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feat(openai): custom tools (#32449)

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2025-08-07 17:30:01 -03:00
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docs/docs/integrations/chat/openai.ipynb
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@@ -447,6 +447,163 @@
")"
]
},
{
"cell_type": "markdown",
"id": "c5d9d19d-8ab1-4d9d-b3a0-56ee4e89c528",
"metadata": {},
"source": [
"### Custom tools\n",
"\n",
":::info Requires ``langchain-openai>=0.3.29``\n",
"\n",
":::\n",
"\n",
"[Custom tools](https://platform.openai.com/docs/guides/function-calling#custom-tools) support tools with arbitrary string inputs. They can be particularly useful when you expect your string arguments to be long or complex."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "a47c809b-852f-46bd-8b9e-d9534c17213d",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"================================\u001b[1m Human Message \u001b[0m=================================\n",
"\n",
"Use the tool to calculate 3^3.\n",
"==================================\u001b[1m Ai Message \u001b[0m==================================\n",
"\n",
"[{'id': 'rs_6894ff5747c0819d9b02fc5645b0be9c000169fd9fb68d99', 'summary': [], 'type': 'reasoning'}, {'call_id': 'call_7SYwMSQPbbEqFcKlKOpXeEux', 'input': 'print(3**3)', 'name': 'execute_code', 'type': 'custom_tool_call', 'id': 'ctc_6894ff5b9f54819d8155a63638d34103000169fd9fb68d99', 'status': 'completed'}]\n",
"Tool Calls:\n",
" execute_code (call_7SYwMSQPbbEqFcKlKOpXeEux)\n",
" Call ID: call_7SYwMSQPbbEqFcKlKOpXeEux\n",
" Args:\n",
" __arg1: print(3**3)\n",
"=================================\u001b[1m Tool Message \u001b[0m=================================\n",
"Name: execute_code\n",
"\n",
"[{'type': 'custom_tool_call_output', 'output': '27'}]\n",
"==================================\u001b[1m Ai Message \u001b[0m==================================\n",
"\n",
"[{'type': 'text', 'text': '27', 'annotations': [], 'id': 'msg_6894ff5db3b8819d9159b3a370a25843000169fd9fb68d99'}]\n"
]
}
],
"source": [
"from langchain_openai import ChatOpenAI, custom_tool\n",
"from langgraph.prebuilt import create_react_agent\n",
"\n",
"\n",
"@custom_tool\n",
"def execute_code(code: str) -> str:\n",
" \"\"\"Execute python code.\"\"\"\n",
" return \"27\"\n",
"\n",
"\n",
"llm = ChatOpenAI(model=\"gpt-5\", output_version=\"responses/v1\")\n",
"\n",
"agent = create_react_agent(llm, [execute_code])\n",
"\n",
"input_message = {\"role\": \"user\", \"content\": \"Use the tool to calculate 3^3.\"}\n",
"for step in agent.stream(\n",
" {\"messages\": [input_message]},\n",
" stream_mode=\"values\",\n",
"):\n",
" step[\"messages\"][-1].pretty_print()"
]
},
{
"cell_type": "markdown",
"id": "5ef93be6-6d4c-4eea-acfd-248774074082",
"metadata": {},
"source": [
"<details>\n",
"<summary>Context-free grammars</summary>\n",
"\n",
"OpenAI supports the specification of a [context-free grammar](https://platform.openai.com/docs/guides/function-calling#context-free-grammars) for custom tool inputs in `lark` or `regex` format. See [OpenAI docs](https://platform.openai.com/docs/guides/function-calling#context-free-grammars) for details. The `format` parameter can be passed into `@custom_tool` as shown below:"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "2ae04586-be33-49c6-8947-7867801d868f",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"================================\u001b[1m Human Message \u001b[0m=================================\n",
"\n",
"Use the tool to calculate 3^3.\n",
"==================================\u001b[1m Ai Message \u001b[0m==================================\n",
"\n",
"[{'id': 'rs_689500828a8481a297ff0f98e328689c0681550c89797f43', 'summary': [], 'type': 'reasoning'}, {'call_id': 'call_jzH01RVhu6EFz7yUrOFXX55s', 'input': '3 * 3 * 3', 'name': 'do_math', 'type': 'custom_tool_call', 'id': 'ctc_6895008d57bc81a2b84d0993517a66b90681550c89797f43', 'status': 'completed'}]\n",
"Tool Calls:\n",
" do_math (call_jzH01RVhu6EFz7yUrOFXX55s)\n",
" Call ID: call_jzH01RVhu6EFz7yUrOFXX55s\n",
" Args:\n",
" __arg1: 3 * 3 * 3\n",
"=================================\u001b[1m Tool Message \u001b[0m=================================\n",
"Name: do_math\n",
"\n",
"[{'type': 'custom_tool_call_output', 'output': '27'}]\n",
"==================================\u001b[1m Ai Message \u001b[0m==================================\n",
"\n",
"[{'type': 'text', 'text': '27', 'annotations': [], 'id': 'msg_6895009776b881a2a25f0be8507d08f20681550c89797f43'}]\n"
]
}
],
"source": [
"from langchain_openai import ChatOpenAI, custom_tool\n",
"from langgraph.prebuilt import create_react_agent\n",
"\n",
"grammar = \"\"\"\n",
"start: expr\n",
"expr: term (SP ADD SP term)* -> add\n",
"| term\n",
"term: factor (SP MUL SP factor)* -> mul\n",
"| factor\n",
"factor: INT\n",
"SP: \" \"\n",
"ADD: \"+\"\n",
"MUL: \"*\"\n",
"%import common.INT\n",
"\"\"\"\n",
"\n",
"format_ = {\"type\": \"grammar\", \"syntax\": \"lark\", \"definition\": grammar}\n",
"\n",
"\n",
"# highlight-next-line\n",
"@custom_tool(format=format_)\n",
"def do_math(input_string: str) -> str:\n",
" \"\"\"Do a mathematical operation.\"\"\"\n",
" return \"27\"\n",
"\n",
"\n",
"llm = ChatOpenAI(model=\"gpt-5\", output_version=\"responses/v1\")\n",
"\n",
"agent = create_react_agent(llm, [do_math])\n",
"\n",
"input_message = {\"role\": \"user\", \"content\": \"Use the tool to calculate 3^3.\"}\n",
"for step in agent.stream(\n",
" {\"messages\": [input_message]},\n",
" stream_mode=\"values\",\n",
"):\n",
" step[\"messages\"][-1].pretty_print()"
]
},
{
"cell_type": "markdown",
"id": "c63430c9-c7b0-4e92-a491-3f165dddeb8f",
"metadata": {},
"source": [
"</details>"
]
},
{
"cell_type": "markdown",
"id": "84833dd0-17e9-4269-82ed-550639d65751",