OpenAI Agent Builder (Assistants API)

from openai import OpenAI

client = OpenAI(api_key="your_api_key")

assistant = client.beta.assistants.create(
    name="Research & Analysis Agent",
    instructions="""You are a research and analysis expert.
    Use tools to search documents, analyze data, and retrieve information.""",
    model="gpt-4-turbo",
    tools=[
        {"type": "code_interpreter"},
        {"type": "file_search"},
        {
            "type": "function",
            "function": {
                "name": "get_customer_data",
                "description": "Retrieve customer info",
                "parameters": {
                    "type": "object",
                    "properties": {
                        "customer_id": {"type": "string"}
                    },
                    "required": ["customer_id"]
                }
            }
        }
    ]
)

print(f"Assistant ID: {assistant.id}")

name: "OpenAI Agent Builder"
model_id: "openai-agent-builder"
description: "Execute OpenAI Agent Builder agents for intelligent task automation"
endpoint: "https://api.openai.com/v1/assistants/{{assistant_id}}/runs"
method: "POST"

headers:
  Authorization: "Bearer {{openai_api_key}}"
  Content-Type: "application/json"
  OpenAI-Beta: "assistants=v2"

request_schema:
  assistant_id: "{{assistant_id}}"
  thread_id: "{{thread_id}}"
  instructions: "{{instructions}}"
  additional_instructions: "{{additional_instructions}}"
  tools:
    - type: "code_interpreter"
    - type: "file_search"
    - type: "function"
      function:
        name: "custom_function"
        description: "Custom function for specific tasks"
  metadata:
    user_id: "{{user_id}}"
    session_id: "{{session_id}}"
    timestamp: "{{timestamp}}"

response_path: "data.run_result"

message_format:
  preset: "openai_assistant"
  mapping:
    role:
      source: "role"
      target: "messages[0].role"
      transform: "openai_role"
    content:
      source: "content"
      target: "messages[0].content"
      transform: "none"
    timestamp:
      source: "timestamp"
      target: "metadata.timestamp"
      transform: "iso8601"
  customFields:
    - name: "agent_configuration"
      value:
        platform: "openai"
        model: "gpt-4-turbo"
        version: "assistants-v2"
        capabilities: ["code_interpreter", "file_search", "function_calling"]
      type: "object"
    - name: "execution_settings"
      value:
        max_completion_tokens: 4000
        temperature: 0.7
        timeout: 300
        stream: false
      type: "object"

suggestion_prompts:
  - "Create an agent to analyze customer feedback and generate insights"
  - "Build an agent for automated code review and suggestions"
  - "Set up an agent for data analysis and report generation"
  - "Create an agent for content moderation and classification"
  - "Build an agent for automated customer support responses"

# Create thread
thread = client.beta.threads.create()

# Add message
message = client.beta.threads.messages.create(
    thread_id=thread.id,
    role="user",
    content="Analyze this data..."
)

# Run assistant
run = client.beta.threads.runs.create(
    thread_id=thread.id,
    assistant_id=assistant.id
)

# Check status
while run.status != "completed":
    run = client.beta.threads.runs.retrieve(
        thread_id=thread.id,
        run_id=run.id
    )
    time.sleep(1)

# Get response
messages = client.beta.threads.messages.list(
    thread_id=thread.id
)

with client.beta.threads.runs.stream(
    thread_id=thread.id,
    assistant_id=assistant.id
) as stream:
    for event in stream:
        if event.type == "thread.message.delta":
            print(event.data.delta.content[0].text.value, end="")

run = client.beta.threads.runs.create(
    thread_id=thread.id,
    assistant_id=assistant.id,
    additional_instructions="""
    For this analysis:
    - Focus on trends from the last quarter
    - Highlight any anomalies
    - Provide actionable recommendations
    """
)

Assistant Config:
  Name: "Data Analysis Expert"
  Model: gpt-4-turbo
  Tools: [code_interpreter]
  Instructions: |
    Analyze data using Python, pandas, numpy.
    Create visualizations with matplotlib.
    Provide statistical insights and predictions.

Example Query:
  "Analyze customer churn data and identify key factors"

Assistant Config:
  Name: "Research Assistant"
  Model: gpt-4-turbo
  Tools: [file_search]
  Instructions: |
    Search through documents to answer questions.
    Always cite sources.
    Provide comprehensive summaries.

Example Query:
  "What does our compliance policy say about data retention?"

Assistant Config:
  Name: "Support Bot"
  Model: gpt-4-turbo
  Tools: [file_search, function(get_ticket_info)]
  Instructions: |
    Help customers with their issues.
    Search knowledge base for solutions.
    Escalate complex issues to humans.

Example Query:
  "Customer CUST-123 is having login issues"

run = client.beta.threads.runs.retrieve(
    thread_id=thread.id,
    run_id=run.id
)

print(f"Status: {run.status}")
# queued, in_progress, requires_action, completed, failed

try:
    run = client.beta.threads.runs.create(
        thread_id=thread.id,
        assistant_id=assistant.id
    )
except openai.APIError as e:
    print(f"API Error: {e}")
except openai.RateLimitError as e:
    print(f"Rate limit exceeded: {e}")
except Exception as e:
    print(f"Unexpected error: {e}")

run = client.beta.threads.runs.retrieve(
    thread_id=thread.id,
    run_id=run.id
)

print(f"Tokens used: {run.usage.total_tokens}")
print(f"Prompt tokens: {run.usage.prompt_tokens}")
print(f"Completion tokens: {run.usage.completion_tokens}")