mirror of
https://github.com/BerriAI/litellm.git
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1eb7122465
Guard the per-request CPU cost of the chat completion, MCP tool and A2A message transforms against regressions on every commit. All benchmarks are pure in-process work with no network I/O so they stay deterministic under CodSpeed's simulation mode, and they import under the base dependency set the benchmark job installs. Inference covers the full SDK overhead via mock_response (simple, multi-turn, tools, streaming) plus convert_to_model_response_object as a deterministic anchor. MCP covers the client-side tool translation and the proxy server-side tool-name prefix round-trip. A2A covers the client request/response transforms and the proxy server-ingress message conversion. Adds the mcp and a2a-sdk packages to the benchmark run since those transform modules need them, and broadens the workflow triggers to litellm_internal_staging so the internal branch flow is benchmarked too.
84 lines
2.6 KiB
Python
84 lines
2.6 KiB
Python
"""
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Performance benchmarks for the MCP tool hot path.
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Two layers are covered: the client-side translation between MCP and OpenAI
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function-calling formats, and the server-side tool-name prefixing that the proxy
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runs on every list-tools (prefix each tool) and call-tool (strip prefix to route)
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request. Both are pure-CPU and deterministic.
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"""
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import pytest
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from mcp.types import Tool as MCPTool
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from litellm.experimental_mcp_client.tools import (
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transform_mcp_tool_to_openai_tool,
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transform_openai_tool_call_request_to_mcp_tool_call_request,
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)
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from litellm.proxy._experimental.mcp_server.utils import (
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add_server_prefix_to_name,
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split_server_prefix_from_name,
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)
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def _make_tool(index: int) -> MCPTool:
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return MCPTool(
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name=f"tool_{index}",
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description=f"Test tool number {index} that performs an operation",
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inputSchema={
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"type": "object",
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"properties": {
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"query": {"type": "string", "description": "The search query"},
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"limit": {"type": "integer", "description": "Max results"},
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},
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"required": ["query"],
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},
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)
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SINGLE_TOOL = _make_tool(0)
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TOOL_LIST = tuple(_make_tool(i) for i in range(20))
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TOOL_NAMES = tuple(t.name for t in TOOL_LIST)
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SERVER_NAME = "github_mcp"
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PREFIXED_TOOL_NAME = add_server_prefix_to_name("tool_0", SERVER_NAME)
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OPENAI_TOOL_CALL = {
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"id": "call_abc123",
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"type": "function",
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"function": {
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"name": "tool_0",
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"arguments": '{"query": "weather in San Francisco", "limit": 5}',
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},
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}
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@pytest.mark.benchmark
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def test_transform_single_mcp_tool_to_openai():
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"""Benchmark translating one MCP tool into OpenAI tool format."""
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transform_mcp_tool_to_openai_tool(mcp_tool=SINGLE_TOOL)
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@pytest.mark.benchmark
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def test_transform_mcp_tool_list_to_openai():
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"""Benchmark translating a full list-tools response into OpenAI format."""
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for tool in TOOL_LIST:
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transform_mcp_tool_to_openai_tool(mcp_tool=tool)
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@pytest.mark.benchmark
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def test_transform_openai_tool_call_to_mcp():
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"""Benchmark translating an OpenAI tool call into an MCP call request."""
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transform_openai_tool_call_request_to_mcp_tool_call_request(openai_tool=OPENAI_TOOL_CALL)
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@pytest.mark.benchmark
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def test_mcp_server_prefix_tool_list():
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"""Benchmark the proxy prefixing every tool name on a list-tools response."""
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for name in TOOL_NAMES:
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add_server_prefix_to_name(name, SERVER_NAME)
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@pytest.mark.benchmark
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def test_mcp_server_strip_prefix_on_call():
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"""Benchmark the proxy stripping the server prefix to route a tool call."""
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split_server_prefix_from_name(PREFIXED_TOOL_NAME)
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