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OpenAI assistants

The Assistants API allows you to build AI assistants within your own applications. An Assistant has instructions and can leverage models, tools, and knowledge to respond to user queries. The Assistants API currently supports three types of tools: Code Interpreter, Retrieval, and Function calling

You can interact with OpenAI Assistants using OpenAI tools or custom tools. When using exclusively OpenAI tools, you can just invoke the assistant directly and get final answers. When using custom tools, you can run the assistant and tool execution loop using the built-in AgentExecutor or easily write your own executor.

Below we show the different ways to interact with Assistants. As a simple example, let's build a math tutor that can write and run code.

Using only OpenAI tools​

from langchain.agents.openai_assistant import OpenAIAssistantRunnable
interpreter_assistant = OpenAIAssistantRunnable.create_assistant(
    name="langchain assistant",
    instructions="You are a personal math tutor. Write and run code to answer math questions.",
    tools=[{"type": "code_interpreter"}],
    model="gpt-4-1106-preview",
)
output = interpreter_assistant.invoke({"content": "What's 10 - 4 raised to the 2.7"})
output

As a LangChain agent with arbitrary tools​

Now let's recreate this functionality using our own tools. For this example we'll use the E2B sandbox runtime tool.

pip install e2b duckduckgo-search
from langchain.tools import E2BDataAnalysisTool, DuckDuckGoSearchRun

tools = [E2BDataAnalysisTool(api_key="..."), DuckDuckGoSearchRun()]
agent = OpenAIAssistantRunnable.create_assistant(
    name="langchain assistant e2b tool",
    instructions="You are a personal math tutor. Write and run code to answer math questions. You can also search the internet.",
    tools=tools,
    model="gpt-4-1106-preview",
    as_agent=True,
)

Using AgentExecutor​

from langchain.agents import AgentExecutor

agent_executor = AgentExecutor(agent=agent, tools=tools)
agent_executor.invoke({"content": "What's the weather in SF today divided by 2.7"})

Custom execution​

agent = OpenAIAssistantRunnable.create_assistant(
    name="langchain assistant e2b tool",
    instructions="You are a personal math tutor. Write and run code to answer math questions.",
    tools=tools,
    model="gpt-4-1106-preview",
    as_agent=True,
)
from langchain.schema.agent import AgentFinish


def execute_agent(agent, tools, input):
    tool_map = {tool.name: tool for tool in tools}
    response = agent.invoke(input)
    while not isinstance(response, AgentFinish):
        tool_outputs = []
        for action in response:
            tool_output = tool_map[action.tool].invoke(action.tool_input)
            print(action.tool, action.tool_input, tool_output, end="\n\n")
            tool_outputs.append(
                {"output": tool_output, "tool_call_id": action.tool_call_id}
            )
        response = agent.invoke(
            {
                "tool_outputs": tool_outputs,
                "run_id": action.run_id,
                "thread_id": action.thread_id,
            }
        )

    return response
response = execute_agent(agent, tools, {"content": "What's 10 - 4 raised to the 2.7"})
print(response.return_values["output"])
next_response = execute_agent(
    agent, tools, {"content": "now add 17.241", "thread_id": response.thread_id}
)
print(next_response.return_values["output"])