Connecting Letta to Remote MCP Servers

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from letta_client import Letta
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from letta_client.types import StreamableHTTPServerConfig
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client = Letta(token="LETTA_API_KEY")
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# Connect a Streamable HTTP server
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streamable_config = StreamableHTTPServerConfig(
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    server_name="my-server",
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    server_url="https://mcp-server.example.com/mcp",
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    auth_token="your-token",  # Optional: Authorization: Bearer your-token
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    custom_headers={"X-API-Version": "v1"}  # Optional: additional headers
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)
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client.tools.add_mcp_server(request=streamable_config)

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from letta_client import Letta
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from letta_client.types import SseServerConfig
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client = Letta(token="LETTA_API_KEY")
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# Connect a SSE server (legacy)
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sse_config = SseServerConfig(
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    server_name="legacy-server",
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    server_url="https://legacy-mcp.example.com/sse",
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    auth_token="optional-token"  # Optional: Authorization: Bearer optional-token
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)
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client.tools.add_mcp_server(request=sse_config)

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from letta_client import Letta
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client = Letta(token="LETTA_API_KEY")
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# List tools from an MCP server
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tools = client.tools.list_mcp_tools_by_server(mcp_server_name="weather-server")
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# Add a specific tool from the MCP server
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tool = client.tools.add_mcp_tool(
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    mcp_server_name="weather-server",
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    mcp_tool_name="get_weather"
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)
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# Create agent with MCP tool
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agent_state = client.agents.create(
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    model="openai/gpt-4o-mini",
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    embedding="openai/text-embedding-3-small",
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    tool_ids=[tool.id]
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)
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# Use the agent with MCP tools
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response = client.agents.messages.create(
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    agent_id=agent_state.id,
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    messages=[
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        {
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            "role": "user",
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            "content": "Use the weather tool to check the forecast"
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        }
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    ]
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)