Centralize agent type registry into single source of truth

Replace ~17 scattered type definitions across 4 files with a unified
AGENT_REGISTRY on both backend (agents.py) and frontend (script.js).
Adding a new agent now only requires one registry entry per side.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

backend/agents.py

@@ -0,0 +1,350 @@
+"""
+Centralized Agent Type Registry.
+
+Adding a new agent type:
+  1. Add an entry to AGENT_REGISTRY below
+  2. Implement its streaming handler (or use "builtin:chat" for simple LLM proxy)
+  3. Add the same entry to AGENT_REGISTRY in frontend/script.js
+
+SSE Event Protocol — every handler emits `data: {JSON}\\n\\n` lines with a `type` field.
+
+Required events (all agents):
+  - done          : stream complete
+  - error         : { content: str } error message
+
+Common optional events:
+  - thinking      : { content: str } reasoning text
+  - content       : { content: str } streamed response tokens
+  - result_preview: { content: str, figures: dict } inline result with optional images
+  - result        : { content: str, figures: dict } final output for command center widget
+  - generating    : still working between turns
+  - retry         : { attempt, max_attempts, delay, message } retrying after error
+
+Code-specific events:
+  - code_start    : { code: str } before code execution
+  - code          : { output, error, images } code cell result
+  - upload        : { paths, output } files uploaded to sandbox
+  - download      : { paths, output } files downloaded from sandbox
+
+Research-specific events:
+  - status        : { message } progress text
+  - queries       : { queries: list, iteration: int } search queries generated
+  - source        : { url, title, query_index, ... } source found
+  - query_stats   : { query_index, relevant_count, irrelevant_count, error_count }
+  - assessment    : { sufficient, missing_aspects, findings_count, reasoning }
+"""
+
+# ============================================================
+# THE REGISTRY — single source of truth for all agent types
+# ============================================================
+
+AGENT_REGISTRY = {
+    "command": {
+        "label": "TASKS",
+        "system_prompt": (
+            "You are a helpful AI assistant in the Productive interface command center.\n\n"
+            "{tools_section}\n\n"
+            "When a user asks you to perform a task that would benefit from a specialized notebook, you can:\n"
+            "1. Briefly acknowledge the request\n"
+            "2. Use the appropriate tool to launch a notebook with the task\n\n"
+            "You can also answer questions directly without launching a notebook if appropriate.\n\n"
+            "Examples:\n"
+            '- User: "Can you help me analyze this CSV file?"\n'
+            "  You: Use launch_code_notebook tool with the task\n\n"
+            '- User: "Research the latest developments in AI"\n'
+            "  You: Use launch_research_notebook tool with the topic\n\n"
+            '- User: "What was the result of the research in 2 sentences?"\n'
+            "  You: Summarize the research results without using tools\n\n"
+            "Be concise and helpful. Don't duplicate effort - either answer directly OR launch a notebook, not both. "
+            "Answer questions about results directly without launching new notebooks.\n\n"
+            "IMPORTANT guidelines when delegating to notebooks:\n"
+            "- Do NOT ask notebooks to save or create files unless the user explicitly requests it or implicitly necessary to solve a task.\n"
+            "- NEVER overwrite existing files without explicit user permission.\n"
+            "- Each notebook has a task_id. If a a new task is clearly related to a existing notebook "
+            "re-use the task id to reuse the notebook. This will reuse the existing context and also the jupyter kernel for code notebooks."
+        ),
+        "tool": None,
+        "tool_arg": None,
+        "has_counter": False,
+        "in_menu": False,
+        "in_launcher": False,
+        "placeholder": "Enter message...",
+    },
+
+    "agent": {
+        "label": "AGENT",
+        "system_prompt": (
+            "You are an autonomous agent assistant specialized in breaking down and executing multi-step tasks.\n\n"
+            "Your role is to:\n"
+            "- Understand complex tasks and break them down into clear steps\n"
+            "- Execute tasks methodically\n"
+            "- Keep track of progress and next steps\n"
+            "- Provide clear status updates\n\n"
+            "Focus on being proactive, organized, and thorough in completing multi-step workflows.\n"
+        ),
+        "tool": {
+            "type": "function",
+            "function": {
+                "name": "launch_agent_notebook",
+                "description": "Launch an autonomous agent notebook for multi-step tasks that need planning and execution. Use this for complex workflows, task organization, or anything requiring multiple coordinated steps.",
+                "parameters": {
+                    "type": "object",
+                    "properties": {
+                        "task": {
+                            "type": "string",
+                            "description": "The task or instruction for the agent. Should contain all necessary context."
+                        },
+                        "task_id": {
+                            "type": "string",
+                            "description": "A 2-3 word summary of the task, separated by dashes."
+                        }
+                    },
+                    "required": ["task", "task_id"]
+                }
+            }
+        },
+        "tool_arg": "task",
+        "has_counter": True,
+        "in_menu": False,
+        "in_launcher": True,
+        "placeholder": "Enter message...",
+    },
+
+    "code": {
+        "label": "CODE",
+        "system_prompt": (
+            "You are a coding assistant with access to a Python code execution environment.\n\n"
+            "Your role is to:\n"
+            "- Write and execute Python code to solve problems\n"
+            "- Analyze data and answer questions\n"
+            "- Debug code and explain errors\n"
+            "- Break down complex tasks into executable steps\n\n"
+            "You have access to a Jupyter kernel with these packages:\n"
+            "pandas, matplotlib, numpy, scipy, scikit-learn, seaborn, plotly, requests, beautifulsoup4, and more.\n\n"
+            "You have three tools available:\n"
+            "- execute_code: Run Python code. The execution environment is stateful - variables and imports persist between calls.\n"
+            "- upload_files: Upload files from the workspace to the execution environment for analysis. "
+            "Files will be available at /home/user/<filename>. Use this when you need to analyze data files, scripts, or other files from the project.\n"
+            "- download_files: Download files from the execution environment to the workspace. "
+            "ONLY use this when the user explicitly asks to save/download files, or when saving files is clearly part of the task "
+            '(e.g., "generate a dataset and save it"). Do NOT automatically save intermediate files, plots, or outputs unless requested.\n\n'
+            "## IMPORTANT Guidelines\n\n"
+            "**Only create artifacts when explicitly requested:**\n"
+            "- Do NOT save files unless the user explicitly asks to save/export/download\n"
+            "- NEVER overwrite existing files without explicit user permission - always ask first or use a new filename\n\n"
+            "When solving problems:\n"
+            "1. Break down the task into logical steps\n"
+            "2. Execute code incrementally\n"
+            "3. Check outputs before proceeding\n"
+            "4. Only create visualizations if explicitly requested\n\n"
+            "IMPORTANT: When you DO generate plots/figures (only when requested), they are automatically named as figure_1, figure_2, etc. "
+            'The execution output will show which figures were created (e.g., "[Generated figures: figure_1, figure_2]").\n\n'
+            "## CRITICAL: You MUST provide a <result> tag\n\n"
+            "When you have completed the task, you MUST provide a brief summary using the <result> tag. "
+            "This is REQUIRED - without it, your work will not be visible in the command center.\n\n"
+            "Keep results SHORT - 1-2 sentences max. Do NOT list implementation details, styling choices, or technical specifics. "
+            "The user can see the code and output above. Just state what was done.\n\n"
+            "To include figures, use self-closing tags like <figure_1> (NOT </figure_1> or <figure_1></figure_1>).\n\n"
+            "Example:\n"
+            "<result>\n"
+            "Here's the sine function plot:\n\n"
+            "<figure_1>\n"
+            "</result>\n"
+        ),
+        "tool": {
+            "type": "function",
+            "function": {
+                "name": "launch_code_notebook",
+                "description": "Launch a code notebook with Python execution environment. Use this for data analysis, creating visualizations, running code, debugging, or anything involving programming.",
+                "parameters": {
+                    "type": "object",
+                    "properties": {
+                        "task": {
+                            "type": "string",
+                            "description": "The coding task or question. Should contain all necessary context."
+                        },
+                        "task_id": {
+                            "type": "string",
+                            "description": "A 2-3 word summary of the task, separated by dashes."
+                        }
+                    },
+                    "required": ["task", "task_id"]
+                }
+            }
+        },
+        "tool_arg": "task",
+        "has_counter": True,
+        "in_menu": True,
+        "in_launcher": True,
+        "placeholder": "Enter message...",
+    },
+
+    "research": {
+        "label": "RESEARCH",
+        "system_prompt": (
+            "You are a research assistant specialized in deep analysis and information gathering.\n\n"
+            "Your role is to:\n"
+            "- Conduct thorough research on topics\n"
+            "- Synthesize information from multiple sources\n"
+            "- Provide well-structured, evidence-based answers\n"
+            "- Identify key insights and trends\n\n"
+            "When presenting your final research report:\n"
+            "1. Be CONCISE - focus on key findings, not lengthy explanations\n"
+            "2. Use TABLES wherever possible to structure information clearly\n"
+            "3. Use markdown table syntax for comparisons, lists of facts, statistics, etc.\n"
+            "4. Example table format:\n"
+            "   | Category | Details |\n"
+            "   |----------|--------|\n"
+            "   | Item 1   | Data    |\n"
+            "   | Item 2   | Data    |\n\n"
+            "5. Only use prose for context and synthesis that can't be tabulated\n"
+            '6. DO NOT include a title or heading like "Research Report:" - start directly with your findings\n\n'
+            "When you have completed your research, wrap your final report in <result> tags:\n\n"
+            "<result>\n"
+            "Your concise, table-based report here (NO title/heading)\n"
+            "</result>\n\n"
+            "The report will be sent back to the main interface.\n\n"
+            "Focus on being comprehensive, analytical, and well-sourced in your research.\n"
+        ),
+        "tool": {
+            "type": "function",
+            "function": {
+                "name": "launch_research_notebook",
+                "description": "Launch a research notebook for deep analysis requiring web search. Use this for researching topics, gathering information from multiple sources, or analyzing current information.",
+                "parameters": {
+                    "type": "object",
+                    "properties": {
+                        "topic": {
+                            "type": "string",
+                            "description": "The research topic or question. Should be clear and specific."
+                        },
+                        "task_id": {
+                            "type": "string",
+                            "description": "A 2-3 word summary of the research topic, separated by dashes."
+                        }
+                    },
+                    "required": ["topic", "task_id"]
+                }
+            }
+        },
+        "tool_arg": "topic",
+        "has_counter": True,
+        "in_menu": True,
+        "in_launcher": True,
+        "placeholder": "Enter message...",
+    },
+
+    "chat": {
+        "label": "CHAT",
+        "system_prompt": (
+            "You are a conversational AI assistant.\n\n"
+            "Your role is to:\n"
+            "- Engage in natural, helpful conversation\n"
+            "- Answer questions clearly and concisely\n"
+            "- Provide thoughtful responses\n"
+            "- Be friendly and approachable\n\n"
+            "Focus on being conversational, helpful, and easy to understand.\n"
+        ),
+        "tool": {
+            "type": "function",
+            "function": {
+                "name": "launch_chat_notebook",
+                "description": "Launch a conversational chat notebook for extended back-and-forth discussion. Use this when the user wants to continue a conversation in a dedicated space.",
+                "parameters": {
+                    "type": "object",
+                    "properties": {
+                        "message": {
+                            "type": "string",
+                            "description": "The initial message or context for the chat."
+                        },
+                        "task_id": {
+                            "type": "string",
+                            "description": "A 2-3 word summary of the conversation topic, separated by dashes."
+                        }
+                    },
+                    "required": ["message", "task_id"]
+                }
+            }
+        },
+        "tool_arg": "message",
+        "has_counter": True,
+        "in_menu": True,
+        "in_launcher": True,
+        "placeholder": "Enter message...",
+    },
+}
+
+
+# ============================================================
+# Derived helpers — replace scattered dicts across the codebase
+# ============================================================
+
+def get_system_prompt(agent_key: str) -> str:
+    """Get system prompt for an agent type."""
+    agent = AGENT_REGISTRY.get(agent_key)
+    if not agent:
+        return ""
+    prompt = agent["system_prompt"]
+    # For command center, fill in the tools section dynamically
+    if "{tools_section}" in prompt:
+        prompt = prompt.replace("{tools_section}", _build_tools_section())
+    return prompt
+
+
+def get_tools() -> list:
+    """Get tool definitions for the command center (replaces TOOLS in command.py)."""
+    return [
+        agent["tool"]
+        for agent in AGENT_REGISTRY.values()
+        if agent["tool"] is not None
+    ]
+
+
+def get_notebook_type_map() -> dict:
+    """Map tool function names to agent keys (replaces notebook_type_map in command.py)."""
+    result = {}
+    for key, agent in AGENT_REGISTRY.items():
+        if agent["tool"] is not None:
+            func_name = agent["tool"]["function"]["name"]
+            result[func_name] = key
+    return result
+
+
+def get_tool_arg(agent_key: str) -> str:
+    """Get the argument name for extracting the initial message from tool call args."""
+    agent = AGENT_REGISTRY.get(agent_key)
+    return agent["tool_arg"] if agent else "task"
+
+
+def get_default_counters() -> dict:
+    """Get default notebook counters (replaces hardcoded dict in get_default_workspace)."""
+    return {
+        key: 0
+        for key, agent in AGENT_REGISTRY.items()
+        if agent["has_counter"]
+    }
+
+
+def get_registry_for_frontend() -> list:
+    """Serialize registry metadata for the frontend /api/agents endpoint."""
+    return [
+        {
+            "key": key,
+            "label": agent["label"],
+            "hasCounter": agent["has_counter"],
+            "inMenu": agent["in_menu"],
+            "inLauncher": agent["in_launcher"],
+            "placeholder": agent["placeholder"],
+        }
+        for key, agent in AGENT_REGISTRY.items()
+    ]
+
+
+def _build_tools_section() -> str:
+    """Build the 'available tools' text for the command center system prompt."""
+    lines = ["You have access to tools that can launch specialized notebooks for different types of tasks:"]
+    for key, agent in AGENT_REGISTRY.items():
+        if agent["tool"] is not None:
+            tool_func = agent["tool"]["function"]
+            lines.append(f"- {tool_func['name']}: {tool_func['description']}")
+    return "\n".join(lines)

backend/command.py

@@ -9,93 +9,10 @@ from typing import List, Dict
 
 logger = logging.getLogger(__name__)
 
-# Tool definitions for launching notebooks
-TOOLS = [
-    {
-        "type": "function",
-        "function": {
-            "name": "launch_agent_notebook",
-            "description": "Launch an autonomous agent notebook for multi-step tasks that need planning and execution. Use this for complex workflows, task organization, or anything requiring multiple coordinated steps.",
-            "parameters": {
-                "type": "object",
-                "properties": {
-                    "task": {
-                        "type": "string",
-                        "description": "The task or instruction for the agent. Should contain all necessary context."
-                    },
-                    "task_id": {
-                        "type": "string",
-                        "description": "A 2-3 word summary of the task, separated by dashes."
-                    }
-                },
-                "required": ["task", "task_id"]
-            }
-        }
-    },
-    {
-        "type": "function",
-        "function": {
-            "name": "launch_code_notebook",
-            "description": "Launch a code notebook with Python execution environment. Use this for data analysis, creating visualizations, running code, debugging, or anything involving programming.",
-            "parameters": {
-                "type": "object",
-                "properties": {
-                    "task": {
-                        "type": "string",
-                        "description": "The coding task or question. Should contain all necessary context."
-                    },
-                    "task_id": {
-                        "type": "string",
-                        "description": "A 2-3 word summary of the task, separated by dashes."
-                    }
-                },
-                "required": ["task", "task_id"]
-            }
-        }
-    },
-    {
-        "type": "function",
-        "function": {
-            "name": "launch_research_notebook",
-            "description": "Launch a research notebook for deep analysis requiring web search. Use this for researching topics, gathering information from multiple sources, or analyzing current information.",
-            "parameters": {
-                "type": "object",
-                "properties": {
-                    "topic": {
-                        "type": "string",
-                        "description": "The research topic or question. Should be clear and specific."
-                    },
-                    "task_id": {
-                        "type": "string",
-                        "description": "A 2-3 word summary of the research topic, separated by dashes."
-                    }
-                },
-                "required": ["topic", "task_id"]
-            }
-        }
-    },
-    {
-        "type": "function",
-        "function": {
-            "name": "launch_chat_notebook",
-            "description": "Launch a conversational chat notebook for extended back-and-forth discussion. Use this when the user wants to continue a conversation in a dedicated space.",
-            "parameters": {
-                "type": "object",
-                "properties": {
-                    "message": {
-                        "type": "string",
-                        "description": "The initial message or context for the chat."
-                    },
-                    "task_id": {
-                        "type": "string",
-                        "description": "A 2-3 word summary of the conversation topic, separated by dashes."
-                    }
-                },
-                "required": ["message", "task_id"]
-            }
-        }
-    }
-]
+# Tool definitions derived from agent registry
+from agents import get_tools, get_notebook_type_map, get_tool_arg
+
+TOOLS = get_tools()
 
 MAX_TURNS = 10  # Limit conversation turns in command center
 MAX_RETRIES = 3  # Maximum retries for LLM calls
@@ -219,19 +136,13 @@ def stream_command_center(client, model: str, messages: List[Dict], extra_params
                     yield {"type": "error", "content": "Failed to parse tool arguments"}
                     return
 
-                # Map function names to notebook types
-                notebook_type_map = {
-                    "launch_agent_notebook": "agent",
-                    "launch_code_notebook": "code",
-                    "launch_research_notebook": "research",
-                    "launch_chat_notebook": "chat"
-                }
-
+                # Map function names to notebook types (derived from registry)
+                notebook_type_map = get_notebook_type_map()
                 notebook_type = notebook_type_map.get(function_name)
 
                 if notebook_type:
-                    # Get the message/task/topic from args
-                    initial_message = args.get("task") or args.get("topic") or args.get("message")
+                    # Get the initial message using the registered arg name for this type
+                    initial_message = args.get(get_tool_arg(notebook_type)) or args.get("task") or args.get("message")
                     task_id = args.get("task_id", "")
 
                     # Send launch action to frontend

backend/main.py

@@ -13,6 +13,7 @@ import signal
 import sys
 from datetime import datetime
 from concurrent.futures import ThreadPoolExecutor
+from agents import AGENT_REGISTRY, get_default_counters, get_registry_for_frontend
 
 # Thread pool for running sync generators without blocking the event loop
 # Use daemon threads so they don't block shutdown
@@ -154,139 +155,7 @@ app.add_middleware(
     allow_headers=["*"],
 )
 
-# System prompts for each notebook type
-SYSTEM_PROMPTS = {
-    "command": """You are a helpful AI assistant in the Productive interface command center.
-
-You have access to tools that can launch specialized notebooks for different types of tasks:
-- launch_agent_notebook: For multi-step tasks that need planning and execution
-- launch_code_notebook: For data analysis, visualizations, running Python code
-- launch_research_notebook: For researching topics using web search
-- launch_chat_notebook: For extended conversational discussions
-
-When a user asks you to perform a task that would benefit from a specialized notebook, you can:
-1. Briefly acknowledge the request
-2. Use the appropriate tool to launch a notebook with the task
-
-You can also answer questions directly without launching a notebook if appropriate.
-
-Examples:
-- User: "Can you help me analyze this CSV file?"
-  You: Use launch_code_notebook tool with the task
-
-- User: "Research the latest developments in AI"
-  You: Use launch_research_notebook tool with the topic
-
-- User: "What was the result of the research in 2 sentences?"
-  You: Summarize the research results without using tools
-
-Be concise and helpful. Don't duplicate effort - either answer directly OR launch a notebook, not both. Answer questions about results directly without launching new notebooks.
-
-IMPORTANT guidelines when delegating to notebooks:
-- Do NOT ask notebooks to save or create files unless the user explicitly requests it or implicitly necessary to solve a task.
-- NEVER overwrite existing files without explicit user permission.
-- Each notebook has a task_id. If a a new task is clearly related to a existing notebook re-use the task id to reuse the notebook. This will reuse the existing context and also the jupyter kernel for code notebooks.""",
-    "agent": """You are an autonomous agent assistant specialized in breaking down and executing multi-step tasks.
-
-Your role is to:
-- Understand complex tasks and break them down into clear steps
-- Execute tasks methodically
-- Keep track of progress and next steps
-- Provide clear status updates
-
-Focus on being proactive, organized, and thorough in completing multi-step workflows.
-""",
-    "code": """You are a coding assistant with access to a Python code execution environment.
-
-Your role is to:
-- Write and execute Python code to solve problems
-- Analyze data and answer questions
-- Debug code and explain errors
-- Break down complex tasks into executable steps
-
-You have access to a Jupyter kernel with these packages:
-pandas, matplotlib, numpy, scipy, scikit-learn, seaborn, plotly, requests, beautifulsoup4, and more.
-
-You have three tools available:
-- execute_code: Run Python code. The execution environment is stateful - variables and imports persist between calls.
-- upload_files: Upload files from the workspace to the execution environment for analysis. Files will be available at /home/user/<filename>. Use this when you need to analyze data files, scripts, or other files from the project.
-- download_files: Download files from the execution environment to the workspace. ONLY use this when the user explicitly asks to save/download files, or when saving files is clearly part of the task (e.g., "generate a dataset and save it"). Do NOT automatically save intermediate files, plots, or outputs unless requested.
-
-## IMPORTANT Guidelines
-
-**Only create artifacts when explicitly requested:**
-- Do NOT save files unless the user explicitly asks to save/export/download
-- NEVER overwrite existing files without explicit user permission - always ask first or use a new filename
-
-When solving problems:
-1. Break down the task into logical steps
-2. Execute code incrementally
-3. Check outputs before proceeding
-4. Only create visualizations if explicitly requested
-
-IMPORTANT: When you DO generate plots/figures (only when requested), they are automatically named as figure_1, figure_2, etc. The execution output will show which figures were created (e.g., "[Generated figures: figure_1, figure_2]").
-
-## CRITICAL: You MUST provide a <result> tag
-
-When you have completed the task, you MUST ALWAYS provide a summary using the <result> tag. This is REQUIRED - without it, your work will not be visible in the command center. To include figures in your result (when you've created them), use self-closing figure tags like <figure_1>, <figure_2>, <figure_3> etc.:
-
-Example:
-<result>
-Created a sine and cosine plot from 0 to 2π:
-
-<figure_1>
-
-The plot shows both functions overlaid on the same axes.
-</result>
-
-IMPORTANT: Use self-closing tags like <figure_1> (NOT </figure_1> or <figure_1></figure_1>). Each tag will be replaced with the actual image.
-
-The result will be sent back to the command center with embedded images. DO NOT forget the <result> tag - it is mandatory for every completed task.
-
-Focus on being precise, practical, and thorough in your coding assistance.
-""",
-    "research": """You are a research assistant specialized in deep analysis and information gathering.
-
-Your role is to:
-- Conduct thorough research on topics
-- Synthesize information from multiple sources
-- Provide well-structured, evidence-based answers
-- Identify key insights and trends
-
-When presenting your final research report:
-1. Be CONCISE - focus on key findings, not lengthy explanations
-2. Use TABLES wherever possible to structure information clearly
-3. Use markdown table syntax for comparisons, lists of facts, statistics, etc.
-4. Example table format:
-   | Category | Details |
-   |----------|---------|
-   | Item 1   | Data    |
-   | Item 2   | Data    |
-
-5. Only use prose for context and synthesis that can't be tabulated
-6. DO NOT include a title or heading like "Research Report:" - start directly with your findings
-
-When you have completed your research, wrap your final report in <result> tags:
-
-<result>
-Your concise, table-based report here (NO title/heading)
-</result>
-
-The report will be sent back to the main interface.
-
-Focus on being comprehensive, analytical, and well-sourced in your research.
-""",
-    "chat": """You are a conversational AI assistant.
-
-Your role is to:
-- Engage in natural, helpful conversation
-- Answer questions clearly and concisely
-- Provide thoughtful responses
-- Be friendly and approachable
-
-Focus on being conversational, helpful, and easy to understand.
-"""
-}
+# Agent type registry is in agents.py — system prompts, tools, and metadata are all defined there
 
 
 def record_api_call(tab_id: str, messages: List[dict]):
@@ -737,6 +606,12 @@ async def api_info():
     }
 
 
+@app.get("/api/agents")
+async def get_agents():
+    """Return agent type registry for frontend consumption."""
+    return {"agents": get_registry_for_frontend()}
+
+
 @app.post("/api/generate-title")
 async def generate_title(request: TitleRequest):
     """Generate a short 2-3 word title for a user query"""
@@ -1275,12 +1150,7 @@ def get_default_workspace():
         "version": 1,
         "tabCounter": 1,
         "activeTabId": 0,
-        "notebookCounters": {
-            "agent": 0,
-            "code": 0,
-            "research": 0,
-            "chat": 0
-        },
+        "notebookCounters": get_default_counters(),
         "tabs": [
             {
                 "id": 0,
@@ -1437,7 +1307,8 @@ Current theme: {name}
 
 def get_system_prompt(notebook_type: str, frontend_context: Optional[Dict] = None) -> str:
     """Get system prompt for a notebook type with dynamic context appended"""
-    base_prompt = SYSTEM_PROMPTS.get(notebook_type, SYSTEM_PROMPTS["command"])
+    from agents import get_system_prompt as _get_agent_prompt
+    base_prompt = _get_agent_prompt(notebook_type) or _get_agent_prompt("command")
     file_tree = get_file_tree_for_prompt()
 
     # Build the full prompt with context sections

frontend/index.html

@@ -20,9 +20,7 @@
             <div class="new-tab-wrapper">
                 <button class="new-tab-btn" id="newTabBtn">+</button>
                 <div class="new-tab-menu" id="newTabMenu">
-                    <div class="menu-item" data-type="chat">BASE</div>
-                    <div class="menu-item" data-type="code">CODE</div>
-                    <div class="menu-item" data-type="research">RESEARCH</div>
+                    <!-- Generated dynamically from AGENT_REGISTRY -->
                 </div>
             </div>
             <div class="tab-bar-spacer"></div>
@@ -60,11 +58,8 @@
                             <div class="notebook-type">TASK CENTER</div>
                             <h2>Task Center</h2>
                         </div>
-                        <div class="header-actions">
-                            <button class="launcher-btn" data-type="agent">AGENT</button>
-                            <button class="launcher-btn" data-type="code">CODE</button>
-                            <button class="launcher-btn" data-type="research">RESEARCH</button>
-                            <button class="launcher-btn" data-type="chat">CHAT</button>
+                        <div class="header-actions" id="launcherButtons">
+                            <!-- Launcher buttons generated dynamically from AGENT_REGISTRY -->
                             <button class="debug-btn" id="debugBtn">DEBUG</button>
                         </div>
                     </div>
@@ -210,21 +205,8 @@
                         <span class="label-text">NOTEBOOK MODELS</span>
                         <span class="label-description">Select which model to use for each notebook type</span>
                     </label>
-                    <div class="notebook-models-grid">
-                        <label>COMMAND:</label>
-                        <select id="setting-notebook-command" class="settings-select"></select>
-
-                        <label>AGENT:</label>
-                        <select id="setting-notebook-agent" class="settings-select"></select>
-
-                        <label>CODE:</label>
-                        <select id="setting-notebook-code" class="settings-select"></select>
-
-                        <label>RESEARCH:</label>
-                        <select id="setting-notebook-research" class="settings-select"></select>
-
-                        <label>CHAT:</label>
-                        <select id="setting-notebook-chat" class="settings-select"></select>
+                    <div class="notebook-models-grid" id="notebookModelsGrid">
+                        <!-- Generated dynamically from AGENT_REGISTRY -->
                     </div>
                 </div>
 
@@ -475,6 +457,6 @@
     <script src="https://cdn.jsdelivr.net/npm/marked/marked.min.js"></script>
     <script src="https://cdn.jsdelivr.net/npm/katex@0.16.9/dist/katex.min.js"></script>
     <script src="research-ui.js?v=23"></script>
-    <script src="script.js?v=56"></script>
+    <script src="script.js?v=57"></script>
 </body>
 </html>

frontend/script.js

@@ -1,3 +1,34 @@
+// ============================================================
+// Agent Type Registry — single source of truth for the frontend
+// To add a new agent type, add an entry here and in backend/agents.py
+// ============================================================
+const AGENT_REGISTRY = {
+    command:  { label: 'TASKS',     hasCounter: false, inMenu: false, inLauncher: false, placeholder: 'Enter message...' },
+    agent:    { label: 'AGENT',     hasCounter: true,  inMenu: false, inLauncher: true,  placeholder: 'Enter message...' },
+    code:     { label: 'CODE',      hasCounter: true,  inMenu: true,  inLauncher: true,  placeholder: 'Enter message...' },
+    research: { label: 'RESEARCH',  hasCounter: true,  inMenu: true,  inLauncher: true,  placeholder: 'Enter message...' },
+    chat:     { label: 'CHAT',      hasCounter: true,  inMenu: true,  inLauncher: true,  placeholder: 'Enter message...' },
+};
+// Virtual types used only in timeline rendering (not real agents)
+const VIRTUAL_TYPE_LABELS = { search: 'SEARCH', browse: 'BROWSE' };
+
+// Derived helpers from registry
+function getTypeLabel(type) {
+    if (AGENT_REGISTRY[type]) return AGENT_REGISTRY[type].label;
+    if (VIRTUAL_TYPE_LABELS[type]) return VIRTUAL_TYPE_LABELS[type];
+    return type.toUpperCase();
+}
+function getPlaceholder(type) {
+    return AGENT_REGISTRY[type]?.placeholder || 'Enter message...';
+}
+function getDefaultCounters() {
+    const counters = {};
+    for (const [key, agent] of Object.entries(AGENT_REGISTRY)) {
+        if (agent.hasCounter) counters[key] = 0;
+    }
+    return counters;
+}
+
 // State management
 let tabCounter = 1;
 let activeTabId = 0;
@@ -22,13 +53,7 @@ let settings = {
     // New provider/model structure
     providers: {},  // providerId -> {name, endpoint, token}
     models: {},     // modelId -> {name, providerId, modelId (API model string)}
-    notebooks: {    // notebook type -> modelId
-        command: '',
-        agent: '',
-        code: '',
-        research: '',
-        chat: ''
-    },
+    notebooks: Object.fromEntries(Object.keys(AGENT_REGISTRY).map(k => [k, ''])),
     // Service API keys
     e2bKey: '',
     serperKey: '',
@@ -51,21 +76,8 @@ const toolCallIds = {};
 // Track notebooks by task_id for reuse (maps task_id -> tabId)
 const taskIdToTabId = {};
 
-// Track notebook counters for each type
-let notebookCounters = {
-    'agent': 0,
-    'code': 0,
-    'research': 0,
-    'chat': 0
-};
-
-const notebookTitles = {
-    'command-center': 'Task Center',
-    'agent': 'AGENT',
-    'code': 'CODE',
-    'research': 'RESEARCH',
-    'chat': 'CHAT'
-};
+// Track notebook counters for each type (derived from registry)
+let notebookCounters = getDefaultCounters();
 
 // Debounce timer for workspace saving
 let saveWorkspaceTimer = null;
@@ -90,7 +102,7 @@ function resetLocalState() {
     Object.keys(taskIdToTabId).forEach(k => delete taskIdToTabId[k]);
     researchQueryTabIds = {};
     showAllTurns = false;
-    notebookCounters = { agent: 0, code: 0, research: 0, chat: 0 };
+    notebookCounters = getDefaultCounters();
 
     // Reset timeline data
     Object.keys(timelineData).forEach(k => delete timelineData[k]);
@@ -362,8 +374,7 @@ function renderTimeline() {
 function renderNotebookTimeline(tabId, notebook, isNested = false) {
     const isActive = activeTabId === tabId;
     const isClosed = notebook.isClosed || false;
-    const typeLabels = { 'agent': 'AGENT', 'code': 'CODE', 'research': 'RESEARCH', 'chat': 'CHAT', 'command': 'TASKS', 'search': 'SEARCH', 'browse': 'BROWSE' };
-    const typeLabel = typeLabels[notebook.type] || notebook.type.toUpperCase();
+    const typeLabel = getTypeLabel(notebook.type);
 
     let html = `<div class="tl-widget${isNested ? '' : ' compact'}${isActive ? ' active' : ''}${isClosed ? ' closed' : ''}" data-tab-id="${tabId}">`;
 
@@ -423,7 +434,7 @@ function renderNotebookTimeline(tabId, notebook, isNested = false) {
             if (event.childTabId !== null) {
                 const childNotebook = timelineData[event.childTabId];
                 if (childNotebook) {
-                    const childTypeLabel = typeLabels[childNotebook.type] || childNotebook.type.toUpperCase();
+                    const childTypeLabel = getTypeLabel(childNotebook.type);
                     const childIsGenerating = childNotebook.isGenerating;
                     const turnCount = childNotebook.events.length;
 
@@ -590,7 +601,60 @@ document.addEventListener('DOMContentLoaded', async () => {
     showSessionSelector(sessionsData.sessions);
 });
 
+// ============================================================
+// Dynamic HTML generation from AGENT_REGISTRY
+// ============================================================
+
+function renderNewTabMenu() {
+    const menu = document.getElementById('newTabMenu');
+    if (!menu) return;
+    menu.innerHTML = '';
+    for (const [key, agent] of Object.entries(AGENT_REGISTRY)) {
+        if (!agent.inMenu) continue;
+        const item = document.createElement('div');
+        item.className = 'menu-item';
+        item.dataset.type = key;
+        item.textContent = agent.label;
+        menu.appendChild(item);
+    }
+}
+
+function renderLauncherButtons() {
+    const container = document.getElementById('launcherButtons');
+    if (!container) return;
+    // Insert before the debug button (keep it at the end)
+    const debugBtn = container.querySelector('#debugBtn');
+    for (const [key, agent] of Object.entries(AGENT_REGISTRY)) {
+        if (!agent.inLauncher) continue;
+        const btn = document.createElement('button');
+        btn.className = 'launcher-btn';
+        btn.dataset.type = key;
+        btn.textContent = agent.label;
+        container.insertBefore(btn, debugBtn);
+    }
+}
+
+function renderNotebookModelSelectors() {
+    const grid = document.getElementById('notebookModelsGrid');
+    if (!grid) return;
+    grid.innerHTML = '';
+    for (const [key, agent] of Object.entries(AGENT_REGISTRY)) {
+        const label = document.createElement('label');
+        label.textContent = `${agent.label}:`;
+        const select = document.createElement('select');
+        select.id = `setting-notebook-${key}`;
+        select.className = 'settings-select';
+        grid.appendChild(label);
+        grid.appendChild(select);
+    }
+}
+
 function initializeEventListeners() {
+    // Generate dynamic UI elements from registry
+    renderLauncherButtons();
+    renderNewTabMenu();
+    renderNotebookModelSelectors();
+
     // Launcher buttons in command center
     document.querySelectorAll('.launcher-btn').forEach(btn => {
         btn.addEventListener('click', (e) => {
@@ -1123,7 +1187,7 @@ function createNotebookTab(type, initialMessage = null, autoSwitch = true, taskI
         notebookCounters[type]++;
         title = `New ${type} task`;
     } else {
-        title = notebookTitles[type];
+        title = getTypeLabel(type);
     }
 
     // Register in timeline
@@ -1205,20 +1269,6 @@ function createNotebookContent(type, tabId, title = null) {
         return document.querySelector('[data-content-id="0"]').innerHTML;
     }
 
-    const placeholders = {
-        'agent': 'Enter message...',
-        'code': 'Enter message...',
-        'research': 'Enter message...',
-        'chat': 'Enter message...'
-    };
-
-    const typeLabels = {
-        'agent': 'AGENT',
-        'code': 'CODE',
-        'research': 'RESEARCH',
-        'chat': 'CHAT'
-    };
-
     // Use unique ID combining type and tabId to ensure unique container IDs
     const uniqueId = `${type}-${tabId}`;
 
@@ -1229,7 +1279,7 @@ function createNotebookContent(type, tabId, title = null) {
         <div class="notebook-interface">
             <div class="notebook-header">
                 <div>
-                    <div class="notebook-type">${typeLabels[type]}</div>
+                    <div class="notebook-type">${getTypeLabel(type)}</div>
                     <h2>${escapeHtml(displayTitle)}</h2>
                 </div>
             </div>
@@ -1239,7 +1289,7 @@ function createNotebookContent(type, tabId, title = null) {
             </div>
             <div class="input-area">
                 <div class="input-container">
-                    <textarea placeholder="${placeholders[type]}" id="input-${uniqueId}" rows="1"></textarea>
+                    <textarea placeholder="${getPlaceholder(type)}" id="input-${uniqueId}" rows="1"></textarea>
                     <button>SEND</button>
                 </div>
             </div>
@@ -2435,12 +2485,7 @@ async function loadWorkspace() {
 function restoreWorkspace(workspace) {
     // Restore counters
     tabCounter = workspace.tabCounter || 1;
-    notebookCounters = workspace.notebookCounters || {
-        'agent': 0,
-        'code': 0,
-        'research': 0,
-        'chat': 0
-    };
+    notebookCounters = workspace.notebookCounters || getDefaultCounters();
 
     // Restore timeline data before tabs so renderTimeline works
     if (workspace.timelineData) {
@@ -2477,7 +2522,7 @@ function restoreTab(tabData) {
     tab.className = 'tab';
     tab.dataset.tabId = tabData.id;
     tab.innerHTML = `
-        <span class="tab-title">${tabData.title || notebookTitles[tabData.type] || 'TAB'}</span>
+        <span class="tab-title">${tabData.title || getTypeLabel(tabData.type) || 'TAB'}</span>
         <span class="tab-status" style="display: none;"><span></span><span></span><span></span></span>
         <span class="tab-close">×</span>
     `;
@@ -2816,7 +2861,7 @@ function serializeTab(tabId, type) {
     const tabData = {
         id: tabId,
         type: type,
-        title: tabEl?.querySelector('.tab-title')?.textContent || notebookTitles[type] || 'TAB',
+        title: tabEl?.querySelector('.tab-title')?.textContent || getTypeLabel(type) || 'TAB',
         messages: []
     };
 
@@ -3045,7 +3090,7 @@ function migrateSettings(oldSettings) {
 
         // Migrate notebook-specific models if they existed
         const oldModels = oldSettings.models || {};
-        const notebookTypes = ['agent', 'code', 'research', 'chat'];
+        const notebookTypes = Object.keys(AGENT_REGISTRY).filter(k => AGENT_REGISTRY[k].hasCounter);
         notebookTypes.forEach(type => {
             if (oldModels[type]) {
                 const specificModelId = `model_${type}`;
@@ -3186,12 +3231,9 @@ function populateModelDropdowns() {
     const models = settings.models || {};
     const notebooks = settings.notebooks || {};
 
+    // Build dropdown IDs from registry + special dropdowns
     const dropdownIds = [
-        'setting-notebook-command',
-        'setting-notebook-agent',
-        'setting-notebook-code',
-        'setting-notebook-research',
-        'setting-notebook-chat',
+        ...Object.keys(AGENT_REGISTRY).map(t => `setting-notebook-${t}`),
         'setting-research-sub-agent-model'
     ];
 
@@ -3218,19 +3260,12 @@ function populateModelDropdowns() {
         }
     });
 
-    // Set values from settings
-    const commandDropdown = document.getElementById('setting-notebook-command');
-    const agentDropdown = document.getElementById('setting-notebook-agent');
-    const codeDropdown = document.getElementById('setting-notebook-code');
-    const researchDropdown = document.getElementById('setting-notebook-research');
-    const chatDropdown = document.getElementById('setting-notebook-chat');
+    // Set values from settings (driven by registry)
+    for (const type of Object.keys(AGENT_REGISTRY)) {
+        const dropdown = document.getElementById(`setting-notebook-${type}`);
+        if (dropdown) dropdown.value = notebooks[type] || '';
+    }
     const subAgentDropdown = document.getElementById('setting-research-sub-agent-model');
-
-    if (commandDropdown) commandDropdown.value = notebooks.command || '';
-    if (agentDropdown) agentDropdown.value = notebooks.agent || '';
-    if (codeDropdown) codeDropdown.value = notebooks.code || '';
-    if (researchDropdown) researchDropdown.value = notebooks.research || '';
-    if (chatDropdown) chatDropdown.value = notebooks.chat || '';
     if (subAgentDropdown) subAgentDropdown.value = settings.researchSubAgentModel || '';
 }
 
@@ -3433,12 +3468,11 @@ function openSettings() {
 }
 
 async function saveSettings() {
-    // Get notebook model selections from dropdowns
-    const commandModel = document.getElementById('setting-notebook-command')?.value || '';
-    const agentModel = document.getElementById('setting-notebook-agent')?.value || '';
-    const codeModel = document.getElementById('setting-notebook-code')?.value || '';
-    const researchModel = document.getElementById('setting-notebook-research')?.value || '';
-    const chatModel = document.getElementById('setting-notebook-chat')?.value || '';
+    // Get notebook model selections from dropdowns (driven by registry)
+    const notebookModels = {};
+    for (const type of Object.keys(AGENT_REGISTRY)) {
+        notebookModels[type] = document.getElementById(`setting-notebook-${type}`)?.value || '';
+    }
     const researchSubAgentModel = document.getElementById('setting-research-sub-agent-model')?.value || '';
 
     // Get other settings
@@ -3460,13 +3494,7 @@ async function saveSettings() {
     }
 
     // Update settings
-    settings.notebooks = {
-        command: commandModel,
-        agent: agentModel,
-        code: codeModel,
-        research: researchModel,
-        chat: chatModel
-    };
+    settings.notebooks = notebookModels;
     settings.e2bKey = e2bKey;
     settings.serperKey = serperKey;
     settings.researchSubAgentModel = researchSubAgentModel;