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layout default
title Chapter 4: Agents and Assistants
nav_order 4
parent OpenAI Python SDK Tutorial

Chapter 4: Agents and Assistants

Welcome to Chapter 4: Agents and Assistants. In this part of OpenAI Python SDK Tutorial: Production API Patterns, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.

This chapter focuses on transition strategy: operate existing assistants safely while moving toward current agent-platform patterns.

Current State

  • Assistants API is still usable in many systems.
  • OpenAI platform docs indicate a target sunset timeline around August 26, 2026.
  • New projects should evaluate Responses API + Agents patterns first.

Existing Assistants Workflow (Legacy/Transition)

from openai import OpenAI

client = OpenAI()

assistant = client.beta.assistants.create(
    model="gpt-5.2",
    name="Ops Assistant",
    instructions="Help with reliability planning and incident response."
)

thread = client.beta.threads.create()
client.beta.threads.messages.create(
    thread_id=thread.id,
    role="user",
    content="Draft a rollback checklist for a risky deployment."
)

Migration Playbook

  1. catalog Assistants API usage and tool dependencies
  2. extract shared prompt/tool contracts
  3. rebuild core flows on Responses/Agents primitives
  4. run side-by-side output comparisons
  5. cut over service by service

Risk Controls During Migration

  • avoid broad rewrites in one release
  • pin SDK versions per service
  • keep rollback path to known-good behavior
  • monitor quality regressions with fixed eval sets

Summary

You can now manage assistant-era systems while executing a controlled migration plan.

Next: Chapter 5: Batch Processing

Source Code Walkthrough

examples/parsing_tools.py

The DynamicValue class in examples/parsing_tools.py handles a key part of this chapter's functionality:

class DynamicValue(BaseModel):
    column_name: str


class Condition(BaseModel):
    column: str
    operator: Operator
    value: Union[str, int, DynamicValue]


class Query(BaseModel):
    table_name: Table
    columns: List[Column]
    conditions: List[Condition]
    order_by: OrderBy


client = OpenAI()

completion = client.chat.completions.parse(
    model="gpt-4o-2024-08-06",
    messages=[
        {
            "role": "system",
            "content": "You are a helpful assistant. The current date is August 6, 2024. You help users query for the data they are looking for by calling the query function.",
        },
        {
            "role": "user",
            "content": "look up all my orders in november of last year that were fulfilled but not delivered on time",
        },

This class is important because it defines how OpenAI Python SDK Tutorial: Production API Patterns implements the patterns covered in this chapter.

examples/parsing_tools.py

The Condition class in examples/parsing_tools.py handles a key part of this chapter's functionality:

class Condition(BaseModel):
    column: str
    operator: Operator
    value: Union[str, int, DynamicValue]


class Query(BaseModel):
    table_name: Table
    columns: List[Column]
    conditions: List[Condition]
    order_by: OrderBy


client = OpenAI()

completion = client.chat.completions.parse(
    model="gpt-4o-2024-08-06",
    messages=[
        {
            "role": "system",
            "content": "You are a helpful assistant. The current date is August 6, 2024. You help users query for the data they are looking for by calling the query function.",
        },
        {
            "role": "user",
            "content": "look up all my orders in november of last year that were fulfilled but not delivered on time",
        },
    ],
    tools=[
        openai.pydantic_function_tool(Query),
    ],

This class is important because it defines how OpenAI Python SDK Tutorial: Production API Patterns implements the patterns covered in this chapter.

examples/parsing_tools.py

The Query class in examples/parsing_tools.py handles a key part of this chapter's functionality:

class Query(BaseModel):
    table_name: Table
    columns: List[Column]
    conditions: List[Condition]
    order_by: OrderBy


client = OpenAI()

completion = client.chat.completions.parse(
    model="gpt-4o-2024-08-06",
    messages=[
        {
            "role": "system",
            "content": "You are a helpful assistant. The current date is August 6, 2024. You help users query for the data they are looking for by calling the query function.",
        },
        {
            "role": "user",
            "content": "look up all my orders in november of last year that were fulfilled but not delivered on time",
        },
    ],
    tools=[
        openai.pydantic_function_tool(Query),
    ],
)

tool_call = (completion.choices[0].message.tool_calls or [])[0]
rich.print(tool_call.function)
assert isinstance(tool_call.function.parsed_arguments, Query)
print(tool_call.function.parsed_arguments.table_name)

This class is important because it defines how OpenAI Python SDK Tutorial: Production API Patterns implements the patterns covered in this chapter.

How These Components Connect

flowchart TD
    A[DynamicValue]
    B[Condition]
    C[Query]
    A --> B
    B --> C
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