SGR Integration & Examples

🚀 Python OpenAI Client Examples - Complete integration guide with streaming & clarifications

Simple Python examples for using OpenAI client with SGR Agent Core system.

Prerequisites

pip install openai

Example 1: Basic Research Request

Simple research query without clarifications.

from openai import OpenAI

# Initialize client
client = OpenAI(
    base_url="http://localhost:8010/v1",
    api_key="dummy",  # Not required for local server
)

# Make research request
response = client.chat.completions.create(
    model="sgr-agent",
    messages=[{"role": "user", "content": "Research BMW X6 2025 prices in Russia"}],
    stream=True,
    temperature=0.4,
)

# Print streaming response
for chunk in response:
    if chunk.choices[0].delta.content:
        print(chunk.choices[0].delta.content, end="")

Example 2: Research with Clarification Support

Handle agent clarification requests and continue conversation.

import json
from openai import OpenAI

client = OpenAI(base_url="http://localhost:8010/v1", api_key="dummy")

# Step 1: Initial research request
print("Starting research...")
response = client.chat.completions.create(
    model="sgr-agent",
    messages=[{"role": "user", "content": "Research AI market trends"}],
    stream=True,
    temperature=0,
)

agent_id = None
clarification_questions = []

# Process streaming response
for chunk in response:
    # Extract agent ID from model field
    if chunk.model and chunk.model.startswith("sgr_agent_"):
        agent_id = chunk.model
        print(f"\nAgent ID: {agent_id}")

    # Check for clarification requests
    if chunk.choices[0].delta.tool_calls:
        for tool_call in chunk.choices[0].delta.tool_calls:
            if tool_call.function and tool_call.function.name == "clarification":
                args = json.loads(tool_call.function.arguments)
                clarification_questions = args.get("questions", [])

    # Print content
    if chunk.choices[0].delta.content:
        print(chunk.choices[0].delta.content, end="")

# Step 2: Handle clarification if needed
if clarification_questions and agent_id:
    print(f"\n\nClarification needed:")
    for i, question in enumerate(clarification_questions, 1):
        print(f"{i}. {question}")

    # Provide clarification
    clarification = "Focus on LLM market trends for 2024-2025, global perspective"
    print(f"\nProviding clarification: {clarification}")

    # Continue with agent ID
    response = client.chat.completions.create(
        model=agent_id,  # Use agent ID as model
        messages=[{"role": "user", "content": clarification}],
        stream=True,
        temperature=0,
    )

    # Print final response
    for chunk in response:
        if chunk.choices[0].delta.content:
            print(chunk.choices[0].delta.content, end="")

print("\n\nResearch completed!")

Usage Notes

• Replace localhost:8010 with your server URL
• The api_key can be any string for local server
• Agent ID is returned in the model field during streaming
• Clarification questions are sent via tool_calls with function name clarification
• Use the agent ID as model name to continue conversation

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⚡ cURL API Examples - Direct HTTP requests with agent interruption & clarification flow

The system provides a fully OpenAI-compatible API with advanced agent interruption and clarification capabilities.

Basic Research Request

curl -X POST "http://localhost:8010/v1/chat/completions" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "sgr_agent",
    "messages": [{"role": "user", "content": "Research BMW X6 2025 prices in Russia"}],
    "stream": true,
    "max_tokens": 1500,
    "temperature": 0.4
  }'

Agent Interruption & Clarification Flow

When the agent needs clarification, it returns a unique agent ID in the streaming response model field. You can then continue the conversation using this agent ID.

Step 1: Initial Request

curl -X POST "http://localhost:8010/v1/chat/completions" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "sgr_agent",
    "messages": [{"role": "user", "content": "Research AI market trends"}],
    "stream": true,
    "max_tokens": 1500,
    "temperature": 0
  }'

Step 2: Agent Requests Clarification

The streaming response includes the agent ID in the model field:

{
  "model": "sgr_agent_b84d5a01-c394-4499-97be-dad6a5d2cb86",
  "choices": [{
    "delta": {
      "tool_calls": [{
        "function": {
          "name": "clarification",
          "arguments": "{\"questions\":[\"Which specific AI market segment are you interested in (LLM, computer vision, robotics)?\", \"What time period should I focus on (2024, next 5 years)?\", \"Are you looking for global trends or specific geographic regions?\", \"Do you need technical analysis or business/investment perspective?\"]}"
        }
      }]
    }
  }]
}

Step 3: Continue with Agent ID

curl -X POST "http://localhost:8010/v1/chat/completions" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "sgr_agent_b84d5a01-c394-4499-97be-dad6a5d2cb86",
    "messages": [{"role": "user", "content": "Focus on LLM market trends for 2024-2025, global perspective, business analysis"}],
    "stream": true,
    "max_tokens": 1500,
    "temperature": 0
  }'

Agent Management

# Get all active agents
curl http://localhost:8010/agents

# Get specific agent state
curl http://localhost:8010/agents/{agent_id}/state

# Direct clarification endpoint
curl -X POST "http://localhost:8010/agents/{agent_id}/provide_clarification" \
  -H "Content-Type: application/json" \
  -d '{
    "messages": [{"role": "user", "content": "Focus on luxury models only"}],
    "stream": true
  }'