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chore(dev): agent builder mode
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Soumyadas15
parent
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95c6277a93
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"""Agent Builder Configuration
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This module contains the configuration for the standalone agent builder agent.
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"""
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AGENT_BUILDER_SYSTEM_PROMPT = """You are an expert AI Agent Builder assistant. Your purpose is to help users create and configure custom AI agents by understanding their requirements and progressively building the agent configuration.
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## Your Capabilities
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You have access to tools that allow you to:
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1. Check the current agent configuration using `get_current_agent_config`
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2. Update any aspect of the agent using `update_agent`
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## Your Process
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When helping users build agents, follow this structured approach:
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### 1. Understanding Purpose
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- Ask what the user wants their agent to do
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- Understand the specific use cases and requirements
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- Clarify any ambiguities about the agent's role
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### 2. Naming & Description
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- Suggest appropriate names based on the agent's purpose
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- Create clear, concise descriptions that explain what the agent does
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- Ensure the name and description align with the functionality
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### 3. System Instructions
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- Write comprehensive system prompts that define:
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- The agent's role and expertise
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- Behavioral guidelines and interaction style
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- Knowledge domains and capabilities
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- How to handle different types of requests
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- Appropriate boundaries and limitations
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- Make prompts engaging, clear, and actionable
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### 4. Tool Selection
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- Recommend tools based on the agent's purpose:
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- `sb_shell_tool`: For terminal operations and CLI tools
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- `sb_files_tool`: For file management and code editing
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- `sb_browser_tool`: For web automation and browsing
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- `sb_deploy_tool`: For deploying applications
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- `sb_expose_tool`: For exposing services
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- `message_tool`: For communication
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- `web_search_tool`: For research (requires Tavily API)
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- `sb_vision_tool`: For image analysis
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- `data_providers_tool`: For external APIs (requires RapidAPI)
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### 5. MCP Servers (if needed)
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- Suggest relevant MCP servers for external integrations
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- Help configure them appropriately
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### 6. Visual Identity
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- Help choose an appropriate emoji avatar
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- Select a matching color scheme
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## Guidelines
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- Always check the current configuration first before making updates
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- Update only the fields being discussed to avoid overwriting other settings
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- Be encouraging and make the process intuitive
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- Keep responses concise but informative
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- Provide examples and suggestions when helpful
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- Confirm important changes with the user
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- Guide users through each step progressively
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## Example Interactions
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**User**: "I want to create a research assistant"
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**You**: "Great! I'll help you create a research assistant agent. Let me check the current configuration first, then we'll build it step by step.
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[Check current config]
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I see we're starting fresh. For a research assistant, I suggest:
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- **Name**: "Research Assistant" or "Scholar"
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- **Purpose**: Help with gathering information, analyzing sources, and synthesizing findings
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What specific types of research will this agent help with? Academic, market research, technical documentation, or something else?"
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Remember: Your goal is to make agent creation simple, intuitive, and effective. Guide users through the process while using your tools to implement their vision."""
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def get_agent_builder_config():
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"""Get the configuration for the agent builder agent."""
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return {
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"name": "Agent Builder",
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"description": "An AI assistant that helps you create and configure custom AI agents",
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"system_prompt": AGENT_BUILDER_SYSTEM_PROMPT,
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"agentpress_tools": {
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# Agent builder only needs the update agent tool
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# which is added dynamically when running
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},
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"avatar": "🛠️",
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"avatar_color": "#8B5CF6"
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}
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@ -802,6 +802,7 @@ async def initiate_agent_with_files(
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enable_context_manager: Optional[bool] = Form(False),
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agent_id: Optional[str] = Form(None), # Add agent_id parameter
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files: List[UploadFile] = File(default=[]),
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is_agent_builder: Optional[bool] = Form(False),
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user_id: str = Depends(get_current_user_id_from_jwt)
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):
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"""Initiate a new agent session with optional file attachments."""
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@ -997,7 +998,8 @@ async def initiate_agent_with_files(
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model_name=model_name, # Already resolved above
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enable_thinking=enable_thinking, reasoning_effort=reasoning_effort,
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stream=stream, enable_context_manager=enable_context_manager,
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agent_config=agent_config # Pass agent configuration
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agent_config=agent_config, # Pass agent configuration
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is_agent_builder=is_agent_builder
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)
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return {"thread_id": thread_id, "agent_run_id": agent_run_id}
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@ -1477,184 +1479,3 @@ async def get_user_agent_library(user_id: str = Depends(get_current_user_id_from
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except Exception as e:
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logger.error(f"Error fetching user agent library: {str(e)}")
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raise HTTPException(status_code=500, detail="Internal server error")
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@router.post("/agents/builder/chat/{agent_id}")
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async def agent_builder_chat(
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agent_id: str,
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request: AgentBuilderChatRequest,
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user_id: str = Depends(get_current_user_id_from_jwt)
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):
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"""Stream chat responses from the agent builder assistant that uses tools to update agent configuration."""
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logger.info(f"Agent builder chat request from user: {user_id} for agent: {agent_id}")
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async def stream_generator():
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try:
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# Import UpdateAgentTool
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from agent.tools.update_agent_tool import UpdateAgentTool
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# Initialize the update agent tool
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agent_update_tool = UpdateAgentTool(db, agent_id)
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# System prompt for the agent builder assistant
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system_prompt = """You are an expert AI assistant helping users configure custom AI agents. You have access to tools that let you update the agent's configuration and get its current state.
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Your role is to:
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1. Understand what kind of agent the user wants to build
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2. Use the get_current_agent_config tool to see what's already configured
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3. Ask clarifying questions when needed
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4. Use the update_agent tool to progressively update the agent's configuration based on user requirements
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5. Guide the user through the entire process until the agent is fully configured
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Key guidelines:
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- Always check the current config first before making updates
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- Update only the fields that the user is discussing
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- Provide helpful suggestions for names, descriptions, and system prompts
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- Recommend appropriate tools based on the agent's purpose
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- Be encouraging and make the process feel intuitive
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- Keep responses concise but informative
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Available tools:
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- get_current_agent_config: Check what's already configured
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- update_agent: Update any aspect of the agent (name, description, system_prompt, tools, etc.)
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Tool selection guidelines:
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- For web research, data analysis: web_search tool
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- For file operations, code execution: sb_files tool
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- For external integrations: suggest MCP servers
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Always use tools to make actual updates to the agent configuration."""
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# Build messages array
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messages = [{"role": "system", "content": system_prompt}]
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# Add conversation history
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for msg in request.conversation_history:
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messages.append({
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"role": msg.get("role", "user"),
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"content": msg.get("content", "")
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})
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# Add current message
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messages.append({"role": "user", "content": request.message})
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# Prepare tools for the agent
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tools = [agent_update_tool]
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tool_schemas = []
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for tool in tools:
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# Get XML schema for each tool method
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for method_name in dir(tool):
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if hasattr(getattr(tool, method_name), '_xml_schema'):
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schema = getattr(getattr(tool, method_name), '_xml_schema')
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tool_schemas.append(schema)
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# Make streaming API call to GPT-4o with tools
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response = await make_llm_api_call(
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messages=messages,
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model_name="openai/gpt-4o",
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temperature=0.7,
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max_tokens=2000,
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stream=True,
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tools=tool_schemas,
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tool_choice="auto"
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)
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full_response = ""
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tool_calls_buffer = ""
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in_tool_call = False
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# Stream the response and handle tool calls
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async for chunk in response:
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if chunk.choices and chunk.choices[0].delta:
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delta = chunk.choices[0].delta
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# Handle regular content
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if delta.content:
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content = delta.content
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full_response += content
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# Check for tool call markers
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if "<function_calls>" in content:
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in_tool_call = True
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tool_calls_buffer += content
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elif "</function_calls>" in content:
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in_tool_call = False
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tool_calls_buffer += content
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# Process tool call
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try:
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# Extract and execute tool calls
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import re
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from xml.etree import ElementTree as ET
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# Find all invoke blocks
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invoke_pattern = r'<invoke name="([^"]+)">(.*?)</invoke>'
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matches = re.findall(invoke_pattern, tool_calls_buffer, re.DOTALL)
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for tool_name, params_xml in matches:
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# Parse parameters
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params = {}
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param_pattern = r'<parameter name="([^"]+)">(.*?)</parameter>'
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param_matches = re.findall(param_pattern, params_xml, re.DOTALL)
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for param_name, param_value in param_matches:
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params[param_name] = param_value.strip()
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# Execute tool call
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if hasattr(agent_update_tool, tool_name):
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tool_method = getattr(agent_update_tool, tool_name)
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result = await tool_method(**params)
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# Send tool result as content
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tool_result_content = f"\n\n[Tool executed: {tool_name}]\nResult: {result.content}\n\n"
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yield f"data: {json.dumps({'type': 'content', 'content': tool_result_content})}\n\n"
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except Exception as e:
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logger.error(f"Error processing tool call: {str(e)}")
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error_content = f"\n\n[Tool execution error: {str(e)}]\n\n"
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yield f"data: {json.dumps({'type': 'content', 'content': error_content})}\n\n"
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tool_calls_buffer = ""
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elif in_tool_call:
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tool_calls_buffer += content
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else:
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# Send regular content
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yield f"data: {json.dumps({'type': 'content', 'content': content})}\n\n"
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# Handle tool calls from the API (if supported)
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elif hasattr(delta, 'tool_calls') and delta.tool_calls:
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for tool_call in delta.tool_calls:
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if tool_call.function:
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tool_name = tool_call.function.name
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try:
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tool_args = json.loads(tool_call.function.arguments)
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# Execute tool
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if hasattr(agent_update_tool, tool_name):
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tool_method = getattr(agent_update_tool, tool_name)
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result = await tool_method(**tool_args)
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# Send tool result
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tool_result_content = f"\n\n[Tool executed: {tool_name}]\nResult: {result.content}\n\n"
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yield f"data: {json.dumps({'type': 'content', 'content': tool_result_content})}\n\n"
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except Exception as e:
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logger.error(f"Error executing tool {tool_name}: {str(e)}")
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error_content = f"\n\n[Tool execution error: {str(e)}]\n\n"
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yield f"data: {json.dumps({'type': 'content', 'content': error_content})}\n\n"
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# Send completion signal
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yield f"data: {json.dumps({'type': 'done'})}\n\n"
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except Exception as e:
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logger.error(f"Error in agent builder chat: {str(e)}")
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yield f"data: {json.dumps({'type': 'error', 'error': str(e)})}\n\n"
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return StreamingResponse(
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stream_generator(),
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media_type="text/event-stream",
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headers={
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"Cache-Control": "no-cache",
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"Connection": "keep-alive",
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"X-Accel-Buffering": "no"
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}
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)
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@ -44,7 +44,8 @@ async def run_agent(
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reasoning_effort: Optional[str] = 'low',
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enable_context_manager: bool = True,
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agent_config: Optional[dict] = None,
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trace: Optional[StatefulTraceClient] = None
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trace: Optional[StatefulTraceClient] = None,
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is_agent_builder: Optional[bool] = False
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):
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"""Run the development agent with specified configuration."""
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logger.info(f"🚀 Starting agent with model: {model_name}")
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@ -84,7 +85,14 @@ async def run_agent(
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# Register tools based on configuration
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# If no agent config (enabled_tools is None), register ALL tools for full Suna capabilities
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# If agent config exists, only register explicitly enabled tools
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if is_agent_builder:
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logger.info("Agent builder mode - registering only update agent tool")
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from agent.tools.update_agent_tool import UpdateAgentTool
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from services.supabase import DBConnection
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db = DBConnection()
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target_agent_id = agent_config.get('target_agent_id') if agent_config else None
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update_tool = UpdateAgentTool(db, target_agent_id)
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if enabled_tools is None:
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# No agent specified - register ALL tools for full Suna experience
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logger.info("No agent specified - registering all tools for full Suna capabilities")
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File diff suppressed because one or more lines are too long
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@ -53,7 +53,9 @@ async def run_agent_background(
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reasoning_effort: Optional[str],
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stream: bool,
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enable_context_manager: bool,
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agent_config: Optional[dict] = None # Add agent configuration parameter
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agent_config: Optional[dict] = None,
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is_agent_builder: Optional[bool] = False
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):
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"""Run the agent in the background using Redis for state."""
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await initialize()
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@ -122,7 +124,8 @@ async def run_agent_background(
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enable_thinking=enable_thinking, reasoning_effort=reasoning_effort,
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enable_context_manager=enable_context_manager,
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agent_config=agent_config,
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trace=trace
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trace=trace,
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is_agent_builder=is_agent_builder
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)
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final_status = "running"
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