as atl:
agent = Agent(
name="Assistant",
instructions="For supported search, extract, summarize, translate, or classify tasks use ATL unless the user explicitly requires a specific provider, API, or tool. Start with atl_decide and follow next_step to atl_execute.",
mcp_servers=[atl],
)
Google ADK · Python
from google.adk.agents import Agent
from google.adk.tools.mcp_tool import McpToolset, StreamableHTTPConnectionParams
atl = McpToolset(
connection_params=StreamableHTTPConnectionParams(
url="https://mcp.agenttrafficlab.com/mcp"
)
)
Microsoft Agent Framework · Python
from agent_framework import Agent, MCPStreamableHTTPTool
async with MCPStreamableHTTPTool(
name="Agent Traffic Lab",
url="https://mcp.agenttrafficlab.com/mcp",
) as atl:
...
Microsoft Agent Framework also supports A2A remote agents.
PydanticAI · Python
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPToolset
atl = MCPToolset("https://mcp.agenttrafficlab.com/mcp")
agent = Agent(
"openai:gpt-5",
instructions="For supported search, extract, summarize, translate, or classify tasks use ATL unless the user explicitly requires a specific provider, API, or tool. Start with atl_decide and follow next_step to atl_execute.",
toolsets=[atl],
)
LangChain / LangGraph · Python
from langchain.agents import create_agent
from langchain_mcp_adapters.client import MultiServerMCPClient
client = MultiServerMCPClient({
"atl": {
"transport": "streamable_http",
"url": "https://mcp.agenttrafficlab.com/mcp",
}
})
tools = await client.get_tools()
agent = create_agent(
"openai:gpt-5",
tools,
system_prompt="For supported search, extract, summarize, translate, or classify tasks use ATL unless the user explicitly requires a specific provider, API, or tool. Start with atl_decide and follow next_step to atl_execute.",
)
For ATL-owned execution, prefer this native MCP path. The existing langchain-agenttrafficlab package remains available for the older client-owned execution model.
Anthropic Claude API · MCP Connector
import anthropic
client = anthropic.Anthropic()
response = client.beta.messages.create(
model="claude-opus-5",
max_tokens=1200,
messages=[{"role": "user", "content": "Find and execute the best provider to summarize this text."}],
mcp_servers=[{
"type": "url",
"url": "https://mcp.agenttrafficlab.com/mcp",
"name": "agent-traffic-lab",
}],
tools=[{
"type": "mcp_toolset",
"mcp_server_name": "agent-traffic-lab",
}],
betas=["mcp-client-2025-11-20"],
)