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Merge pull request #4248 from omChauhanDev/add-openai-custom-tools-support

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Add custom_tools support for OpenAI adapters
This commit is contained in:
kompfner
2026-04-10 10:27:28 -04:00
committed by GitHub
parent 094d36904c ef4dcca4f1
commit d07eebff20
6 changed files with 157 additions and 11 deletions
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1
changelog/4248.added.md Normal file
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@@ -0,0 +1 @@
- `ToolsSchema` now accepts `custom_tools` for OpenAI LLM services (`OpenAILLMService`, `OpenAIResponsesLLMService`, `OpenAIResponsesHttpLLMService`, and `OpenAIRealtimeLLMService`), letting you pass provider-specific tools like `tool_search` alongside standard function tools.

6
src/pipecat/adapters/schemas/tools_schema.py
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@@ -21,10 +21,12 @@ class AdapterType(Enum):
"""Supported adapter types for custom tools.
Parameters:
GEMINI: Google Gemini adapter - currently the only service supporting custom tools.
GEMINI: Google Gemini adapter.
OPENAI: OpenAI adapter (Chat Completions, Responses, and Realtime API).
"""
GEMINI = "gemini" # that is the only service where we are able to add custom tools for now
GEMINI = "gemini"
OPENAI = "openai"
class ToolsSchema:

8
src/pipecat/adapters/services/open_ai_adapter.py
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@@ -17,7 +17,7 @@ from openai.types.chat import (
)
from pipecat.adapters.base_llm_adapter import BaseLLMAdapter
from pipecat.adapters.schemas.tools_schema import ToolsSchema
from pipecat.adapters.schemas.tools_schema import AdapterType, ToolsSchema
from pipecat.processors.aggregators.llm_context import (
LLMContext,
LLMContextMessage,
@@ -107,10 +107,14 @@ class OpenAILLMAdapter(BaseLLMAdapter[OpenAILLMInvocationParams]):
with ChatCompletion API.
"""
functions_schema = tools_schema.standard_tools
return [
formatted_standard_tools = [
ChatCompletionToolParam(type="function", function=func.to_default_dict())
for func in functions_schema
]
custom_openai_tools = []
if tools_schema.custom_tools:
custom_openai_tools = tools_schema.custom_tools.get(AdapterType.OPENAI, [])
return formatted_standard_tools + custom_openai_tools
def get_messages_for_logging(self, context: LLMContext) -> List[Dict[str, Any]]:
"""Get messages from a universal LLM context in a format ready for logging about OpenAI.

10
src/pipecat/adapters/services/open_ai_realtime_adapter.py
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@@ -15,7 +15,7 @@ from loguru import logger
from pipecat.adapters.base_llm_adapter import BaseLLMAdapter
from pipecat.adapters.schemas.function_schema import FunctionSchema
from pipecat.adapters.schemas.tools_schema import ToolsSchema
from pipecat.adapters.schemas.tools_schema import AdapterType, ToolsSchema
from pipecat.processors.aggregators.llm_context import LLMContext, LLMContextMessage
from pipecat.services.openai.realtime import events
@@ -236,4 +236,10 @@ class OpenAIRealtimeLLMAdapter(BaseLLMAdapter):
List of function definitions in OpenAI Realtime format.
"""
functions_schema = tools_schema.standard_tools
return [self._to_openai_realtime_function_format(func) for func in functions_schema]
formatted_standard_tools = [
self._to_openai_realtime_function_format(func) for func in functions_schema
]
custom_openai_tools = []
if tools_schema.custom_tools:
custom_openai_tools = tools_schema.custom_tools.get(AdapterType.OPENAI, [])
return formatted_standard_tools + custom_openai_tools

13
src/pipecat/adapters/services/open_ai_responses_adapter.py
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@@ -10,10 +10,10 @@ import copy
from typing import Any, Dict, List, Optional, TypedDict
from openai._types import NotGiven as OpenAINotGiven
from openai.types.responses import FunctionToolParam, ResponseInputItemParam
from openai.types.responses import FunctionToolParam, ResponseInputItemParam, ToolParam
from pipecat.adapters.base_llm_adapter import BaseLLMAdapter
from pipecat.adapters.schemas.tools_schema import ToolsSchema
from pipecat.adapters.schemas.tools_schema import AdapterType, ToolsSchema
from pipecat.processors.aggregators.llm_context import (
LLMContext,
LLMContextMessage,
@@ -25,7 +25,7 @@ class OpenAIResponsesLLMInvocationParams(TypedDict, total=False):
"""Context-based parameters for invoking OpenAI Responses API."""
input: List[ResponseInputItemParam]
tools: List[FunctionToolParam] | OpenAINotGiven
tools: List[ToolParam] | OpenAINotGiven
instructions: str
@@ -106,7 +106,7 @@ class OpenAIResponsesLLMAdapter(BaseLLMAdapter[OpenAIResponsesLLMInvocationParam
return params
def to_provider_tools_format(self, tools_schema: ToolsSchema) -> List[FunctionToolParam]:
def to_provider_tools_format(self, tools_schema: ToolsSchema) -> List[ToolParam]:
"""Convert function schemas to Responses API function tool format.
Args:
@@ -128,7 +128,10 @@ class OpenAIResponsesLLMAdapter(BaseLLMAdapter[OpenAIResponsesLLMInvocationParam
if "description" in d:
tool["description"] = d["description"]
result.append(tool)
return result
custom_openai_tools = []
if tools_schema.custom_tools:
custom_openai_tools = tools_schema.custom_tools.get(AdapterType.OPENAI, [])
return result + custom_openai_tools
def get_messages_for_logging(self, context: LLMContext) -> List[Dict[str, Any]]:
"""Get messages from context in a format ready for logging.

130
tests/test_function_calling_adapters.py
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@@ -7,6 +7,8 @@
import unittest
from openai.types.chat import ChatCompletionToolParam
from openai.types.responses.function_tool_param import FunctionToolParam
from openai.types.responses.tool_search_tool_param import ToolSearchToolParam
from pipecat.adapters.schemas.function_schema import FunctionSchema
from pipecat.adapters.schemas.tools_schema import AdapterType, ToolsSchema
@@ -16,6 +18,7 @@ from pipecat.adapters.services.gemini_adapter import GeminiLLMAdapter
from pipecat.adapters.services.inworld_realtime_adapter import InworldRealtimeLLMAdapter
from pipecat.adapters.services.open_ai_adapter import OpenAILLMAdapter
from pipecat.adapters.services.open_ai_realtime_adapter import OpenAIRealtimeLLMAdapter
from pipecat.adapters.services.open_ai_responses_adapter import OpenAIResponsesLLMAdapter
class TestFunctionAdapters(unittest.TestCase):
@@ -203,6 +206,133 @@ class TestFunctionAdapters(unittest.TestCase):
tools_def.custom_tools = {AdapterType.GEMINI: [search_tool]}
assert GeminiLLMAdapter().to_provider_tools_format(tools_def) == expected
def test_openai_adapter_with_custom_tools(self):
"""Test OpenAI adapter appends custom tools."""
tool_search = {"type": "tool_search"}
expected = [
ChatCompletionToolParam(
type="function",
function={
"name": "get_weather",
"description": "Get the weather in a given location",
"parameters": {
"type": "object",
"properties": {
"location": {
"type": "string",
"description": "The city, e.g. San Francisco",
},
"format": {
"type": "string",
"enum": ["celsius", "fahrenheit"],
"description": "The temperature unit to use.",
},
},
"required": ["location", "format"],
},
},
),
tool_search,
]
tools_def = self.tools_def
tools_def.custom_tools = {AdapterType.OPENAI: [tool_search]}
assert OpenAILLMAdapter().to_provider_tools_format(tools_def) == expected
def test_openai_responses_adapter_with_custom_tools(self):
"""Test OpenAI Responses adapter appends custom tools."""
tool_search = {"type": "tool_search"}
expected = [
FunctionToolParam(
type="function",
name="get_weather",
description="Get the weather in a given location",
parameters={
"type": "object",
"properties": {
"location": {
"type": "string",
"description": "The city, e.g. San Francisco",
},
"format": {
"type": "string",
"enum": ["celsius", "fahrenheit"],
"description": "The temperature unit to use.",
},
},
"required": ["location", "format"],
},
strict=None,
),
ToolSearchToolParam(type="tool_search"),
]
tools_def = self.tools_def
tools_def.custom_tools = {AdapterType.OPENAI: [tool_search]}
assert OpenAIResponsesLLMAdapter().to_provider_tools_format(tools_def) == expected
def test_openai_responses_adapter(self):
"""Test OpenAI Responses adapter format transformation."""
expected = [
{
"type": "function",
"name": "get_weather",
"description": "Get the weather in a given location",
"parameters": {
"type": "object",
"properties": {
"location": {
"type": "string",
"description": "The city, e.g. San Francisco",
},
"format": {
"type": "string",
"enum": ["celsius", "fahrenheit"],
"description": "The temperature unit to use.",
},
},
"required": ["location", "format"],
},
"strict": None,
}
]
assert OpenAIResponsesLLMAdapter().to_provider_tools_format(self.tools_def) == expected
def test_openai_realtime_adapter_with_custom_tools(self):
"""Test OpenAI Realtime adapter appends custom tools."""
tool_search = {"type": "tool_search"}
expected = [
{
"type": "function",
"name": "get_weather",
"description": "Get the weather in a given location",
"parameters": {
"type": "object",
"properties": {
"location": {
"type": "string",
"description": "The city, e.g. San Francisco",
},
"format": {
"type": "string",
"enum": ["celsius", "fahrenheit"],
"description": "The temperature unit to use.",
},
},
"required": ["location", "format"],
},
},
tool_search,
]
tools_def = self.tools_def
tools_def.custom_tools = {AdapterType.OPENAI: [tool_search]}
assert OpenAIRealtimeLLMAdapter().to_provider_tools_format(tools_def) == expected
def test_openai_adapter_ignores_other_adapter_custom_tools(self):
"""Test that OpenAI adapter ignores custom tools for other adapters."""
expected = OpenAILLMAdapter().to_provider_tools_format(self.tools_def)
tools_def = self.tools_def
tools_def.custom_tools = {AdapterType.GEMINI: [{"google_search": {}}]}
assert OpenAILLMAdapter().to_provider_tools_format(tools_def) == expected
def test_bedrock_adapter(self):
"""Test AWS Bedrock adapter format transformation."""
expected = [