port backend changes ontop of a fresh dev branch

## Changes 🏗️

<img width="250" height="504" alt="Screenshot 2026-01-06 at 17 53 26"
src="https://github.com/user-attachments/assets/52013448-f49c-46b6-b86a-39f98270cbc3"
/>

<img width="300" height="544" alt="Screenshot 2026-01-06 at 17 53 29"
src="https://github.com/user-attachments/assets/e6334034-68e4-4346-9092-3774ab3e8445"
/>

On the **New Builder**:
- right-click on a node menu make it show the context menu
- use the same menu for right-click and when clicking on `...`

## Checklist 📋

### For code changes:
- [x] I have clearly listed my changes in the PR description
- [x] I have made a test plan
- [x] I have tested my changes according to the test plan:
  - [x] Run locally and test the above



<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit

* **New Features**
* Added a custom right-click context menu for nodes with Copy, Open
agent (when available), and Delete actions; browser default menu is
suppressed while preserving zoom/drag/wiring.
* Introduced reusable SecondaryMenu primitives for context and dropdown
menus.

* **Documentation**
* Added Storybook examples demonstrating the context menu and dropdown
menu usage.

* **Style**
* Updated menu styling and icons with improved consistency and dark-mode
support.

<sub>✏️ Tip: You can customize this high-level summary in your review
settings.</sub>
<!-- end of auto-generated comment: release notes by coderabbit.ai -->

autogpt_platform/.claude/settings.local.json

@@ -1,9 +0,0 @@
-{
-  "permissions": {
-    "allow": [
-      "Bash(ls:*)",
-      "WebFetch(domain:langfuse.com)",
-      "Bash(poetry install:*)"
-    ]
-  }
-}

autogpt_platform/Makefile

@@ -1,4 +1,4 @@
-.PHONY: start-core stop-core logs-core format lint migrate run-backend stop-backend run-frontend load-store-agents backfill-store-embeddings
+.PHONY: start-core stop-core logs-core format lint migrate run-backend run-frontend load-store-agents
 
 # Run just Supabase + Redis + RabbitMQ
 start-core:
@@ -6,12 +6,11 @@ start-core:
 
 # Stop core services
 stop-core:
-	docker compose stop deps
+	docker compose stop 
 
 reset-db:
+	docker compose stop db
 	rm -rf db/docker/volumes/db/data
-	cd backend && poetry run prisma migrate deploy
-	cd backend && poetry run prisma generate
 	
 # View logs for core services
 logs-core:
@@ -34,14 +33,7 @@ migrate:
 	cd backend && poetry run prisma migrate deploy
 	cd backend && poetry run prisma generate
 
-stop-backend:
-	@echo "Stopping backend processes..."
-	@cd backend && poetry run cli stop 2>/dev/null || true
-	@echo "Killing any processes using backend ports..."
-	@lsof -ti:8001,8002,8003,8004,8005,8006,8007 | xargs kill -9 2>/dev/null || true
-	@echo "Backend stopped"
-
-run-backend: stop-backend
+run-backend:
 	cd backend && poetry run app
 
 run-frontend:
@@ -53,9 +45,6 @@ test-data:
 load-store-agents:
 	cd backend && poetry run load-store-agents
 
-backfill-store-embeddings:
-	cd backend && poetry run python -m backend.api.features.store.backfill_embeddings
-
 help:
 	@echo "Usage: make <target>"
 	@echo "Targets:"
@@ -65,9 +54,7 @@ help:
 	@echo "  logs-core - Tail the logs for core services"
 	@echo "  format - Format & lint backend (Python) and frontend (TypeScript) code"
 	@echo "  migrate - Run backend database migrations"
-	@echo "  stop-backend - Stop any running backend processes"
-	@echo "  run-backend - Run the backend FastAPI server (stops existing processes first)"
+	@echo "  run-backend - Run the backend FastAPI server"
 	@echo "  run-frontend - Run the frontend Next.js development server"
 	@echo "  test-data - Run the test data creator"
 	@echo "  load-store-agents - Load store agents from agents/ folder into test database"
-	@echo "  backfill-store-embeddings - Generate embeddings for store agents that don't have them"

autogpt_platform/backend/.env.default

@@ -58,13 +58,6 @@ V0_API_KEY=
 OPEN_ROUTER_API_KEY=
 NVIDIA_API_KEY=
 
-# Langfuse Prompt Management
-# Used for managing the CoPilot system prompt externally
-# Get credentials from https://cloud.langfuse.com or your self-hosted instance
-LANGFUSE_PUBLIC_KEY=
-LANGFUSE_SECRET_KEY=
-LANGFUSE_HOST=https://cloud.langfuse.com
-
 # OAuth Credentials
 # For the OAuth callback URL, use <your_frontend_url>/auth/integrations/oauth_callback,
 # e.g. http://localhost:3000/auth/integrations/oauth_callback

autogpt_platform/backend/backend/api/features/admin/store_admin_routes.py

@@ -9,7 +9,6 @@ import prisma.enums
 
 import backend.api.features.store.cache as store_cache
 import backend.api.features.store.db as store_db
-import backend.api.features.store.embeddings as store_embeddings
 import backend.api.features.store.model as store_model
 import backend.util.json
 
@@ -151,54 +150,3 @@ async def admin_download_agent_file(
         return fastapi.responses.FileResponse(
             tmp_file.name, filename=file_name, media_type="application/json"
         )
-
-
-@router.get(
-    "/embeddings/stats",
-    summary="Get Embedding Statistics",
-)
-async def get_embedding_stats() -> dict[str, typing.Any]:
-    """
-    Get statistics about embedding coverage for store listings.
-
-    Returns counts of total approved listings, listings with embeddings,
-    listings without embeddings, and coverage percentage.
-    """
-    try:
-        stats = await store_embeddings.get_embedding_stats()
-        return stats
-    except Exception as e:
-        logger.exception("Error getting embedding stats: %s", e)
-        raise fastapi.HTTPException(
-            status_code=500,
-            detail="An error occurred while retrieving embedding stats",
-        )
-
-
-@router.post(
-    "/embeddings/backfill",
-    summary="Backfill Missing Embeddings",
-)
-async def backfill_embeddings(
-    batch_size: int = 10,
-) -> dict[str, typing.Any]:
-    """
-    Trigger backfill of embeddings for approved listings that don't have them.
-
-    Args:
-        batch_size: Number of embeddings to generate in one call (default 10)
-
-    Returns:
-        Dict with processed count, success count, failure count, and message
-    """
-    try:
-        result = await store_embeddings.backfill_missing_embeddings(
-            batch_size=batch_size
-        )
-        return result
-    except Exception as e:
-        logger.exception("Error backfilling embeddings: %s", e)
-        raise fastapi.HTTPException(
-            status_code=500,
-            detail="An error occurred while backfilling embeddings",
-        )

autogpt_platform/backend/backend/api/features/chat/config.py

@@ -45,13 +45,6 @@ class ChatConfig(BaseSettings):
         default=3, description="Maximum number of agent schedules"
     )
 
-    # Langfuse Prompt Management Configuration
-    # Note: Langfuse credentials are in Settings().secrets (settings.py)
-    langfuse_prompt_name: str = Field(
-        default="CoPilot Prompt",
-        description="Name of the prompt in Langfuse to fetch",
-    )
-
     @field_validator("api_key", mode="before")
     @classmethod
     def get_api_key(cls, v):

autogpt_platform/backend/backend/api/features/chat/db.py

@@ -2,11 +2,15 @@
 
 import logging
 from datetime import UTC, datetime
-from typing import Any
+from typing import Any, cast
 
 from prisma.models import ChatMessage as PrismaChatMessage
 from prisma.models import ChatSession as PrismaChatSession
-from prisma.types import ChatSessionUpdateInput
+from prisma.types import (
+    ChatMessageCreateInput,
+    ChatSessionCreateInput,
+    ChatSessionUpdateInput,
+)
 
 from backend.util.json import SafeJson
 
@@ -30,13 +34,13 @@ async def create_chat_session(
     user_id: str | None,
 ) -> PrismaChatSession:
     """Create a new chat session in the database."""
-    data = {
-        "id": session_id,
-        "userId": user_id,
-        "credentials": SafeJson({}),
-        "successfulAgentRuns": SafeJson({}),
-        "successfulAgentSchedules": SafeJson({}),
-    }
+    data = ChatSessionCreateInput(
+        id=session_id,
+        userId=user_id,
+        credentials=SafeJson({}),
+        successfulAgentRuns=SafeJson({}),
+        successfulAgentSchedules=SafeJson({}),
+    )
     return await PrismaChatSession.prisma().create(
         data=data,
         include={"Messages": True},
@@ -90,12 +94,16 @@ async def add_chat_message(
     function_call: dict[str, Any] | None = None,
 ) -> PrismaChatMessage:
     """Add a message to a chat session."""
+    # Build the input dict dynamically - only include optional fields when they
+    # have values, as Prisma TypedDict validation fails when optional fields
+    # are explicitly set to None
     data: dict[str, Any] = {
         "Session": {"connect": {"id": session_id}},
         "role": role,
         "sequence": sequence,
     }
 
+    # Add optional string fields
     if content is not None:
         data["content"] = content
     if name is not None:
@@ -104,6 +112,8 @@ async def add_chat_message(
         data["toolCallId"] = tool_call_id
     if refusal is not None:
         data["refusal"] = refusal
+
+    # Add optional JSON fields only when they have values
     if tool_calls is not None:
         data["toolCalls"] = SafeJson(tool_calls)
     if function_call is not None:
@@ -115,7 +125,9 @@ async def add_chat_message(
         data={"updatedAt": datetime.now(UTC)},
     )
 
-    return await PrismaChatMessage.prisma().create(data=data)
+    return await PrismaChatMessage.prisma().create(
+        data=cast(ChatMessageCreateInput, data)
+    )
 
 
 async def add_chat_messages_batch(
@@ -129,12 +141,16 @@ async def add_chat_messages_batch(
 
     created_messages = []
     for i, msg in enumerate(messages):
+        # Build the input dict dynamically - only include optional JSON fields
+        # when they have values, as Prisma TypedDict validation fails when
+        # optional fields are explicitly set to None
         data: dict[str, Any] = {
             "Session": {"connect": {"id": session_id}},
             "role": msg["role"],
             "sequence": start_sequence + i,
         }
 
+        # Add optional string fields
         if msg.get("content") is not None:
             data["content"] = msg["content"]
         if msg.get("name") is not None:
@@ -143,12 +159,16 @@ async def add_chat_messages_batch(
             data["toolCallId"] = msg["tool_call_id"]
         if msg.get("refusal") is not None:
             data["refusal"] = msg["refusal"]
+
+        # Add optional JSON fields only when they have values
         if msg.get("tool_calls") is not None:
             data["toolCalls"] = SafeJson(msg["tool_calls"])
         if msg.get("function_call") is not None:
             data["functionCall"] = SafeJson(msg["function_call"])
 
-        created = await PrismaChatMessage.prisma().create(data=data)
+        created = await PrismaChatMessage.prisma().create(
+            data=cast(ChatMessageCreateInput, data)
+        )
         created_messages.append(created)
 
     # Update session's updatedAt timestamp

autogpt_platform/backend/backend/api/features/chat/model_test.py

@@ -78,7 +78,7 @@ async def test_chatsession_db_storage():
     # Create session with messages including assistant message
     s = ChatSession.new(user_id=None)
     s.messages = messages  # Contains user, assistant, and tool messages
-
+    assert s.session_id is not None, "Session id is not set"
     # Upsert to save to both cache and DB
     s = await upsert_chat_session(s)
 

autogpt_platform/backend/backend/api/features/chat/service.py

@@ -4,7 +4,6 @@ from datetime import UTC, datetime
 from typing import Any
 
 import orjson
-from langfuse import Langfuse
 from openai import AsyncOpenAI
 from openai.types.chat import ChatCompletionChunk, ChatCompletionToolParam
 
@@ -13,20 +12,12 @@ from backend.data.understanding import (
     get_business_understanding,
 )
 from backend.util.exceptions import NotFoundError
-from backend.util.settings import Settings
 
 from . import db as chat_db
 from .config import ChatConfig
-from .model import (
-    ChatMessage,
-    ChatSession,
-    Usage,
-    get_chat_session,
-    upsert_chat_session,
-)
-from .model import (
-    create_chat_session as model_create_chat_session,
-)
+from .model import ChatMessage, ChatSession, Usage
+from .model import create_chat_session as model_create_chat_session
+from .model import get_chat_session, upsert_chat_session
 from .response_model import (
     StreamBaseResponse,
     StreamEnd,
@@ -43,53 +34,8 @@ from .tools import execute_tool, tools
 logger = logging.getLogger(__name__)
 
 config = ChatConfig()
-settings = Settings()
 client = AsyncOpenAI(api_key=config.api_key, base_url=config.base_url)
 
-# Langfuse client (lazy initialization)
-_langfuse_client: Langfuse | None = None
-
-
-def _get_langfuse_client() -> Langfuse:
-    """Get or create the Langfuse client for prompt management."""
-    global _langfuse_client
-    if _langfuse_client is None:
-        if not settings.secrets.langfuse_public_key or not settings.secrets.langfuse_secret_key:
-            raise ValueError(
-                "Langfuse credentials not configured. "
-                "Set LANGFUSE_PUBLIC_KEY and LANGFUSE_SECRET_KEY environment variables."
-            )
-        _langfuse_client = Langfuse(
-            public_key=settings.secrets.langfuse_public_key,
-            secret_key=settings.secrets.langfuse_secret_key,
-            host=settings.secrets.langfuse_host or "https://cloud.langfuse.com",
-        )
-    return _langfuse_client
-
-
-def _get_langfuse_prompt() -> str:
-    """Fetch the latest production prompt from Langfuse.
-
-    Returns:
-        The compiled prompt text from Langfuse.
-
-    Raises:
-        Exception: If Langfuse is unavailable or prompt fetch fails.
-    """
-    try:
-        langfuse = _get_langfuse_client()
-        # cache_ttl_seconds=0 disables SDK caching to always get the latest prompt
-        prompt = langfuse.get_prompt(config.langfuse_prompt_name, cache_ttl_seconds=0)
-        compiled = prompt.compile()
-        logger.info(
-            f"Fetched prompt '{config.langfuse_prompt_name}' from Langfuse "
-            f"(version: {prompt.version})"
-        )
-        return compiled
-    except Exception as e:
-        logger.error(f"Failed to fetch prompt from Langfuse: {e}")
-        raise
-
 
 async def _is_first_session(user_id: str) -> bool:
     """Check if this is the user's first chat session.
@@ -125,12 +71,7 @@ async def _build_system_prompt(
             effective_prompt_type = "onboarding"
 
     # Start with the base system prompt for the specified type
-    if effective_prompt_type == "default":
-        # Fetch from Langfuse for the default prompt
-        base_prompt = _get_langfuse_prompt()
-    else:
-        # Use local file for other prompt types (e.g., onboarding)
-        base_prompt = config.get_system_prompt_for_type(effective_prompt_type)
+    base_prompt = config.get_system_prompt_for_type(effective_prompt_type)
 
     # If user is authenticated, try to fetch their business understanding
     if user_id:

autogpt_platform/backend/backend/api/features/chat/tools/__init__.py

@@ -7,41 +7,26 @@ from backend.api.features.chat.model import ChatSession
 from .add_understanding import AddUnderstandingTool
 from .agent_output import AgentOutputTool
 from .base import BaseTool
-from .create_agent import CreateAgentTool
-from .edit_agent import EditAgentTool
 from .find_agent import FindAgentTool
-from .find_block import FindBlockTool
 from .find_library_agent import FindLibraryAgentTool
 from .run_agent import RunAgentTool
-from .run_block import RunBlockTool
-from .search_docs import SearchDocsTool
 
 if TYPE_CHECKING:
     from backend.api.features.chat.response_model import StreamToolExecutionResult
 
 # Initialize tool instances
 add_understanding_tool = AddUnderstandingTool()
-create_agent_tool = CreateAgentTool()
-edit_agent_tool = EditAgentTool()
 find_agent_tool = FindAgentTool()
-find_block_tool = FindBlockTool()
 find_library_agent_tool = FindLibraryAgentTool()
 run_agent_tool = RunAgentTool()
-run_block_tool = RunBlockTool()
-search_docs_tool = SearchDocsTool()
 agent_output_tool = AgentOutputTool()
 
 # Export tools as OpenAI format
 tools: list[ChatCompletionToolParam] = [
     add_understanding_tool.as_openai_tool(),
-    create_agent_tool.as_openai_tool(),
-    edit_agent_tool.as_openai_tool(),
     find_agent_tool.as_openai_tool(),
-    find_block_tool.as_openai_tool(),
     find_library_agent_tool.as_openai_tool(),
     run_agent_tool.as_openai_tool(),
-    run_block_tool.as_openai_tool(),
-    search_docs_tool.as_openai_tool(),
     agent_output_tool.as_openai_tool(),
 ]
 
@@ -56,14 +41,9 @@ async def execute_tool(
 
     tool_map: dict[str, BaseTool] = {
         "add_understanding": add_understanding_tool,
-        "create_agent": create_agent_tool,
-        "edit_agent": edit_agent_tool,
         "find_agent": find_agent_tool,
-        "find_block": find_block_tool,
         "find_library_agent": find_library_agent_tool,
         "run_agent": run_agent_tool,
-        "run_block": run_block_tool,
-        "search_platform_docs": search_docs_tool,
         "agent_output": agent_output_tool,
     }
     if tool_name not in tool_map:

autogpt_platform/backend/backend/api/features/chat/tools/_test_data.py

@@ -3,6 +3,7 @@ from datetime import UTC, datetime
 from os import getenv
 
 import pytest
+from prisma.types import ProfileCreateInput
 from pydantic import SecretStr
 
 from backend.api.features.chat.model import ChatSession
@@ -49,13 +50,13 @@ async def setup_test_data():
     # 1b. Create a profile with username for the user (required for store agent lookup)
     username = user.email.split("@")[0]
     await prisma.profile.create(
-        data={
-            "userId": user.id,
-            "username": username,
-            "name": f"Test User {username}",
-            "description": "Test user profile",
-            "links": [],  # Required field - empty array for test profiles
-        }
+        data=ProfileCreateInput(
+            userId=user.id,
+            username=username,
+            name=f"Test User {username}",
+            description="Test user profile",
+            links=[],  # Required field - empty array for test profiles
+        )
     )
 
     # 2. Create a test graph with agent input -> agent output
@@ -172,13 +173,13 @@ async def setup_llm_test_data():
     # 1b. Create a profile with username for the user (required for store agent lookup)
     username = user.email.split("@")[0]
     await prisma.profile.create(
-        data={
-            "userId": user.id,
-            "username": username,
-            "name": f"Test User {username}",
-            "description": "Test user profile for LLM tests",
-            "links": [],  # Required field - empty array for test profiles
-        }
+        data=ProfileCreateInput(
+            userId=user.id,
+            username=username,
+            name=f"Test User {username}",
+            description="Test user profile for LLM tests",
+            links=[],  # Required field - empty array for test profiles
+        )
     )
 
     # 2. Create test OpenAI credentials for the user
@@ -332,13 +333,13 @@ async def setup_firecrawl_test_data():
     # 1b. Create a profile with username for the user (required for store agent lookup)
     username = user.email.split("@")[0]
     await prisma.profile.create(
-        data={
-            "userId": user.id,
-            "username": username,
-            "name": f"Test User {username}",
-            "description": "Test user profile for Firecrawl tests",
-            "links": [],  # Required field - empty array for test profiles
-        }
+        data=ProfileCreateInput(
+            userId=user.id,
+            username=username,
+            name=f"Test User {username}",
+            description="Test user profile for Firecrawl tests",
+            links=[],  # Required field - empty array for test profiles
+        )
     )
 
     # NOTE: We deliberately do NOT create Firecrawl credentials for this user

autogpt_platform/backend/backend/api/features/chat/tools/add_understanding.py

@@ -10,11 +10,7 @@ from backend.data.understanding import (
 )
 
 from .base import BaseTool
-from .models import (
-    ErrorResponse,
-    ToolResponseBase,
-    UnderstandingUpdatedResponse,
-)
+from .models import ErrorResponse, ToolResponseBase, UnderstandingUpdatedResponse
 
 logger = logging.getLogger(__name__)
 

autogpt_platform/backend/backend/api/features/chat/tools/agent_generator/__init__.py

@@ -1,29 +0,0 @@
-"""Agent generator package - Creates agents from natural language."""
-
-from .core import (
-    apply_agent_patch,
-    decompose_goal,
-    generate_agent,
-    generate_agent_patch,
-    get_agent_as_json,
-    save_agent_to_library,
-)
-from .fixer import apply_all_fixes
-from .utils import get_blocks_info
-from .validator import validate_agent
-
-__all__ = [
-    # Core functions
-    "decompose_goal",
-    "generate_agent",
-    "generate_agent_patch",
-    "apply_agent_patch",
-    "save_agent_to_library",
-    "get_agent_as_json",
-    # Fixer
-    "apply_all_fixes",
-    # Validator
-    "validate_agent",
-    # Utils
-    "get_blocks_info",
-]

autogpt_platform/backend/backend/api/features/chat/tools/agent_generator/client.py

@@ -1,25 +0,0 @@
-"""OpenRouter client configuration for agent generation."""
-
-import os
-
-from openai import AsyncOpenAI
-
-# Configuration - use OPEN_ROUTER_API_KEY for consistency with chat/config.py
-OPENROUTER_API_KEY = os.getenv("OPEN_ROUTER_API_KEY") or os.getenv("OPENROUTER_API_KEY")
-AGENT_GENERATOR_MODEL = os.getenv("AGENT_GENERATOR_MODEL", "anthropic/claude-opus-4.5")
-
-# OpenRouter client (OpenAI-compatible API)
-_client: AsyncOpenAI | None = None
-
-
-def get_client() -> AsyncOpenAI:
-    """Get or create the OpenRouter client."""
-    global _client
-    if _client is None:
-        if not OPENROUTER_API_KEY:
-            raise ValueError("OPENROUTER_API_KEY environment variable is required")
-        _client = AsyncOpenAI(
-            base_url="https://openrouter.ai/api/v1",
-            api_key=OPENROUTER_API_KEY,
-        )
-    return _client

autogpt_platform/backend/backend/api/features/chat/tools/agent_generator/core.py

@@ -1,390 +0,0 @@
-"""Core agent generation functions."""
-
-import copy
-import json
-import logging
-import uuid
-from typing import Any
-
-from backend.api.features.library import db as library_db
-from backend.data.graph import Graph, Link, Node, create_graph
-
-from .client import AGENT_GENERATOR_MODEL, get_client
-from .prompts import DECOMPOSITION_PROMPT, GENERATION_PROMPT, PATCH_PROMPT
-from .utils import get_block_summaries, parse_json_from_llm
-
-logger = logging.getLogger(__name__)
-
-
-async def decompose_goal(description: str, context: str = "") -> dict[str, Any] | None:
-    """Break down a goal into steps or return clarifying questions.
-
-    Args:
-        description: Natural language goal description
-        context: Additional context (e.g., answers to previous questions)
-
-    Returns:
-        Dict with either:
-        - {"type": "clarifying_questions", "questions": [...]}
-        - {"type": "instructions", "steps": [...]}
-        Or None on error
-    """
-    client = get_client()
-    prompt = DECOMPOSITION_PROMPT.format(block_summaries=get_block_summaries())
-
-    full_description = description
-    if context:
-        full_description = f"{description}\n\nAdditional context:\n{context}"
-
-    try:
-        response = await client.chat.completions.create(
-            model=AGENT_GENERATOR_MODEL,
-            messages=[
-                {"role": "system", "content": prompt},
-                {"role": "user", "content": full_description},
-            ],
-            temperature=0,
-        )
-
-        content = response.choices[0].message.content
-        if content is None:
-            logger.error("LLM returned empty content for decomposition")
-            return None
-
-        result = parse_json_from_llm(content)
-
-        if result is None:
-            logger.error(f"Failed to parse decomposition response: {content[:200]}")
-            return None
-
-        return result
-
-    except Exception as e:
-        logger.error(f"Error decomposing goal: {e}")
-        return None
-
-
-async def generate_agent(instructions: dict[str, Any]) -> dict[str, Any] | None:
-    """Generate agent JSON from instructions.
-
-    Args:
-        instructions: Structured instructions from decompose_goal
-
-    Returns:
-        Agent JSON dict or None on error
-    """
-    client = get_client()
-    prompt = GENERATION_PROMPT.format(block_summaries=get_block_summaries())
-
-    try:
-        response = await client.chat.completions.create(
-            model=AGENT_GENERATOR_MODEL,
-            messages=[
-                {"role": "system", "content": prompt},
-                {"role": "user", "content": json.dumps(instructions, indent=2)},
-            ],
-            temperature=0,
-        )
-
-        content = response.choices[0].message.content
-        if content is None:
-            logger.error("LLM returned empty content for agent generation")
-            return None
-
-        result = parse_json_from_llm(content)
-
-        if result is None:
-            logger.error(f"Failed to parse agent JSON: {content[:200]}")
-            return None
-
-        # Ensure required fields
-        if "id" not in result:
-            result["id"] = str(uuid.uuid4())
-        if "version" not in result:
-            result["version"] = 1
-        if "is_active" not in result:
-            result["is_active"] = True
-
-        return result
-
-    except Exception as e:
-        logger.error(f"Error generating agent: {e}")
-        return None
-
-
-def json_to_graph(agent_json: dict[str, Any]) -> Graph:
-    """Convert agent JSON dict to Graph model.
-
-    Args:
-        agent_json: Agent JSON with nodes and links
-
-    Returns:
-        Graph ready for saving
-    """
-    nodes = []
-    for n in agent_json.get("nodes", []):
-        node = Node(
-            id=n.get("id", str(uuid.uuid4())),
-            block_id=n["block_id"],
-            input_default=n.get("input_default", {}),
-            metadata=n.get("metadata", {}),
-        )
-        nodes.append(node)
-
-    links = []
-    for link_data in agent_json.get("links", []):
-        link = Link(
-            id=link_data.get("id", str(uuid.uuid4())),
-            source_id=link_data["source_id"],
-            sink_id=link_data["sink_id"],
-            source_name=link_data["source_name"],
-            sink_name=link_data["sink_name"],
-            is_static=link_data.get("is_static", False),
-        )
-        links.append(link)
-
-    return Graph(
-        id=agent_json.get("id", str(uuid.uuid4())),
-        version=agent_json.get("version", 1),
-        is_active=agent_json.get("is_active", True),
-        name=agent_json.get("name", "Generated Agent"),
-        description=agent_json.get("description", ""),
-        nodes=nodes,
-        links=links,
-    )
-
-
-def _reassign_node_ids(graph: Graph) -> None:
-    """Reassign all node and link IDs to new UUIDs.
-
-    This is needed when creating a new version to avoid unique constraint violations.
-    """
-    # Create mapping from old node IDs to new UUIDs
-    id_map = {node.id: str(uuid.uuid4()) for node in graph.nodes}
-
-    # Reassign node IDs
-    for node in graph.nodes:
-        node.id = id_map[node.id]
-
-    # Update link references to use new node IDs
-    for link in graph.links:
-        link.id = str(uuid.uuid4())  # Also give links new IDs
-        if link.source_id in id_map:
-            link.source_id = id_map[link.source_id]
-        if link.sink_id in id_map:
-            link.sink_id = id_map[link.sink_id]
-
-
-async def save_agent_to_library(
-    agent_json: dict[str, Any], user_id: str, is_update: bool = False
-) -> tuple[Graph, Any]:
-    """Save agent to database and user's library.
-
-    Args:
-        agent_json: Agent JSON dict
-        user_id: User ID
-        is_update: Whether this is an update to an existing agent
-
-    Returns:
-        Tuple of (created Graph, LibraryAgent)
-    """
-    from backend.data.graph import get_graph_all_versions
-
-    graph = json_to_graph(agent_json)
-
-    if is_update:
-        # For updates, keep the same graph ID but increment version
-        # and reassign node/link IDs to avoid conflicts
-        if graph.id:
-            existing_versions = await get_graph_all_versions(graph.id, user_id)
-            if existing_versions:
-                latest_version = max(v.version for v in existing_versions)
-                graph.version = latest_version + 1
-                # Reassign node IDs (but keep graph ID the same)
-                _reassign_node_ids(graph)
-                logger.info(f"Updating agent {graph.id} to version {graph.version}")
-    else:
-        # For new agents, always generate a fresh UUID to avoid collisions
-        graph.id = str(uuid.uuid4())
-        graph.version = 1
-        # Reassign all node IDs as well
-        _reassign_node_ids(graph)
-        logger.info(f"Creating new agent with ID {graph.id}")
-
-    # Save to database
-    created_graph = await create_graph(graph, user_id)
-
-    # Add to user's library (or update existing library agent)
-    library_agents = await library_db.create_library_agent(
-        graph=created_graph,
-        user_id=user_id,
-        create_library_agents_for_sub_graphs=False,
-    )
-
-    return created_graph, library_agents[0]
-
-
-async def get_agent_as_json(
-    graph_id: str, user_id: str | None
-) -> dict[str, Any] | None:
-    """Fetch an agent and convert to JSON format for editing.
-
-    Args:
-        graph_id: Graph ID or library agent ID
-        user_id: User ID
-
-    Returns:
-        Agent as JSON dict or None if not found
-    """
-    from backend.data.graph import get_graph
-
-    # Try to get the graph (version=None gets the active version)
-    graph = await get_graph(graph_id, version=None, user_id=user_id)
-    if not graph:
-        return None
-
-    # Convert to JSON format
-    nodes = []
-    for node in graph.nodes:
-        nodes.append(
-            {
-                "id": node.id,
-                "block_id": node.block_id,
-                "input_default": node.input_default,
-                "metadata": node.metadata,
-            }
-        )
-
-    links = []
-    for node in graph.nodes:
-        for link in node.output_links:
-            links.append(
-                {
-                    "id": link.id,
-                    "source_id": link.source_id,
-                    "sink_id": link.sink_id,
-                    "source_name": link.source_name,
-                    "sink_name": link.sink_name,
-                    "is_static": link.is_static,
-                }
-            )
-
-    return {
-        "id": graph.id,
-        "name": graph.name,
-        "description": graph.description,
-        "version": graph.version,
-        "is_active": graph.is_active,
-        "nodes": nodes,
-        "links": links,
-    }
-
-
-async def generate_agent_patch(
-    update_request: str, current_agent: dict[str, Any]
-) -> dict[str, Any] | None:
-    """Generate a patch to update an existing agent.
-
-    Args:
-        update_request: Natural language description of changes
-        current_agent: Current agent JSON
-
-    Returns:
-        Patch dict or clarifying questions, or None on error
-    """
-    client = get_client()
-    prompt = PATCH_PROMPT.format(
-        current_agent=json.dumps(current_agent, indent=2),
-        block_summaries=get_block_summaries(),
-    )
-
-    try:
-        response = await client.chat.completions.create(
-            model=AGENT_GENERATOR_MODEL,
-            messages=[
-                {"role": "system", "content": prompt},
-                {"role": "user", "content": update_request},
-            ],
-            temperature=0,
-        )
-
-        content = response.choices[0].message.content
-        if content is None:
-            logger.error("LLM returned empty content for patch generation")
-            return None
-
-        return parse_json_from_llm(content)
-
-    except Exception as e:
-        logger.error(f"Error generating patch: {e}")
-        return None
-
-
-def apply_agent_patch(
-    current_agent: dict[str, Any], patch: dict[str, Any]
-) -> dict[str, Any]:
-    """Apply a patch to an existing agent.
-
-    Args:
-        current_agent: Current agent JSON
-        patch: Patch dict with operations
-
-    Returns:
-        Updated agent JSON
-    """
-    agent = copy.deepcopy(current_agent)
-    patches = patch.get("patches", [])
-
-    for p in patches:
-        patch_type = p.get("type")
-
-        if patch_type == "modify":
-            node_id = p.get("node_id")
-            changes = p.get("changes", {})
-
-            for node in agent.get("nodes", []):
-                if node["id"] == node_id:
-                    _deep_update(node, changes)
-                    logger.debug(f"Modified node {node_id}")
-                    break
-
-        elif patch_type == "add":
-            new_nodes = p.get("new_nodes", [])
-            new_links = p.get("new_links", [])
-
-            agent["nodes"] = agent.get("nodes", []) + new_nodes
-            agent["links"] = agent.get("links", []) + new_links
-            logger.debug(f"Added {len(new_nodes)} nodes, {len(new_links)} links")
-
-        elif patch_type == "remove":
-            node_ids_to_remove = set(p.get("node_ids", []))
-            link_ids_to_remove = set(p.get("link_ids", []))
-
-            # Remove nodes
-            agent["nodes"] = [
-                n for n in agent.get("nodes", []) if n["id"] not in node_ids_to_remove
-            ]
-
-            # Remove links (both explicit and those referencing removed nodes)
-            agent["links"] = [
-                link
-                for link in agent.get("links", [])
-                if link["id"] not in link_ids_to_remove
-                and link["source_id"] not in node_ids_to_remove
-                and link["sink_id"] not in node_ids_to_remove
-            ]
-
-            logger.debug(
-                f"Removed {len(node_ids_to_remove)} nodes, {len(link_ids_to_remove)} links"
-            )
-
-    return agent
-
-
-def _deep_update(target: dict, source: dict) -> None:
-    """Recursively update a dict with another dict."""
-    for key, value in source.items():
-        if key in target and isinstance(target[key], dict) and isinstance(value, dict):
-            _deep_update(target[key], value)
-        else:
-            target[key] = value

autogpt_platform/backend/backend/api/features/chat/tools/agent_generator/fixer.py

@@ -1,606 +0,0 @@
-"""Agent fixer - Fixes common LLM generation errors."""
-
-import logging
-import re
-import uuid
-from typing import Any
-
-from .utils import (
-    ADDTODICTIONARY_BLOCK_ID,
-    ADDTOLIST_BLOCK_ID,
-    CODE_EXECUTION_BLOCK_ID,
-    CONDITION_BLOCK_ID,
-    CREATEDICT_BLOCK_ID,
-    CREATELIST_BLOCK_ID,
-    DATA_SAMPLING_BLOCK_ID,
-    DOUBLE_CURLY_BRACES_BLOCK_IDS,
-    GET_CURRENT_DATE_BLOCK_ID,
-    STORE_VALUE_BLOCK_ID,
-    UNIVERSAL_TYPE_CONVERTER_BLOCK_ID,
-    get_blocks_info,
-    is_valid_uuid,
-)
-
-logger = logging.getLogger(__name__)
-
-
-def fix_agent_ids(agent: dict[str, Any]) -> dict[str, Any]:
-    """Fix invalid UUIDs in agent and link IDs."""
-    # Fix agent ID
-    if not is_valid_uuid(agent.get("id", "")):
-        agent["id"] = str(uuid.uuid4())
-        logger.debug(f"Fixed agent ID: {agent['id']}")
-
-    # Fix node IDs
-    id_mapping = {}  # Old ID -> New ID
-    for node in agent.get("nodes", []):
-        if not is_valid_uuid(node.get("id", "")):
-            old_id = node.get("id", "")
-            new_id = str(uuid.uuid4())
-            id_mapping[old_id] = new_id
-            node["id"] = new_id
-            logger.debug(f"Fixed node ID: {old_id} -> {new_id}")
-
-    # Fix link IDs and update references
-    for link in agent.get("links", []):
-        if not is_valid_uuid(link.get("id", "")):
-            link["id"] = str(uuid.uuid4())
-            logger.debug(f"Fixed link ID: {link['id']}")
-
-        # Update source/sink IDs if they were remapped
-        if link.get("source_id") in id_mapping:
-            link["source_id"] = id_mapping[link["source_id"]]
-        if link.get("sink_id") in id_mapping:
-            link["sink_id"] = id_mapping[link["sink_id"]]
-
-    return agent
-
-
-def fix_double_curly_braces(agent: dict[str, Any]) -> dict[str, Any]:
-    """Fix single curly braces to double in template blocks."""
-    for node in agent.get("nodes", []):
-        if node.get("block_id") not in DOUBLE_CURLY_BRACES_BLOCK_IDS:
-            continue
-
-        input_data = node.get("input_default", {})
-        for key in ("prompt", "format"):
-            if key in input_data and isinstance(input_data[key], str):
-                original = input_data[key]
-                # Fix simple variable references: {var} -> {{var}}
-                fixed = re.sub(
-                    r"(?<!\{)\{([a-zA-Z_][a-zA-Z0-9_]*)\}(?!\})",
-                    r"{{\1}}",
-                    original,
-                )
-                if fixed != original:
-                    input_data[key] = fixed
-                    logger.debug(f"Fixed curly braces in {key}")
-
-    return agent
-
-
-def fix_storevalue_before_condition(agent: dict[str, Any]) -> dict[str, Any]:
-    """Add StoreValueBlock before ConditionBlock if needed for value2."""
-    nodes = agent.get("nodes", [])
-    links = agent.get("links", [])
-
-    # Find all ConditionBlock nodes
-    condition_node_ids = {
-        node["id"] for node in nodes if node.get("block_id") == CONDITION_BLOCK_ID
-    }
-
-    if not condition_node_ids:
-        return agent
-
-    new_nodes = []
-    new_links = []
-    processed_conditions = set()
-
-    for link in links:
-        sink_id = link.get("sink_id")
-        sink_name = link.get("sink_name")
-
-        # Check if this link goes to a ConditionBlock's value2
-        if sink_id in condition_node_ids and sink_name == "value2":
-            source_node = next(
-                (n for n in nodes if n["id"] == link.get("source_id")), None
-            )
-
-            # Skip if source is already a StoreValueBlock
-            if source_node and source_node.get("block_id") == STORE_VALUE_BLOCK_ID:
-                continue
-
-            # Skip if we already processed this condition
-            if sink_id in processed_conditions:
-                continue
-
-            processed_conditions.add(sink_id)
-
-            # Create StoreValueBlock
-            store_node_id = str(uuid.uuid4())
-            store_node = {
-                "id": store_node_id,
-                "block_id": STORE_VALUE_BLOCK_ID,
-                "input_default": {"data": None},
-                "metadata": {"position": {"x": 0, "y": -100}},
-            }
-            new_nodes.append(store_node)
-
-            # Create link: original source -> StoreValueBlock
-            new_links.append(
-                {
-                    "id": str(uuid.uuid4()),
-                    "source_id": link["source_id"],
-                    "source_name": link["source_name"],
-                    "sink_id": store_node_id,
-                    "sink_name": "input",
-                    "is_static": False,
-                }
-            )
-
-            # Update original link: StoreValueBlock -> ConditionBlock
-            link["source_id"] = store_node_id
-            link["source_name"] = "output"
-
-            logger.debug(f"Added StoreValueBlock before ConditionBlock {sink_id}")
-
-    if new_nodes:
-        agent["nodes"] = nodes + new_nodes
-
-    return agent
-
-
-def fix_addtolist_blocks(agent: dict[str, Any]) -> dict[str, Any]:
-    """Fix AddToList blocks by adding prerequisite empty AddToList block.
-
-    When an AddToList block is found:
-    1. Checks if there's a CreateListBlock before it
-    2. Removes CreateListBlock if linked directly to AddToList
-    3. Adds an empty AddToList block before the original
-    4. Ensures the original has a self-referencing link
-    """
-    nodes = agent.get("nodes", [])
-    links = agent.get("links", [])
-    new_nodes = []
-    original_addtolist_ids = set()
-    nodes_to_remove = set()
-    links_to_remove = []
-
-    # First pass: identify CreateListBlock nodes to remove
-    for link in links:
-        source_node = next(
-            (n for n in nodes if n.get("id") == link.get("source_id")), None
-        )
-        sink_node = next((n for n in nodes if n.get("id") == link.get("sink_id")), None)
-
-        if (
-            source_node
-            and sink_node
-            and source_node.get("block_id") == CREATELIST_BLOCK_ID
-            and sink_node.get("block_id") == ADDTOLIST_BLOCK_ID
-        ):
-            nodes_to_remove.add(source_node.get("id"))
-            links_to_remove.append(link)
-            logger.debug(f"Removing CreateListBlock {source_node.get('id')}")
-
-    # Second pass: process AddToList blocks
-    filtered_nodes = []
-    for node in nodes:
-        if node.get("id") in nodes_to_remove:
-            continue
-
-        if node.get("block_id") == ADDTOLIST_BLOCK_ID:
-            original_addtolist_ids.add(node.get("id"))
-            node_id = node.get("id")
-            pos = node.get("metadata", {}).get("position", {"x": 0, "y": 0})
-
-            # Check if already has prerequisite
-            has_prereq = any(
-                link.get("sink_id") == node_id
-                and link.get("sink_name") == "list"
-                and link.get("source_name") == "updated_list"
-                for link in links
-            )
-
-            if not has_prereq:
-                # Remove links to "list" input (except self-reference)
-                for link in links:
-                    if (
-                        link.get("sink_id") == node_id
-                        and link.get("sink_name") == "list"
-                        and link.get("source_id") != node_id
-                        and link not in links_to_remove
-                    ):
-                        links_to_remove.append(link)
-
-                # Create prerequisite AddToList block
-                prereq_id = str(uuid.uuid4())
-                prereq_node = {
-                    "id": prereq_id,
-                    "block_id": ADDTOLIST_BLOCK_ID,
-                    "input_default": {"list": [], "entry": None, "entries": []},
-                    "metadata": {
-                        "position": {"x": pos.get("x", 0) - 800, "y": pos.get("y", 0)}
-                    },
-                }
-                new_nodes.append(prereq_node)
-
-                # Link prerequisite to original
-                links.append(
-                    {
-                        "id": str(uuid.uuid4()),
-                        "source_id": prereq_id,
-                        "source_name": "updated_list",
-                        "sink_id": node_id,
-                        "sink_name": "list",
-                        "is_static": False,
-                    }
-                )
-                logger.debug(f"Added prerequisite AddToList block for {node_id}")
-
-        filtered_nodes.append(node)
-
-    # Remove marked links
-    filtered_links = [link for link in links if link not in links_to_remove]
-
-    # Add self-referencing links for original AddToList blocks
-    for node in filtered_nodes + new_nodes:
-        if (
-            node.get("block_id") == ADDTOLIST_BLOCK_ID
-            and node.get("id") in original_addtolist_ids
-        ):
-            node_id = node.get("id")
-            has_self_ref = any(
-                link["source_id"] == node_id
-                and link["sink_id"] == node_id
-                and link["source_name"] == "updated_list"
-                and link["sink_name"] == "list"
-                for link in filtered_links
-            )
-            if not has_self_ref:
-                filtered_links.append(
-                    {
-                        "id": str(uuid.uuid4()),
-                        "source_id": node_id,
-                        "source_name": "updated_list",
-                        "sink_id": node_id,
-                        "sink_name": "list",
-                        "is_static": False,
-                    }
-                )
-                logger.debug(f"Added self-reference for AddToList {node_id}")
-
-    agent["nodes"] = filtered_nodes + new_nodes
-    agent["links"] = filtered_links
-    return agent
-
-
-def fix_addtodictionary_blocks(agent: dict[str, Any]) -> dict[str, Any]:
-    """Fix AddToDictionary blocks by removing empty CreateDictionary nodes."""
-    nodes = agent.get("nodes", [])
-    links = agent.get("links", [])
-    nodes_to_remove = set()
-    links_to_remove = []
-
-    for link in links:
-        source_node = next(
-            (n for n in nodes if n.get("id") == link.get("source_id")), None
-        )
-        sink_node = next((n for n in nodes if n.get("id") == link.get("sink_id")), None)
-
-        if (
-            source_node
-            and sink_node
-            and source_node.get("block_id") == CREATEDICT_BLOCK_ID
-            and sink_node.get("block_id") == ADDTODICTIONARY_BLOCK_ID
-        ):
-            nodes_to_remove.add(source_node.get("id"))
-            links_to_remove.append(link)
-            logger.debug(f"Removing CreateDictionary {source_node.get('id')}")
-
-    agent["nodes"] = [n for n in nodes if n.get("id") not in nodes_to_remove]
-    agent["links"] = [link for link in links if link not in links_to_remove]
-    return agent
-
-
-def fix_code_execution_output(agent: dict[str, Any]) -> dict[str, Any]:
-    """Fix CodeExecutionBlock output: change 'response' to 'stdout_logs'."""
-    nodes = agent.get("nodes", [])
-    links = agent.get("links", [])
-
-    for link in links:
-        source_node = next(
-            (n for n in nodes if n.get("id") == link.get("source_id")), None
-        )
-        if (
-            source_node
-            and source_node.get("block_id") == CODE_EXECUTION_BLOCK_ID
-            and link.get("source_name") == "response"
-        ):
-            link["source_name"] = "stdout_logs"
-            logger.debug("Fixed CodeExecutionBlock output: response -> stdout_logs")
-
-    return agent
-
-
-def fix_data_sampling_sample_size(agent: dict[str, Any]) -> dict[str, Any]:
-    """Fix DataSamplingBlock by setting sample_size to 1 as default."""
-    nodes = agent.get("nodes", [])
-    links = agent.get("links", [])
-    links_to_remove = []
-
-    for node in nodes:
-        if node.get("block_id") == DATA_SAMPLING_BLOCK_ID:
-            node_id = node.get("id")
-            input_default = node.get("input_default", {})
-
-            # Remove links to sample_size
-            for link in links:
-                if (
-                    link.get("sink_id") == node_id
-                    and link.get("sink_name") == "sample_size"
-                ):
-                    links_to_remove.append(link)
-
-            # Set default
-            input_default["sample_size"] = 1
-            node["input_default"] = input_default
-            logger.debug(f"Fixed DataSamplingBlock {node_id} sample_size to 1")
-
-    if links_to_remove:
-        agent["links"] = [link for link in links if link not in links_to_remove]
-
-    return agent
-
-
-def fix_node_x_coordinates(agent: dict[str, Any]) -> dict[str, Any]:
-    """Fix node x-coordinates to ensure 800+ unit spacing between linked nodes."""
-    nodes = agent.get("nodes", [])
-    links = agent.get("links", [])
-    node_lookup = {n.get("id"): n for n in nodes}
-
-    for link in links:
-        source_id = link.get("source_id")
-        sink_id = link.get("sink_id")
-
-        source_node = node_lookup.get(source_id)
-        sink_node = node_lookup.get(sink_id)
-
-        if not source_node or not sink_node:
-            continue
-
-        source_pos = source_node.get("metadata", {}).get("position", {})
-        sink_pos = sink_node.get("metadata", {}).get("position", {})
-
-        source_x = source_pos.get("x", 0)
-        sink_x = sink_pos.get("x", 0)
-
-        if abs(sink_x - source_x) < 800:
-            new_x = source_x + 800
-            if "metadata" not in sink_node:
-                sink_node["metadata"] = {}
-            if "position" not in sink_node["metadata"]:
-                sink_node["metadata"]["position"] = {}
-            sink_node["metadata"]["position"]["x"] = new_x
-            logger.debug(f"Fixed node {sink_id} x: {sink_x} -> {new_x}")
-
-    return agent
-
-
-def fix_getcurrentdate_offset(agent: dict[str, Any]) -> dict[str, Any]:
-    """Fix GetCurrentDateBlock offset to ensure it's positive."""
-    for node in agent.get("nodes", []):
-        if node.get("block_id") == GET_CURRENT_DATE_BLOCK_ID:
-            input_default = node.get("input_default", {})
-            if "offset" in input_default:
-                offset = input_default["offset"]
-                if isinstance(offset, (int, float)) and offset < 0:
-                    input_default["offset"] = abs(offset)
-                    logger.debug(f"Fixed offset: {offset} -> {abs(offset)}")
-
-    return agent
-
-
-def fix_ai_model_parameter(
-    agent: dict[str, Any],
-    blocks_info: list[dict[str, Any]],
-    default_model: str = "gpt-4o",
-) -> dict[str, Any]:
-    """Add default model parameter to AI blocks if missing."""
-    block_map = {b.get("id"): b for b in blocks_info}
-
-    for node in agent.get("nodes", []):
-        block_id = node.get("block_id")
-        block = block_map.get(block_id)
-
-        if not block:
-            continue
-
-        # Check if block has AI category
-        categories = block.get("categories", [])
-        is_ai_block = any(
-            cat.get("category") == "AI" for cat in categories if isinstance(cat, dict)
-        )
-
-        if is_ai_block:
-            input_default = node.get("input_default", {})
-            if "model" not in input_default:
-                input_default["model"] = default_model
-                node["input_default"] = input_default
-                logger.debug(
-                    f"Added model '{default_model}' to AI block {node.get('id')}"
-                )
-
-    return agent
-
-
-def fix_link_static_properties(
-    agent: dict[str, Any], blocks_info: list[dict[str, Any]]
-) -> dict[str, Any]:
-    """Fix is_static property based on source block's staticOutput."""
-    block_map = {b.get("id"): b for b in blocks_info}
-    node_lookup = {n.get("id"): n for n in agent.get("nodes", [])}
-
-    for link in agent.get("links", []):
-        source_node = node_lookup.get(link.get("source_id"))
-        if not source_node:
-            continue
-
-        source_block = block_map.get(source_node.get("block_id"))
-        if not source_block:
-            continue
-
-        static_output = source_block.get("staticOutput", False)
-        if link.get("is_static") != static_output:
-            link["is_static"] = static_output
-            logger.debug(f"Fixed link {link.get('id')} is_static to {static_output}")
-
-    return agent
-
-
-def fix_data_type_mismatch(
-    agent: dict[str, Any], blocks_info: list[dict[str, Any]]
-) -> dict[str, Any]:
-    """Fix data type mismatches by inserting UniversalTypeConverterBlock."""
-    nodes = agent.get("nodes", [])
-    links = agent.get("links", [])
-    block_map = {b.get("id"): b for b in blocks_info}
-    node_lookup = {n.get("id"): n for n in nodes}
-
-    def get_property_type(schema: dict, name: str) -> str | None:
-        if "_#_" in name:
-            parent, child = name.split("_#_", 1)
-            parent_schema = schema.get(parent, {})
-            if "properties" in parent_schema:
-                return parent_schema["properties"].get(child, {}).get("type")
-            return None
-        return schema.get(name, {}).get("type")
-
-    def are_types_compatible(src: str, sink: str) -> bool:
-        if {src, sink} <= {"integer", "number"}:
-            return True
-        return src == sink
-
-    type_mapping = {
-        "string": "string",
-        "text": "string",
-        "integer": "number",
-        "number": "number",
-        "float": "number",
-        "boolean": "boolean",
-        "bool": "boolean",
-        "array": "list",
-        "list": "list",
-        "object": "dictionary",
-        "dict": "dictionary",
-        "dictionary": "dictionary",
-    }
-
-    new_links = []
-    nodes_to_add = []
-
-    for link in links:
-        source_node = node_lookup.get(link.get("source_id"))
-        sink_node = node_lookup.get(link.get("sink_id"))
-
-        if not source_node or not sink_node:
-            new_links.append(link)
-            continue
-
-        source_block = block_map.get(source_node.get("block_id"))
-        sink_block = block_map.get(sink_node.get("block_id"))
-
-        if not source_block or not sink_block:
-            new_links.append(link)
-            continue
-
-        source_outputs = source_block.get("outputSchema", {}).get("properties", {})
-        sink_inputs = sink_block.get("inputSchema", {}).get("properties", {})
-
-        source_type = get_property_type(source_outputs, link.get("source_name", ""))
-        sink_type = get_property_type(sink_inputs, link.get("sink_name", ""))
-
-        if (
-            source_type
-            and sink_type
-            and not are_types_compatible(source_type, sink_type)
-        ):
-            # Insert type converter
-            converter_id = str(uuid.uuid4())
-            target_type = type_mapping.get(sink_type, sink_type)
-
-            converter_node = {
-                "id": converter_id,
-                "block_id": UNIVERSAL_TYPE_CONVERTER_BLOCK_ID,
-                "input_default": {"type": target_type},
-                "metadata": {"position": {"x": 0, "y": 100}},
-            }
-            nodes_to_add.append(converter_node)
-
-            # source -> converter
-            new_links.append(
-                {
-                    "id": str(uuid.uuid4()),
-                    "source_id": link["source_id"],
-                    "source_name": link["source_name"],
-                    "sink_id": converter_id,
-                    "sink_name": "value",
-                    "is_static": False,
-                }
-            )
-
-            # converter -> sink
-            new_links.append(
-                {
-                    "id": str(uuid.uuid4()),
-                    "source_id": converter_id,
-                    "source_name": "value",
-                    "sink_id": link["sink_id"],
-                    "sink_name": link["sink_name"],
-                    "is_static": False,
-                }
-            )
-
-            logger.debug(f"Inserted type converter: {source_type} -> {target_type}")
-        else:
-            new_links.append(link)
-
-    if nodes_to_add:
-        agent["nodes"] = nodes + nodes_to_add
-        agent["links"] = new_links
-
-    return agent
-
-
-def apply_all_fixes(
-    agent: dict[str, Any], blocks_info: list[dict[str, Any]] | None = None
-) -> dict[str, Any]:
-    """Apply all fixes to an agent JSON.
-
-    Args:
-        agent: Agent JSON dict
-        blocks_info: Optional list of block info dicts for advanced fixes
-
-    Returns:
-        Fixed agent JSON
-    """
-    # Basic fixes (no block info needed)
-    agent = fix_agent_ids(agent)
-    agent = fix_double_curly_braces(agent)
-    agent = fix_storevalue_before_condition(agent)
-    agent = fix_addtolist_blocks(agent)
-    agent = fix_addtodictionary_blocks(agent)
-    agent = fix_code_execution_output(agent)
-    agent = fix_data_sampling_sample_size(agent)
-    agent = fix_node_x_coordinates(agent)
-    agent = fix_getcurrentdate_offset(agent)
-
-    # Advanced fixes (require block info)
-    if blocks_info is None:
-        blocks_info = get_blocks_info()
-
-    agent = fix_ai_model_parameter(agent, blocks_info)
-    agent = fix_link_static_properties(agent, blocks_info)
-    agent = fix_data_type_mismatch(agent, blocks_info)
-
-    return agent

autogpt_platform/backend/backend/api/features/chat/tools/agent_generator/prompts.py

@@ -1,225 +0,0 @@
-"""Prompt templates for agent generation."""
-
-DECOMPOSITION_PROMPT = """
-You are an expert AutoGPT Workflow Decomposer. Your task is to analyze a user's high-level goal and break it down into a clear, step-by-step plan using the available blocks.
-
-Each step should represent a distinct, automatable action suitable for execution by an AI automation system.
-
----
-
-FIRST: Analyze the user's goal and determine:
-1) Design-time configuration (fixed settings that won't change per run)
-2) Runtime inputs (values the agent's end-user will provide each time it runs)
-
-For anything that can vary per run (email addresses, names, dates, search terms, etc.):
-- DO NOT ask for the actual value
-- Instead, define it as an Agent Input with a clear name, type, and description
-
-Only ask clarifying questions about design-time config that affects how you build the workflow:
-- Which external service to use (e.g., "Gmail vs Outlook", "Notion vs Google Docs")
-- Required formats or structures (e.g., "CSV, JSON, or PDF output?")
-- Business rules that must be hard-coded
-
-IMPORTANT CLARIFICATIONS POLICY:
-- Ask no more than five essential questions
-- Do not ask for concrete values that can be provided at runtime as Agent Inputs
-- Do not ask for API keys or credentials; the platform handles those directly
-- If there is enough information to infer reasonable defaults, prefer to propose defaults
-
----
-
-GUIDELINES:
-1. List each step as a numbered item
-2. Describe the action clearly and specify inputs/outputs
-3. Ensure steps are in logical, sequential order
-4. Mention block names naturally (e.g., "Use GetWeatherByLocationBlock to...")
-5. Help the user reach their goal efficiently
-
----
-
-RULES:
-1. OUTPUT FORMAT: Only output either clarifying questions OR step-by-step instructions, not both
-2. USE ONLY THE BLOCKS PROVIDED
-3. ALL required_input fields must be provided
-4. Data types of linked properties must match
-5. Write expert-level prompts for AI-related blocks
-
----
-
-CRITICAL BLOCK RESTRICTIONS:
-1. AddToListBlock: Outputs updated list EVERY addition, not after all additions
-2. SendEmailBlock: Draft the email for user review; set SMTP config based on email type
-3. ConditionBlock: value2 is reference, value1 is contrast
-4. CodeExecutionBlock: DO NOT USE - use AI blocks instead
-5. ReadCsvBlock: Only use the 'rows' output, not 'row'
-
----
-
-OUTPUT FORMAT:
-
-If more information is needed:
-```json
-{{
-  "type": "clarifying_questions",
-  "questions": [
-    {{
-      "question": "Which email provider should be used? (Gmail, Outlook, custom SMTP)",
-      "keyword": "email_provider",
-      "example": "Gmail"
-    }}
-  ]
-}}
-```
-
-If ready to proceed:
-```json
-{{
-  "type": "instructions",
-  "steps": [
-    {{
-      "step_number": 1,
-      "block_name": "AgentShortTextInputBlock",
-      "description": "Get the URL of the content to analyze.",
-      "inputs": [{{"name": "name", "value": "URL"}}],
-      "outputs": [{{"name": "result", "description": "The URL entered by user"}}]
-    }}
-  ]
-}}
-```
-
----
-
-AVAILABLE BLOCKS:
-{block_summaries}
-"""
-
-GENERATION_PROMPT = """
-You are an expert AI workflow builder. Generate a valid agent JSON from the given instructions.
-
----
-
-NODES:
-Each node must include:
-- `id`: Unique UUID v4 (e.g. `a8f5b1e2-c3d4-4e5f-8a9b-0c1d2e3f4a5b`)
-- `block_id`: The block identifier (must match an Allowed Block)
-- `input_default`: Dict of inputs (can be empty if no static inputs needed)
-- `metadata`: Must contain:
-  - `position`: {{"x": number, "y": number}} - adjacent nodes should differ by 800+ in X
-  - `customized_name`: Clear name describing this block's purpose in the workflow
-
----
-
-LINKS:
-Each link connects a source node's output to a sink node's input:
-- `id`: MUST be UUID v4 (NOT "link-1", "link-2", etc.)
-- `source_id`: ID of the source node
-- `source_name`: Output field name from the source block
-- `sink_id`: ID of the sink node
-- `sink_name`: Input field name on the sink block
-- `is_static`: true only if source block has static_output: true
-
-CRITICAL: All IDs must be valid UUID v4 format!
-
----
-
-AGENT (GRAPH):
-Wrap nodes and links in:
-- `id`: UUID of the agent
-- `name`: Short, generic name (avoid specific company names, URLs)
-- `description`: Short, generic description
-- `nodes`: List of all nodes
-- `links`: List of all links
-- `version`: 1
-- `is_active`: true
-
----
-
-TIPS:
-- All required_input fields must be provided via input_default or a valid link
-- Ensure consistent source_id and sink_id references
-- Avoid dangling links
-- Input/output pins must match block schemas
-- Do not invent unknown block_ids
-
----
-
-ALLOWED BLOCKS:
-{block_summaries}
-
----
-
-Generate the complete agent JSON. Output ONLY valid JSON, no explanation.
-"""
-
-PATCH_PROMPT = """
-You are an expert at modifying AutoGPT agent workflows. Given the current agent and a modification request, generate a JSON patch to update the agent.
-
-CURRENT AGENT:
-{current_agent}
-
-AVAILABLE BLOCKS:
-{block_summaries}
-
----
-
-PATCH FORMAT:
-Return a JSON object with the following structure:
-
-```json
-{{
-  "type": "patch",
-  "intent": "Brief description of what the patch does",
-  "patches": [
-    {{
-      "type": "modify",
-      "node_id": "uuid-of-node-to-modify",
-      "changes": {{
-        "input_default": {{"field": "new_value"}},
-        "metadata": {{"customized_name": "New Name"}}
-      }}
-    }},
-    {{
-      "type": "add",
-      "new_nodes": [
-        {{
-          "id": "new-uuid",
-          "block_id": "block-uuid",
-          "input_default": {{}},
-          "metadata": {{"position": {{"x": 0, "y": 0}}, "customized_name": "Name"}}
-        }}
-      ],
-      "new_links": [
-        {{
-          "id": "link-uuid",
-          "source_id": "source-node-id",
-          "source_name": "output_field",
-          "sink_id": "sink-node-id",
-          "sink_name": "input_field"
-        }}
-      ]
-    }},
-    {{
-      "type": "remove",
-      "node_ids": ["uuid-of-node-to-remove"],
-      "link_ids": ["uuid-of-link-to-remove"]
-    }}
-  ]
-}}
-```
-
-If you need more information, return:
-```json
-{{
-  "type": "clarifying_questions",
-  "questions": [
-    {{
-      "question": "What specific change do you want?",
-      "keyword": "change_type",
-      "example": "Add error handling"
-    }}
-  ]
-}}
-```
-
-Generate the minimal patch needed. Output ONLY valid JSON.
-"""

autogpt_platform/backend/backend/api/features/chat/tools/agent_generator/utils.py

@@ -1,213 +0,0 @@
-"""Utilities for agent generation."""
-
-import json
-import re
-from typing import Any
-
-from backend.data.block import get_blocks
-
-# UUID validation regex
-UUID_REGEX = re.compile(
-    r"^[a-f0-9]{8}-[a-f0-9]{4}-4[a-f0-9]{3}-[89ab][a-f0-9]{3}-[a-f0-9]{12}$"
-)
-
-# Block IDs for various fixes
-STORE_VALUE_BLOCK_ID = "1ff065e9-88e8-4358-9d82-8dc91f622ba9"
-CONDITION_BLOCK_ID = "715696a0-e1da-45c8-b209-c2fa9c3b0be6"
-ADDTOLIST_BLOCK_ID = "aeb08fc1-2fc1-4141-bc8e-f758f183a822"
-ADDTODICTIONARY_BLOCK_ID = "31d1064e-7446-4693-a7d4-65e5ca1180d1"
-CREATELIST_BLOCK_ID = "a912d5c7-6e00-4542-b2a9-8034136930e4"
-CREATEDICT_BLOCK_ID = "b924ddf4-de4f-4b56-9a85-358930dcbc91"
-CODE_EXECUTION_BLOCK_ID = "0b02b072-abe7-11ef-8372-fb5d162dd712"
-DATA_SAMPLING_BLOCK_ID = "4a448883-71fa-49cf-91cf-70d793bd7d87"
-UNIVERSAL_TYPE_CONVERTER_BLOCK_ID = "95d1b990-ce13-4d88-9737-ba5c2070c97b"
-GET_CURRENT_DATE_BLOCK_ID = "b29c1b50-5d0e-4d9f-8f9d-1b0e6fcbf0b1"
-
-DOUBLE_CURLY_BRACES_BLOCK_IDS = [
-    "44f6c8ad-d75c-4ae1-8209-aad1c0326928",  # FillTextTemplateBlock
-    "6ab085e2-20b3-4055-bc3e-08036e01eca6",
-    "90f8c45e-e983-4644-aa0b-b4ebe2f531bc",
-    "363ae599-353e-4804-937e-b2ee3cef3da4",  # AgentOutputBlock
-    "3b191d9f-356f-482d-8238-ba04b6d18381",
-    "db7d8f02-2f44-4c55-ab7a-eae0941f0c30",
-    "3a7c4b8d-6e2f-4a5d-b9c1-f8d23c5a9b0e",
-    "ed1ae7a0-b770-4089-b520-1f0005fad19a",
-    "a892b8d9-3e4e-4e9c-9c1e-75f8efcf1bfa",
-    "b29c1b50-5d0e-4d9f-8f9d-1b0e6fcbf0b1",
-    "716a67b3-6760-42e7-86dc-18645c6e00fc",
-    "530cf046-2ce0-4854-ae2c-659db17c7a46",
-    "ed55ac19-356e-4243-a6cb-bc599e9b716f",
-    "1f292d4a-41a4-4977-9684-7c8d560b9f91",  # LLM blocks
-    "32a87eab-381e-4dd4-bdb8-4c47151be35a",
-]
-
-
-def is_valid_uuid(value: str) -> bool:
-    """Check if a string is a valid UUID v4."""
-    return isinstance(value, str) and UUID_REGEX.match(value) is not None
-
-
-def _compact_schema(schema: dict) -> dict[str, str]:
-    """Extract compact type info from a JSON schema properties dict.
-
-    Returns a dict of {field_name: type_string} for essential info only.
-    """
-    props = schema.get("properties", {})
-    result = {}
-
-    for name, prop in props.items():
-        # Skip internal/complex fields
-        if name.startswith("_"):
-            continue
-
-        # Get type string
-        type_str = prop.get("type", "any")
-
-        # Handle anyOf/oneOf (optional types)
-        if "anyOf" in prop:
-            types = [t.get("type", "?") for t in prop["anyOf"] if t.get("type")]
-            type_str = "|".join(types) if types else "any"
-        elif "allOf" in prop:
-            type_str = "object"
-
-        # Add array item type if present
-        if type_str == "array" and "items" in prop:
-            items = prop["items"]
-            if isinstance(items, dict):
-                item_type = items.get("type", "any")
-                type_str = f"array[{item_type}]"
-
-        result[name] = type_str
-
-    return result
-
-
-def get_block_summaries(include_schemas: bool = True) -> str:
-    """Generate compact block summaries for prompts.
-
-    Args:
-        include_schemas: Whether to include input/output type info
-
-    Returns:
-        Formatted string of block summaries (compact format)
-    """
-    blocks = get_blocks()
-    summaries = []
-
-    for block_id, block_cls in blocks.items():
-        block = block_cls()
-        name = block.name
-        desc = getattr(block, "description", "") or ""
-
-        # Truncate description
-        if len(desc) > 150:
-            desc = desc[:147] + "..."
-
-        if not include_schemas:
-            summaries.append(f"- {name} (id: {block_id}): {desc}")
-        else:
-            # Compact format with type info only
-            inputs = {}
-            outputs = {}
-            required = []
-
-            if hasattr(block, "input_schema"):
-                try:
-                    schema = block.input_schema.jsonschema()
-                    inputs = _compact_schema(schema)
-                    required = schema.get("required", [])
-                except Exception:
-                    pass
-
-            if hasattr(block, "output_schema"):
-                try:
-                    schema = block.output_schema.jsonschema()
-                    outputs = _compact_schema(schema)
-                except Exception:
-                    pass
-
-            # Build compact line format
-            # Format: NAME (id): desc | in: {field:type, ...} [required] | out: {field:type}
-            in_str = ", ".join(f"{k}:{v}" for k, v in inputs.items())
-            out_str = ", ".join(f"{k}:{v}" for k, v in outputs.items())
-            req_str = f" req=[{','.join(required)}]" if required else ""
-
-            static = " [static]" if getattr(block, "static_output", False) else ""
-
-            line = f"- {name} (id: {block_id}): {desc}"
-            if in_str:
-                line += f"\n  in: {{{in_str}}}{req_str}"
-            if out_str:
-                line += f"\n  out: {{{out_str}}}{static}"
-
-            summaries.append(line)
-
-    return "\n".join(summaries)
-
-
-def get_blocks_info() -> list[dict[str, Any]]:
-    """Get block information with schemas for validation and fixing."""
-    blocks = get_blocks()
-    blocks_info = []
-    for block_id, block_cls in blocks.items():
-        block = block_cls()
-        blocks_info.append(
-            {
-                "id": block_id,
-                "name": block.name,
-                "description": getattr(block, "description", ""),
-                "categories": getattr(block, "categories", []),
-                "staticOutput": getattr(block, "static_output", False),
-                "inputSchema": (
-                    block.input_schema.jsonschema()
-                    if hasattr(block, "input_schema")
-                    else {}
-                ),
-                "outputSchema": (
-                    block.output_schema.jsonschema()
-                    if hasattr(block, "output_schema")
-                    else {}
-                ),
-            }
-        )
-    return blocks_info
-
-
-def parse_json_from_llm(text: str) -> dict[str, Any] | None:
-    """Extract JSON from LLM response (handles markdown code blocks)."""
-    if not text:
-        return None
-
-    # Try fenced code block
-    match = re.search(r"```(?:json)?\s*([\s\S]*?)```", text, re.IGNORECASE)
-    if match:
-        try:
-            return json.loads(match.group(1).strip())
-        except json.JSONDecodeError:
-            pass
-
-    # Try raw text
-    try:
-        return json.loads(text.strip())
-    except json.JSONDecodeError:
-        pass
-
-    # Try finding {...} span
-    start = text.find("{")
-    end = text.rfind("}")
-    if start != -1 and end > start:
-        try:
-            return json.loads(text[start : end + 1])
-        except json.JSONDecodeError:
-            pass
-
-    # Try finding [...] span
-    start = text.find("[")
-    end = text.rfind("]")
-    if start != -1 and end > start:
-        try:
-            return json.loads(text[start : end + 1])
-        except json.JSONDecodeError:
-            pass
-
-    return None

autogpt_platform/backend/backend/api/features/chat/tools/agent_generator/validator.py

@@ -1,279 +0,0 @@
-"""Agent validator - Validates agent structure and connections."""
-
-import logging
-import re
-from typing import Any
-
-from .utils import get_blocks_info
-
-logger = logging.getLogger(__name__)
-
-
-class AgentValidator:
-    """Validator for AutoGPT agents with detailed error reporting."""
-
-    def __init__(self):
-        self.errors: list[str] = []
-
-    def add_error(self, error: str) -> None:
-        """Add an error message."""
-        self.errors.append(error)
-
-    def validate_block_existence(
-        self, agent: dict[str, Any], blocks_info: list[dict[str, Any]]
-    ) -> bool:
-        """Validate all block IDs exist in the blocks library."""
-        valid = True
-        valid_block_ids = {b.get("id") for b in blocks_info if b.get("id")}
-
-        for node in agent.get("nodes", []):
-            block_id = node.get("block_id")
-            node_id = node.get("id")
-
-            if not block_id:
-                self.add_error(f"Node '{node_id}' is missing 'block_id' field.")
-                valid = False
-                continue
-
-            if block_id not in valid_block_ids:
-                self.add_error(
-                    f"Node '{node_id}' references block_id '{block_id}' which does not exist."
-                )
-                valid = False
-
-        return valid
-
-    def validate_link_node_references(self, agent: dict[str, Any]) -> bool:
-        """Validate all node IDs referenced in links exist."""
-        valid = True
-        valid_node_ids = {n.get("id") for n in agent.get("nodes", []) if n.get("id")}
-
-        for link in agent.get("links", []):
-            link_id = link.get("id", "Unknown")
-            source_id = link.get("source_id")
-            sink_id = link.get("sink_id")
-
-            if not source_id:
-                self.add_error(f"Link '{link_id}' is missing 'source_id'.")
-                valid = False
-            elif source_id not in valid_node_ids:
-                self.add_error(
-                    f"Link '{link_id}' references non-existent source_id '{source_id}'."
-                )
-                valid = False
-
-            if not sink_id:
-                self.add_error(f"Link '{link_id}' is missing 'sink_id'.")
-                valid = False
-            elif sink_id not in valid_node_ids:
-                self.add_error(
-                    f"Link '{link_id}' references non-existent sink_id '{sink_id}'."
-                )
-                valid = False
-
-        return valid
-
-    def validate_required_inputs(
-        self, agent: dict[str, Any], blocks_info: list[dict[str, Any]]
-    ) -> bool:
-        """Validate required inputs are provided."""
-        valid = True
-        block_map = {b.get("id"): b for b in blocks_info}
-
-        for node in agent.get("nodes", []):
-            block_id = node.get("block_id")
-            block = block_map.get(block_id)
-
-            if not block:
-                continue
-
-            required_inputs = block.get("inputSchema", {}).get("required", [])
-            input_defaults = node.get("input_default", {})
-            node_id = node.get("id")
-
-            # Get linked inputs
-            linked_inputs = {
-                link["sink_name"]
-                for link in agent.get("links", [])
-                if link.get("sink_id") == node_id
-            }
-
-            for req_input in required_inputs:
-                if (
-                    req_input not in input_defaults
-                    and req_input not in linked_inputs
-                    and req_input != "credentials"
-                ):
-                    block_name = block.get("name", "Unknown Block")
-                    self.add_error(
-                        f"Node '{node_id}' ({block_name}) is missing required input '{req_input}'."
-                    )
-                    valid = False
-
-        return valid
-
-    def validate_data_type_compatibility(
-        self, agent: dict[str, Any], blocks_info: list[dict[str, Any]]
-    ) -> bool:
-        """Validate linked data types are compatible."""
-        valid = True
-        block_map = {b.get("id"): b for b in blocks_info}
-        node_lookup = {n.get("id"): n for n in agent.get("nodes", [])}
-
-        def get_type(schema: dict, name: str) -> str | None:
-            if "_#_" in name:
-                parent, child = name.split("_#_", 1)
-                parent_schema = schema.get(parent, {})
-                if "properties" in parent_schema:
-                    return parent_schema["properties"].get(child, {}).get("type")
-                return None
-            return schema.get(name, {}).get("type")
-
-        def are_compatible(src: str, sink: str) -> bool:
-            if {src, sink} <= {"integer", "number"}:
-                return True
-            return src == sink
-
-        for link in agent.get("links", []):
-            source_node = node_lookup.get(link.get("source_id"))
-            sink_node = node_lookup.get(link.get("sink_id"))
-
-            if not source_node or not sink_node:
-                continue
-
-            source_block = block_map.get(source_node.get("block_id"))
-            sink_block = block_map.get(sink_node.get("block_id"))
-
-            if not source_block or not sink_block:
-                continue
-
-            source_outputs = source_block.get("outputSchema", {}).get("properties", {})
-            sink_inputs = sink_block.get("inputSchema", {}).get("properties", {})
-
-            source_type = get_type(source_outputs, link.get("source_name", ""))
-            sink_type = get_type(sink_inputs, link.get("sink_name", ""))
-
-            if source_type and sink_type and not are_compatible(source_type, sink_type):
-                self.add_error(
-                    f"Type mismatch: {source_block.get('name')} output '{link['source_name']}' "
-                    f"({source_type}) -> {sink_block.get('name')} input '{link['sink_name']}' ({sink_type})."
-                )
-                valid = False
-
-        return valid
-
-    def validate_nested_sink_links(
-        self, agent: dict[str, Any], blocks_info: list[dict[str, Any]]
-    ) -> bool:
-        """Validate nested sink links (with _#_ notation)."""
-        valid = True
-        block_map = {b.get("id"): b for b in blocks_info}
-        node_lookup = {n.get("id"): n for n in agent.get("nodes", [])}
-
-        for link in agent.get("links", []):
-            sink_name = link.get("sink_name", "")
-
-            if "_#_" in sink_name:
-                parent, child = sink_name.split("_#_", 1)
-
-                sink_node = node_lookup.get(link.get("sink_id"))
-                if not sink_node:
-                    continue
-
-                block = block_map.get(sink_node.get("block_id"))
-                if not block:
-                    continue
-
-                input_props = block.get("inputSchema", {}).get("properties", {})
-                parent_schema = input_props.get(parent)
-
-                if not parent_schema:
-                    self.add_error(
-                        f"Invalid nested link '{sink_name}': parent '{parent}' not found."
-                    )
-                    valid = False
-                    continue
-
-                if not parent_schema.get("additionalProperties"):
-                    if not (
-                        isinstance(parent_schema, dict)
-                        and "properties" in parent_schema
-                        and child in parent_schema.get("properties", {})
-                    ):
-                        self.add_error(
-                            f"Invalid nested link '{sink_name}': child '{child}' not found in '{parent}'."
-                        )
-                        valid = False
-
-        return valid
-
-    def validate_prompt_spaces(self, agent: dict[str, Any]) -> bool:
-        """Validate prompts don't have spaces in template variables."""
-        valid = True
-
-        for node in agent.get("nodes", []):
-            input_default = node.get("input_default", {})
-            prompt = input_default.get("prompt", "")
-
-            if not isinstance(prompt, str):
-                continue
-
-            # Find {{...}} with spaces
-            matches = re.finditer(r"\{\{([^}]+)\}\}", prompt)
-            for match in matches:
-                content = match.group(1)
-                if " " in content:
-                    self.add_error(
-                        f"Node '{node.get('id')}' has spaces in template variable: "
-                        f"'{{{{{content}}}}}' should be '{{{{{content.replace(' ', '_')}}}}}'."
-                    )
-                    valid = False
-
-        return valid
-
-    def validate(
-        self, agent: dict[str, Any], blocks_info: list[dict[str, Any]] | None = None
-    ) -> tuple[bool, str | None]:
-        """Run all validations.
-
-        Returns:
-            Tuple of (is_valid, error_message)
-        """
-        self.errors = []
-
-        if blocks_info is None:
-            blocks_info = get_blocks_info()
-
-        checks = [
-            self.validate_block_existence(agent, blocks_info),
-            self.validate_link_node_references(agent),
-            self.validate_required_inputs(agent, blocks_info),
-            self.validate_data_type_compatibility(agent, blocks_info),
-            self.validate_nested_sink_links(agent, blocks_info),
-            self.validate_prompt_spaces(agent),
-        ]
-
-        all_passed = all(checks)
-
-        if all_passed:
-            logger.info("Agent validation successful")
-            return True, None
-
-        error_message = "Agent validation failed:\n"
-        for i, error in enumerate(self.errors, 1):
-            error_message += f"{i}. {error}\n"
-
-        logger.warning(f"Agent validation failed with {len(self.errors)} errors")
-        return False, error_message
-
-
-def validate_agent(
-    agent: dict[str, Any], blocks_info: list[dict[str, Any]] | None = None
-) -> tuple[bool, str | None]:
-    """Convenience function to validate an agent.
-
-    Returns:
-        Tuple of (is_valid, error_message)
-    """
-    validator = AgentValidator()
-    return validator.validate(agent, blocks_info)

autogpt_platform/backend/backend/api/features/chat/tools/create_agent.py

@@ -1,279 +0,0 @@
-"""CreateAgentTool - Creates agents from natural language descriptions."""
-
-import logging
-from typing import Any
-
-from backend.api.features.chat.model import ChatSession
-
-from .agent_generator import (
-    apply_all_fixes,
-    decompose_goal,
-    generate_agent,
-    get_blocks_info,
-    save_agent_to_library,
-    validate_agent,
-)
-from .base import BaseTool
-from .models import (
-    AgentPreviewResponse,
-    AgentSavedResponse,
-    ClarificationNeededResponse,
-    ClarifyingQuestion,
-    ErrorResponse,
-    ToolResponseBase,
-)
-
-logger = logging.getLogger(__name__)
-
-# Maximum retries for agent generation with validation feedback
-MAX_GENERATION_RETRIES = 2
-
-
-class CreateAgentTool(BaseTool):
-    """Tool for creating agents from natural language descriptions."""
-
-    @property
-    def name(self) -> str:
-        return "create_agent"
-
-    @property
-    def description(self) -> str:
-        return (
-            "Create a new agent workflow from a natural language description. "
-            "First generates a preview, then saves to library if save=true."
-        )
-
-    @property
-    def requires_auth(self) -> bool:
-        return True
-
-    @property
-    def parameters(self) -> dict[str, Any]:
-        return {
-            "type": "object",
-            "properties": {
-                "description": {
-                    "type": "string",
-                    "description": (
-                        "Natural language description of what the agent should do. "
-                        "Be specific about inputs, outputs, and the workflow steps."
-                    ),
-                },
-                "context": {
-                    "type": "string",
-                    "description": (
-                        "Additional context or answers to previous clarifying questions. "
-                        "Include any preferences or constraints mentioned by the user."
-                    ),
-                },
-                "save": {
-                    "type": "boolean",
-                    "description": (
-                        "Whether to save the agent to the user's library. "
-                        "Default is true. Set to false for preview only."
-                    ),
-                    "default": True,
-                },
-            },
-            "required": ["description"],
-        }
-
-    async def _execute(
-        self,
-        user_id: str | None,
-        session: ChatSession,
-        **kwargs,
-    ) -> ToolResponseBase:
-        """Execute the create_agent tool.
-
-        Flow:
-        1. Decompose the description into steps (may return clarifying questions)
-        2. Generate agent JSON from the steps
-        3. Apply fixes to correct common LLM errors
-        4. Preview or save based on the save parameter
-        """
-        description = kwargs.get("description", "").strip()
-        context = kwargs.get("context", "")
-        save = kwargs.get("save", True)
-        session_id = session.session_id if session else None
-
-        if not description:
-            return ErrorResponse(
-                message="Please provide a description of what the agent should do.",
-                error="Missing description parameter",
-                session_id=session_id,
-            )
-
-        # Step 1: Decompose goal into steps
-        try:
-            decomposition_result = await decompose_goal(description, context)
-        except ValueError as e:
-            # Handle missing API key or configuration errors
-            return ErrorResponse(
-                message=f"Agent generation is not configured: {str(e)}",
-                error="configuration_error",
-                session_id=session_id,
-            )
-
-        if decomposition_result is None:
-            return ErrorResponse(
-                message="Failed to analyze the goal. Please try rephrasing.",
-                error="Decomposition failed",
-                session_id=session_id,
-            )
-
-        # Check if LLM returned clarifying questions
-        if decomposition_result.get("type") == "clarifying_questions":
-            questions = decomposition_result.get("questions", [])
-            return ClarificationNeededResponse(
-                message=(
-                    "I need some more information to create this agent. "
-                    "Please answer the following questions:"
-                ),
-                questions=[
-                    ClarifyingQuestion(
-                        question=q.get("question", ""),
-                        keyword=q.get("keyword", ""),
-                        example=q.get("example"),
-                    )
-                    for q in questions
-                ],
-                session_id=session_id,
-            )
-
-        # Check for unachievable/vague goals
-        if decomposition_result.get("type") == "unachievable_goal":
-            suggested = decomposition_result.get("suggested_goal", "")
-            reason = decomposition_result.get("reason", "")
-            return ErrorResponse(
-                message=(
-                    f"This goal cannot be accomplished with the available blocks. "
-                    f"{reason} "
-                    f"Suggestion: {suggested}"
-                ),
-                error="unachievable_goal",
-                details={"suggested_goal": suggested, "reason": reason},
-                session_id=session_id,
-            )
-
-        if decomposition_result.get("type") == "vague_goal":
-            suggested = decomposition_result.get("suggested_goal", "")
-            return ErrorResponse(
-                message=(
-                    f"The goal is too vague to create a specific workflow. "
-                    f"Suggestion: {suggested}"
-                ),
-                error="vague_goal",
-                details={"suggested_goal": suggested},
-                session_id=session_id,
-            )
-
-        # Step 2: Generate agent JSON with retry on validation failure
-        blocks_info = get_blocks_info()
-        agent_json = None
-        validation_errors = None
-
-        for attempt in range(MAX_GENERATION_RETRIES + 1):
-            # Generate agent (include validation errors from previous attempt)
-            if attempt == 0:
-                agent_json = await generate_agent(decomposition_result)
-            else:
-                # Retry with validation error feedback
-                logger.info(
-                    f"Retry {attempt}/{MAX_GENERATION_RETRIES} with validation feedback"
-                )
-                retry_instructions = {
-                    **decomposition_result,
-                    "previous_errors": validation_errors,
-                    "retry_instructions": (
-                        "The previous generation had validation errors. "
-                        "Please fix these issues in the new generation:\n"
-                        f"{validation_errors}"
-                    ),
-                }
-                agent_json = await generate_agent(retry_instructions)
-
-            if agent_json is None:
-                if attempt == MAX_GENERATION_RETRIES:
-                    return ErrorResponse(
-                        message="Failed to generate the agent. Please try again.",
-                        error="Generation failed",
-                        session_id=session_id,
-                    )
-                continue
-
-            # Step 3: Apply fixes to correct common errors
-            agent_json = apply_all_fixes(agent_json, blocks_info)
-
-            # Step 4: Validate the agent
-            is_valid, validation_errors = validate_agent(agent_json, blocks_info)
-
-            if is_valid:
-                logger.info(f"Agent generated successfully on attempt {attempt + 1}")
-                break
-
-            logger.warning(
-                f"Validation failed on attempt {attempt + 1}: {validation_errors}"
-            )
-
-            if attempt == MAX_GENERATION_RETRIES:
-                # Return error with validation details
-                return ErrorResponse(
-                    message=(
-                        f"Generated agent has validation errors after {MAX_GENERATION_RETRIES + 1} attempts. "
-                        f"Please try rephrasing your request or simplify the workflow."
-                    ),
-                    error="validation_failed",
-                    details={"validation_errors": validation_errors},
-                    session_id=session_id,
-                )
-
-        agent_name = agent_json.get("name", "Generated Agent")
-        agent_description = agent_json.get("description", "")
-        node_count = len(agent_json.get("nodes", []))
-        link_count = len(agent_json.get("links", []))
-
-        # Step 4: Preview or save
-        if not save:
-            return AgentPreviewResponse(
-                message=(
-                    f"I've generated an agent called '{agent_name}' with {node_count} blocks. "
-                    f"Review it and call create_agent with save=true to save it to your library."
-                ),
-                agent_json=agent_json,
-                agent_name=agent_name,
-                description=agent_description,
-                node_count=node_count,
-                link_count=link_count,
-                session_id=session_id,
-            )
-
-        # Save to library
-        if not user_id:
-            return ErrorResponse(
-                message="You must be logged in to save agents.",
-                error="auth_required",
-                session_id=session_id,
-            )
-
-        try:
-            created_graph, library_agent = await save_agent_to_library(
-                agent_json, user_id
-            )
-
-            return AgentSavedResponse(
-                message=f"Agent '{created_graph.name}' has been saved to your library!",
-                agent_id=created_graph.id,
-                agent_name=created_graph.name,
-                library_agent_id=library_agent.id,
-                library_agent_link=f"/library/{library_agent.id}",
-                agent_page_link=f"/build?flowID={created_graph.id}",
-                session_id=session_id,
-            )
-        except Exception as e:
-            return ErrorResponse(
-                message=f"Failed to save the agent: {str(e)}",
-                error="save_failed",
-                details={"exception": str(e)},
-                session_id=session_id,
-            )

autogpt_platform/backend/backend/api/features/chat/tools/edit_agent.py

@@ -1,294 +0,0 @@
-"""EditAgentTool - Edits existing agents using natural language."""
-
-import logging
-from typing import Any
-
-from backend.api.features.chat.model import ChatSession
-
-from .agent_generator import (
-    apply_agent_patch,
-    apply_all_fixes,
-    generate_agent_patch,
-    get_agent_as_json,
-    get_blocks_info,
-    save_agent_to_library,
-    validate_agent,
-)
-from .base import BaseTool
-from .models import (
-    AgentPreviewResponse,
-    AgentSavedResponse,
-    ClarificationNeededResponse,
-    ClarifyingQuestion,
-    ErrorResponse,
-    ToolResponseBase,
-)
-
-logger = logging.getLogger(__name__)
-
-# Maximum retries for patch generation with validation feedback
-MAX_GENERATION_RETRIES = 2
-
-
-class EditAgentTool(BaseTool):
-    """Tool for editing existing agents using natural language."""
-
-    @property
-    def name(self) -> str:
-        return "edit_agent"
-
-    @property
-    def description(self) -> str:
-        return (
-            "Edit an existing agent from the user's library using natural language. "
-            "Generates a patch to update the agent while preserving unchanged parts."
-        )
-
-    @property
-    def requires_auth(self) -> bool:
-        return True
-
-    @property
-    def parameters(self) -> dict[str, Any]:
-        return {
-            "type": "object",
-            "properties": {
-                "agent_id": {
-                    "type": "string",
-                    "description": (
-                        "The ID of the agent to edit. "
-                        "Can be a graph ID or library agent ID."
-                    ),
-                },
-                "changes": {
-                    "type": "string",
-                    "description": (
-                        "Natural language description of what changes to make. "
-                        "Be specific about what to add, remove, or modify."
-                    ),
-                },
-                "context": {
-                    "type": "string",
-                    "description": (
-                        "Additional context or answers to previous clarifying questions."
-                    ),
-                },
-                "save": {
-                    "type": "boolean",
-                    "description": (
-                        "Whether to save the changes. "
-                        "Default is true. Set to false for preview only."
-                    ),
-                    "default": True,
-                },
-            },
-            "required": ["agent_id", "changes"],
-        }
-
-    async def _execute(
-        self,
-        user_id: str | None,
-        session: ChatSession,
-        **kwargs,
-    ) -> ToolResponseBase:
-        """Execute the edit_agent tool.
-
-        Flow:
-        1. Fetch the current agent
-        2. Generate a patch based on the requested changes
-        3. Apply the patch to create an updated agent
-        4. Preview or save based on the save parameter
-        """
-        agent_id = kwargs.get("agent_id", "").strip()
-        changes = kwargs.get("changes", "").strip()
-        context = kwargs.get("context", "")
-        save = kwargs.get("save", True)
-        session_id = session.session_id if session else None
-
-        if not agent_id:
-            return ErrorResponse(
-                message="Please provide the agent ID to edit.",
-                error="Missing agent_id parameter",
-                session_id=session_id,
-            )
-
-        if not changes:
-            return ErrorResponse(
-                message="Please describe what changes you want to make.",
-                error="Missing changes parameter",
-                session_id=session_id,
-            )
-
-        # Step 1: Fetch current agent
-        current_agent = await get_agent_as_json(agent_id, user_id)
-
-        if current_agent is None:
-            return ErrorResponse(
-                message=f"Could not find agent with ID '{agent_id}' in your library.",
-                error="agent_not_found",
-                session_id=session_id,
-            )
-
-        # Build the update request with context
-        update_request = changes
-        if context:
-            update_request = f"{changes}\n\nAdditional context:\n{context}"
-
-        # Step 2: Generate patch with retry on validation failure
-        blocks_info = get_blocks_info()
-        updated_agent = None
-        validation_errors = None
-        intent = "Applied requested changes"
-
-        for attempt in range(MAX_GENERATION_RETRIES + 1):
-            # Generate patch (include validation errors from previous attempt)
-            try:
-                if attempt == 0:
-                    patch_result = await generate_agent_patch(
-                        update_request, current_agent
-                    )
-                else:
-                    # Retry with validation error feedback
-                    logger.info(
-                        f"Retry {attempt}/{MAX_GENERATION_RETRIES} with validation feedback"
-                    )
-                    retry_request = (
-                        f"{update_request}\n\n"
-                        f"IMPORTANT: The previous edit had validation errors. "
-                        f"Please fix these issues:\n{validation_errors}"
-                    )
-                    patch_result = await generate_agent_patch(
-                        retry_request, current_agent
-                    )
-            except ValueError as e:
-                # Handle missing API key or configuration errors
-                return ErrorResponse(
-                    message=f"Agent generation is not configured: {str(e)}",
-                    error="configuration_error",
-                    session_id=session_id,
-                )
-
-            if patch_result is None:
-                if attempt == MAX_GENERATION_RETRIES:
-                    return ErrorResponse(
-                        message="Failed to generate changes. Please try rephrasing.",
-                        error="Patch generation failed",
-                        session_id=session_id,
-                    )
-                continue
-
-            # Check if LLM returned clarifying questions
-            if patch_result.get("type") == "clarifying_questions":
-                questions = patch_result.get("questions", [])
-                return ClarificationNeededResponse(
-                    message=(
-                        "I need some more information about the changes. "
-                        "Please answer the following questions:"
-                    ),
-                    questions=[
-                        ClarifyingQuestion(
-                            question=q.get("question", ""),
-                            keyword=q.get("keyword", ""),
-                            example=q.get("example"),
-                        )
-                        for q in questions
-                    ],
-                    session_id=session_id,
-                )
-
-            # Step 3: Apply patch and fixes
-            try:
-                updated_agent = apply_agent_patch(current_agent, patch_result)
-                updated_agent = apply_all_fixes(updated_agent, blocks_info)
-            except Exception as e:
-                if attempt == MAX_GENERATION_RETRIES:
-                    return ErrorResponse(
-                        message=f"Failed to apply changes: {str(e)}",
-                        error="patch_apply_failed",
-                        details={"exception": str(e)},
-                        session_id=session_id,
-                    )
-                validation_errors = str(e)
-                continue
-
-            # Step 4: Validate the updated agent
-            is_valid, validation_errors = validate_agent(updated_agent, blocks_info)
-
-            if is_valid:
-                logger.info(f"Agent edited successfully on attempt {attempt + 1}")
-                intent = patch_result.get("intent", "Applied requested changes")
-                break
-
-            logger.warning(
-                f"Validation failed on attempt {attempt + 1}: {validation_errors}"
-            )
-
-            if attempt == MAX_GENERATION_RETRIES:
-                # Return error with validation details
-                return ErrorResponse(
-                    message=(
-                        f"Updated agent has validation errors after "
-                        f"{MAX_GENERATION_RETRIES + 1} attempts. "
-                        f"Please try rephrasing your request or simplify the changes."
-                    ),
-                    error="validation_failed",
-                    details={"validation_errors": validation_errors},
-                    session_id=session_id,
-                )
-
-        # At this point, updated_agent is guaranteed to be set (we return on all failure paths)
-        assert updated_agent is not None
-
-        agent_name = updated_agent.get("name", "Updated Agent")
-        agent_description = updated_agent.get("description", "")
-        node_count = len(updated_agent.get("nodes", []))
-        link_count = len(updated_agent.get("links", []))
-
-        # Step 5: Preview or save
-        if not save:
-            return AgentPreviewResponse(
-                message=(
-                    f"I've updated the agent. Changes: {intent}. "
-                    f"The agent now has {node_count} blocks. "
-                    f"Review it and call edit_agent with save=true to save the changes."
-                ),
-                agent_json=updated_agent,
-                agent_name=agent_name,
-                description=agent_description,
-                node_count=node_count,
-                link_count=link_count,
-                session_id=session_id,
-            )
-
-        # Save to library (creates a new version)
-        if not user_id:
-            return ErrorResponse(
-                message="You must be logged in to save agents.",
-                error="auth_required",
-                session_id=session_id,
-            )
-
-        try:
-            created_graph, library_agent = await save_agent_to_library(
-                updated_agent, user_id, is_update=True
-            )
-
-            return AgentSavedResponse(
-                message=(
-                    f"Updated agent '{created_graph.name}' has been saved to your library! "
-                    f"Changes: {intent}"
-                ),
-                agent_id=created_graph.id,
-                agent_name=created_graph.name,
-                library_agent_id=library_agent.id,
-                library_agent_link=f"/library/{library_agent.id}",
-                agent_page_link=f"/build?flowID={created_graph.id}",
-                session_id=session_id,
-            )
-        except Exception as e:
-            return ErrorResponse(
-                message=f"Failed to save the updated agent: {str(e)}",
-                error="save_failed",
-                details={"exception": str(e)},
-                session_id=session_id,
-            )

autogpt_platform/backend/backend/api/features/chat/tools/find_block.py

@@ -1,253 +0,0 @@
-"""Tool for searching available blocks using hybrid search."""
-
-import logging
-from typing import Any
-
-from backend.api.features.chat.model import ChatSession
-from backend.blocks import load_all_blocks
-
-from .base import BaseTool
-from .models import (
-    BlockInfoSummary,
-    BlockListResponse,
-    ErrorResponse,
-    NoResultsResponse,
-    ToolResponseBase,
-)
-from .search_blocks import get_block_search_index
-
-logger = logging.getLogger(__name__)
-
-
-class FindBlockTool(BaseTool):
-    """Tool for searching available blocks."""
-
-    @property
-    def name(self) -> str:
-        return "find_block"
-
-    @property
-    def description(self) -> str:
-        return (
-            "Search for available blocks by name or description. "
-            "Blocks are reusable components that perform specific tasks like "
-            "sending emails, making API calls, processing text, etc. "
-            "Use this to find blocks that can be executed directly."
-        )
-
-    @property
-    def parameters(self) -> dict[str, Any]:
-        return {
-            "type": "object",
-            "properties": {
-                "query": {
-                    "type": "string",
-                    "description": (
-                        "Search query to find blocks by name or description. "
-                        "Use keywords like 'email', 'http', 'text', 'ai', etc."
-                    ),
-                },
-            },
-            "required": ["query"],
-        }
-
-    @property
-    def requires_auth(self) -> bool:
-        return True
-
-    def _matches_query(self, block, query: str) -> tuple[int, bool]:
-        """
-        Check if a block matches the query and return a priority score.
-
-        Returns (priority, matches) where:
-        - priority 0: exact name match
-        - priority 1: name contains query
-        - priority 2: description contains query
-        - priority 3: category contains query
-        """
-        query_lower = query.lower()
-        name_lower = block.name.lower()
-        desc_lower = block.description.lower()
-
-        # Exact name match
-        if query_lower == name_lower:
-            return 0, True
-
-        # Name contains query
-        if query_lower in name_lower:
-            return 1, True
-
-        # Description contains query
-        if query_lower in desc_lower:
-            return 2, True
-
-        # Category contains query
-        for category in block.categories:
-            if query_lower in category.name.lower():
-                return 3, True
-
-        return 4, False
-
-    async def _execute(
-        self,
-        user_id: str | None,
-        session: ChatSession,
-        **kwargs,
-    ) -> ToolResponseBase:
-        """Search for blocks matching the query.
-
-        Args:
-            user_id: User ID (required)
-            session: Chat session
-            query: Search query
-
-        Returns:
-            BlockListResponse: List of matching blocks
-            NoResultsResponse: No blocks found
-            ErrorResponse: Error message
-        """
-        query = kwargs.get("query", "").strip()
-        session_id = session.session_id
-
-        if not query:
-            return ErrorResponse(
-                message="Please provide a search query",
-                session_id=session_id,
-            )
-
-        try:
-            # Try hybrid search first
-            search_results = self._hybrid_search(query)
-
-            if search_results is not None:
-                # Hybrid search succeeded
-                if not search_results:
-                    return NoResultsResponse(
-                        message=f"No blocks found matching '{query}'",
-                        session_id=session_id,
-                        suggestions=[
-                            "Try more general terms",
-                            "Search by category: ai, text, social, search, etc.",
-                            "Check block names like 'SendEmail', 'HttpRequest', etc.",
-                        ],
-                    )
-
-                # Get full block info for each result
-                all_blocks = load_all_blocks()
-                blocks = []
-                for result in search_results:
-                    block_cls = all_blocks.get(result.block_id)
-                    if block_cls:
-                        block = block_cls()
-                        blocks.append(
-                            BlockInfoSummary(
-                                id=block.id,
-                                name=block.name,
-                                description=block.description,
-                                categories=[cat.name for cat in block.categories],
-                                input_schema=block.input_schema.jsonschema(),
-                                output_schema=block.output_schema.jsonschema(),
-                            )
-                        )
-
-                return BlockListResponse(
-                    message=(
-                        f"Found {len(blocks)} block{'s' if len(blocks) != 1 else ''} "
-                        f"matching '{query}'. Use run_block to execute a block with "
-                        "the required inputs."
-                    ),
-                    blocks=blocks,
-                    count=len(blocks),
-                    query=query,
-                    session_id=session_id,
-                )
-
-            # Fallback to simple search if hybrid search failed
-            return self._simple_search(query, session_id)
-
-        except Exception as e:
-            logger.error(f"Error searching blocks: {e}", exc_info=True)
-            return ErrorResponse(
-                message="Failed to search blocks. Please try again.",
-                error=str(e),
-                session_id=session_id,
-            )
-
-    def _hybrid_search(self, query: str) -> list | None:
-        """
-        Perform hybrid search using embeddings and BM25.
-
-        Returns:
-            List of BlockSearchResult if successful, None if index not available
-        """
-        try:
-            index = get_block_search_index()
-            if not index.load():
-                logger.info(
-                    "Block search index not available, falling back to simple search"
-                )
-                return None
-
-            results = index.search(query, top_k=10)
-            logger.info(f"Hybrid search found {len(results)} blocks for: {query}")
-            return results
-
-        except Exception as e:
-            logger.warning(f"Hybrid search failed, falling back to simple: {e}")
-            return None
-
-    def _simple_search(self, query: str, session_id: str) -> ToolResponseBase:
-        """Fallback simple search using substring matching."""
-        all_blocks = load_all_blocks()
-        logger.info(f"Simple searching {len(all_blocks)} blocks for: {query}")
-
-        # Find matching blocks with priority scores
-        matches: list[tuple[int, Any]] = []
-        for block_id, block_cls in all_blocks.items():
-            block = block_cls()
-            priority, is_match = self._matches_query(block, query)
-            if is_match:
-                matches.append((priority, block))
-
-        # Sort by priority (lower is better)
-        matches.sort(key=lambda x: x[0])
-
-        # Take top 10 results
-        top_matches = [block for _, block in matches[:10]]
-
-        if not top_matches:
-            return NoResultsResponse(
-                message=f"No blocks found matching '{query}'",
-                session_id=session_id,
-                suggestions=[
-                    "Try more general terms",
-                    "Search by category: ai, text, social, search, etc.",
-                    "Check block names like 'SendEmail', 'HttpRequest', etc.",
-                ],
-            )
-
-        # Build response
-        blocks = []
-        for block in top_matches:
-            blocks.append(
-                BlockInfoSummary(
-                    id=block.id,
-                    name=block.name,
-                    description=block.description,
-                    categories=[cat.name for cat in block.categories],
-                    input_schema=block.input_schema.jsonschema(),
-                    output_schema=block.output_schema.jsonschema(),
-                )
-            )
-
-        return BlockListResponse(
-            message=(
-                f"Found {len(blocks)} block{'s' if len(blocks) != 1 else ''} "
-                f"matching '{query}'. Use run_block to execute a block with "
-                "the required inputs."
-            ),
-            blocks=blocks,
-            count=len(blocks),
-            query=query,
-            session_id=session_id,
-        )

autogpt_platform/backend/backend/api/features/chat/tools/index_blocks.py

@@ -1,483 +0,0 @@
-#!/usr/bin/env python3
-"""
-Block Indexer for Hybrid Search
-
-Creates a hybrid search index from blocks:
-- OpenAI embeddings (text-embedding-3-small)
-- BM25 index for lexical search
-- Name index for title matching boost
-
-Supports incremental updates by tracking content hashes.
-
-Usage:
-    python -m backend.server.v2.chat.tools.index_blocks [--force]
-"""
-
-import argparse
-import base64
-import hashlib
-import json
-import logging
-import os
-import re
-import sys
-from collections import defaultdict
-from datetime import datetime, timezone
-from pathlib import Path
-from typing import Any
-
-import numpy as np
-
-logger = logging.getLogger(__name__)
-
-# Check for OpenAI availability
-try:
-    import openai  # noqa: F401
-
-    HAS_OPENAI = True
-except ImportError:
-    HAS_OPENAI = False
-    print("Warning: openai not installed. Run: pip install openai")
-
-# Default embedding model (OpenAI)
-DEFAULT_EMBEDDING_MODEL = "text-embedding-3-small"
-DEFAULT_EMBEDDING_DIM = 1536
-
-# Output path (relative to this file)
-INDEX_PATH = Path(__file__).parent / "blocks_index.json"
-
-# Stopwords for tokenization
-STOPWORDS = {
-    "the",
-    "a",
-    "an",
-    "is",
-    "are",
-    "was",
-    "were",
-    "be",
-    "been",
-    "being",
-    "have",
-    "has",
-    "had",
-    "do",
-    "does",
-    "did",
-    "will",
-    "would",
-    "could",
-    "should",
-    "may",
-    "might",
-    "must",
-    "shall",
-    "can",
-    "need",
-    "dare",
-    "ought",
-    "used",
-    "to",
-    "of",
-    "in",
-    "for",
-    "on",
-    "with",
-    "at",
-    "by",
-    "from",
-    "as",
-    "into",
-    "through",
-    "during",
-    "before",
-    "after",
-    "above",
-    "below",
-    "between",
-    "under",
-    "again",
-    "further",
-    "then",
-    "once",
-    "and",
-    "but",
-    "or",
-    "nor",
-    "so",
-    "yet",
-    "both",
-    "either",
-    "neither",
-    "not",
-    "only",
-    "own",
-    "same",
-    "than",
-    "too",
-    "very",
-    "just",
-    "also",
-    "now",
-    "here",
-    "there",
-    "when",
-    "where",
-    "why",
-    "how",
-    "all",
-    "each",
-    "every",
-    "few",
-    "more",
-    "most",
-    "other",
-    "some",
-    "such",
-    "no",
-    "any",
-    "this",
-    "that",
-    "these",
-    "those",
-    "it",
-    "its",
-    "block",  # Too common in block context
-}
-
-
-def tokenize(text: str) -> list[str]:
-    """Simple tokenizer for BM25."""
-    text = text.lower()
-    # Remove code blocks if any
-    text = re.sub(r"```[\s\S]*?```", "", text)
-    text = re.sub(r"`[^`]+`", "", text)
-    # Extract words (including camelCase split)
-    # First, split camelCase
-    text = re.sub(r"([a-z])([A-Z])", r"\1 \2", text)
-    # Extract words
-    words = re.findall(r"\b[a-z][a-z0-9_-]*\b", text)
-    # Remove very short words and stopwords
-    return [w for w in words if len(w) > 2 and w not in STOPWORDS]
-
-
-def build_searchable_text(block: Any) -> str:
-    """Build searchable text from block attributes."""
-    parts = []
-
-    # Block name (split camelCase for better tokenization)
-    name = block.name
-    # Split camelCase: GetCurrentTimeBlock -> Get Current Time Block
-    name_split = re.sub(r"([a-z])([A-Z])", r"\1 \2", name)
-    parts.append(name_split)
-
-    # Description
-    if block.description:
-        parts.append(block.description)
-
-    # Categories
-    for category in block.categories:
-        parts.append(category.name)
-
-    # Input schema field names and descriptions
-    try:
-        input_schema = block.input_schema.jsonschema()
-        if "properties" in input_schema:
-            for field_name, field_info in input_schema["properties"].items():
-                parts.append(field_name)
-                if "description" in field_info:
-                    parts.append(field_info["description"])
-    except Exception:
-        pass
-
-    # Output schema field names
-    try:
-        output_schema = block.output_schema.jsonschema()
-        if "properties" in output_schema:
-            for field_name in output_schema["properties"]:
-                parts.append(field_name)
-    except Exception:
-        pass
-
-    return " ".join(parts)
-
-
-def compute_content_hash(text: str) -> str:
-    """Compute MD5 hash of text for change detection."""
-    return hashlib.md5(text.encode()).hexdigest()
-
-
-def load_existing_index(index_path: Path) -> dict[str, Any] | None:
-    """Load existing index if present."""
-    if not index_path.exists():
-        return None
-
-    try:
-        with open(index_path, "r", encoding="utf-8") as f:
-            return json.load(f)
-    except Exception as e:
-        logger.warning(f"Failed to load existing index: {e}")
-        return None
-
-
-def create_embeddings(
-    texts: list[str],
-    model_name: str = DEFAULT_EMBEDDING_MODEL,
-    batch_size: int = 100,
-) -> np.ndarray:
-    """Create embeddings using OpenAI API."""
-    if not HAS_OPENAI:
-        raise RuntimeError("openai not installed. Run: pip install openai")
-
-    # Import here to satisfy type checker
-    from openai import OpenAI
-
-    # Check for API key
-    api_key = os.getenv("OPENAI_API_KEY")
-    if not api_key:
-        raise RuntimeError("OPENAI_API_KEY environment variable not set")
-
-    client = OpenAI(api_key=api_key)
-    embeddings = []
-
-    print(f"Creating embeddings for {len(texts)} texts using {model_name}...")
-
-    for i in range(0, len(texts), batch_size):
-        batch = texts[i : i + batch_size]
-        # Truncate texts to max token limit (8191 tokens for text-embedding-3-small)
-        # Roughly 4 chars per token, so ~32000 chars max
-        batch = [text[:32000] for text in batch]
-
-        response = client.embeddings.create(
-            model=model_name,
-            input=batch,
-        )
-
-        for embedding_data in response.data:
-            embeddings.append(embedding_data.embedding)
-
-        print(f"  Processed {min(i + batch_size, len(texts))}/{len(texts)} texts")
-
-    return np.array(embeddings, dtype=np.float32)
-
-
-def build_bm25_data(
-    blocks_data: list[dict[str, Any]],
-) -> dict[str, Any]:
-    """Build BM25 metadata from block data."""
-    # Tokenize all searchable texts
-    tokenized_docs = []
-    for block in blocks_data:
-        tokens = tokenize(block["searchable_text"])
-        tokenized_docs.append(tokens)
-
-    # Calculate document frequencies
-    doc_freq: dict[str, int] = {}
-    for tokens in tokenized_docs:
-        seen = set()
-        for token in tokens:
-            if token not in seen:
-                doc_freq[token] = doc_freq.get(token, 0) + 1
-                seen.add(token)
-
-    n_docs = len(tokenized_docs)
-    doc_lens = [len(d) for d in tokenized_docs]
-    avgdl = sum(doc_lens) / max(n_docs, 1)
-
-    return {
-        "n_docs": n_docs,
-        "avgdl": avgdl,
-        "df": doc_freq,
-        "doc_lens": doc_lens,
-    }
-
-
-def build_name_index(
-    blocks_data: list[dict[str, Any]],
-) -> dict[str, list[list[int | float]]]:
-    """Build inverted index for name search boost."""
-    index: dict[str, list[list[int | float]]] = defaultdict(list)
-
-    for idx, block in enumerate(blocks_data):
-        # Tokenize block name
-        name_tokens = tokenize(block["name"])
-        seen = set()
-
-        for i, token in enumerate(name_tokens):
-            if token in seen:
-                continue
-            seen.add(token)
-
-            # Score: first token gets higher weight
-            score = 1.5 if i == 0 else 1.0
-            index[token].append([idx, score])
-
-    return dict(index)
-
-
-def build_block_index(
-    force_rebuild: bool = False,
-    output_path: Path = INDEX_PATH,
-) -> dict[str, Any]:
-    """
-    Build the block search index.
-
-    Args:
-        force_rebuild: If True, rebuild all embeddings even if unchanged
-        output_path: Path to save the index
-
-    Returns:
-        The generated index dictionary
-    """
-    # Import here to avoid circular imports
-    from backend.blocks import load_all_blocks
-
-    print("Loading all blocks...")
-    all_blocks = load_all_blocks()
-    print(f"Found {len(all_blocks)} blocks")
-
-    # Load existing index for incremental updates
-    existing_index = None if force_rebuild else load_existing_index(output_path)
-    existing_blocks: dict[str, dict[str, Any]] = {}
-
-    if existing_index:
-        print(
-            f"Loaded existing index with {len(existing_index.get('blocks', []))} blocks"
-        )
-        for block in existing_index.get("blocks", []):
-            existing_blocks[block["id"]] = block
-
-    # Process each block
-    blocks_data: list[dict[str, Any]] = []
-    blocks_needing_embedding: list[tuple[int, str]] = []  # (index, searchable_text)
-
-    for block_id, block_cls in all_blocks.items():
-        try:
-            block = block_cls()
-
-            # Skip disabled blocks
-            if block.disabled:
-                continue
-
-            searchable_text = build_searchable_text(block)
-            content_hash = compute_content_hash(searchable_text)
-
-            block_data = {
-                "id": block.id,
-                "name": block.name,
-                "description": block.description,
-                "categories": [cat.name for cat in block.categories],
-                "searchable_text": searchable_text,
-                "content_hash": content_hash,
-                "emb": None,  # Will be filled later
-            }
-
-            # Check if we can reuse existing embedding
-            if (
-                block.id in existing_blocks
-                and existing_blocks[block.id].get("content_hash") == content_hash
-                and existing_blocks[block.id].get("emb")
-            ):
-                # Reuse existing embedding
-                block_data["emb"] = existing_blocks[block.id]["emb"]
-            else:
-                # Need new embedding
-                blocks_needing_embedding.append((len(blocks_data), searchable_text))
-
-            blocks_data.append(block_data)
-
-        except Exception as e:
-            logger.warning(f"Failed to process block {block_id}: {e}")
-            continue
-
-    print(f"Processed {len(blocks_data)} blocks")
-    print(f"Blocks needing new embeddings: {len(blocks_needing_embedding)}")
-
-    # Create embeddings for new/changed blocks
-    if blocks_needing_embedding and HAS_OPENAI:
-        texts_to_embed = [text for _, text in blocks_needing_embedding]
-        try:
-            embeddings = create_embeddings(texts_to_embed)
-
-            # Assign embeddings to blocks
-            for i, (block_idx, _) in enumerate(blocks_needing_embedding):
-                emb = embeddings[i].astype(np.float32)
-                # Encode as base64
-                blocks_data[block_idx]["emb"] = base64.b64encode(emb.tobytes()).decode(
-                    "ascii"
-                )
-        except Exception as e:
-            print(f"Warning: Failed to create embeddings: {e}")
-    elif blocks_needing_embedding:
-        print(
-            "Warning: Cannot create embeddings (openai not installed or OPENAI_API_KEY not set)"
-        )
-
-    # Build BM25 data
-    print("Building BM25 index...")
-    bm25_data = build_bm25_data(blocks_data)
-
-    # Build name index
-    print("Building name index...")
-    name_index = build_name_index(blocks_data)
-
-    # Build final index
-    index = {
-        "version": "1.0.0",
-        "embedding_model": DEFAULT_EMBEDDING_MODEL,
-        "embedding_dim": DEFAULT_EMBEDDING_DIM,
-        "generated_at": datetime.now(timezone.utc).isoformat(),
-        "blocks": blocks_data,
-        "bm25": bm25_data,
-        "name_index": name_index,
-    }
-
-    # Save index
-    print(f"Saving index to {output_path}...")
-    with open(output_path, "w", encoding="utf-8") as f:
-        json.dump(index, f, separators=(",", ":"))
-
-    size_kb = output_path.stat().st_size / 1024
-    print(f"Index saved ({size_kb:.1f} KB)")
-
-    # Print statistics
-    print("\nIndex Statistics:")
-    print(f"  Blocks indexed: {len(blocks_data)}")
-    print(f"  BM25 vocabulary size: {len(bm25_data['df'])}")
-    print(f"  Name index terms: {len(name_index)}")
-    print(f"  Embeddings: {'Yes' if any(b.get('emb') for b in blocks_data) else 'No'}")
-
-    return index
-
-
-def main():
-    parser = argparse.ArgumentParser(description="Build hybrid search index for blocks")
-    parser.add_argument(
-        "--force",
-        action="store_true",
-        help="Force rebuild all embeddings even if unchanged",
-    )
-    parser.add_argument(
-        "--output",
-        type=Path,
-        default=INDEX_PATH,
-        help=f"Output index file path (default: {INDEX_PATH})",
-    )
-
-    args = parser.parse_args()
-
-    try:
-        build_block_index(
-            force_rebuild=args.force,
-            output_path=args.output,
-        )
-    except Exception as e:
-        print(f"Error building index: {e}")
-        import traceback
-
-        traceback.print_exc()
-        sys.exit(1)
-
-
-if __name__ == "__main__":
-    main()

autogpt_platform/backend/backend/api/features/chat/tools/run_block.py

@@ -1,287 +0,0 @@
-"""Tool for executing blocks directly."""
-
-import logging
-from collections import defaultdict
-from typing import Any
-
-from backend.api.features.chat.model import ChatSession
-from backend.data.block import get_block
-from backend.data.model import CredentialsMetaInput
-from backend.integrations.creds_manager import IntegrationCredentialsManager
-from backend.util.exceptions import BlockError
-
-from .base import BaseTool
-from .models import (
-    BlockOutputResponse,
-    ErrorResponse,
-    SetupInfo,
-    SetupRequirementsResponse,
-    ToolResponseBase,
-    UserReadiness,
-)
-
-logger = logging.getLogger(__name__)
-
-
-class RunBlockTool(BaseTool):
-    """Tool for executing a block and returning its outputs."""
-
-    @property
-    def name(self) -> str:
-        return "run_block"
-
-    @property
-    def description(self) -> str:
-        return (
-            "Execute a specific block with the provided input data. "
-            "Use find_block to discover available blocks and their input schemas. "
-            "The block will run and return its outputs once complete."
-        )
-
-    @property
-    def parameters(self) -> dict[str, Any]:
-        return {
-            "type": "object",
-            "properties": {
-                "block_id": {
-                    "type": "string",
-                    "description": "The UUID of the block to execute",
-                },
-                "input_data": {
-                    "type": "object",
-                    "description": (
-                        "Input values for the block. Must match the block's input schema. "
-                        "Check the block's input_schema from find_block for required fields."
-                    ),
-                },
-            },
-            "required": ["block_id", "input_data"],
-        }
-
-    @property
-    def requires_auth(self) -> bool:
-        return True
-
-    async def _check_block_credentials(
-        self,
-        user_id: str,
-        block: Any,
-    ) -> tuple[dict[str, CredentialsMetaInput], list[CredentialsMetaInput]]:
-        """
-        Check if user has required credentials for a block.
-
-        Returns:
-            tuple[matched_credentials, missing_credentials]
-        """
-        matched_credentials: dict[str, CredentialsMetaInput] = {}
-        missing_credentials: list[CredentialsMetaInput] = []
-
-        # Get credential field info from block's input schema
-        credentials_fields_info = block.input_schema.get_credentials_fields_info()
-
-        if not credentials_fields_info:
-            return matched_credentials, missing_credentials
-
-        # Get user's available credentials
-        creds_manager = IntegrationCredentialsManager()
-        available_creds = await creds_manager.store.get_all_creds(user_id)
-
-        for field_name, field_info in credentials_fields_info.items():
-            # field_info.provider is a frozenset of acceptable providers
-            # field_info.supported_types is a frozenset of acceptable types
-            matching_cred = next(
-                (
-                    cred
-                    for cred in available_creds
-                    if cred.provider in field_info.provider
-                    and cred.type in field_info.supported_types
-                ),
-                None,
-            )
-
-            if matching_cred:
-                matched_credentials[field_name] = CredentialsMetaInput(
-                    id=matching_cred.id,
-                    provider=matching_cred.provider,  # type: ignore
-                    type=matching_cred.type,
-                    title=matching_cred.title,
-                )
-            else:
-                # Create a placeholder for the missing credential
-                provider = next(iter(field_info.provider), "unknown")
-                cred_type = next(iter(field_info.supported_types), "api_key")
-                missing_credentials.append(
-                    CredentialsMetaInput(
-                        id=field_name,
-                        provider=provider,  # type: ignore
-                        type=cred_type,  # type: ignore
-                        title=field_name.replace("_", " ").title(),
-                    )
-                )
-
-        return matched_credentials, missing_credentials
-
-    async def _execute(
-        self,
-        user_id: str | None,
-        session: ChatSession,
-        **kwargs,
-    ) -> ToolResponseBase:
-        """Execute a block with the given input data.
-
-        Args:
-            user_id: User ID (required)
-            session: Chat session
-            block_id: Block UUID to execute
-            input_data: Input values for the block
-
-        Returns:
-            BlockOutputResponse: Block execution outputs
-            SetupRequirementsResponse: Missing credentials
-            ErrorResponse: Error message
-        """
-        block_id = kwargs.get("block_id", "").strip()
-        input_data = kwargs.get("input_data", {})
-        session_id = session.session_id
-
-        if not block_id:
-            return ErrorResponse(
-                message="Please provide a block_id",
-                session_id=session_id,
-            )
-
-        if not isinstance(input_data, dict):
-            return ErrorResponse(
-                message="input_data must be an object",
-                session_id=session_id,
-            )
-
-        if not user_id:
-            return ErrorResponse(
-                message="Authentication required",
-                session_id=session_id,
-            )
-
-        # Get the block
-        block = get_block(block_id)
-        if not block:
-            return ErrorResponse(
-                message=f"Block '{block_id}' not found",
-                session_id=session_id,
-            )
-
-        logger.info(f"Executing block {block.name} ({block_id}) for user {user_id}")
-
-        # Check credentials
-        creds_manager = IntegrationCredentialsManager()
-        matched_credentials, missing_credentials = await self._check_block_credentials(
-            user_id, block
-        )
-
-        if missing_credentials:
-            # Return setup requirements response with missing credentials
-            missing_creds_dict = {c.id: c.model_dump() for c in missing_credentials}
-
-            return SetupRequirementsResponse(
-                message=(
-                    f"Block '{block.name}' requires credentials that are not configured. "
-                    "Please set up the required credentials before running this block."
-                ),
-                session_id=session_id,
-                setup_info=SetupInfo(
-                    agent_id=block_id,
-                    agent_name=block.name,
-                    user_readiness=UserReadiness(
-                        has_all_credentials=False,
-                        missing_credentials=missing_creds_dict,
-                        ready_to_run=False,
-                    ),
-                    requirements={
-                        "credentials": [c.model_dump() for c in missing_credentials],
-                        "inputs": self._get_inputs_list(block),
-                        "execution_modes": ["immediate"],
-                    },
-                ),
-                graph_id=None,
-                graph_version=None,
-            )
-
-        try:
-            # Fetch actual credentials and prepare kwargs for block execution
-            exec_kwargs: dict[str, Any] = {"user_id": user_id}
-
-            for field_name, cred_meta in matched_credentials.items():
-                # Inject metadata into input_data (for validation)
-                if field_name not in input_data:
-                    input_data[field_name] = cred_meta.model_dump()
-
-                # Fetch actual credentials and pass as kwargs (for execution)
-                actual_credentials = await creds_manager.get(
-                    user_id, cred_meta.id, lock=False
-                )
-                if actual_credentials:
-                    exec_kwargs[field_name] = actual_credentials
-                else:
-                    return ErrorResponse(
-                        message=f"Failed to retrieve credentials for {field_name}",
-                        session_id=session_id,
-                    )
-
-            # Execute the block and collect outputs
-            outputs: dict[str, list[Any]] = defaultdict(list)
-            async for output_name, output_data in block.execute(
-                input_data,
-                **exec_kwargs,
-            ):
-                outputs[output_name].append(output_data)
-
-            return BlockOutputResponse(
-                message=f"Block '{block.name}' executed successfully",
-                block_id=block_id,
-                block_name=block.name,
-                outputs=dict(outputs),
-                success=True,
-                session_id=session_id,
-            )
-
-        except BlockError as e:
-            logger.warning(f"Block execution failed: {e}")
-            return ErrorResponse(
-                message=f"Block execution failed: {e}",
-                error=str(e),
-                session_id=session_id,
-            )
-        except Exception as e:
-            logger.error(f"Unexpected error executing block: {e}", exc_info=True)
-            return ErrorResponse(
-                message=f"Failed to execute block: {str(e)}",
-                error=str(e),
-                session_id=session_id,
-            )
-
-    def _get_inputs_list(self, block: Any) -> list[dict[str, Any]]:
-        """Extract non-credential inputs from block schema."""
-        inputs_list = []
-        schema = block.input_schema.jsonschema()
-        properties = schema.get("properties", {})
-        required_fields = set(schema.get("required", []))
-
-        # Get credential field names to exclude
-        credentials_fields = set(block.input_schema.get_credentials_fields().keys())
-
-        for field_name, field_schema in properties.items():
-            # Skip credential fields
-            if field_name in credentials_fields:
-                continue
-
-            inputs_list.append(
-                {
-                    "name": field_name,
-                    "title": field_schema.get("title", field_name),
-                    "type": field_schema.get("type", "string"),
-                    "description": field_schema.get("description", ""),
-                    "required": field_name in required_fields,
-                }
-            )
-
-        return inputs_list

autogpt_platform/backend/backend/api/features/chat/tools/search_blocks.py

@@ -1,460 +0,0 @@
-"""
-Block Hybrid Search
-
-Combines multiple ranking signals for block search:
-- Semantic search (OpenAI embeddings + cosine similarity)
-- Lexical search (BM25)
-- Name matching (boost for block name matches)
-- Category matching (boost for category matches)
-
-Based on the docs search implementation.
-"""
-
-import base64
-import json
-import logging
-import math
-import os
-import re
-from dataclasses import dataclass
-from pathlib import Path
-from typing import Any, Optional
-
-import numpy as np
-
-logger = logging.getLogger(__name__)
-
-# OpenAI embedding model
-EMBEDDING_MODEL = "text-embedding-3-small"
-
-# Path to the JSON index file
-INDEX_PATH = Path(__file__).parent / "blocks_index.json"
-
-# Stopwords for tokenization (same as index_blocks.py)
-STOPWORDS = {
-    "the",
-    "a",
-    "an",
-    "is",
-    "are",
-    "was",
-    "were",
-    "be",
-    "been",
-    "being",
-    "have",
-    "has",
-    "had",
-    "do",
-    "does",
-    "did",
-    "will",
-    "would",
-    "could",
-    "should",
-    "may",
-    "might",
-    "must",
-    "shall",
-    "can",
-    "need",
-    "dare",
-    "ought",
-    "used",
-    "to",
-    "of",
-    "in",
-    "for",
-    "on",
-    "with",
-    "at",
-    "by",
-    "from",
-    "as",
-    "into",
-    "through",
-    "during",
-    "before",
-    "after",
-    "above",
-    "below",
-    "between",
-    "under",
-    "again",
-    "further",
-    "then",
-    "once",
-    "and",
-    "but",
-    "or",
-    "nor",
-    "so",
-    "yet",
-    "both",
-    "either",
-    "neither",
-    "not",
-    "only",
-    "own",
-    "same",
-    "than",
-    "too",
-    "very",
-    "just",
-    "also",
-    "now",
-    "here",
-    "there",
-    "when",
-    "where",
-    "why",
-    "how",
-    "all",
-    "each",
-    "every",
-    "few",
-    "more",
-    "most",
-    "other",
-    "some",
-    "such",
-    "no",
-    "any",
-    "this",
-    "that",
-    "these",
-    "those",
-    "it",
-    "its",
-    "block",
-}
-
-
-def tokenize(text: str) -> list[str]:
-    """Simple tokenizer for search."""
-    text = text.lower()
-    # Remove code blocks if any
-    text = re.sub(r"```[\s\S]*?```", "", text)
-    text = re.sub(r"`[^`]+`", "", text)
-    # Split camelCase
-    text = re.sub(r"([a-z])([A-Z])", r"\1 \2", text)
-    # Extract words
-    words = re.findall(r"\b[a-z][a-z0-9_-]*\b", text)
-    # Remove very short words and stopwords
-    return [w for w in words if len(w) > 2 and w not in STOPWORDS]
-
-
-@dataclass
-class SearchWeights:
-    """Configuration for hybrid search signal weights."""
-
-    semantic: float = 0.40  # Embedding similarity
-    bm25: float = 0.25  # Lexical matching
-    name_match: float = 0.25  # Block name matches
-    category_match: float = 0.10  # Category matches
-
-
-@dataclass
-class BlockSearchResult:
-    """A single block search result."""
-
-    block_id: str
-    name: str
-    description: str
-    categories: list[str]
-    score: float
-
-    # Individual signal scores (for debugging)
-    semantic_score: float = 0.0
-    bm25_score: float = 0.0
-    name_score: float = 0.0
-    category_score: float = 0.0
-
-
-class BlockSearchIndex:
-    """Hybrid search index for blocks combining BM25 + embeddings."""
-
-    def __init__(self, index_path: Path = INDEX_PATH):
-        self.blocks: list[dict[str, Any]] = []
-        self.bm25_data: dict[str, Any] = {}
-        self.name_index: dict[str, list[list[int | float]]] = {}
-        self.embeddings: Optional[np.ndarray] = None
-        self.normalized_embeddings: Optional[np.ndarray] = None
-        self._loaded = False
-        self._index_path = index_path
-        self._embedding_model: Any = None
-
-    def load(self) -> bool:
-        """Load the index from JSON file."""
-        if self._loaded:
-            return True
-
-        if not self._index_path.exists():
-            logger.warning(f"Block index not found at {self._index_path}")
-            return False
-
-        try:
-            with open(self._index_path, "r", encoding="utf-8") as f:
-                data = json.load(f)
-
-            self.blocks = data.get("blocks", [])
-            self.bm25_data = data.get("bm25", {})
-            self.name_index = data.get("name_index", {})
-
-            # Decode embeddings from base64
-            embeddings_list = []
-            for block in self.blocks:
-                if block.get("emb"):
-                    emb_bytes = base64.b64decode(block["emb"])
-                    emb = np.frombuffer(emb_bytes, dtype=np.float32)
-                    embeddings_list.append(emb)
-                else:
-                    # No embedding, use zeros
-                    dim = data.get("embedding_dim", 384)
-                    embeddings_list.append(np.zeros(dim, dtype=np.float32))
-
-            if embeddings_list:
-                self.embeddings = np.stack(embeddings_list)
-                # Precompute normalized embeddings for cosine similarity
-                norms = np.linalg.norm(self.embeddings, axis=1, keepdims=True)
-                self.normalized_embeddings = self.embeddings / (norms + 1e-10)
-
-            self._loaded = True
-            logger.info(f"Loaded block index with {len(self.blocks)} blocks")
-            return True
-
-        except Exception as e:
-            logger.error(f"Failed to load block index: {e}")
-            return False
-
-    def _get_openai_client(self) -> Any:
-        """Get OpenAI client for query embedding."""
-        if self._embedding_model is None:
-            try:
-                from openai import OpenAI
-
-                api_key = os.getenv("OPENAI_API_KEY")
-                if not api_key:
-                    logger.warning("OPENAI_API_KEY not set")
-                    return None
-                self._embedding_model = OpenAI(api_key=api_key)
-            except ImportError:
-                logger.warning("openai not installed")
-                return None
-        return self._embedding_model
-
-    def _embed_query(self, query: str) -> Optional[np.ndarray]:
-        """Embed the search query using OpenAI."""
-        client = self._get_openai_client()
-        if client is None:
-            return None
-
-        try:
-            response = client.embeddings.create(
-                model=EMBEDDING_MODEL,
-                input=query,
-            )
-            embedding = response.data[0].embedding
-            return np.array(embedding, dtype=np.float32)
-        except Exception as e:
-            logger.warning(f"Failed to embed query: {e}")
-            return None
-
-    def _compute_semantic_scores(self, query_embedding: np.ndarray) -> np.ndarray:
-        """Compute cosine similarity between query and all blocks."""
-        if self.normalized_embeddings is None:
-            return np.zeros(len(self.blocks))
-
-        # Normalize query embedding
-        query_norm = query_embedding / (np.linalg.norm(query_embedding) + 1e-10)
-
-        # Cosine similarity via dot product
-        similarities = self.normalized_embeddings @ query_norm
-
-        # Scale to [0, 1] (cosine ranges from -1 to 1)
-        return (similarities + 1) / 2
-
-    def _compute_bm25_scores(self, query_tokens: list[str]) -> np.ndarray:
-        """Compute BM25 scores for all blocks."""
-        scores = np.zeros(len(self.blocks))
-
-        if not self.bm25_data or not query_tokens:
-            return scores
-
-        # BM25 parameters
-        k1 = 1.5
-        b = 0.75
-        n_docs = self.bm25_data.get("n_docs", len(self.blocks))
-        avgdl = self.bm25_data.get("avgdl", 100)
-        df = self.bm25_data.get("df", {})
-        doc_lens = self.bm25_data.get("doc_lens", [100] * len(self.blocks))
-
-        for i, block in enumerate(self.blocks):
-            # Tokenize block's searchable text
-            block_tokens = tokenize(block.get("searchable_text", ""))
-            doc_len = doc_lens[i] if i < len(doc_lens) else len(block_tokens)
-
-            # Calculate BM25 score
-            score = 0.0
-            for token in query_tokens:
-                if token not in df:
-                    continue
-
-                # Term frequency in this document
-                tf = block_tokens.count(token)
-                if tf == 0:
-                    continue
-
-                # IDF
-                doc_freq = df.get(token, 0)
-                idf = math.log((n_docs - doc_freq + 0.5) / (doc_freq + 0.5) + 1)
-
-                # BM25 score component
-                numerator = tf * (k1 + 1)
-                denominator = tf + k1 * (1 - b + b * doc_len / avgdl)
-                score += idf * numerator / denominator
-
-            scores[i] = score
-
-        # Normalize to [0, 1]
-        max_score = scores.max()
-        if max_score > 0:
-            scores = scores / max_score
-
-        return scores
-
-    def _compute_name_scores(self, query_tokens: list[str]) -> np.ndarray:
-        """Compute name match scores using the name index."""
-        scores = np.zeros(len(self.blocks))
-
-        if not self.name_index or not query_tokens:
-            return scores
-
-        for token in query_tokens:
-            if token in self.name_index:
-                for block_idx, weight in self.name_index[token]:
-                    if block_idx < len(scores):
-                        scores[int(block_idx)] += weight
-
-        # Also check for partial matches in block names
-        for i, block in enumerate(self.blocks):
-            name_lower = block.get("name", "").lower()
-            for token in query_tokens:
-                if token in name_lower:
-                    scores[i] += 0.5
-
-        # Normalize to [0, 1]
-        max_score = scores.max()
-        if max_score > 0:
-            scores = scores / max_score
-
-        return scores
-
-    def _compute_category_scores(self, query_tokens: list[str]) -> np.ndarray:
-        """Compute category match scores."""
-        scores = np.zeros(len(self.blocks))
-
-        if not query_tokens:
-            return scores
-
-        for i, block in enumerate(self.blocks):
-            categories = block.get("categories", [])
-            category_text = " ".join(categories).lower()
-
-            for token in query_tokens:
-                if token in category_text:
-                    scores[i] += 1.0
-
-        # Normalize to [0, 1]
-        max_score = scores.max()
-        if max_score > 0:
-            scores = scores / max_score
-
-        return scores
-
-    def search(
-        self,
-        query: str,
-        top_k: int = 10,
-        weights: Optional[SearchWeights] = None,
-    ) -> list[BlockSearchResult]:
-        """
-        Perform hybrid search combining multiple signals.
-
-        Args:
-            query: Search query string
-            top_k: Number of results to return
-            weights: Optional custom weights for signals
-
-        Returns:
-            List of BlockSearchResult sorted by score
-        """
-        if not self._loaded and not self.load():
-            return []
-
-        if weights is None:
-            weights = SearchWeights()
-
-        # Tokenize query
-        query_tokens = tokenize(query)
-        if not query_tokens:
-            # Fallback: try raw query words
-            query_tokens = query.lower().split()
-
-        # Compute semantic scores
-        semantic_scores = np.zeros(len(self.blocks))
-        if self.normalized_embeddings is not None:
-            query_embedding = self._embed_query(query)
-            if query_embedding is not None:
-                semantic_scores = self._compute_semantic_scores(query_embedding)
-
-        # Compute other scores
-        bm25_scores = self._compute_bm25_scores(query_tokens)
-        name_scores = self._compute_name_scores(query_tokens)
-        category_scores = self._compute_category_scores(query_tokens)
-
-        # Combine scores using weights
-        combined_scores = (
-            weights.semantic * semantic_scores
-            + weights.bm25 * bm25_scores
-            + weights.name_match * name_scores
-            + weights.category_match * category_scores
-        )
-
-        # Get top-k indices
-        top_indices = np.argsort(combined_scores)[::-1][:top_k]
-
-        # Build results
-        results = []
-        for idx in top_indices:
-            if combined_scores[idx] <= 0:
-                continue
-
-            block = self.blocks[idx]
-            results.append(
-                BlockSearchResult(
-                    block_id=block["id"],
-                    name=block["name"],
-                    description=block["description"],
-                    categories=block.get("categories", []),
-                    score=float(combined_scores[idx]),
-                    semantic_score=float(semantic_scores[idx]),
-                    bm25_score=float(bm25_scores[idx]),
-                    name_score=float(name_scores[idx]),
-                    category_score=float(category_scores[idx]),
-                )
-            )
-
-        return results
-
-
-# Global index instance (lazy loaded)
-_block_search_index: Optional[BlockSearchIndex] = None
-
-
-def get_block_search_index() -> BlockSearchIndex:
-    """Get or create the block search index singleton."""
-    global _block_search_index
-    if _block_search_index is None:
-        _block_search_index = BlockSearchIndex(INDEX_PATH)
-    return _block_search_index

autogpt_platform/backend/backend/api/features/chat/tools/search_docs.py

@@ -1,386 +0,0 @@
-"""Tool for searching platform documentation."""
-
-import json
-import logging
-import math
-import re
-from pathlib import Path
-from typing import Any
-
-from backend.api.features.chat.model import ChatSession
-
-from .base import BaseTool
-from .models import (
-    DocSearchResult,
-    DocSearchResultsResponse,
-    ErrorResponse,
-    NoResultsResponse,
-    ToolResponseBase,
-)
-
-logger = logging.getLogger(__name__)
-
-# Documentation base URL
-DOCS_BASE_URL = "https://docs.agpt.co/platform"
-
-# Path to the JSON index file (relative to this file)
-INDEX_PATH = Path(__file__).parent / "docs_index.json"
-
-
-def tokenize(text: str) -> list[str]:
-    """Simple tokenizer for BM25."""
-    text = text.lower()
-    # Remove code blocks
-    text = re.sub(r"```[\s\S]*?```", "", text)
-    text = re.sub(r"`[^`]+`", "", text)
-    # Extract words
-    words = re.findall(r"\b[a-z][a-z0-9_-]*\b", text)
-    # Remove very short words and stopwords
-    stopwords = {
-        "the",
-        "a",
-        "an",
-        "is",
-        "are",
-        "was",
-        "were",
-        "be",
-        "been",
-        "being",
-        "have",
-        "has",
-        "had",
-        "do",
-        "does",
-        "did",
-        "will",
-        "would",
-        "could",
-        "should",
-        "may",
-        "might",
-        "must",
-        "shall",
-        "can",
-        "need",
-        "dare",
-        "ought",
-        "used",
-        "to",
-        "of",
-        "in",
-        "for",
-        "on",
-        "with",
-        "at",
-        "by",
-        "from",
-        "as",
-        "into",
-        "through",
-        "during",
-        "before",
-        "after",
-        "above",
-        "below",
-        "between",
-        "under",
-        "again",
-        "further",
-        "then",
-        "once",
-        "and",
-        "but",
-        "or",
-        "nor",
-        "so",
-        "yet",
-        "both",
-        "either",
-        "neither",
-        "not",
-        "only",
-        "own",
-        "same",
-        "than",
-        "too",
-        "very",
-        "just",
-        "also",
-        "now",
-        "here",
-        "there",
-        "when",
-        "where",
-        "why",
-        "how",
-        "all",
-        "each",
-        "every",
-        "both",
-        "few",
-        "more",
-        "most",
-        "other",
-        "some",
-        "such",
-        "no",
-        "any",
-        "this",
-        "that",
-        "these",
-        "those",
-        "it",
-        "its",
-    }
-    return [w for w in words if len(w) > 2 and w not in stopwords]
-
-
-class DocSearchIndex:
-    """Lightweight documentation search index using BM25."""
-
-    def __init__(self, index_path: Path):
-        self.chunks: list[dict] = []
-        self.bm25_data: dict = {}
-        self._loaded = False
-        self._index_path = index_path
-
-    def load(self) -> bool:
-        """Load the index from JSON file."""
-        if self._loaded:
-            return True
-
-        if not self._index_path.exists():
-            logger.warning(f"Documentation index not found at {self._index_path}")
-            return False
-
-        try:
-            with open(self._index_path, "r", encoding="utf-8") as f:
-                data = json.load(f)
-
-            self.chunks = data.get("chunks", [])
-            self.bm25_data = data.get("bm25", {})
-            self._loaded = True
-            logger.info(f"Loaded documentation index with {len(self.chunks)} chunks")
-            return True
-        except Exception as e:
-            logger.error(f"Failed to load documentation index: {e}")
-            return False
-
-    def search(self, query: str, top_k: int = 5) -> list[dict]:
-        """Search the index using BM25."""
-        if not self._loaded and not self.load():
-            return []
-
-        query_tokens = tokenize(query)
-        if not query_tokens:
-            return []
-
-        # BM25 parameters
-        k1 = 1.5
-        b = 0.75
-        n_docs = self.bm25_data.get("n_docs", len(self.chunks))
-        avgdl = self.bm25_data.get("avgdl", 100)
-        df = self.bm25_data.get("df", {})
-        doc_lens = self.bm25_data.get("doc_lens", [100] * len(self.chunks))
-
-        scores = []
-        for i, chunk in enumerate(self.chunks):
-            # Tokenize chunk text
-            chunk_tokens = tokenize(chunk.get("text", ""))
-            doc_len = doc_lens[i] if i < len(doc_lens) else len(chunk_tokens)
-
-            # Calculate BM25 score
-            score = 0.0
-            for token in query_tokens:
-                if token not in df:
-                    continue
-
-                # Term frequency in this document
-                tf = chunk_tokens.count(token)
-                if tf == 0:
-                    continue
-
-                # IDF
-                doc_freq = df.get(token, 0)
-                idf = math.log((n_docs - doc_freq + 0.5) / (doc_freq + 0.5) + 1)
-
-                # BM25 score component
-                numerator = tf * (k1 + 1)
-                denominator = tf + k1 * (1 - b + b * doc_len / avgdl)
-                score += idf * numerator / denominator
-
-            # Boost for title/heading matches
-            title = chunk.get("title", "").lower()
-            heading = chunk.get("heading", "").lower()
-            for token in query_tokens:
-                if token in title:
-                    score *= 1.5
-                if token in heading:
-                    score *= 1.2
-
-            scores.append((i, score))
-
-        # Sort by score and return top_k
-        scores.sort(key=lambda x: x[1], reverse=True)
-
-        results = []
-        seen_sections = set()
-        for idx, score in scores:
-            if score <= 0:
-                continue
-
-            chunk = self.chunks[idx]
-            section_key = (chunk.get("doc", ""), chunk.get("heading", ""))
-
-            # Deduplicate by section
-            if section_key in seen_sections:
-                continue
-            seen_sections.add(section_key)
-
-            results.append(
-                {
-                    "title": chunk.get("title", ""),
-                    "path": chunk.get("doc", ""),
-                    "heading": chunk.get("heading", ""),
-                    "text": chunk.get("text", ""),  # Full text for LLM comprehension
-                    "score": score,
-                }
-            )
-
-            if len(results) >= top_k:
-                break
-
-        return results
-
-
-# Global index instance (lazy loaded)
-_search_index: DocSearchIndex | None = None
-
-
-def get_search_index() -> DocSearchIndex:
-    """Get or create the search index singleton."""
-    global _search_index
-    if _search_index is None:
-        _search_index = DocSearchIndex(INDEX_PATH)
-    return _search_index
-
-
-class SearchDocsTool(BaseTool):
-    """Tool for searching AutoGPT platform documentation."""
-
-    @property
-    def name(self) -> str:
-        return "search_platform_docs"
-
-    @property
-    def description(self) -> str:
-        return (
-            "Search the AutoGPT platform documentation and support Q&A for information about "
-            "how to use the platform, create agents, configure blocks, "
-            "set up integrations, troubleshoot issues, and more. Use this when users ask "
-            "support questions or want to learn how to do something with AutoGPT."
-        )
-
-    @property
-    def parameters(self) -> dict[str, Any]:
-        return {
-            "type": "object",
-            "properties": {
-                "query": {
-                    "type": "string",
-                    "description": (
-                        "Search query describing what the user wants to learn about. "
-                        "Use keywords like 'blocks', 'agents', 'credentials', 'API', etc."
-                    ),
-                },
-            },
-            "required": ["query"],
-        }
-
-    async def _execute(
-        self,
-        user_id: str | None,
-        session: ChatSession,
-        **kwargs,
-    ) -> ToolResponseBase:
-        """Search documentation for the query.
-
-        Args:
-            user_id: User ID (may be anonymous)
-            session: Chat session
-            query: Search query
-
-        Returns:
-            DocSearchResultsResponse: List of matching documentation sections
-            NoResultsResponse: No results found
-            ErrorResponse: Error message
-        """
-        query = kwargs.get("query", "").strip()
-        session_id = session.session_id
-
-        if not query:
-            return ErrorResponse(
-                message="Please provide a search query",
-                session_id=session_id,
-            )
-
-        try:
-            index = get_search_index()
-            results = index.search(query, top_k=5)
-
-            if not results:
-                return NoResultsResponse(
-                    message=f"No documentation found for '{query}'. Try different keywords.",
-                    session_id=session_id,
-                    suggestions=[
-                        "Try more general terms like 'blocks', 'agents', 'setup'",
-                        "Check the documentation at docs.agpt.co",
-                    ],
-                )
-
-            # Convert to response format
-            doc_results = []
-            for r in results:
-                # Build documentation URL
-                path = r["path"]
-                if path.endswith(".md"):
-                    path = path[:-3]  # Remove .md extension
-                doc_url = f"{DOCS_BASE_URL}/{path}"
-
-                full_text = r["text"]
-                doc_results.append(
-                    DocSearchResult(
-                        title=r["title"],
-                        path=r["path"],
-                        section=r["heading"],
-                        snippet=(
-                            full_text[:300] + "..."
-                            if len(full_text) > 300
-                            else full_text
-                        ),
-                        content=full_text,  # Full text for LLM to read and understand
-                        score=round(r["score"], 3),
-                        doc_url=doc_url,
-                    )
-                )
-
-            return DocSearchResultsResponse(
-                message=(
-                    f"Found {len(doc_results)} relevant documentation sections. "
-                    "Use these to help answer the user's question. "
-                    "Include links to the documentation when helpful."
-                ),
-                results=doc_results,
-                count=len(doc_results),
-                query=query,
-                session_id=session_id,
-            )
-
-        except Exception as e:
-            logger.error(f"Error searching documentation: {e}", exc_info=True)
-            return ErrorResponse(
-                message="Failed to search documentation. Please try again.",
-                error=str(e),
-                session_id=session_id,
-            )

autogpt_platform/backend/backend/api/features/store/backfill_embeddings.py

@@ -1,72 +0,0 @@
-#!/usr/bin/env python3
-"""
-CLI script to backfill embeddings for store agents.
-
-Usage:
-    poetry run python -m backend.server.v2.store.backfill_embeddings [--batch-size N]
-"""
-
-import argparse
-import asyncio
-import sys
-
-import prisma
-
-
-async def main(batch_size: int = 100) -> int:
-    """Run the backfill process."""
-    # Initialize Prisma client
-    client = prisma.Prisma()
-    await client.connect()
-    prisma.register(client)
-
-    try:
-        from backend.api.features.store.embeddings import (
-            backfill_missing_embeddings,
-            get_embedding_stats,
-        )
-
-        # Get current stats
-        print("Current embedding stats:")
-        stats = await get_embedding_stats()
-        print(f"  Total approved: {stats['total_approved']}")
-        print(f"  With embeddings: {stats['with_embeddings']}")
-        print(f"  Without embeddings: {stats['without_embeddings']}")
-        print(f"  Coverage: {stats['coverage_percent']}%")
-
-        if stats["without_embeddings"] == 0:
-            print("\nAll agents already have embeddings. Nothing to do.")
-            return 0
-
-        # Run backfill
-        print(f"\nBackfilling up to {batch_size} embeddings...")
-        result = await backfill_missing_embeddings(batch_size=batch_size)
-        print(f"  Processed: {result['processed']}")
-        print(f"  Success: {result['success']}")
-        print(f"  Failed: {result['failed']}")
-
-        # Get final stats
-        print("\nFinal embedding stats:")
-        stats = await get_embedding_stats()
-        print(f"  Total approved: {stats['total_approved']}")
-        print(f"  With embeddings: {stats['with_embeddings']}")
-        print(f"  Without embeddings: {stats['without_embeddings']}")
-        print(f"  Coverage: {stats['coverage_percent']}%")
-
-        return 0 if result["failed"] == 0 else 1
-
-    finally:
-        await client.disconnect()
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser(description="Backfill embeddings for store agents")
-    parser.add_argument(
-        "--batch-size",
-        type=int,
-        default=100,
-        help="Number of embeddings to generate (default: 100)",
-    )
-    args = parser.parse_args()
-
-    sys.exit(asyncio.run(main(batch_size=args.batch_size)))

autogpt_platform/backend/backend/api/features/store/db.py

@@ -1,5 +1,6 @@
 import asyncio
 import logging
+import typing
 from datetime import datetime, timezone
 from typing import Literal
 
@@ -9,7 +10,7 @@ import prisma.errors
 import prisma.models
 import prisma.types
 
-from backend.data.db import transaction
+from backend.data.db import query_raw_with_schema, transaction
 from backend.data.graph import (
     GraphMeta,
     GraphModel,
@@ -56,21 +57,95 @@ async def get_store_agents(
     )
 
     try:
-        # If search_query is provided, use hybrid search (embeddings + tsvector)
+        # If search_query is provided, use full-text search
         if search_query:
-            from backend.api.features.store.hybrid_search import hybrid_search
+            offset = (page - 1) * page_size
 
-            # Use hybrid search combining semantic and lexical signals
-            agents, total = await hybrid_search(
-                query=search_query,
-                featured=featured,
-                creators=creators,
-                category=category,
-                sorted_by="relevance",  # Use hybrid scoring for relevance
-                page=page,
-                page_size=page_size,
-            )
+            # Whitelist allowed order_by columns
+            ALLOWED_ORDER_BY = {
+                "rating": "rating DESC, rank DESC",
+                "runs": "runs DESC, rank DESC",
+                "name": "agent_name ASC, rank ASC",
+                "updated_at": "updated_at DESC, rank DESC",
+            }
 
+            # Validate and get order clause
+            if sorted_by and sorted_by in ALLOWED_ORDER_BY:
+                order_by_clause = ALLOWED_ORDER_BY[sorted_by]
+            else:
+                order_by_clause = "updated_at DESC, rank DESC"
+
+            # Build WHERE conditions and parameters list
+            where_parts: list[str] = []
+            params: list[typing.Any] = [search_query]  # $1 - search term
+            param_index = 2  # Start at $2 for next parameter
+
+            # Always filter for available agents
+            where_parts.append("is_available = true")
+
+            if featured:
+                where_parts.append("featured = true")
+
+            if creators and creators:
+                # Use ANY with array parameter
+                where_parts.append(f"creator_username = ANY(${param_index})")
+                params.append(creators)
+                param_index += 1
+
+            if category and category:
+                where_parts.append(f"${param_index} = ANY(categories)")
+                params.append(category)
+                param_index += 1
+
+            sql_where_clause: str = " AND ".join(where_parts) if where_parts else "1=1"
+
+            # Add pagination params
+            params.extend([page_size, offset])
+            limit_param = f"${param_index}"
+            offset_param = f"${param_index + 1}"
+
+            # Execute full-text search query with parameterized values
+            sql_query = f"""
+                SELECT
+                    slug,
+                    agent_name,
+                    agent_image,
+                    creator_username,
+                    creator_avatar,
+                    sub_heading,
+                    description,
+                    runs,
+                    rating,
+                    categories,
+                    featured,
+                    is_available,
+                    updated_at,
+                    ts_rank_cd(search, query) AS rank
+                FROM {{schema_prefix}}"StoreAgent",
+                    plainto_tsquery('english', $1) AS query
+                WHERE {sql_where_clause}
+                    AND search @@ query
+                ORDER BY {order_by_clause}
+                LIMIT {limit_param} OFFSET {offset_param}
+            """
+
+            # Count query for pagination - only uses search term parameter
+            count_query = f"""
+                SELECT COUNT(*) as count
+                FROM {{schema_prefix}}"StoreAgent",
+                    plainto_tsquery('english', $1) AS query
+                WHERE {sql_where_clause}
+                    AND search @@ query
+            """
+
+            # Execute both queries with parameters
+            agents = await query_raw_with_schema(sql_query, *params)
+
+            # For count, use params without pagination (last 2 params)
+            count_params = params[:-2]
+            count_result = await query_raw_with_schema(count_query, *count_params)
+
+            total = count_result[0]["count"] if count_result else 0
             total_pages = (total + page_size - 1) // page_size
 
             # Convert raw results to StoreAgent models
@@ -1489,24 +1564,6 @@ async def review_store_submission(
                     },
                 )
 
-            # Generate embedding for approved listing (non-blocking)
-            try:
-                from backend.api.features.store.embeddings import ensure_embedding
-
-                await ensure_embedding(
-                    version_id=store_listing_version_id,
-                    name=store_listing_version.name,
-                    description=store_listing_version.description,
-                    sub_heading=store_listing_version.subHeading,
-                    categories=store_listing_version.categories or [],
-                )
-            except Exception as e:
-                # Don't fail approval if embedding generation fails
-                logger.warning(
-                    f"Failed to generate embedding for approved listing "
-                    f"{store_listing_version_id}: {e}"
-                )
-
         # If rejecting an approved agent, update the StoreListing accordingly
         if is_rejecting_approved:
             # Check if there are other approved versions

autogpt_platform/backend/backend/api/features/store/embeddings.py

@@ -1,408 +0,0 @@
-"""
-Store Listing Embeddings Service
-
-Handles generation and storage of OpenAI embeddings for store listings
-to enable semantic/hybrid search.
-"""
-
-import hashlib
-import logging
-import os
-from typing import Any
-
-import prisma
-
-logger = logging.getLogger(__name__)
-
-# OpenAI embedding model configuration
-EMBEDDING_MODEL = "text-embedding-3-small"
-EMBEDDING_DIM = 1536
-
-
-def build_searchable_text(
-    name: str,
-    description: str,
-    sub_heading: str,
-    categories: list[str],
-) -> str:
-    """
-    Build searchable text from listing version fields.
-
-    Combines relevant fields into a single string for embedding.
-    """
-    parts = []
-
-    # Name is important - include it
-    if name:
-        parts.append(name)
-
-    # Sub-heading provides context
-    if sub_heading:
-        parts.append(sub_heading)
-
-    # Description is the main content
-    if description:
-        parts.append(description)
-
-    # Categories help with semantic matching
-    if categories:
-        parts.append(" ".join(categories))
-
-    return " ".join(parts)
-
-
-def compute_content_hash(text: str) -> str:
-    """Compute MD5 hash of text for change detection."""
-    return hashlib.md5(text.encode()).hexdigest()
-
-
-async def generate_embedding(text: str) -> list[float] | None:
-    """
-    Generate embedding for text using OpenAI API.
-
-    Returns None if embedding generation fails.
-    """
-    try:
-        from openai import OpenAI
-
-        api_key = os.getenv("OPENAI_API_KEY")
-        if not api_key:
-            logger.warning("OPENAI_API_KEY not set, cannot generate embedding")
-            return None
-
-        client = OpenAI(api_key=api_key)
-
-        # Truncate text to avoid token limits (~32k chars for safety)
-        truncated_text = text[:32000]
-
-        response = client.embeddings.create(
-            model=EMBEDDING_MODEL,
-            input=truncated_text,
-        )
-
-        embedding = response.data[0].embedding
-        logger.debug(f"Generated embedding with {len(embedding)} dimensions")
-        return embedding
-
-    except Exception as e:
-        logger.error(f"Failed to generate embedding: {e}")
-        return None
-
-
-async def store_embedding(
-    version_id: str,
-    embedding: list[float],
-    searchable_text: str,
-    content_hash: str,
-    tx: prisma.Prisma | None = None,
-) -> bool:
-    """
-    Store embedding in the database.
-
-    Uses raw SQL since Prisma doesn't natively support pgvector.
-    """
-    try:
-        client = tx if tx else prisma.get_client()
-
-        # Convert embedding to PostgreSQL vector format
-        embedding_str = "[" + ",".join(str(x) for x in embedding) + "]"
-
-        # Upsert the embedding
-        # Set search_path to include public for vector type visibility
-        await client.execute_raw(
-            """
-            SET LOCAL search_path TO platform, public;
-            INSERT INTO platform."StoreListingEmbedding" (
-                "id", "storeListingVersionId", "embedding",
-                "searchableText", "contentHash", "createdAt", "updatedAt"
-            )
-            VALUES (
-                gen_random_uuid(), $1, $2::vector,
-                $3, $4, NOW(), NOW()
-            )
-            ON CONFLICT ("storeListingVersionId")
-            DO UPDATE SET
-                "embedding" = $2::vector,
-                "searchableText" = $3,
-                "contentHash" = $4,
-                "updatedAt" = NOW()
-            """,
-            version_id,
-            embedding_str,
-            searchable_text,
-            content_hash,
-        )
-
-        logger.info(f"Stored embedding for version {version_id}")
-        return True
-
-    except Exception as e:
-        logger.error(f"Failed to store embedding for version {version_id}: {e}")
-        return False
-
-
-async def get_embedding(version_id: str) -> dict[str, Any] | None:
-    """
-    Retrieve embedding record for a listing version.
-
-    Returns dict with embedding, searchableText, contentHash or None if not found.
-    """
-    try:
-        client = prisma.get_client()
-
-        result = await client.query_raw(
-            """
-            SELECT
-                "id",
-                "storeListingVersionId",
-                "embedding"::text as "embedding",
-                "searchableText",
-                "contentHash",
-                "createdAt",
-                "updatedAt"
-            FROM platform."StoreListingEmbedding"
-            WHERE "storeListingVersionId" = $1
-            """,
-            version_id,
-        )
-
-        if result and len(result) > 0:
-            return result[0]
-        return None
-
-    except Exception as e:
-        logger.error(f"Failed to get embedding for version {version_id}: {e}")
-        return None
-
-
-async def ensure_embedding(
-    version_id: str,
-    name: str,
-    description: str,
-    sub_heading: str,
-    categories: list[str],
-    force: bool = False,
-    tx: prisma.Prisma | None = None,
-) -> bool:
-    """
-    Ensure an embedding exists for the listing version.
-
-    Creates embedding if missing or if content has changed.
-    Skips if content hash matches existing embedding.
-
-    Args:
-        version_id: The StoreListingVersion ID
-        name: Agent name
-        description: Agent description
-        sub_heading: Agent sub-heading
-        categories: Agent categories
-        force: Force regeneration even if hash matches
-        tx: Optional transaction client
-
-    Returns:
-        True if embedding exists/was created, False on failure
-    """
-    try:
-        # Build searchable text and compute hash
-        searchable_text = build_searchable_text(
-            name, description, sub_heading, categories
-        )
-        content_hash = compute_content_hash(searchable_text)
-
-        # Check if embedding already exists with same hash
-        if not force:
-            existing = await get_embedding(version_id)
-            if existing and existing.get("contentHash") == content_hash:
-                logger.debug(
-                    f"Embedding for version {version_id} is up to date (hash match)"
-                )
-                return True
-
-        # Generate new embedding
-        embedding = await generate_embedding(searchable_text)
-        if embedding is None:
-            logger.warning(f"Could not generate embedding for version {version_id}")
-            return False
-
-        # Store the embedding
-        return await store_embedding(
-            version_id=version_id,
-            embedding=embedding,
-            searchable_text=searchable_text,
-            content_hash=content_hash,
-            tx=tx,
-        )
-
-    except Exception as e:
-        logger.error(f"Failed to ensure embedding for version {version_id}: {e}")
-        return False
-
-
-async def delete_embedding(version_id: str) -> bool:
-    """
-    Delete embedding for a listing version.
-
-    Note: This is usually handled automatically by CASCADE delete,
-    but provided for manual cleanup if needed.
-    """
-    try:
-        client = prisma.get_client()
-
-        await client.execute_raw(
-            """
-            DELETE FROM platform."StoreListingEmbedding"
-            WHERE "storeListingVersionId" = $1
-            """,
-            version_id,
-        )
-
-        logger.info(f"Deleted embedding for version {version_id}")
-        return True
-
-    except Exception as e:
-        logger.error(f"Failed to delete embedding for version {version_id}: {e}")
-        return False
-
-
-async def get_embedding_stats() -> dict[str, Any]:
-    """
-    Get statistics about embedding coverage.
-
-    Returns counts of:
-    - Total approved listing versions
-    - Versions with embeddings
-    - Versions without embeddings
-    """
-    try:
-        client = prisma.get_client()
-
-        # Count approved versions
-        approved_result = await client.query_raw(
-            """
-            SELECT COUNT(*) as count
-            FROM platform."StoreListingVersion"
-            WHERE "submissionStatus" = 'APPROVED'
-            AND "isDeleted" = false
-            """
-        )
-        total_approved = approved_result[0]["count"] if approved_result else 0
-
-        # Count versions with embeddings
-        embedded_result = await client.query_raw(
-            """
-            SELECT COUNT(*) as count
-            FROM platform."StoreListingVersion" slv
-            JOIN platform."StoreListingEmbedding" sle ON slv.id = sle."storeListingVersionId"
-            WHERE slv."submissionStatus" = 'APPROVED'
-            AND slv."isDeleted" = false
-            """
-        )
-        with_embeddings = embedded_result[0]["count"] if embedded_result else 0
-
-        return {
-            "total_approved": total_approved,
-            "with_embeddings": with_embeddings,
-            "without_embeddings": total_approved - with_embeddings,
-            "coverage_percent": (
-                round(with_embeddings / total_approved * 100, 1)
-                if total_approved > 0
-                else 0
-            ),
-        }
-
-    except Exception as e:
-        logger.error(f"Failed to get embedding stats: {e}")
-        return {
-            "total_approved": 0,
-            "with_embeddings": 0,
-            "without_embeddings": 0,
-            "coverage_percent": 0,
-            "error": str(e),
-        }
-
-
-async def backfill_missing_embeddings(batch_size: int = 10) -> dict[str, Any]:
-    """
-    Generate embeddings for approved listings that don't have them.
-
-    Args:
-        batch_size: Number of embeddings to generate in one call
-
-    Returns:
-        Dict with success/failure counts
-    """
-    try:
-        client = prisma.get_client()
-
-        # Find approved versions without embeddings
-        missing = await client.query_raw(
-            """
-            SELECT
-                slv.id,
-                slv.name,
-                slv.description,
-                slv."subHeading",
-                slv.categories
-            FROM platform."StoreListingVersion" slv
-            LEFT JOIN platform."StoreListingEmbedding" sle ON slv.id = sle."storeListingVersionId"
-            WHERE slv."submissionStatus" = 'APPROVED'
-            AND slv."isDeleted" = false
-            AND sle.id IS NULL
-            LIMIT $1
-            """,
-            batch_size,
-        )
-
-        if not missing:
-            return {
-                "processed": 0,
-                "success": 0,
-                "failed": 0,
-                "message": "No missing embeddings",
-            }
-
-        success = 0
-        failed = 0
-
-        for row in missing:
-            result = await ensure_embedding(
-                version_id=row["id"],
-                name=row["name"],
-                description=row["description"],
-                sub_heading=row["subHeading"],
-                categories=row["categories"] or [],
-            )
-            if result:
-                success += 1
-            else:
-                failed += 1
-
-        return {
-            "processed": len(missing),
-            "success": success,
-            "failed": failed,
-            "message": f"Backfilled {success} embeddings, {failed} failed",
-        }
-
-    except Exception as e:
-        logger.error(f"Failed to backfill embeddings: {e}")
-        return {
-            "processed": 0,
-            "success": 0,
-            "failed": 0,
-            "error": str(e),
-        }
-
-
-async def embed_query(query: str) -> list[float] | None:
-    """
-    Generate embedding for a search query.
-
-    Same as generate_embedding but with clearer intent.
-    """
-    return await generate_embedding(query)
-
-
-def embedding_to_vector_string(embedding: list[float]) -> str:
-    """Convert embedding list to PostgreSQL vector string format."""
-    return "[" + ",".join(str(x) for x in embedding) + "]"

autogpt_platform/backend/backend/api/features/store/hybrid_search.py

@@ -1,440 +0,0 @@
-"""
-Hybrid Search for Store Agents
-
-Combines semantic (embedding) search with lexical (tsvector) search
-for improved relevance in marketplace agent discovery.
-"""
-
-import logging
-from dataclasses import dataclass
-from datetime import datetime
-from typing import Any, Literal
-
-import prisma
-
-from backend.api.features.store.embeddings import (
-    embed_query,
-    embedding_to_vector_string,
-)
-
-logger = logging.getLogger(__name__)
-
-
-@dataclass
-class HybridSearchWeights:
-    """Weights for combining search signals."""
-
-    semantic: float = 0.35  # Embedding cosine similarity
-    lexical: float = 0.35  # tsvector ts_rank_cd score
-    category: float = 0.20  # Category match boost
-    recency: float = 0.10  # Newer agents ranked higher
-
-
-DEFAULT_WEIGHTS = HybridSearchWeights()
-
-# Minimum relevance score threshold - agents below this are filtered out
-# With weights (0.35 semantic + 0.35 lexical + 0.20 category + 0.10 recency):
-# - 0.20 means at least ~50% semantic match OR strong lexical match required
-# - Ensures only genuinely relevant results are returned
-# - Recency alone (0.10 max) won't pass the threshold
-DEFAULT_MIN_SCORE = 0.20
-
-
-@dataclass
-class HybridSearchResult:
-    """A single search result with score breakdown."""
-
-    slug: str
-    agent_name: str
-    agent_image: str
-    creator_username: str
-    creator_avatar: str
-    sub_heading: str
-    description: str
-    runs: int
-    rating: float
-    categories: list[str]
-    featured: bool
-    is_available: bool
-    updated_at: datetime
-
-    # Score breakdown (for debugging/tuning)
-    combined_score: float
-    semantic_score: float = 0.0
-    lexical_score: float = 0.0
-    category_score: float = 0.0
-    recency_score: float = 0.0
-
-
-async def hybrid_search(
-    query: str,
-    featured: bool = False,
-    creators: list[str] | None = None,
-    category: str | None = None,
-    sorted_by: (
-        Literal["relevance", "rating", "runs", "name", "updated_at"] | None
-    ) = None,
-    page: int = 1,
-    page_size: int = 20,
-    weights: HybridSearchWeights | None = None,
-    min_score: float | None = None,
-) -> tuple[list[dict[str, Any]], int]:
-    """
-    Perform hybrid search combining semantic and lexical signals.
-
-    Args:
-        query: Search query string
-        featured: Filter for featured agents only
-        creators: Filter by creator usernames
-        category: Filter by category
-        sorted_by: Sort order (relevance uses hybrid scoring)
-        page: Page number (1-indexed)
-        page_size: Results per page
-        weights: Custom weights for search signals
-        min_score: Minimum relevance score threshold (0-1). Results below
-                   this score are filtered out. Defaults to DEFAULT_MIN_SCORE.
-
-    Returns:
-        Tuple of (results list, total count). Returns empty list if no
-        results meet the minimum relevance threshold.
-    """
-    if weights is None:
-        weights = DEFAULT_WEIGHTS
-    if min_score is None:
-        min_score = DEFAULT_MIN_SCORE
-
-    offset = (page - 1) * page_size
-    client = prisma.get_client()
-
-    # Generate query embedding
-    query_embedding = await embed_query(query)
-
-    # Build WHERE clause conditions
-    where_parts: list[str] = ["sa.is_available = true"]
-    params: list[Any] = []
-    param_index = 1
-
-    # Add search query for lexical matching
-    params.append(query)
-    query_param = f"${param_index}"
-    param_index += 1
-
-    if featured:
-        where_parts.append("sa.featured = true")
-
-    if creators:
-        where_parts.append(f"sa.creator_username = ANY(${param_index})")
-        params.append(creators)
-        param_index += 1
-
-    if category:
-        where_parts.append(f"${param_index} = ANY(sa.categories)")
-        params.append(category)
-        param_index += 1
-
-    where_clause = " AND ".join(where_parts)
-
-    # Determine if we can use hybrid search (have query embedding)
-    use_hybrid = query_embedding is not None
-
-    if use_hybrid:
-        # Add embedding parameter
-        embedding_str = embedding_to_vector_string(query_embedding)
-        params.append(embedding_str)
-        embedding_param = f"${param_index}"
-        param_index += 1
-
-        # Build hybrid search query with weighted scoring
-        # The semantic score is (1 - cosine_distance), normalized to [0,1]
-        # The lexical score is ts_rank_cd, normalized by max value
-        # Set search_path to include public for vector type visibility
-        sql_query = f"""
-            SET LOCAL search_path TO platform, public;
-            WITH search_scores AS (
-                SELECT
-                    sa.*,
-                    -- Semantic score: cosine similarity (1 - distance)
-                    COALESCE(1 - (sle.embedding <=> {embedding_param}::vector), 0) as semantic_score,
-                    -- Lexical score: ts_rank_cd normalized
-                    COALESCE(ts_rank_cd(sa.search, plainto_tsquery('english', {query_param})), 0) as lexical_raw,
-                    -- Category match: 1 if query term appears in categories, else 0
-                    CASE
-                        WHEN EXISTS (
-                            SELECT 1 FROM unnest(sa.categories) cat
-                            WHERE LOWER(cat) LIKE '%' || LOWER({query_param}) || '%'
-                        ) THEN 1.0
-                        ELSE 0.0
-                    END as category_score,
-                    -- Recency score: exponential decay over 90 days
-                    EXP(-EXTRACT(EPOCH FROM (NOW() - sa.updated_at)) / (90 * 24 * 3600)) as recency_score
-                FROM platform."StoreAgent" sa
-                LEFT JOIN platform."StoreListing" sl ON sa.slug = sl.slug
-                LEFT JOIN platform."StoreListingVersion" slv ON sl."activeVersionId" = slv.id
-                LEFT JOIN platform."StoreListingEmbedding" sle ON slv.id = sle."storeListingVersionId"
-                WHERE {where_clause}
-                AND (
-                    sa.search @@ plainto_tsquery('english', {query_param})
-                    OR sle.embedding IS NOT NULL
-                )
-            ),
-            normalized AS (
-                SELECT
-                    *,
-                    -- Normalize lexical score by max in result set
-                    CASE
-                        WHEN MAX(lexical_raw) OVER () > 0
-                        THEN lexical_raw / MAX(lexical_raw) OVER ()
-                        ELSE 0
-                    END as lexical_score
-                FROM search_scores
-            ),
-            scored AS (
-                SELECT
-                    slug,
-                    agent_name,
-                    agent_image,
-                    creator_username,
-                    creator_avatar,
-                    sub_heading,
-                    description,
-                    runs,
-                    rating,
-                    categories,
-                    featured,
-                    is_available,
-                    updated_at,
-                    semantic_score,
-                    lexical_score,
-                    category_score,
-                    recency_score,
-                    (
-                        {weights.semantic} * semantic_score +
-                        {weights.lexical} * lexical_score +
-                        {weights.category} * category_score +
-                        {weights.recency} * recency_score
-                    ) as combined_score
-                FROM normalized
-            )
-            SELECT * FROM scored
-            WHERE combined_score >= {min_score}
-            ORDER BY combined_score DESC
-            LIMIT ${param_index} OFFSET ${param_index + 1}
-        """
-
-        # Add pagination params
-        params.extend([page_size, offset])
-
-        # Count query - must also filter by min_score
-        count_query = f"""
-            SET LOCAL search_path TO platform, public;
-            WITH search_scores AS (
-                SELECT
-                    sa.slug,
-                    COALESCE(1 - (sle.embedding <=> {embedding_param}::vector), 0) as semantic_score,
-                    COALESCE(ts_rank_cd(sa.search, plainto_tsquery('english', {query_param})), 0) as lexical_raw,
-                    CASE
-                        WHEN EXISTS (
-                            SELECT 1 FROM unnest(sa.categories) cat
-                            WHERE LOWER(cat) LIKE '%' || LOWER({query_param}) || '%'
-                        ) THEN 1.0
-                        ELSE 0.0
-                    END as category_score,
-                    EXP(-EXTRACT(EPOCH FROM (NOW() - sa.updated_at)) / (90 * 24 * 3600)) as recency_score
-                FROM platform."StoreAgent" sa
-                LEFT JOIN platform."StoreListing" sl ON sa.slug = sl.slug
-                LEFT JOIN platform."StoreListingVersion" slv ON sl."activeVersionId" = slv.id
-                LEFT JOIN platform."StoreListingEmbedding" sle ON slv.id = sle."storeListingVersionId"
-                WHERE {where_clause}
-                AND (
-                    sa.search @@ plainto_tsquery('english', {query_param})
-                    OR sle.embedding IS NOT NULL
-                )
-            ),
-            normalized AS (
-                SELECT
-                    slug,
-                    semantic_score,
-                    category_score,
-                    recency_score,
-                    CASE
-                        WHEN MAX(lexical_raw) OVER () > 0
-                        THEN lexical_raw / MAX(lexical_raw) OVER ()
-                        ELSE 0
-                    END as lexical_score
-                FROM search_scores
-            ),
-            scored AS (
-                SELECT
-                    slug,
-                    (
-                        {weights.semantic} * semantic_score +
-                        {weights.lexical} * lexical_score +
-                        {weights.category} * category_score +
-                        {weights.recency} * recency_score
-                    ) as combined_score
-                FROM normalized
-            )
-            SELECT COUNT(*) as count FROM scored
-            WHERE combined_score >= {min_score}
-        """
-
-    else:
-        # Fallback to lexical-only search (existing behavior)
-        # Note: For lexical-only, we still require tsvector match but don't
-        # apply min_score since ts_rank_cd isn't normalized to [0,1]
-        logger.warning("Falling back to lexical-only search (no query embedding)")
-
-        sql_query = f"""
-            WITH lexical_scores AS (
-                SELECT
-                    slug,
-                    agent_name,
-                    agent_image,
-                    creator_username,
-                    creator_avatar,
-                    sub_heading,
-                    description,
-                    runs,
-                    rating,
-                    categories,
-                    featured,
-                    is_available,
-                    updated_at,
-                    0.0 as semantic_score,
-                    ts_rank_cd(search, plainto_tsquery('english', {query_param})) as lexical_raw,
-                    CASE
-                        WHEN EXISTS (
-                            SELECT 1 FROM unnest(categories) cat
-                            WHERE LOWER(cat) LIKE '%' || LOWER({query_param}) || '%'
-                        ) THEN 1.0
-                        ELSE 0.0
-                    END as category_score,
-                    EXP(-EXTRACT(EPOCH FROM (NOW() - updated_at)) / (90 * 24 * 3600)) as recency_score
-                FROM platform."StoreAgent" sa
-                WHERE {where_clause}
-                AND search @@ plainto_tsquery('english', {query_param})
-            ),
-            normalized AS (
-                SELECT
-                    *,
-                    CASE
-                        WHEN MAX(lexical_raw) OVER () > 0
-                        THEN lexical_raw / MAX(lexical_raw) OVER ()
-                        ELSE 0
-                    END as lexical_score
-                FROM lexical_scores
-            ),
-            scored AS (
-                SELECT
-                    slug,
-                    agent_name,
-                    agent_image,
-                    creator_username,
-                    creator_avatar,
-                    sub_heading,
-                    description,
-                    runs,
-                    rating,
-                    categories,
-                    featured,
-                    is_available,
-                    updated_at,
-                    semantic_score,
-                    lexical_score,
-                    category_score,
-                    recency_score,
-                    (
-                        {weights.lexical} * lexical_score +
-                        {weights.category} * category_score +
-                        {weights.recency} * recency_score
-                    ) as combined_score
-                FROM normalized
-            )
-            SELECT * FROM scored
-            WHERE combined_score >= {min_score}
-            ORDER BY combined_score DESC
-            LIMIT ${param_index} OFFSET ${param_index + 1}
-        """
-
-        params.extend([page_size, offset])
-
-        count_query = f"""
-            WITH lexical_scores AS (
-                SELECT
-                    slug,
-                    ts_rank_cd(search, plainto_tsquery('english', {query_param})) as lexical_raw,
-                    CASE
-                        WHEN EXISTS (
-                            SELECT 1 FROM unnest(categories) cat
-                            WHERE LOWER(cat) LIKE '%' || LOWER({query_param}) || '%'
-                        ) THEN 1.0
-                        ELSE 0.0
-                    END as category_score,
-                    EXP(-EXTRACT(EPOCH FROM (NOW() - updated_at)) / (90 * 24 * 3600)) as recency_score
-                FROM platform."StoreAgent" sa
-                WHERE {where_clause}
-                AND search @@ plainto_tsquery('english', {query_param})
-            ),
-            normalized AS (
-                SELECT
-                    slug,
-                    category_score,
-                    recency_score,
-                    CASE
-                        WHEN MAX(lexical_raw) OVER () > 0
-                        THEN lexical_raw / MAX(lexical_raw) OVER ()
-                        ELSE 0
-                    END as lexical_score
-                FROM lexical_scores
-            ),
-            scored AS (
-                SELECT
-                    slug,
-                    (
-                        {weights.lexical} * lexical_score +
-                        {weights.category} * category_score +
-                        {weights.recency} * recency_score
-                    ) as combined_score
-                FROM normalized
-            )
-            SELECT COUNT(*) as count FROM scored
-            WHERE combined_score >= {min_score}
-        """
-
-    try:
-        # Execute search query
-        # Dynamic SQL is safe here - all user inputs are parameterized ($1, $2, etc.)
-        results = await client.query_raw(sql_query, *params)  # type: ignore[arg-type]
-
-        # Execute count query (without pagination params)
-        count_params = params[:-2]  # Remove LIMIT and OFFSET params
-        count_result = await client.query_raw(count_query, *count_params)  # type: ignore[arg-type]
-        total = count_result[0]["count"] if count_result else 0
-
-        logger.info(
-            f"Hybrid search for '{query}': {len(results)} results, {total} total "
-            f"(hybrid={use_hybrid})"
-        )
-
-        return results, total
-
-    except Exception as e:
-        logger.error(f"Hybrid search failed: {e}")
-        raise
-
-
-async def hybrid_search_simple(
-    query: str,
-    page: int = 1,
-    page_size: int = 20,
-) -> tuple[list[dict[str, Any]], int]:
-    """
-    Simplified hybrid search for common use cases.
-
-    Uses default weights and no filters.
-    """
-    return await hybrid_search(
-        query=query,
-        page=page,
-        page_size=page_size,
-    )

autogpt_platform/backend/backend/util/settings.py

@@ -658,14 +658,6 @@ class Secrets(UpdateTrackingModel["Secrets"], BaseSettings):
 
     ayrshare_api_key: str = Field(default="", description="Ayrshare API Key")
     ayrshare_jwt_key: str = Field(default="", description="Ayrshare private Key")
-
-    # Langfuse prompt management
-    langfuse_public_key: str = Field(default="", description="Langfuse public key")
-    langfuse_secret_key: str = Field(default="", description="Langfuse secret key")
-    langfuse_host: str = Field(
-        default="https://cloud.langfuse.com", description="Langfuse host URL"
-    )
-
     # Add more secret fields as needed
     model_config = SettingsConfigDict(
         env_file=".env",

autogpt_platform/backend/migrations/20251216181700_add_store_embeddings/migration.sql

@@ -1,41 +0,0 @@
--- Migration: Add pgvector extension and StoreListingEmbedding table
--- This enables hybrid search combining semantic (embedding) and lexical (tsvector) search
-
--- Enable pgvector extension for vector similarity search
-CREATE EXTENSION IF NOT EXISTS vector;
-
--- Create table to store embeddings for store listing versions
-CREATE TABLE "StoreListingEmbedding" (
-    "id" TEXT NOT NULL DEFAULT gen_random_uuid(),
-    "storeListingVersionId" TEXT NOT NULL,
-    "embedding" vector(1536),  -- OpenAI text-embedding-3-small produces 1536 dimensions
-    "searchableText" TEXT,     -- The text that was embedded (for debugging/recomputation)
-    "contentHash" TEXT,        -- MD5 hash of searchable text for change detection
-    "createdAt" TIMESTAMP(3) NOT NULL DEFAULT CURRENT_TIMESTAMP,
-    "updatedAt" TIMESTAMP(3) NOT NULL DEFAULT CURRENT_TIMESTAMP,
-
-    CONSTRAINT "StoreListingEmbedding_pkey" PRIMARY KEY ("id")
-);
-
--- Unique constraint: one embedding per listing version
-CREATE UNIQUE INDEX "StoreListingEmbedding_storeListingVersionId_key"
-    ON "StoreListingEmbedding"("storeListingVersionId");
-
--- HNSW index for fast approximate nearest neighbor search
--- Using cosine distance (vector_cosine_ops) which is standard for text embeddings
-CREATE INDEX "StoreListingEmbedding_embedding_idx"
-    ON "StoreListingEmbedding"
-    USING hnsw ("embedding" vector_cosine_ops);
-
--- Index on content hash for fast lookup during change detection
-CREATE INDEX "StoreListingEmbedding_contentHash_idx"
-    ON "StoreListingEmbedding"("contentHash");
-
--- Foreign key to StoreListingVersion with CASCADE delete
--- When a listing version is deleted, its embedding is automatically removed
-ALTER TABLE "StoreListingEmbedding"
-    ADD CONSTRAINT "StoreListingEmbedding_storeListingVersionId_fkey"
-    FOREIGN KEY ("storeListingVersionId")
-    REFERENCES "StoreListingVersion"("id")
-    ON DELETE CASCADE
-    ON UPDATE CASCADE;

autogpt_platform/backend/migrations/20251216181803_enhance_search/migration.sql

@@ -1,5 +0,0 @@
--- DropIndex
-DROP INDEX "StoreListingEmbedding_embedding_idx";
-
--- AlterTable
-ALTER TABLE "StoreListingEmbedding" ALTER COLUMN "id" DROP DEFAULT;

autogpt_platform/backend/migrations/20260108110428_add_chat_tables/migration.sql

@@ -1,3 +1,6 @@
+-- DropIndex
+DROP INDEX "StoreListingVersion_storeListingId_version_key";
+
 -- CreateTable
 CREATE TABLE "UserBusinessUnderstanding" (
     "id" TEXT NOT NULL,

autogpt_platform/backend/pyproject.toml

@@ -33,7 +33,6 @@ html2text = "^2024.2.26"
 jinja2 = "^3.1.6"
 jsonref = "^1.1.0"
 jsonschema = "^4.25.0"
-langfuse = "^2.0.0"
 launchdarkly-server-sdk = "^9.12.0"
 mem0ai = "^0.1.115"
 moviepy = "^2.1.2"

autogpt_platform/backend/schema.prisma

@@ -1,15 +1,14 @@
 datasource db {
-  provider   = "postgresql"
-  url        = env("DATABASE_URL")
-  directUrl  = env("DIRECT_URL")
-  extensions = [pgvector(map: "vector", schema: "public")]
+  provider  = "postgresql"
+  url       = env("DATABASE_URL")
+  directUrl = env("DIRECT_URL")
 }
 
 generator client {
   provider               = "prisma-client-py"
   recursive_type_depth   = -1
   interface              = "asyncio"
-  previewFeatures        = ["views", "fullTextSearch", "postgresqlExtensions"]
+  previewFeatures        = ["views", "fullTextSearch"]
   partial_type_generator = "backend/data/partial_types.py"
 }
 
@@ -991,10 +990,6 @@ model StoreListingVersion {
   // Reviews for this specific version
   Reviews StoreListingReview[]
 
-  // Embedding for semantic search (one-to-one)
-  Embedding StoreListingEmbedding?
-
-  @@unique([storeListingId, version])
   @@index([storeListingId, submissionStatus, isAvailable])
   @@index([submissionStatus])
   @@index([reviewerId])
@@ -1019,24 +1014,6 @@ model StoreListingReview {
   @@index([reviewByUserId])
 }
 
-// Stores vector embeddings for semantic search of store listings
-// Uses pgvector extension for efficient similarity search
-model StoreListingEmbedding {
-  id        String   @id @default(uuid())
-  createdAt DateTime @default(now())
-  updatedAt DateTime @default(now()) @updatedAt
-
-  storeListingVersionId String              @unique
-  StoreListingVersion   StoreListingVersion @relation(fields: [storeListingVersionId], references: [id], onDelete: Cascade)
-
-  // pgvector embedding - stored as Unsupported type since Prisma doesn't natively support vector
-  embedding      Unsupported("vector(1536)")?
-  searchableText String? // The text that was embedded (for debugging/recomputation)
-  contentHash    String? // MD5 hash for change detection
-
-  @@index([contentHash])
-}
-
 enum SubmissionStatus {
   DRAFT // Being prepared, not yet submitted
   PENDING // Submitted, awaiting review

autogpt_platform/frontend/package.json

@@ -54,7 +54,7 @@
     "@radix-ui/react-tooltip": "1.2.8",
     "@rjsf/core": "6.1.2",
     "@rjsf/utils": "6.1.2",
-    "@rjsf/validator-ajv8": "5.24.13",
+    "@rjsf/validator-ajv8": "6.1.2",
     "@sentry/nextjs": "10.27.0",
     "@supabase/ssr": "0.7.0",
     "@supabase/supabase-js": "2.78.0",

autogpt_platform/frontend/src/app/(no-navbar)/onboarding/5-run/components/AgentOnboardingCredentials/AgentOnboardingCredentials.tsx

@@ -1,6 +1,6 @@
+import { CredentialsInput } from "@/app/(platform)/library/agents/[id]/components/NewAgentLibraryView/components/modals/CredentialsInputs/CredentialsInputs";
 import { CredentialsMetaInput } from "@/app/api/__generated__/models/credentialsMetaInput";
 import { GraphMeta } from "@/app/api/__generated__/models/graphMeta";
-import { CredentialsInput } from "@/components/contextual/CredentialsInputs/CredentialsInputs";
 import { useState } from "react";
 import { getSchemaDefaultCredentials } from "../../helpers";
 import { areAllCredentialsSet, getCredentialFields } from "./helpers";

autogpt_platform/frontend/src/app/(no-navbar)/onboarding/5-run/page.tsx

@@ -1,12 +1,12 @@
 "use client";
 
+import { RunAgentInputs } from "@/app/(platform)/library/agents/[id]/components/NewAgentLibraryView/components/modals/RunAgentInputs/RunAgentInputs";
 import {
   Card,
   CardContent,
   CardHeader,
   CardTitle,
 } from "@/components/__legacy__/ui/card";
-import { RunAgentInputs } from "@/components/contextual/RunAgentInputs/RunAgentInputs";
 import { ErrorCard } from "@/components/molecules/ErrorCard/ErrorCard";
 import { CircleNotchIcon } from "@phosphor-icons/react/dist/ssr";
 import { Play } from "lucide-react";

autogpt_platform/frontend/src/app/(platform)/PlatformLayoutContent.tsx

@@ -1,48 +0,0 @@
-"use client";
-
-import { ChatDrawer } from "@/components/contextual/Chat/ChatDrawer";
-import { usePathname } from "next/navigation";
-import { Children, ReactNode } from "react";
-
-interface PlatformLayoutContentProps {
-  children: ReactNode;
-}
-
-export function PlatformLayoutContent({
-  children,
-}: PlatformLayoutContentProps) {
-  const pathname = usePathname();
-  const isAuthPage =
-    pathname?.includes("/login") || pathname?.includes("/signup");
-
-  // Extract Navbar, AdminImpersonationBanner, and page content from children
-  const childrenArray = Children.toArray(children);
-  const navbar = childrenArray[0];
-  const adminBanner = childrenArray[1];
-  const pageContent = childrenArray.slice(2);
-
-  // For login/signup pages, use a simpler layout that doesn't interfere with centering
-  if (isAuthPage) {
-    return (
-      <main className="flex min-h-screen w-full flex-col">
-        {navbar}
-        {adminBanner}
-        <section className="flex-1">{pageContent}</section>
-        {/* ChatDrawer must always be rendered to maintain consistent hook count */}
-        <ChatDrawer />
-      </main>
-    );
-  }
-
-  // For logged-in pages, use the drawer layout
-  return (
-    <main className="flex h-screen w-full flex-col overflow-hidden">
-      {navbar}
-      {adminBanner}
-      <section className="flex min-h-0 flex-1 overflow-auto">
-        {pageContent}
-      </section>
-      <ChatDrawer />
-    </main>
-  );
-}

autogpt_platform/frontend/src/app/(platform)/auth/integrations/setup-wizard/page.tsx

@@ -8,7 +8,7 @@ import { AuthCard } from "@/components/auth/AuthCard";
 import { Text } from "@/components/atoms/Text/Text";
 import { Button } from "@/components/atoms/Button/Button";
 import { ErrorCard } from "@/components/molecules/ErrorCard/ErrorCard";
-import { CredentialsInput } from "@/components/contextual/CredentialsInputs/CredentialsInputs";
+import { CredentialsInput } from "@/app/(platform)/library/agents/[id]/components/NewAgentLibraryView/components/modals/CredentialsInputs/CredentialsInputs";
 import type {
   BlockIOCredentialsSubSchema,
   CredentialsMetaInput,

autogpt_platform/frontend/src/app/(platform)/build/components/BuilderActions/components/AgentOutputs/AgentOutputs.tsx

@@ -1,6 +1,11 @@
 import { BlockUIType } from "@/app/(platform)/build/components/types";
 import { useGraphStore } from "@/app/(platform)/build/stores/graphStore";
 import { useNodeStore } from "@/app/(platform)/build/stores/nodeStore";
+import {
+  globalRegistry,
+  OutputActions,
+  OutputItem,
+} from "@/app/(platform)/library/agents/[id]/components/NewAgentLibraryView/components/selected-views/OutputRenderers";
 import { Label } from "@/components/__legacy__/ui/label";
 import { ScrollArea } from "@/components/__legacy__/ui/scroll-area";
 import {
@@ -18,11 +23,6 @@ import {
   TooltipProvider,
   TooltipTrigger,
 } from "@/components/atoms/Tooltip/BaseTooltip";
-import {
-  globalRegistry,
-  OutputActions,
-  OutputItem,
-} from "@/components/contextual/OutputRenderers";
 import { BookOpenIcon } from "@phosphor-icons/react";
 import { useMemo } from "react";
 import { useShallow } from "zustand/react/shallow";

autogpt_platform/frontend/src/app/(platform)/build/components/FlowEditor/Flow/Flow.tsx

@@ -97,6 +97,9 @@ export const Flow = () => {
           onConnect={onConnect}
           onEdgesChange={onEdgesChange}
           onNodeDragStop={onNodeDragStop}
+          onNodeContextMenu={(event) => {
+            event.preventDefault();
+          }}
           maxZoom={2}
           minZoom={0.1}
           onDragOver={onDragOver}

autogpt_platform/frontend/src/app/(platform)/build/components/FlowEditor/nodes/CustomNode/CustomNode.tsx

@@ -1,24 +1,25 @@
-import React from "react";
-import { Node as XYNode, NodeProps } from "@xyflow/react";
-import { RJSFSchema } from "@rjsf/utils";
-import { BlockUIType } from "../../../types";
-import { StickyNoteBlock } from "./components/StickyNoteBlock";
-import { BlockInfoCategoriesItem } from "@/app/api/__generated__/models/blockInfoCategoriesItem";
-import { BlockCost } from "@/app/api/__generated__/models/blockCost";
 import { AgentExecutionStatus } from "@/app/api/__generated__/models/agentExecutionStatus";
+import { BlockCost } from "@/app/api/__generated__/models/blockCost";
+import { BlockInfoCategoriesItem } from "@/app/api/__generated__/models/blockInfoCategoriesItem";
 import { NodeExecutionResult } from "@/app/api/__generated__/models/nodeExecutionResult";
-import { NodeContainer } from "./components/NodeContainer";
-import { NodeHeader } from "./components/NodeHeader";
-import { FormCreator } from "../FormCreator";
-import { preprocessInputSchema } from "@/components/renderers/InputRenderer/utils/input-schema-pre-processor";
-import { OutputHandler } from "../OutputHandler";
-import { NodeAdvancedToggle } from "./components/NodeAdvancedToggle";
-import { NodeDataRenderer } from "./components/NodeOutput/NodeOutput";
-import { NodeExecutionBadge } from "./components/NodeExecutionBadge";
-import { cn } from "@/lib/utils";
-import { WebhookDisclaimer } from "./components/WebhookDisclaimer";
-import { AyrshareConnectButton } from "./components/AyrshareConnectButton";
 import { NodeModelMetadata } from "@/app/api/__generated__/models/nodeModelMetadata";
+import { preprocessInputSchema } from "@/components/renderers/InputRenderer/utils/input-schema-pre-processor";
+import { cn } from "@/lib/utils";
+import { RJSFSchema } from "@rjsf/utils";
+import { NodeProps, Node as XYNode } from "@xyflow/react";
+import React from "react";
+import { BlockUIType } from "../../../types";
+import { FormCreator } from "../FormCreator";
+import { OutputHandler } from "../OutputHandler";
+import { AyrshareConnectButton } from "./components/AyrshareConnectButton";
+import { NodeAdvancedToggle } from "./components/NodeAdvancedToggle";
+import { NodeContainer } from "./components/NodeContainer";
+import { NodeExecutionBadge } from "./components/NodeExecutionBadge";
+import { NodeHeader } from "./components/NodeHeader";
+import { NodeDataRenderer } from "./components/NodeOutput/NodeOutput";
+import { NodeRightClickMenu } from "./components/NodeRightClickMenu";
+import { StickyNoteBlock } from "./components/StickyNoteBlock";
+import { WebhookDisclaimer } from "./components/WebhookDisclaimer";
 
 export type CustomNodeData = {
   hardcodedValues: {
@@ -88,7 +89,7 @@ export const CustomNode: React.FC<NodeProps<CustomNode>> = React.memo(
 
     // Currently all blockTypes design are similar - that's why i am using the same component for all of them
     // If in future - if we need some drastic change in some blockTypes design - we can create separate components for them
-    return (
+    const node = (
       <NodeContainer selected={selected} nodeId={nodeId} hasErrors={hasErrors}>
         <div className="rounded-xlarge bg-white">
           <NodeHeader data={data} nodeId={nodeId} />
@@ -117,6 +118,15 @@ export const CustomNode: React.FC<NodeProps<CustomNode>> = React.memo(
         <NodeExecutionBadge nodeId={nodeId} />
       </NodeContainer>
     );
+
+    return (
+      <NodeRightClickMenu
+        nodeId={nodeId}
+        subGraphID={data.hardcodedValues?.graph_id}
+      >
+        {node}
+      </NodeRightClickMenu>
+    );
   },
 );
 

autogpt_platform/frontend/src/app/(platform)/build/components/FlowEditor/nodes/CustomNode/components/NodeContextMenu.tsx

@@ -1,26 +1,31 @@
-import { Separator } from "@/components/__legacy__/ui/separator";
+import { useCopyPasteStore } from "@/app/(platform)/build/stores/copyPasteStore";
+import { useNodeStore } from "@/app/(platform)/build/stores/nodeStore";
 import {
   DropdownMenu,
-  DropdownMenuContent,
-  DropdownMenuItem,
   DropdownMenuTrigger,
 } from "@/components/molecules/DropdownMenu/DropdownMenu";
-import { DotsThreeOutlineVerticalIcon } from "@phosphor-icons/react";
-import { Copy, Trash2, ExternalLink } from "lucide-react";
-import { useNodeStore } from "@/app/(platform)/build/stores/nodeStore";
-import { useCopyPasteStore } from "@/app/(platform)/build/stores/copyPasteStore";
+import {
+  SecondaryDropdownMenuContent,
+  SecondaryDropdownMenuItem,
+  SecondaryDropdownMenuSeparator,
+} from "@/components/molecules/SecondaryMenu/SecondaryMenu";
+import {
+  ArrowSquareOutIcon,
+  CopyIcon,
+  DotsThreeOutlineVerticalIcon,
+  TrashIcon,
+} from "@phosphor-icons/react";
 import { useReactFlow } from "@xyflow/react";
 
-export const NodeContextMenu = ({
-  nodeId,
-  subGraphID,
-}: {
+type Props = {
   nodeId: string;
   subGraphID?: string;
-}) => {
+};
+
+export const NodeContextMenu = ({ nodeId, subGraphID }: Props) => {
   const { deleteElements } = useReactFlow();
 
-  const handleCopy = () => {
+  function handleCopy() {
     useNodeStore.setState((state) => ({
       nodes: state.nodes.map((node) => ({
         ...node,
@@ -30,47 +35,47 @@ export const NodeContextMenu = ({
 
     useCopyPasteStore.getState().copySelectedNodes();
     useCopyPasteStore.getState().pasteNodes();
-  };
+  }
 
-  const handleDelete = () => {
+  function handleDelete() {
     deleteElements({ nodes: [{ id: nodeId }] });
-  };
+  }
 
   return (
     <DropdownMenu>
       <DropdownMenuTrigger className="py-2">
         <DotsThreeOutlineVerticalIcon size={16} weight="fill" />
       </DropdownMenuTrigger>
-      <DropdownMenuContent
-        side="right"
-        align="start"
-        className="rounded-xlarge"
-      >
-        <DropdownMenuItem onClick={handleCopy} className="hover:rounded-xlarge">
-          <Copy className="mr-2 h-4 w-4" />
-          Copy Node
-        </DropdownMenuItem>
+      <SecondaryDropdownMenuContent side="right" align="start">
+        <SecondaryDropdownMenuItem onClick={handleCopy}>
+          <CopyIcon size={20} className="mr-2 dark:text-gray-100" />
+          <span className="dark:text-gray-100">Copy</span>
+        </SecondaryDropdownMenuItem>
+        <SecondaryDropdownMenuSeparator />
 
         {subGraphID && (
-          <DropdownMenuItem
-            onClick={() => window.open(`/build?flowID=${subGraphID}`)}
-            className="hover:rounded-xlarge"
-          >
-            <ExternalLink className="mr-2 h-4 w-4" />
-            Open Agent
-          </DropdownMenuItem>
+          <>
+            <SecondaryDropdownMenuItem
+              onClick={() => window.open(`/build?flowID=${subGraphID}`)}
+            >
+              <ArrowSquareOutIcon
+                size={20}
+                className="mr-2 dark:text-gray-100"
+              />
+              <span className="dark:text-gray-100">Open agent</span>
+            </SecondaryDropdownMenuItem>
+            <SecondaryDropdownMenuSeparator />
+          </>
         )}
 
-        <Separator className="my-2" />
-
-        <DropdownMenuItem
-          onClick={handleDelete}
-          className="text-red-600 hover:rounded-xlarge"
-        >
-          <Trash2 className="mr-2 h-4 w-4" />
-          Delete
-        </DropdownMenuItem>
-      </DropdownMenuContent>
+        <SecondaryDropdownMenuItem variant="destructive" onClick={handleDelete}>
+          <TrashIcon
+            size={20}
+            className="mr-2 text-red-500 dark:text-red-400"
+          />
+          <span className="dark:text-red-400">Delete</span>
+        </SecondaryDropdownMenuItem>
+      </SecondaryDropdownMenuContent>
     </DropdownMenu>
   );
 };

autogpt_platform/frontend/src/app/(platform)/build/components/FlowEditor/nodes/CustomNode/components/NodeHeader.tsx

@@ -1,25 +1,24 @@
-import { Text } from "@/components/atoms/Text/Text";
-import { beautifyString, cn } from "@/lib/utils";
-import { NodeCost } from "./NodeCost";
-import { NodeBadges } from "./NodeBadges";
-import { NodeContextMenu } from "./NodeContextMenu";
-import { CustomNodeData } from "../CustomNode";
 import { useNodeStore } from "@/app/(platform)/build/stores/nodeStore";
-import { useState } from "react";
+import { Text } from "@/components/atoms/Text/Text";
 import {
   Tooltip,
   TooltipContent,
   TooltipProvider,
   TooltipTrigger,
 } from "@/components/atoms/Tooltip/BaseTooltip";
+import { beautifyString, cn } from "@/lib/utils";
+import { useState } from "react";
+import { CustomNodeData } from "../CustomNode";
+import { NodeBadges } from "./NodeBadges";
+import { NodeContextMenu } from "./NodeContextMenu";
+import { NodeCost } from "./NodeCost";
 
-export const NodeHeader = ({
-  data,
-  nodeId,
-}: {
+type Props = {
   data: CustomNodeData;
   nodeId: string;
-}) => {
+};
+
+export const NodeHeader = ({ data, nodeId }: Props) => {
   const updateNodeData = useNodeStore((state) => state.updateNodeData);
   const title = (data.metadata?.customized_name as string) || data.title;
   const [isEditingTitle, setIsEditingTitle] = useState(false);

autogpt_platform/frontend/src/app/(platform)/build/components/FlowEditor/nodes/CustomNode/components/NodeOutput/components/ContentRenderer.tsx

@@ -1,7 +1,7 @@
 "use client";
 
-import type { OutputMetadata } from "@/components/contextual/OutputRenderers";
-import { globalRegistry } from "@/components/contextual/OutputRenderers";
+import type { OutputMetadata } from "@/app/(platform)/library/agents/[id]/components/NewAgentLibraryView/components/selected-views/OutputRenderers";
+import { globalRegistry } from "@/app/(platform)/library/agents/[id]/components/NewAgentLibraryView/components/selected-views/OutputRenderers";
 
 export const TextRenderer: React.FC<{
   value: any;

autogpt_platform/frontend/src/app/(platform)/build/components/FlowEditor/nodes/CustomNode/components/NodeOutput/components/NodeDataViewer/NodeDataViewer.tsx

@@ -1,3 +1,7 @@
+import {
+  OutputActions,
+  OutputItem,
+} from "@/app/(platform)/library/agents/[id]/components/NewAgentLibraryView/components/selected-views/OutputRenderers";
 import { ScrollArea } from "@/components/__legacy__/ui/scroll-area";
 import { Button } from "@/components/atoms/Button/Button";
 import { Text } from "@/components/atoms/Text/Text";
@@ -7,10 +11,6 @@ import {
   TooltipProvider,
   TooltipTrigger,
 } from "@/components/atoms/Tooltip/BaseTooltip";
-import {
-  OutputActions,
-  OutputItem,
-} from "@/components/contextual/OutputRenderers";
 import { Dialog } from "@/components/molecules/Dialog/Dialog";
 import { beautifyString } from "@/lib/utils";
 import {

autogpt_platform/frontend/src/app/(platform)/build/components/FlowEditor/nodes/CustomNode/components/NodeOutput/components/NodeDataViewer/useNodeDataViewer.ts

@@ -1,6 +1,6 @@
-import type { OutputMetadata } from "@/components/contextual/OutputRenderers";
-import { globalRegistry } from "@/components/contextual/OutputRenderers";
-import { downloadOutputs } from "@/components/contextual/OutputRenderers/utils/download";
+import type { OutputMetadata } from "@/app/(platform)/library/agents/[id]/components/NewAgentLibraryView/components/selected-views/OutputRenderers";
+import { globalRegistry } from "@/app/(platform)/library/agents/[id]/components/NewAgentLibraryView/components/selected-views/OutputRenderers";
+import { downloadOutputs } from "@/app/(platform)/library/agents/[id]/components/NewAgentLibraryView/components/selected-views/OutputRenderers/utils/download";
 import { useToast } from "@/components/molecules/Toast/use-toast";
 import { beautifyString } from "@/lib/utils";
 import React, { useMemo, useState } from "react";

autogpt_platform/frontend/src/app/(platform)/build/components/FlowEditor/nodes/CustomNode/components/NodeRightClickMenu.tsx

@@ -0,0 +1,104 @@
+import { useCopyPasteStore } from "@/app/(platform)/build/stores/copyPasteStore";
+import { useNodeStore } from "@/app/(platform)/build/stores/nodeStore";
+import {
+  SecondaryMenuContent,
+  SecondaryMenuItem,
+  SecondaryMenuSeparator,
+} from "@/components/molecules/SecondaryMenu/SecondaryMenu";
+import { ArrowSquareOutIcon, CopyIcon, TrashIcon } from "@phosphor-icons/react";
+import * as ContextMenu from "@radix-ui/react-context-menu";
+import { useReactFlow } from "@xyflow/react";
+import { useEffect, useRef } from "react";
+import { CustomNode } from "../CustomNode";
+
+type Props = {
+  nodeId: string;
+  subGraphID?: string;
+  children: React.ReactNode;
+};
+
+const DOUBLE_CLICK_TIMEOUT = 300;
+
+export function NodeRightClickMenu({ nodeId, subGraphID, children }: Props) {
+  const { deleteElements } = useReactFlow<CustomNode>();
+  const lastRightClickTime = useRef<number>(0);
+  const containerRef = useRef<HTMLDivElement>(null);
+
+  function copyNode() {
+    useNodeStore.setState((state) => ({
+      nodes: state.nodes.map((node) => ({
+        ...node,
+        selected: node.id === nodeId,
+      })),
+    }));
+
+    useCopyPasteStore.getState().copySelectedNodes();
+    useCopyPasteStore.getState().pasteNodes();
+  }
+
+  function deleteNode() {
+    deleteElements({ nodes: [{ id: nodeId }] });
+  }
+
+  useEffect(() => {
+    const container = containerRef.current;
+    if (!container) return;
+
+    function handleContextMenu(e: MouseEvent) {
+      const now = Date.now();
+      const timeSinceLastClick = now - lastRightClickTime.current;
+
+      if (timeSinceLastClick < DOUBLE_CLICK_TIMEOUT) {
+        e.stopImmediatePropagation();
+        lastRightClickTime.current = 0;
+        return;
+      }
+
+      lastRightClickTime.current = now;
+    }
+
+    container.addEventListener("contextmenu", handleContextMenu, true);
+
+    return () => {
+      container.removeEventListener("contextmenu", handleContextMenu, true);
+    };
+  }, []);
+
+  return (
+    <ContextMenu.Root>
+      <ContextMenu.Trigger asChild>
+        <div ref={containerRef}>{children}</div>
+      </ContextMenu.Trigger>
+      <SecondaryMenuContent>
+        <SecondaryMenuItem onSelect={copyNode}>
+          <CopyIcon size={20} className="mr-2 dark:text-gray-100" />
+          <span className="dark:text-gray-100">Copy</span>
+        </SecondaryMenuItem>
+        <SecondaryMenuSeparator />
+
+        {subGraphID && (
+          <>
+            <SecondaryMenuItem
+              onClick={() => window.open(`/build?flowID=${subGraphID}`)}
+            >
+              <ArrowSquareOutIcon
+                size={20}
+                className="mr-2 dark:text-gray-100"
+              />
+              <span className="dark:text-gray-100">Open agent</span>
+            </SecondaryMenuItem>
+            <SecondaryMenuSeparator />
+          </>
+        )}
+
+        <SecondaryMenuItem variant="destructive" onSelect={deleteNode}>
+          <TrashIcon
+            size={20}
+            className="mr-2 text-red-500 dark:text-red-400"
+          />
+          <span className="dark:text-red-400">Delete</span>
+        </SecondaryMenuItem>
+      </SecondaryMenuContent>
+    </ContextMenu.Root>
+  );
+}

autogpt_platform/frontend/src/app/(platform)/build/components/FlowEditor/nodes/CustomNode/components/WebhookDisclaimer.tsx

@@ -1,10 +1,10 @@
+import { Alert, AlertDescription } from "@/components/molecules/Alert/Alert";
+import { Text } from "@/components/atoms/Text/Text";
+import Link from "next/link";
 import { useGetV2GetLibraryAgentByGraphId } from "@/app/api/__generated__/endpoints/library/library";
 import { LibraryAgent } from "@/app/api/__generated__/models/libraryAgent";
-import { Text } from "@/components/atoms/Text/Text";
-import { isValidUUID } from "@/components/contextual/Chat/helpers";
-import { Alert, AlertDescription } from "@/components/molecules/Alert/Alert";
-import Link from "next/link";
-import { parseAsString, useQueryStates } from "nuqs";
+import { useQueryStates, parseAsString } from "nuqs";
+import { isValidUUID } from "@/app/(platform)/chat/helpers";
 
 export const WebhookDisclaimer = ({ nodeId }: { nodeId: string }) => {
   const [{ flowID }] = useQueryStates({

autogpt_platform/frontend/src/app/(platform)/build/components/NewControlPanel/NewBlockMenu/BlockMenuFilters/BlockMenuFilters.tsx

@@ -0,0 +1,57 @@
+import { useBlockMenuStore } from "@/app/(platform)/build/stores/blockMenuStore";
+import { FilterChip } from "../FilterChip";
+import { categories } from "./constants";
+import { FilterSheet } from "../FilterSheet/FilterSheet";
+import { GetV2BuilderSearchFilterAnyOfItem } from "@/app/api/__generated__/models/getV2BuilderSearchFilterAnyOfItem";
+
+export const BlockMenuFilters = () => {
+  const {
+    filters,
+    addFilter,
+    removeFilter,
+    categoryCounts,
+    creators,
+    addCreator,
+    removeCreator,
+  } = useBlockMenuStore();
+
+  const handleFilterClick = (filter: GetV2BuilderSearchFilterAnyOfItem) => {
+    if (filters.includes(filter)) {
+      removeFilter(filter);
+    } else {
+      addFilter(filter);
+    }
+  };
+
+  const handleCreatorClick = (creator: string) => {
+    if (creators.includes(creator)) {
+      removeCreator(creator);
+    } else {
+      addCreator(creator);
+    }
+  };
+
+  return (
+    <div className="flex flex-wrap gap-2">
+      <FilterSheet categories={categories} />
+      {creators.length > 0 &&
+        creators.map((creator) => (
+          <FilterChip
+            key={creator}
+            name={"Created by " + creator.slice(0, 10) + "..."}
+            selected={creators.includes(creator)}
+            onClick={() => handleCreatorClick(creator)}
+          />
+        ))}
+      {categories.map((category) => (
+        <FilterChip
+          key={category.key}
+          name={category.name}
+          selected={filters.includes(category.key)}
+          onClick={() => handleFilterClick(category.key)}
+          number={categoryCounts[category.key] ?? 0}
+        />
+      ))}
+    </div>
+  );
+};

autogpt_platform/frontend/src/app/(platform)/build/components/NewControlPanel/NewBlockMenu/BlockMenuFilters/constants.ts

@@ -0,0 +1,15 @@
+import { GetV2BuilderSearchFilterAnyOfItem } from "@/app/api/__generated__/models/getV2BuilderSearchFilterAnyOfItem";
+import { CategoryKey } from "./types";
+
+export const categories: Array<{ key: CategoryKey; name: string }> = [
+  { key: GetV2BuilderSearchFilterAnyOfItem.blocks, name: "Blocks" },
+  {
+    key: GetV2BuilderSearchFilterAnyOfItem.integrations,
+    name: "Integrations",
+  },
+  {
+    key: GetV2BuilderSearchFilterAnyOfItem.marketplace_agents,
+    name: "Marketplace agents",
+  },
+  { key: GetV2BuilderSearchFilterAnyOfItem.my_agents, name: "My agents" },
+];

autogpt_platform/frontend/src/app/(platform)/build/components/NewControlPanel/NewBlockMenu/BlockMenuFilters/types.ts

@@ -0,0 +1,26 @@
+import { GetV2BuilderSearchFilterAnyOfItem } from "@/app/api/__generated__/models/getV2BuilderSearchFilterAnyOfItem";
+
+export type DefaultStateType =
+  | "suggestion"
+  | "all_blocks"
+  | "input_blocks"
+  | "action_blocks"
+  | "output_blocks"
+  | "integrations"
+  | "marketplace_agents"
+  | "my_agents";
+
+export type CategoryKey = GetV2BuilderSearchFilterAnyOfItem;
+
+export interface Filters {
+  categories: {
+    blocks: boolean;
+    integrations: boolean;
+    marketplace_agents: boolean;
+    my_agents: boolean;
+    providers: boolean;
+  };
+  createdBy: string[];
+}
+
+export type CategoryCounts = Record<CategoryKey, number>;

autogpt_platform/frontend/src/app/(platform)/build/components/NewControlPanel/NewBlockMenu/BlockMenuSearch/BlockMenuSearch.tsx

@@ -1,111 +1,14 @@
 import { Text } from "@/components/atoms/Text/Text";
-import { useBlockMenuSearch } from "./useBlockMenuSearch";
-import { InfiniteScroll } from "@/components/contextual/InfiniteScroll/InfiniteScroll";
-import { LoadingSpinner } from "@/components/__legacy__/ui/loading";
-import { SearchResponseItemsItem } from "@/app/api/__generated__/models/searchResponseItemsItem";
-import { MarketplaceAgentBlock } from "../MarketplaceAgentBlock";
-import { Block } from "../Block";
-import { UGCAgentBlock } from "../UGCAgentBlock";
-import { getSearchItemType } from "./helper";
-import { useBlockMenuStore } from "../../../../stores/blockMenuStore";
 import { blockMenuContainerStyle } from "../style";
-import { cn } from "@/lib/utils";
-import { NoSearchResult } from "../NoSearchResult";
+import { BlockMenuFilters } from "../BlockMenuFilters/BlockMenuFilters";
+import { BlockMenuSearchContent } from "../BlockMenuSearchContent/BlockMenuSearchContent";
 
 export const BlockMenuSearch = () => {
-  const {
-    searchResults,
-    isFetchingNextPage,
-    fetchNextPage,
-    hasNextPage,
-    searchLoading,
-    handleAddLibraryAgent,
-    handleAddMarketplaceAgent,
-    addingLibraryAgentId,
-    addingMarketplaceAgentSlug,
-  } = useBlockMenuSearch();
-  const { searchQuery } = useBlockMenuStore();
-
-  if (searchLoading) {
-    return (
-      <div
-        className={cn(
-          blockMenuContainerStyle,
-          "flex items-center justify-center",
-        )}
-      >
-        <LoadingSpinner className="size-13" />
-      </div>
-    );
-  }
-
-  if (searchResults.length === 0) {
-    return <NoSearchResult />;
-  }
-
   return (
     <div className={blockMenuContainerStyle}>
+      <BlockMenuFilters />
       <Text variant="body-medium">Search results</Text>
-      <InfiniteScroll
-        isFetchingNextPage={isFetchingNextPage}
-        fetchNextPage={fetchNextPage}
-        hasNextPage={hasNextPage}
-        loader={<LoadingSpinner className="size-13" />}
-        className="space-y-2.5"
-      >
-        {searchResults.map((item: SearchResponseItemsItem, index: number) => {
-          const { type, data } = getSearchItemType(item);
-          // backend give support to these 3 types only [right now] - we need to give support to integration and ai agent types in follow up PRs
-          switch (type) {
-            case "store_agent":
-              return (
-                <MarketplaceAgentBlock
-                  key={index}
-                  slug={data.slug}
-                  highlightedText={searchQuery}
-                  title={data.agent_name}
-                  image_url={data.agent_image}
-                  creator_name={data.creator}
-                  number_of_runs={data.runs}
-                  loading={addingMarketplaceAgentSlug === data.slug}
-                  onClick={() =>
-                    handleAddMarketplaceAgent({
-                      creator_name: data.creator,
-                      slug: data.slug,
-                    })
-                  }
-                />
-              );
-            case "block":
-              return (
-                <Block
-                  key={index}
-                  title={data.name}
-                  highlightedText={searchQuery}
-                  description={data.description}
-                  blockData={data}
-                />
-              );
-
-            case "library_agent":
-              return (
-                <UGCAgentBlock
-                  key={index}
-                  title={data.name}
-                  highlightedText={searchQuery}
-                  image_url={data.image_url}
-                  version={data.graph_version}
-                  edited_time={data.updated_at}
-                  isLoading={addingLibraryAgentId === data.id}
-                  onClick={() => handleAddLibraryAgent(data)}
-                />
-              );
-
-            default:
-              return null;
-          }
-        })}
-      </InfiniteScroll>
+      <BlockMenuSearchContent />
     </div>
   );
 };

autogpt_platform/frontend/src/app/(platform)/build/components/NewControlPanel/NewBlockMenu/BlockMenuSearchContent/BlockMenuSearchContent.tsx

@@ -0,0 +1,108 @@
+import { SearchResponseItemsItem } from "@/app/api/__generated__/models/searchResponseItemsItem";
+import { LoadingSpinner } from "@/components/atoms/LoadingSpinner/LoadingSpinner";
+import { InfiniteScroll } from "@/components/contextual/InfiniteScroll/InfiniteScroll";
+import { getSearchItemType } from "./helper";
+import { MarketplaceAgentBlock } from "../MarketplaceAgentBlock";
+import { Block } from "../Block";
+import { UGCAgentBlock } from "../UGCAgentBlock";
+import { useBlockMenuSearchContent } from "./useBlockMenuSearchContent";
+import { useBlockMenuStore } from "@/app/(platform)/build/stores/blockMenuStore";
+import { cn } from "@/lib/utils";
+import { blockMenuContainerStyle } from "../style";
+import { NoSearchResult } from "../NoSearchResult";
+
+export const BlockMenuSearchContent = () => {
+  const {
+    searchResults,
+    isFetchingNextPage,
+    fetchNextPage,
+    hasNextPage,
+    searchLoading,
+    handleAddLibraryAgent,
+    handleAddMarketplaceAgent,
+    addingLibraryAgentId,
+    addingMarketplaceAgentSlug,
+  } = useBlockMenuSearchContent();
+
+  const { searchQuery } = useBlockMenuStore();
+
+  if (searchLoading) {
+    return (
+      <div
+        className={cn(
+          blockMenuContainerStyle,
+          "flex items-center justify-center",
+        )}
+      >
+        <LoadingSpinner className="size-13" />
+      </div>
+    );
+  }
+
+  if (searchResults.length === 0) {
+    return <NoSearchResult />;
+  }
+
+  return (
+    <InfiniteScroll
+      isFetchingNextPage={isFetchingNextPage}
+      fetchNextPage={fetchNextPage}
+      hasNextPage={hasNextPage}
+      loader={<LoadingSpinner className="size-13" />}
+      className="space-y-2.5"
+    >
+      {searchResults.map((item: SearchResponseItemsItem, index: number) => {
+        const { type, data } = getSearchItemType(item);
+        // backend give support to these 3 types only [right now] - we need to give support to integration and ai agent types in follow up PRs
+        switch (type) {
+          case "store_agent":
+            return (
+              <MarketplaceAgentBlock
+                key={index}
+                slug={data.slug}
+                highlightedText={searchQuery}
+                title={data.agent_name}
+                image_url={data.agent_image}
+                creator_name={data.creator}
+                number_of_runs={data.runs}
+                loading={addingMarketplaceAgentSlug === data.slug}
+                onClick={() =>
+                  handleAddMarketplaceAgent({
+                    creator_name: data.creator,
+                    slug: data.slug,
+                  })
+                }
+              />
+            );
+          case "block":
+            return (
+              <Block
+                key={index}
+                title={data.name}
+                highlightedText={searchQuery}
+                description={data.description}
+                blockData={data}
+              />
+            );
+
+          case "library_agent":
+            return (
+              <UGCAgentBlock
+                key={index}
+                title={data.name}
+                highlightedText={searchQuery}
+                image_url={data.image_url}
+                version={data.graph_version}
+                edited_time={data.updated_at}
+                isLoading={addingLibraryAgentId === data.id}
+                onClick={() => handleAddLibraryAgent(data)}
+              />
+            );
+
+          default:
+            return null;
+        }
+      })}
+    </InfiniteScroll>
+  );
+};

autogpt_platform/frontend/src/app/(platform)/build/components/NewControlPanel/NewBlockMenu/BlockMenuSearchContent/useBlockMenuSearchContent.tsx

@@ -23,9 +23,19 @@ import { LibraryAgent } from "@/app/api/__generated__/models/libraryAgent";
 import { getQueryClient } from "@/lib/react-query/queryClient";
 import { useToast } from "@/components/molecules/Toast/use-toast";
 import * as Sentry from "@sentry/nextjs";
+import { GetV2BuilderSearchFilterAnyOfItem } from "@/app/api/__generated__/models/getV2BuilderSearchFilterAnyOfItem";
+
+export const useBlockMenuSearchContent = () => {
+  const {
+    searchQuery,
+    searchId,
+    setSearchId,
+    filters,
+    setCreatorsList,
+    creators,
+    setCategoryCounts,
+  } = useBlockMenuStore();
 
-export const useBlockMenuSearch = () => {
-  const { searchQuery, searchId, setSearchId } = useBlockMenuStore();
   const { toast } = useToast();
   const { addAgentToBuilder, addLibraryAgentToBuilder } =
     useAddAgentToBuilder();
@@ -57,6 +67,8 @@ export const useBlockMenuSearch = () => {
       page_size: 8,
       search_query: searchQuery,
       search_id: searchId,
+      filter: filters.length > 0 ? filters : undefined,
+      by_creator: creators.length > 0 ? creators : undefined,
     },
     {
       query: { getNextPageParam: getPaginationNextPageNumber },
@@ -98,6 +110,26 @@ export const useBlockMenuSearch = () => {
     }
   }, [searchQueryData, searchId, setSearchId]);
 
+  // from all the results, we need to get all the unique creators
+  useEffect(() => {
+    if (!searchQueryData?.pages?.length) {
+      return;
+    }
+    const latestData = okData(searchQueryData.pages.at(-1));
+    setCategoryCounts(
+      (latestData?.total_items as Record<
+        GetV2BuilderSearchFilterAnyOfItem,
+        number
+      >) || {
+        blocks: 0,
+        integrations: 0,
+        marketplace_agents: 0,
+        my_agents: 0,
+      },
+    );
+    setCreatorsList(latestData?.items || []);
+  }, [searchQueryData]);
+
   useEffect(() => {
     if (searchId && !searchQuery) {
       resetSearchSession();

autogpt_platform/frontend/src/app/(platform)/build/components/NewControlPanel/NewBlockMenu/FilterChip.tsx

@@ -1,7 +1,9 @@
 import { Button } from "@/components/__legacy__/ui/button";
 import { cn } from "@/lib/utils";
-import { X } from "lucide-react";
-import React, { ButtonHTMLAttributes } from "react";
+import { XIcon } from "@phosphor-icons/react";
+import { AnimatePresence, motion } from "framer-motion";
+
+import React, { ButtonHTMLAttributes, useState } from "react";
 
 interface Props extends ButtonHTMLAttributes<HTMLButtonElement> {
   selected?: boolean;
@@ -16,39 +18,51 @@ export const FilterChip: React.FC<Props> = ({
   className,
   ...rest
 }) => {
+  const [isHovered, setIsHovered] = useState(false);
   return (
-    <Button
-      className={cn(
-        "group w-fit space-x-1 rounded-[1.5rem] border border-zinc-300 bg-transparent px-[0.625rem] py-[0.375rem] shadow-none transition-transform duration-300 ease-in-out",
-        "hover:border-violet-500 hover:bg-transparent focus:ring-0 disabled:cursor-not-allowed",
-        selected && "border-0 bg-violet-700 hover:border",
-        className,
-      )}
-      {...rest}
-    >
-      <span
+    <AnimatePresence mode="wait">
+      <Button
+        onMouseEnter={() => setIsHovered(true)}
+        onMouseLeave={() => setIsHovered(false)}
         className={cn(
-          "font-sans text-sm font-medium leading-[1.375rem] text-zinc-600 group-hover:text-zinc-600 group-disabled:text-zinc-400",
-          selected && "text-zinc-50",
+          "group w-fit space-x-1 rounded-[1.5rem] border border-zinc-300 bg-transparent px-[0.625rem] py-[0.375rem] shadow-none",
+          "hover:border-violet-500 hover:bg-transparent focus:ring-0 disabled:cursor-not-allowed",
+          selected && "border-0 bg-violet-700 hover:border",
+          className,
         )}
+        {...rest}
       >
-        {name}
-      </span>
-      {selected && (
-        <>
-          <span className="flex h-4 w-4 items-center justify-center rounded-full bg-zinc-50 transition-all duration-300 ease-in-out group-hover:hidden">
-            <X
-              className="h-3 w-3 rounded-full text-violet-700"
-              strokeWidth={2}
-            />
-          </span>
-          {number !== undefined && (
-            <span className="hidden h-[1.375rem] items-center rounded-[1.25rem] bg-violet-700 p-[0.375rem] text-zinc-50 transition-all duration-300 ease-in-out animate-in fade-in zoom-in group-hover:flex">
-              {number > 100 ? "100+" : number}
-            </span>
+        <span
+          className={cn(
+            "font-sans text-sm font-medium leading-[1.375rem] text-zinc-600 group-hover:text-zinc-600 group-disabled:text-zinc-400",
+            selected && "text-zinc-50",
           )}
-        </>
-      )}
-    </Button>
+        >
+          {name}
+        </span>
+        {selected && !isHovered && (
+          <motion.span
+            initial={{ opacity: 0.5, scale: 0.5, filter: "blur(20px)" }}
+            animate={{ opacity: 1, scale: 1, filter: "blur(0px)" }}
+            exit={{ opacity: 0.5, scale: 0.5, filter: "blur(20px)" }}
+            transition={{ duration: 0.3, type: "spring", bounce: 0.2 }}
+            className="flex h-4 w-4 items-center justify-center rounded-full bg-zinc-50"
+          >
+            <XIcon size={12} weight="bold" className="text-violet-700" />
+          </motion.span>
+        )}
+        {number !== undefined && isHovered && (
+          <motion.span
+            initial={{ opacity: 0.5, scale: 0.5, filter: "blur(10px)" }}
+            animate={{ opacity: 1, scale: 1, filter: "blur(0px)" }}
+            exit={{ opacity: 0.5, scale: 0.5, filter: "blur(10px)" }}
+            transition={{ duration: 0.3, type: "spring", bounce: 0.2 }}
+            className="flex h-[1.375rem] items-center rounded-[1.25rem] bg-violet-700 p-[0.375rem] text-zinc-50"
+          >
+            {number > 100 ? "100+" : number}
+          </motion.span>
+        )}
+      </Button>
+    </AnimatePresence>
   );
 };

autogpt_platform/frontend/src/app/(platform)/build/components/NewControlPanel/NewBlockMenu/FilterSheet/FilterSheet.tsx

@@ -0,0 +1,156 @@
+import { FilterChip } from "../FilterChip";
+import { cn } from "@/lib/utils";
+import { CategoryKey } from "../BlockMenuFilters/types";
+import { AnimatePresence, motion } from "framer-motion";
+import { XIcon } from "@phosphor-icons/react";
+import { Button } from "@/components/atoms/Button/Button";
+import { Text } from "@/components/atoms/Text/Text";
+import { Separator } from "@/components/__legacy__/ui/separator";
+import { Checkbox } from "@/components/__legacy__/ui/checkbox";
+import { useFilterSheet } from "./useFilterSheet";
+import { INITIAL_CREATORS_TO_SHOW } from "./constant";
+
+export function FilterSheet({
+  categories,
+}: {
+  categories: Array<{ key: CategoryKey; name: string }>;
+}) {
+  const {
+    isOpen,
+    localCategories,
+    localCreators,
+    displayedCreatorsCount,
+    handleLocalCategoryChange,
+    handleToggleShowMoreCreators,
+    handleLocalCreatorChange,
+    handleClearFilters,
+    handleCloseButton,
+    handleApplyFilters,
+    hasLocalActiveFilters,
+    visibleCreators,
+    creators,
+    handleOpenFilters,
+    hasActiveFilters,
+  } = useFilterSheet();
+
+  return (
+    <div className="m-0 inline w-fit p-0">
+      <FilterChip
+        name={hasActiveFilters() ? "Edit filters" : "All filters"}
+        onClick={handleOpenFilters}
+      />
+
+      <AnimatePresence>
+        {isOpen && (
+          <motion.div
+            className={cn(
+              "absolute bottom-2 left-2 top-2 z-20 w-3/4 max-w-[22.5rem] space-y-4 overflow-hidden rounded-[0.75rem] bg-white pb-4 shadow-[0_4px_12px_2px_rgba(0,0,0,0.1)]",
+            )}
+            initial={{ x: "-100%", filter: "blur(10px)" }}
+            animate={{ x: 0, filter: "blur(0px)" }}
+            exit={{ x: "-110%", filter: "blur(10px)" }}
+            transition={{ duration: 0.4, type: "spring", bounce: 0.2 }}
+          >
+            {/* Top section */}
+            <div className="flex items-center justify-between px-5 pt-4">
+              <Text variant="body">Filters</Text>
+              <Button
+                className="p-0"
+                variant="ghost"
+                size="icon"
+                onClick={handleCloseButton}
+              >
+                <XIcon size={20} />
+              </Button>
+            </div>
+
+            <Separator className="h-[1px] w-full text-zinc-300" />
+
+            {/* Category section */}
+            <div className="space-y-4 px-5">
+              <Text variant="large">Categories</Text>
+              <div className="space-y-2">
+                {categories.map((category) => (
+                  <div
+                    key={category.key}
+                    className="flex items-center space-x-2"
+                  >
+                    <Checkbox
+                      id={category.key}
+                      checked={localCategories.includes(category.key)}
+                      onCheckedChange={() =>
+                        handleLocalCategoryChange(category.key)
+                      }
+                      className="border border-[#D4D4D4] shadow-none data-[state=checked]:border-none data-[state=checked]:bg-violet-700 data-[state=checked]:text-white"
+                    />
+                    <label
+                      htmlFor={category.key}
+                      className="font-sans text-sm leading-[1.375rem] text-zinc-600"
+                    >
+                      {category.name}
+                    </label>
+                  </div>
+                ))}
+              </div>
+            </div>
+
+            {/* Created by section */}
+            <div className="space-y-4 px-5">
+              <p className="font-sans text-base font-medium text-zinc-800">
+                Created by
+              </p>
+              <div className="space-y-2">
+                {visibleCreators.map((creator, i) => (
+                  <div key={i} className="flex items-center space-x-2">
+                    <Checkbox
+                      id={`creator-${creator}`}
+                      checked={localCreators.includes(creator)}
+                      onCheckedChange={() => handleLocalCreatorChange(creator)}
+                      className="border border-[#D4D4D4] shadow-none data-[state=checked]:border-none data-[state=checked]:bg-violet-700 data-[state=checked]:text-white"
+                    />
+                    <label
+                      htmlFor={`creator-${creator}`}
+                      className="font-sans text-sm leading-[1.375rem] text-zinc-600"
+                    >
+                      {creator}
+                    </label>
+                  </div>
+                ))}
+              </div>
+              {creators.length > INITIAL_CREATORS_TO_SHOW && (
+                <Button
+                  variant={"link"}
+                  className="m-0 p-0 font-sans text-sm font-medium leading-[1.375rem] text-zinc-800 underline hover:text-zinc-600"
+                  onClick={handleToggleShowMoreCreators}
+                >
+                  {displayedCreatorsCount < creators.length ? "More" : "Less"}
+                </Button>
+              )}
+            </div>
+
+            {/* Footer section */}
+            <div className="fixed bottom-0 flex w-full justify-between gap-3 border-t border-zinc-200 bg-white px-5 py-3">
+              <Button
+                size="small"
+                variant={"outline"}
+                onClick={handleClearFilters}
+                className="rounded-[8px] px-2 py-1.5"
+              >
+                Clear
+              </Button>
+
+              <Button
+                size="small"
+                onClick={handleApplyFilters}
+                disabled={!hasLocalActiveFilters()}
+                className="rounded-[8px] px-2 py-1.5"
+              >
+                Apply filters
+              </Button>
+            </div>
+          </motion.div>
+        )}
+      </AnimatePresence>
+    </div>
+  );
+}

autogpt_platform/frontend/src/app/(platform)/build/components/NewControlPanel/NewBlockMenu/FilterSheet/constant.ts

@@ -0,0 +1 @@
+export const INITIAL_CREATORS_TO_SHOW = 5;

autogpt_platform/frontend/src/app/(platform)/build/components/NewControlPanel/NewBlockMenu/FilterSheet/useFilterSheet.ts

@@ -0,0 +1,100 @@
+import { useBlockMenuStore } from "@/app/(platform)/build/stores/blockMenuStore";
+import { useState } from "react";
+import { INITIAL_CREATORS_TO_SHOW } from "./constant";
+import { GetV2BuilderSearchFilterAnyOfItem } from "@/app/api/__generated__/models/getV2BuilderSearchFilterAnyOfItem";
+
+export const useFilterSheet = () => {
+  const { filters, creators_list, creators, setFilters, setCreators } =
+    useBlockMenuStore();
+
+  const [isOpen, setIsOpen] = useState(false);
+  const [localCategories, setLocalCategories] =
+    useState<GetV2BuilderSearchFilterAnyOfItem[]>(filters);
+  const [localCreators, setLocalCreators] = useState<string[]>(creators);
+  const [displayedCreatorsCount, setDisplayedCreatorsCount] = useState(
+    INITIAL_CREATORS_TO_SHOW,
+  );
+
+  const handleLocalCategoryChange = (
+    category: GetV2BuilderSearchFilterAnyOfItem,
+  ) => {
+    setLocalCategories((prev) => {
+      if (prev.includes(category)) {
+        return prev.filter((c) => c !== category);
+      }
+      return [...prev, category];
+    });
+  };
+
+  const hasActiveFilters = () => {
+    return filters.length > 0 || creators.length > 0;
+  };
+
+  const handleToggleShowMoreCreators = () => {
+    if (displayedCreatorsCount < creators.length) {
+      setDisplayedCreatorsCount(creators.length);
+    } else {
+      setDisplayedCreatorsCount(INITIAL_CREATORS_TO_SHOW);
+    }
+  };
+
+  const handleLocalCreatorChange = (creator: string) => {
+    setLocalCreators((prev) => {
+      if (prev.includes(creator)) {
+        return prev.filter((c) => c !== creator);
+      }
+      return [...prev, creator];
+    });
+  };
+
+  const handleClearFilters = () => {
+    setLocalCategories([]);
+    setLocalCreators([]);
+    setDisplayedCreatorsCount(INITIAL_CREATORS_TO_SHOW);
+  };
+
+  const handleCloseButton = () => {
+    setIsOpen(false);
+    setLocalCategories(filters);
+    setLocalCreators(creators);
+    setDisplayedCreatorsCount(INITIAL_CREATORS_TO_SHOW);
+  };
+
+  const handleApplyFilters = () => {
+    setFilters(localCategories);
+    setCreators(localCreators);
+    setIsOpen(false);
+  };
+
+  const handleOpenFilters = () => {
+    setIsOpen(true);
+    setLocalCategories(filters);
+    setLocalCreators(creators);
+  };
+
+  const hasLocalActiveFilters = () => {
+    return localCategories.length > 0 || localCreators.length > 0;
+  };
+
+  const visibleCreators = creators_list.slice(0, displayedCreatorsCount);
+
+  return {
+    creators,
+    isOpen,
+    setIsOpen,
+    localCategories,
+    localCreators,
+    displayedCreatorsCount,
+    setDisplayedCreatorsCount,
+    handleLocalCategoryChange,
+    handleToggleShowMoreCreators,
+    handleLocalCreatorChange,
+    handleClearFilters,
+    handleCloseButton,
+    handleOpenFilters,
+    handleApplyFilters,
+    hasLocalActiveFilters,
+    visibleCreators,
+    hasActiveFilters,
+  };
+};

autogpt_platform/frontend/src/app/(platform)/build/components/legacy-builder/ExpandableOutputDialog.tsx

@@ -1,9 +1,9 @@
-import type { OutputMetadata } from "@/components/contextual/OutputRenderers";
+import type { OutputMetadata } from "@/app/(platform)/library/agents/[id]/components/NewAgentLibraryView/components/selected-views/OutputRenderers";
 import {
   globalRegistry,
   OutputActions,
   OutputItem,
-} from "@/components/contextual/OutputRenderers";
+} from "@/app/(platform)/library/agents/[id]/components/NewAgentLibraryView/components/selected-views/OutputRenderers";
 import { Dialog } from "@/components/molecules/Dialog/Dialog";
 import { beautifyString } from "@/lib/utils";
 import { Flag, useGetFlag } from "@/services/feature-flags/use-get-flag";

autogpt_platform/frontend/src/app/(platform)/build/components/legacy-builder/NodeInputs.tsx

@@ -3,6 +3,7 @@ import {
   CustomNodeData,
 } from "@/app/(platform)/build/components/legacy-builder/CustomNode/CustomNode";
 import { NodeTableInput } from "@/app/(platform)/build/components/legacy-builder/NodeTableInput";
+import { CredentialsInput } from "@/app/(platform)/library/agents/[id]/components/NewAgentLibraryView/components/modals/CredentialsInputs/CredentialsInputs";
 import { Button } from "@/components/__legacy__/ui/button";
 import { Calendar } from "@/components/__legacy__/ui/calendar";
 import { LocalValuedInput } from "@/components/__legacy__/ui/input";
@@ -27,7 +28,6 @@ import {
   SelectValue,
 } from "@/components/__legacy__/ui/select";
 import { Switch } from "@/components/atoms/Switch/Switch";
-import { CredentialsInput } from "@/components/contextual/CredentialsInputs/CredentialsInputs";
 import { GoogleDrivePickerInput } from "@/components/contextual/GoogleDrivePicker/GoogleDrivePickerInput";
 import {
   BlockIOArraySubSchema,

autogpt_platform/frontend/src/app/(platform)/build/stores/blockMenuStore.ts

@@ -1,12 +1,30 @@
 import { create } from "zustand";
 import { DefaultStateType } from "../components/NewControlPanel/NewBlockMenu/types";
+import { SearchResponseItemsItem } from "@/app/api/__generated__/models/searchResponseItemsItem";
+import { getSearchItemType } from "../components/NewControlPanel/NewBlockMenu/BlockMenuSearchContent/helper";
+import { StoreAgent } from "@/app/api/__generated__/models/storeAgent";
+import { GetV2BuilderSearchFilterAnyOfItem } from "@/app/api/__generated__/models/getV2BuilderSearchFilterAnyOfItem";
 
 type BlockMenuStore = {
   searchQuery: string;
   searchId: string | undefined;
   defaultState: DefaultStateType;
   integration: string | undefined;
+  filters: GetV2BuilderSearchFilterAnyOfItem[];
+  creators: string[];
+  creators_list: string[];
+  categoryCounts: Record<GetV2BuilderSearchFilterAnyOfItem, number>;
 
+  setCategoryCounts: (
+    counts: Record<GetV2BuilderSearchFilterAnyOfItem, number>,
+  ) => void;
+  setCreatorsList: (searchData: SearchResponseItemsItem[]) => void;
+  addCreator: (creator: string) => void;
+  setCreators: (creators: string[]) => void;
+  removeCreator: (creator: string) => void;
+  addFilter: (filter: GetV2BuilderSearchFilterAnyOfItem) => void;
+  setFilters: (filters: GetV2BuilderSearchFilterAnyOfItem[]) => void;
+  removeFilter: (filter: GetV2BuilderSearchFilterAnyOfItem) => void;
   setSearchQuery: (query: string) => void;
   setSearchId: (id: string | undefined) => void;
   setDefaultState: (state: DefaultStateType) => void;
@@ -19,11 +37,44 @@ export const useBlockMenuStore = create<BlockMenuStore>((set) => ({
   searchId: undefined,
   defaultState: DefaultStateType.SUGGESTION,
   integration: undefined,
+  filters: [],
+  creators: [], // creator filters that are applied to the search results
+  creators_list: [], // all creators that are available to filter by
+  categoryCounts: {
+    blocks: 0,
+    integrations: 0,
+    marketplace_agents: 0,
+    my_agents: 0,
+  },
 
+  setCategoryCounts: (counts) => set({ categoryCounts: counts }),
+  setCreatorsList: (searchData) => {
+    const marketplaceAgents = searchData.filter((item) => {
+      return getSearchItemType(item).type === "store_agent";
+    }) as StoreAgent[];
+
+    const newCreators = marketplaceAgents.map((agent) => agent.creator);
+
+    set((state) => ({
+      creators_list: Array.from(
+        new Set([...state.creators_list, ...newCreators]),
+      ),
+    }));
+  },
+  setCreators: (creators) => set({ creators }),
+  setFilters: (filters) => set({ filters }),
   setSearchQuery: (query) => set({ searchQuery: query }),
   setSearchId: (id) => set({ searchId: id }),
   setDefaultState: (state) => set({ defaultState: state }),
   setIntegration: (integration) => set({ integration }),
+  addFilter: (filter) =>
+    set((state) => ({ filters: [...state.filters, filter] })),
+  removeFilter: (filter) =>
+    set((state) => ({ filters: state.filters.filter((f) => f !== filter) })),
+  addCreator: (creator) =>
+    set((state) => ({ creators: [...state.creators, creator] })),
+  removeCreator: (creator) =>
+    set((state) => ({ creators: state.creators.filter((c) => c !== creator) })),
   reset: () =>
     set({
       searchQuery: "",

autogpt_platform/frontend/src/app/(platform)/chat/components/AgentCarouselMessage/AgentCarouselMessage.tsx

@@ -1,16 +1,15 @@
+import React from "react";
+import { Text } from "@/components/atoms/Text/Text";
 import { Button } from "@/components/atoms/Button/Button";
 import { Card } from "@/components/atoms/Card/Card";
-import { Text } from "@/components/atoms/Text/Text";
+import { List, Robot, ArrowRight } from "@phosphor-icons/react";
 import { cn } from "@/lib/utils";
-import { ArrowRight, List, Robot } from "@phosphor-icons/react";
-import Image from "next/image";
 
 export interface Agent {
   id: string;
   name: string;
   description: string;
   version?: number;
-  image_url?: string;
 }
 
 export interface AgentCarouselMessageProps {
@@ -31,7 +30,7 @@ export function AgentCarouselMessage({
   return (
     <div
       className={cn(
-        "mx-4 my-2 flex flex-col gap-4 rounded-lg border border-purple-200 bg-purple-50 p-6",
+        "mx-4 my-2 flex flex-col gap-4 rounded-lg border border-purple-200 bg-purple-50 p-6 dark:border-purple-900 dark:bg-purple-950",
         className,
       )}
     >
@@ -41,10 +40,13 @@ export function AgentCarouselMessage({
           <List size={24} weight="bold" className="text-white" />
         </div>
         <div>
-          <Text variant="h3" className="text-purple-900">
+          <Text variant="h3" className="text-purple-900 dark:text-purple-100">
             Found {displayCount} {displayCount === 1 ? "Agent" : "Agents"}
           </Text>
-          <Text variant="small" className="text-purple-700">
+          <Text
+            variant="small"
+            className="text-purple-700 dark:text-purple-300"
+          >
             Select an agent to view details or run it
           </Text>
         </div>
@@ -55,49 +57,40 @@ export function AgentCarouselMessage({
         {agents.map((agent) => (
           <Card
             key={agent.id}
-            className="border border-purple-200 bg-white p-4"
+            className="border border-purple-200 bg-white p-4 dark:border-purple-800 dark:bg-purple-900"
           >
             <div className="flex gap-3">
-              <div className="relative h-10 w-10 flex-shrink-0 overflow-hidden rounded-lg bg-purple-100">
-                {agent.image_url ? (
-                  <Image
-                    src={agent.image_url}
-                    alt={`${agent.name} preview image`}
-                    fill
-                    className="object-cover"
-                  />
-                ) : (
-                  <div className="flex h-full w-full items-center justify-center">
-                    <Robot
-                      size={20}
-                      weight="bold"
-                      className="text-purple-600"
-                    />
-                  </div>
-                )}
+              <div className="flex h-10 w-10 flex-shrink-0 items-center justify-center rounded-lg bg-purple-100 dark:bg-purple-800">
+                <Robot size={20} weight="bold" className="text-purple-600" />
               </div>
               <div className="flex-1 space-y-2">
                 <div>
                   <Text
                     variant="body"
-                    className="font-semibold text-purple-900"
+                    className="font-semibold text-purple-900 dark:text-purple-100"
                   >
                     {agent.name}
                   </Text>
                   {agent.version && (
-                    <Text variant="small" className="text-purple-600">
+                    <Text
+                      variant="small"
+                      className="text-purple-600 dark:text-purple-400"
+                    >
                       v{agent.version}
                     </Text>
                   )}
                 </div>
-                <Text variant="small" className="line-clamp-2 text-purple-700">
+                <Text
+                  variant="small"
+                  className="line-clamp-2 text-purple-700 dark:text-purple-300"
+                >
                   {agent.description}
                 </Text>
                 {onSelectAgent && (
                   <Button
                     onClick={() => onSelectAgent(agent.id)}
                     variant="ghost"
-                    className="mt-2 flex items-center gap-1 p-0 text-sm text-purple-600 hover:text-purple-800"
+                    className="mt-2 flex items-center gap-1 p-0 text-sm text-purple-600 hover:text-purple-800 dark:text-purple-400 dark:hover:text-purple-200"
                   >
                     View details
                     <ArrowRight size={16} weight="bold" />
@@ -110,7 +103,10 @@ export function AgentCarouselMessage({
       </div>
 
       {totalCount && totalCount > agents.length && (
-        <Text variant="small" className="text-center text-purple-600">
+        <Text
+          variant="small"
+          className="text-center text-purple-600 dark:text-purple-400"
+        >
           Showing {agents.length} of {totalCount} results
         </Text>
       )}

autogpt_platform/frontend/src/app/(platform)/chat/components/AuthPromptWidget/AuthPromptWidget.tsx

@@ -1,9 +1,10 @@
 "use client";
 
-import { Button } from "@/components/atoms/Button/Button";
-import { cn } from "@/lib/utils";
-import { ShieldIcon, SignInIcon, UserPlusIcon } from "@phosphor-icons/react";
+import React from "react";
 import { useRouter } from "next/navigation";
+import { Button } from "@/components/atoms/Button/Button";
+import { SignInIcon, UserPlusIcon, ShieldIcon } from "@phosphor-icons/react";
+import { cn } from "@/lib/utils";
 
 export interface AuthPromptWidgetProps {
   message: string;
@@ -53,8 +54,8 @@ export function AuthPromptWidget({
   return (
     <div
       className={cn(
-        "my-4 overflow-hidden rounded-lg border border-violet-200",
-        "bg-gradient-to-br from-violet-50 to-purple-50",
+        "my-4 overflow-hidden rounded-lg border border-violet-200 dark:border-violet-800",
+        "bg-gradient-to-br from-violet-50 to-purple-50 dark:from-violet-950/30 dark:to-purple-950/30",
         "duration-500 animate-in fade-in-50 slide-in-from-bottom-2",
         className,
       )}
@@ -65,19 +66,21 @@ export function AuthPromptWidget({
             <ShieldIcon size={20} weight="fill" className="text-white" />
           </div>
           <div>
-            <h3 className="text-lg font-semibold text-neutral-900">
+            <h3 className="text-lg font-semibold text-neutral-900 dark:text-neutral-100">
               Authentication Required
             </h3>
-            <p className="text-sm text-neutral-600">
+            <p className="text-sm text-neutral-600 dark:text-neutral-400">
               Sign in to set up and manage agents
             </p>
           </div>
         </div>
 
-        <div className="mb-5 rounded-md bg-white/50 p-4">
-          <p className="text-sm text-neutral-700">{message}</p>
+        <div className="mb-5 rounded-md bg-white/50 p-4 dark:bg-neutral-900/50">
+          <p className="text-sm text-neutral-700 dark:text-neutral-300">
+            {message}
+          </p>
           {agentInfo && (
-            <div className="mt-3 text-xs text-neutral-600">
+            <div className="mt-3 text-xs text-neutral-600 dark:text-neutral-400">
               <p>
                 Ready to set up:{" "}
                 <span className="font-medium">{agentInfo.name}</span>
@@ -111,7 +114,7 @@ export function AuthPromptWidget({
           </Button>
         </div>
 
-        <div className="mt-4 text-center text-xs text-neutral-500">
+        <div className="mt-4 text-center text-xs text-neutral-500 dark:text-neutral-500">
           Your chat session will be preserved after signing in
         </div>
       </div>

autogpt_platform/frontend/src/app/(platform)/chat/components/ChatContainer/ChatContainer.tsx

@@ -1,11 +1,9 @@
-import type { SessionDetailResponse } from "@/app/api/__generated__/models/sessionDetailResponse";
 import { cn } from "@/lib/utils";
-import { useCallback } from "react";
-import { usePageContext } from "../../usePageContext";
-import { ChatInput } from "../ChatInput/ChatInput";
-import { MessageList } from "../MessageList/MessageList";
-import { QuickActionsWelcome } from "../QuickActionsWelcome/QuickActionsWelcome";
+import { ChatInput } from "@/app/(platform)/chat/components/ChatInput/ChatInput";
+import { MessageList } from "@/app/(platform)/chat/components/MessageList/MessageList";
+import { QuickActionsWelcome } from "@/app/(platform)/chat/components/QuickActionsWelcome/QuickActionsWelcome";
 import { useChatContainer } from "./useChatContainer";
+import type { SessionDetailResponse } from "@/app/api/__generated__/models/sessionDetailResponse";
 
 export interface ChatContainerProps {
   sessionId: string | null;
@@ -26,16 +24,6 @@ export function ChatContainer({
       initialMessages,
       onRefreshSession,
     });
-  const { capturePageContext } = usePageContext();
-
-  // Wrap sendMessage to automatically capture page context
-  const sendMessageWithContext = useCallback(
-    async (content: string, isUserMessage: boolean = true) => {
-      const context = capturePageContext();
-      await sendMessage(content, isUserMessage, context);
-    },
-    [sendMessage, capturePageContext],
-  );
 
   const quickActions = [
     "Find agents for social media management",
@@ -45,23 +33,14 @@ export function ChatContainer({
   ];
 
   return (
-    <div
-      className={cn("flex h-full flex-col", className)}
-      style={{
-        backgroundColor: "#ffffff",
-        backgroundImage:
-          "radial-gradient(#e5e5e5 0.5px, transparent 0.5px), radial-gradient(#e5e5e5 0.5px, #ffffff 0.5px)",
-        backgroundSize: "20px 20px",
-        backgroundPosition: "0 0, 10px 10px",
-      }}
-    >
+    <div className={cn("flex h-full flex-col", className)}>
       {/* Messages or Welcome Screen */}
       {messages.length === 0 ? (
         <QuickActionsWelcome
-          title="Welcome to AutoGPT Copilot"
+          title="Welcome to AutoGPT Chat"
           description="Start a conversation to discover and run AI agents."
           actions={quickActions}
-          onActionClick={sendMessageWithContext}
+          onActionClick={sendMessage}
           disabled={isStreaming || !sessionId}
         />
       ) : (
@@ -69,15 +48,15 @@ export function ChatContainer({
           messages={messages}
           streamingChunks={streamingChunks}
           isStreaming={isStreaming}
-          onSendMessage={sendMessageWithContext}
+          onSendMessage={sendMessage}
           className="flex-1"
         />
       )}
 
       {/* Input - Always visible */}
-      <div className="border-t border-zinc-200 p-4">
+      <div className="border-t border-zinc-200 p-4 dark:border-zinc-800">
         <ChatInput
-          onSend={sendMessageWithContext}
+          onSend={sendMessage}
           disabled={isStreaming || !sessionId}
           placeholder={
             sessionId ? "Type your message..." : "Creating session..."

autogpt_platform/frontend/src/app/(platform)/chat/components/ChatContainer/createStreamEventDispatcher.ts

@@ -1,14 +1,14 @@
-import type { StreamChunk } from "@/components/contextual/Chat/useChatStream";
 import { toast } from "sonner";
+import type { StreamChunk } from "@/app/(platform)/chat/useChatStream";
 import type { HandlerDependencies } from "./useChatContainer.handlers";
 import {
-  handleError,
-  handleLoginNeeded,
-  handleStreamEnd,
   handleTextChunk,
   handleTextEnded,
   handleToolCallStart,
   handleToolResponse,
+  handleLoginNeeded,
+  handleStreamEnd,
+  handleError,
 } from "./useChatContainer.handlers";
 
 export function createStreamEventDispatcher(

autogpt_platform/frontend/src/app/(platform)/chat/components/ChatContainer/helpers.ts

@@ -1,24 +1,5 @@
+import type { ChatMessageData } from "@/app/(platform)/chat/components/ChatMessage/useChatMessage";
 import type { ToolResult } from "@/types/chat";
-import type { ChatMessageData } from "../ChatMessage/useChatMessage";
-
-export function removePageContext(content: string): string {
-  // Remove "Page URL: ..." pattern (case insensitive, handles various formats)
-  let cleaned = content.replace(/Page URL:\s*[^\n\r]*/gi, "");
-
-  // Find "User Message:" marker to preserve the actual user message
-  const userMessageMatch = cleaned.match(/User Message:\s*([\s\S]*)$/i);
-  if (userMessageMatch) {
-    // If we found "User Message:", extract everything after it
-    cleaned = userMessageMatch[1];
-  } else {
-    // If no "User Message:" marker, remove "Page Content:" and everything after it
-    cleaned = cleaned.replace(/Page Content:[\s\S]*$/gi, "");
-  }
-
-  // Clean up extra whitespace and newlines
-  cleaned = cleaned.replace(/\n\s*\n\s*\n+/g, "\n\n").trim();
-  return cleaned;
-}
 
 export function createUserMessage(content: string): ChatMessageData {
   return {
@@ -82,7 +63,6 @@ export function isAgentArray(value: unknown): value is Array<{
   name: string;
   description: string;
   version?: number;
-  image_url?: string;
 }> {
   if (!Array.isArray(value)) {
     return false;
@@ -97,8 +77,7 @@ export function isAgentArray(value: unknown): value is Array<{
       typeof item.name === "string" &&
       "description" in item &&
       typeof item.description === "string" &&
-      (!("version" in item) || typeof item.version === "number") &&
-      (!("image_url" in item) || typeof item.image_url === "string"),
+      (!("version" in item) || typeof item.version === "number"),
   );
 }
 
@@ -253,7 +232,6 @@ export function isSetupInfo(value: unknown): value is {
 
 export function extractCredentialsNeeded(
   parsedResult: Record<string, unknown>,
-  toolName: string = "run_agent",
 ): ChatMessageData | null {
   try {
     const setupInfo = parsedResult?.setup_info as
@@ -266,7 +244,7 @@ export function extractCredentialsNeeded(
       | Record<string, Record<string, unknown>>
       | undefined;
     if (missingCreds && Object.keys(missingCreds).length > 0) {
-      const agentName = (setupInfo?.agent_name as string) || "this block";
+      const agentName = (setupInfo?.agent_name as string) || "this agent";
       const credentials = Object.values(missingCreds).map((credInfo) => ({
         provider: (credInfo.provider as string) || "unknown",
         providerName:
@@ -286,7 +264,7 @@ export function extractCredentialsNeeded(
       }));
       return {
         type: "credentials_needed",
-        toolName,
+        toolName: "run_agent",
         credentials,
         message: `To run ${agentName}, you need to add ${credentials.length === 1 ? "credentials" : `${credentials.length} credentials`}.`,
         agentName,
@@ -299,81 +277,3 @@ export function extractCredentialsNeeded(
     return null;
   }
 }
-
-export function extractInputsNeeded(
-  parsedResult: Record<string, unknown>,
-  toolName: string = "run_agent",
-): ChatMessageData | null {
-  try {
-    const setupInfo = parsedResult?.setup_info as
-      | Record<string, unknown>
-      | undefined;
-    const requirements = setupInfo?.requirements as
-      | Record<string, unknown>
-      | undefined;
-    const inputs = requirements?.inputs as
-      | Array<Record<string, unknown>>
-      | undefined;
-    const credentials = requirements?.credentials as
-      | Array<Record<string, unknown>>
-      | undefined;
-
-    if (!inputs || inputs.length === 0) {
-      return null;
-    }
-
-    const agentName = (setupInfo?.agent_name as string) || "this agent";
-    const agentId = parsedResult?.graph_id as string | undefined;
-    const graphVersion = parsedResult?.graph_version as number | undefined;
-
-    const inputSchema: Record<string, any> = {};
-    inputs.forEach((input) => {
-      const name = input.name as string;
-      if (name) {
-        inputSchema[name] = {
-          title: input.name as string,
-          description: (input.description as string) || "",
-          type: (input.type as string) || "string",
-          default: input.default,
-          required: (input.required as boolean) || false,
-          enum: input.options,
-          format: input.format,
-        };
-      }
-    });
-
-    const credentialsSchema: Record<string, any> = {};
-    if (credentials && credentials.length > 0) {
-      credentials.forEach((cred) => {
-        const id = cred.id as string;
-        if (id) {
-          credentialsSchema[id] = {
-            type: "object",
-            properties: {},
-            credentials_provider: [cred.provider as string],
-            credentials_types: [(cred.type as string) || "api_key"],
-            credentials_scopes: cred.scopes as string[] | undefined,
-          };
-        }
-      });
-    }
-
-    return {
-      type: "inputs_needed",
-      toolName,
-      agentName,
-      agentId,
-      graphVersion,
-      inputSchema,
-      credentialsSchema:
-        Object.keys(credentialsSchema).length > 0
-          ? credentialsSchema
-          : undefined,
-      message: `Please provide the required inputs to run ${agentName}.`,
-      timestamp: new Date(),
-    };
-  } catch (err) {
-    console.error("Failed to extract inputs from setup info:", err);
-    return null;
-  }
-}

autogpt_platform/frontend/src/app/(platform)/chat/components/ChatContainer/useChatContainer.handlers.ts

@@ -1,18 +1,13 @@
-import type { StreamChunk } from "@/components/contextual/Chat/useChatStream";
-import type { Dispatch, MutableRefObject, SetStateAction } from "react";
-import type { ChatMessageData } from "../ChatMessage/useChatMessage";
-import {
-  extractCredentialsNeeded,
-  extractInputsNeeded,
-  parseToolResponse,
-} from "./helpers";
+import type { Dispatch, SetStateAction, MutableRefObject } from "react";
+import type { StreamChunk } from "@/app/(platform)/chat/useChatStream";
+import type { ChatMessageData } from "@/app/(platform)/chat/components/ChatMessage/useChatMessage";
+import { parseToolResponse, extractCredentialsNeeded } from "./helpers";
 
 export interface HandlerDependencies {
   setHasTextChunks: Dispatch<SetStateAction<boolean>>;
   setStreamingChunks: Dispatch<SetStateAction<string[]>>;
   streamingChunksRef: MutableRefObject<string[]>;
   setMessages: Dispatch<SetStateAction<ChatMessageData[]>>;
-  setIsStreamingInitiated: Dispatch<SetStateAction<boolean>>;
   sessionId: string;
 }
 
@@ -44,7 +39,6 @@ export function handleTextEnded(
   deps.setStreamingChunks([]);
   deps.streamingChunksRef.current = [];
   deps.setHasTextChunks(false);
-  deps.setIsStreamingInitiated(false);
 }
 
 export function handleToolCallStart(
@@ -106,18 +100,11 @@ export function handleToolResponse(
       parsedResult = null;
     }
     if (
-      (chunk.tool_name === "run_agent" || chunk.tool_name === "run_block") &&
+      chunk.tool_name === "run_agent" &&
       chunk.success &&
       parsedResult?.type === "setup_requirements"
     ) {
-      const inputsMessage = extractInputsNeeded(parsedResult, chunk.tool_name);
-      if (inputsMessage) {
-        deps.setMessages((prev) => [...prev, inputsMessage]);
-      }
-      const credentialsMessage = extractCredentialsNeeded(
-        parsedResult,
-        chunk.tool_name,
-      );
+      const credentialsMessage = extractCredentialsNeeded(parsedResult);
       if (credentialsMessage) {
         deps.setMessages((prev) => [...prev, credentialsMessage]);
       }
@@ -210,15 +197,10 @@ export function handleStreamEnd(
   deps.setStreamingChunks([]);
   deps.streamingChunksRef.current = [];
   deps.setHasTextChunks(false);
-  deps.setIsStreamingInitiated(false);
   console.log("[Stream End] Stream complete, messages in local state");
 }
 
-export function handleError(chunk: StreamChunk, deps: HandlerDependencies) {
+export function handleError(chunk: StreamChunk, _deps: HandlerDependencies) {
   const errorMessage = chunk.message || chunk.content || "An error occurred";
   console.error("Stream error:", errorMessage);
-  deps.setIsStreamingInitiated(false);
-  deps.setHasTextChunks(false);
-  deps.setStreamingChunks([]);
-  deps.streamingChunksRef.current = [];
 }

autogpt_platform/frontend/src/app/(platform)/chat/components/ChatContainer/useChatContainer.ts

@@ -0,0 +1,130 @@
+import { useState, useCallback, useRef, useMemo } from "react";
+import { toast } from "sonner";
+import { useChatStream } from "@/app/(platform)/chat/useChatStream";
+import type { SessionDetailResponse } from "@/app/api/__generated__/models/sessionDetailResponse";
+import type { ChatMessageData } from "@/app/(platform)/chat/components/ChatMessage/useChatMessage";
+import {
+  parseToolResponse,
+  isValidMessage,
+  isToolCallArray,
+  createUserMessage,
+  filterAuthMessages,
+} from "./helpers";
+import { createStreamEventDispatcher } from "./createStreamEventDispatcher";
+
+interface UseChatContainerArgs {
+  sessionId: string | null;
+  initialMessages: SessionDetailResponse["messages"];
+  onRefreshSession: () => Promise<void>;
+}
+
+export function useChatContainer({
+  sessionId,
+  initialMessages,
+}: UseChatContainerArgs) {
+  const [messages, setMessages] = useState<ChatMessageData[]>([]);
+  const [streamingChunks, setStreamingChunks] = useState<string[]>([]);
+  const [hasTextChunks, setHasTextChunks] = useState(false);
+  const streamingChunksRef = useRef<string[]>([]);
+  const { error, sendMessage: sendStreamMessage } = useChatStream();
+  const isStreaming = hasTextChunks;
+
+  const allMessages = useMemo(() => {
+    const processedInitialMessages = initialMessages
+      .filter((msg: Record<string, unknown>) => {
+        if (!isValidMessage(msg)) {
+          console.warn("Invalid message structure from backend:", msg);
+          return false;
+        }
+        const content = String(msg.content || "").trim();
+        const toolCalls = msg.tool_calls;
+        return (
+          content.length > 0 ||
+          (toolCalls && Array.isArray(toolCalls) && toolCalls.length > 0)
+        );
+      })
+      .map((msg: Record<string, unknown>) => {
+        const content = String(msg.content || "");
+        const role = String(msg.role || "assistant").toLowerCase();
+        const toolCalls = msg.tool_calls;
+        if (
+          role === "assistant" &&
+          toolCalls &&
+          isToolCallArray(toolCalls) &&
+          toolCalls.length > 0
+        ) {
+          return null;
+        }
+        if (role === "tool") {
+          const timestamp = msg.timestamp
+            ? new Date(msg.timestamp as string)
+            : undefined;
+          const toolResponse = parseToolResponse(
+            content,
+            (msg.tool_call_id as string) || "",
+            "unknown",
+            timestamp,
+          );
+          if (!toolResponse) {
+            return null;
+          }
+          return toolResponse;
+        }
+        return {
+          type: "message",
+          role: role as "user" | "assistant" | "system",
+          content,
+          timestamp: msg.timestamp
+            ? new Date(msg.timestamp as string)
+            : undefined,
+        };
+      })
+      .filter((msg): msg is ChatMessageData => msg !== null);
+
+    return [...processedInitialMessages, ...messages];
+  }, [initialMessages, messages]);
+
+  const sendMessage = useCallback(
+    async function sendMessage(content: string, isUserMessage: boolean = true) {
+      if (!sessionId) {
+        console.error("Cannot send message: no session ID");
+        return;
+      }
+      if (isUserMessage) {
+        const userMessage = createUserMessage(content);
+        setMessages((prev) => [...filterAuthMessages(prev), userMessage]);
+      } else {
+        setMessages((prev) => filterAuthMessages(prev));
+      }
+      setStreamingChunks([]);
+      streamingChunksRef.current = [];
+      setHasTextChunks(false);
+      const dispatcher = createStreamEventDispatcher({
+        setHasTextChunks,
+        setStreamingChunks,
+        streamingChunksRef,
+        setMessages,
+        sessionId,
+      });
+      try {
+        await sendStreamMessage(sessionId, content, dispatcher, isUserMessage);
+      } catch (err) {
+        console.error("Failed to send message:", err);
+        const errorMessage =
+          err instanceof Error ? err.message : "Failed to send message";
+        toast.error("Failed to send message", {
+          description: errorMessage,
+        });
+      }
+    },
+    [sessionId, sendStreamMessage],
+  );
+
+  return {
+    messages: allMessages,
+    streamingChunks,
+    isStreaming,
+    error,
+    sendMessage,
+  };
+}

autogpt_platform/frontend/src/app/(platform)/chat/components/ChatCredentialsSetup/ChatCredentialsSetup.tsx

@@ -0,0 +1,153 @@
+import { CredentialsInput } from "@/app/(platform)/library/agents/[id]/components/NewAgentLibraryView/components/modals/CredentialsInputs/CredentialsInputs";
+import { Card } from "@/components/atoms/Card/Card";
+import { Text } from "@/components/atoms/Text/Text";
+import type { BlockIOCredentialsSubSchema } from "@/lib/autogpt-server-api";
+import { cn } from "@/lib/utils";
+import { CheckIcon, KeyIcon, WarningIcon } from "@phosphor-icons/react";
+import { useEffect, useRef } from "react";
+import { useChatCredentialsSetup } from "./useChatCredentialsSetup";
+
+export interface CredentialInfo {
+  provider: string;
+  providerName: string;
+  credentialType: "api_key" | "oauth2" | "user_password" | "host_scoped";
+  title: string;
+  scopes?: string[];
+}
+
+interface Props {
+  credentials: CredentialInfo[];
+  agentName?: string;
+  message: string;
+  onAllCredentialsComplete: () => void;
+  onCancel: () => void;
+  className?: string;
+}
+
+function createSchemaFromCredentialInfo(
+  credential: CredentialInfo,
+): BlockIOCredentialsSubSchema {
+  return {
+    type: "object",
+    properties: {},
+    credentials_provider: [credential.provider],
+    credentials_types: [credential.credentialType],
+    credentials_scopes: credential.scopes,
+    discriminator: undefined,
+    discriminator_mapping: undefined,
+    discriminator_values: undefined,
+  };
+}
+
+export function ChatCredentialsSetup({
+  credentials,
+  agentName: _agentName,
+  message,
+  onAllCredentialsComplete,
+  onCancel: _onCancel,
+  className,
+}: Props) {
+  const { selectedCredentials, isAllComplete, handleCredentialSelect } =
+    useChatCredentialsSetup(credentials);
+
+  // Track if we've already called completion to prevent double calls
+  const hasCalledCompleteRef = useRef(false);
+
+  // Reset the completion flag when credentials change (new credential setup flow)
+  useEffect(
+    function resetCompletionFlag() {
+      hasCalledCompleteRef.current = false;
+    },
+    [credentials],
+  );
+
+  // Auto-call completion when all credentials are configured
+  useEffect(
+    function autoCompleteWhenReady() {
+      if (isAllComplete && !hasCalledCompleteRef.current) {
+        hasCalledCompleteRef.current = true;
+        onAllCredentialsComplete();
+      }
+    },
+    [isAllComplete, onAllCredentialsComplete],
+  );
+
+  return (
+    <Card
+      className={cn(
+        "mx-4 my-2 overflow-hidden border-orange-200 bg-orange-50 dark:border-orange-900 dark:bg-orange-950",
+        className,
+      )}
+    >
+      <div className="flex items-start gap-4 p-6">
+        <div className="flex h-12 w-12 flex-shrink-0 items-center justify-center rounded-full bg-orange-500">
+          <KeyIcon size={24} weight="bold" className="text-white" />
+        </div>
+        <div className="flex-1">
+          <Text
+            variant="h3"
+            className="mb-2 text-orange-900 dark:text-orange-100"
+          >
+            Credentials Required
+          </Text>
+          <Text
+            variant="body"
+            className="mb-4 text-orange-700 dark:text-orange-300"
+          >
+            {message}
+          </Text>
+
+          <div className="space-y-3">
+            {credentials.map((cred, index) => {
+              const schema = createSchemaFromCredentialInfo(cred);
+              const isSelected = !!selectedCredentials[cred.provider];
+
+              return (
+                <div
+                  key={`${cred.provider}-${index}`}
+                  className={cn(
+                    "relative rounded-lg border border-orange-200 bg-white p-4 dark:border-orange-800 dark:bg-orange-900/20",
+                    isSelected &&
+                      "border-green-500 bg-green-50 dark:border-green-700 dark:bg-green-950/30",
+                  )}
+                >
+                  <div className="mb-2 flex items-center justify-between">
+                    <div className="flex items-center gap-2">
+                      {isSelected ? (
+                        <CheckIcon
+                          size={20}
+                          className="text-green-500"
+                          weight="bold"
+                        />
+                      ) : (
+                        <WarningIcon
+                          size={20}
+                          className="text-orange-500"
+                          weight="bold"
+                        />
+                      )}
+                      <Text
+                        variant="body"
+                        className="font-semibold text-orange-900 dark:text-orange-100"
+                      >
+                        {cred.providerName}
+                      </Text>
+                    </div>
+                  </div>
+
+                  <CredentialsInput
+                    schema={schema}
+                    selectedCredentials={selectedCredentials[cred.provider]}
+                    onSelectCredentials={(credMeta) =>
+                      handleCredentialSelect(cred.provider, credMeta)
+                    }
+                  />
+                </div>
+              );
+            })}
+          </div>
+        </div>
+      </div>
+    </Card>
+  );
+}

autogpt_platform/frontend/src/app/(platform)/chat/components/ChatInput/ChatInput.tsx

@@ -0,0 +1,63 @@
+import { cn } from "@/lib/utils";
+import { PaperPlaneRightIcon } from "@phosphor-icons/react";
+import { Button } from "@/components/atoms/Button/Button";
+import { useChatInput } from "./useChatInput";
+
+export interface ChatInputProps {
+  onSend: (message: string) => void;
+  disabled?: boolean;
+  placeholder?: string;
+  className?: string;
+}
+
+export function ChatInput({
+  onSend,
+  disabled = false,
+  placeholder = "Type your message...",
+  className,
+}: ChatInputProps) {
+  const { value, setValue, handleKeyDown, handleSend, textareaRef } =
+    useChatInput({
+      onSend,
+      disabled,
+      maxRows: 5,
+    });
+
+  return (
+    <div className={cn("flex gap-2", className)}>
+      <textarea
+        ref={textareaRef}
+        value={value}
+        onChange={(e) => setValue(e.target.value)}
+        onKeyDown={handleKeyDown}
+        placeholder={placeholder}
+        disabled={disabled}
+        rows={1}
+        autoComplete="off"
+        aria-label="Chat message input"
+        aria-describedby="chat-input-hint"
+        className={cn(
+          "flex-1 resize-none rounded-lg border border-neutral-200 bg-white px-4 py-2 text-sm",
+          "placeholder:text-neutral-400",
+          "focus:border-violet-600 focus:outline-none focus:ring-2 focus:ring-violet-600/20",
+          "dark:border-neutral-800 dark:bg-neutral-900 dark:text-neutral-100 dark:placeholder:text-neutral-500",
+          "disabled:cursor-not-allowed disabled:opacity-50",
+        )}
+      />
+      <span id="chat-input-hint" className="sr-only">
+        Press Enter to send, Shift+Enter for new line
+      </span>
+
+      <Button
+        variant="primary"
+        size="small"
+        onClick={handleSend}
+        disabled={disabled || !value.trim()}
+        className="self-end"
+        aria-label="Send message"
+      >
+        <PaperPlaneRightIcon className="h-4 w-4" weight="fill" />
+      </Button>
+    </div>
+  );
+}

autogpt_platform/frontend/src/app/(platform)/chat/components/ChatInput/useChatInput.ts

@@ -1,22 +1,21 @@
-import { KeyboardEvent, useCallback, useEffect, useState } from "react";
+import { KeyboardEvent, useCallback, useState, useRef, useEffect } from "react";
 
 interface UseChatInputArgs {
   onSend: (message: string) => void;
   disabled?: boolean;
   maxRows?: number;
-  inputId?: string;
 }
 
 export function useChatInput({
   onSend,
   disabled = false,
   maxRows = 5,
-  inputId = "chat-input",
 }: UseChatInputArgs) {
   const [value, setValue] = useState("");
+  const textareaRef = useRef<HTMLTextAreaElement>(null);
 
   useEffect(() => {
-    const textarea = document.getElementById(inputId) as HTMLTextAreaElement;
+    const textarea = textareaRef.current;
     if (!textarea) return;
     textarea.style.height = "auto";
     const lineHeight = parseInt(
@@ -28,25 +27,23 @@ export function useChatInput({
     textarea.style.height = `${newHeight}px`;
     textarea.style.overflowY =
       textarea.scrollHeight > maxHeight ? "auto" : "hidden";
-  }, [value, maxRows, inputId]);
+  }, [value, maxRows]);
 
   const handleSend = useCallback(() => {
     if (disabled || !value.trim()) return;
     onSend(value.trim());
     setValue("");
-    const textarea = document.getElementById(inputId) as HTMLTextAreaElement;
-    if (textarea) {
-      textarea.style.height = "auto";
+    if (textareaRef.current) {
+      textareaRef.current.style.height = "auto";
     }
-  }, [value, onSend, disabled, inputId]);
+  }, [value, onSend, disabled]);
 
   const handleKeyDown = useCallback(
-    (event: KeyboardEvent<HTMLInputElement | HTMLTextAreaElement>) => {
+    (event: KeyboardEvent<HTMLTextAreaElement>) => {
       if (event.key === "Enter" && !event.shiftKey) {
         event.preventDefault();
         handleSend();
       }
-      // Shift+Enter allows default behavior (new line) - no need to handle explicitly
     },
     [handleSend],
   );
@@ -56,5 +53,6 @@ export function useChatInput({
     setValue,
     handleKeyDown,
     handleSend,
+    textareaRef,
   };
 }

autogpt_platform/frontend/src/app/(platform)/chat/components/ChatLoadingState/ChatLoadingState.tsx

@@ -0,0 +1,31 @@
+import React from "react";
+import { Text } from "@/components/atoms/Text/Text";
+import { ArrowClockwiseIcon } from "@phosphor-icons/react";
+import { cn } from "@/lib/utils";
+
+export interface ChatLoadingStateProps {
+  message?: string;
+  className?: string;
+}
+
+export function ChatLoadingState({
+  message = "Loading...",
+  className,
+}: ChatLoadingStateProps) {
+  return (
+    <div
+      className={cn("flex flex-1 items-center justify-center p-6", className)}
+    >
+      <div className="flex flex-col items-center gap-4 text-center">
+        <ArrowClockwiseIcon
+          size={32}
+          weight="bold"
+          className="animate-spin text-purple-500"
+        />
+        <Text variant="body" className="text-zinc-600 dark:text-zinc-400">
+          {message}
+        </Text>
+      </div>
+    </div>
+  );
+}

autogpt_platform/frontend/src/app/(platform)/chat/components/ChatMessage/ChatMessage.tsx

@@ -0,0 +1,194 @@
+"use client";
+
+import { cn } from "@/lib/utils";
+import { RobotIcon, UserIcon, CheckCircleIcon } from "@phosphor-icons/react";
+import { useCallback } from "react";
+import { MessageBubble } from "@/app/(platform)/chat/components/MessageBubble/MessageBubble";
+import { MarkdownContent } from "@/app/(platform)/chat/components/MarkdownContent/MarkdownContent";
+import { ToolCallMessage } from "@/app/(platform)/chat/components/ToolCallMessage/ToolCallMessage";
+import { ToolResponseMessage } from "@/app/(platform)/chat/components/ToolResponseMessage/ToolResponseMessage";
+import { AuthPromptWidget } from "@/app/(platform)/chat/components/AuthPromptWidget/AuthPromptWidget";
+import { ChatCredentialsSetup } from "@/app/(platform)/chat/components/ChatCredentialsSetup/ChatCredentialsSetup";
+import { useSupabase } from "@/lib/supabase/hooks/useSupabase";
+import { useChatMessage, type ChatMessageData } from "./useChatMessage";
+import { getToolActionPhrase } from "@/app/(platform)/chat/helpers";
+export interface ChatMessageProps {
+  message: ChatMessageData;
+  className?: string;
+  onDismissLogin?: () => void;
+  onDismissCredentials?: () => void;
+  onSendMessage?: (content: string, isUserMessage?: boolean) => void;
+}
+
+export function ChatMessage({
+  message,
+  className,
+  onDismissCredentials,
+  onSendMessage,
+}: ChatMessageProps) {
+  const { user } = useSupabase();
+  const {
+    formattedTimestamp,
+    isUser,
+    isAssistant,
+    isToolCall,
+    isToolResponse,
+    isLoginNeeded,
+    isCredentialsNeeded,
+  } = useChatMessage(message);
+
+  const handleAllCredentialsComplete = useCallback(
+    function handleAllCredentialsComplete() {
+      // Send a user message that explicitly asks to retry the setup
+      // This ensures the LLM calls get_required_setup_info again and proceeds with execution
+      if (onSendMessage) {
+        onSendMessage(
+          "I've configured the required credentials. Please check if everything is ready and proceed with setting up the agent.",
+        );
+      }
+      // Optionally dismiss the credentials prompt
+      if (onDismissCredentials) {
+        onDismissCredentials();
+      }
+    },
+    [onSendMessage, onDismissCredentials],
+  );
+
+  function handleCancelCredentials() {
+    // Dismiss the credentials prompt
+    if (onDismissCredentials) {
+      onDismissCredentials();
+    }
+  }
+
+  // Render credentials needed messages
+  if (isCredentialsNeeded && message.type === "credentials_needed") {
+    return (
+      <ChatCredentialsSetup
+        credentials={message.credentials}
+        agentName={message.agentName}
+        message={message.message}
+        onAllCredentialsComplete={handleAllCredentialsComplete}
+        onCancel={handleCancelCredentials}
+        className={className}
+      />
+    );
+  }
+
+  // Render login needed messages
+  if (isLoginNeeded && message.type === "login_needed") {
+    // If user is already logged in, show success message instead of auth prompt
+    if (user) {
+      return (
+        <div className={cn("px-4 py-2", className)}>
+          <div className="my-4 overflow-hidden rounded-lg border border-green-200 bg-gradient-to-br from-green-50 to-emerald-50 dark:border-green-800 dark:from-green-950/30 dark:to-emerald-950/30">
+            <div className="px-6 py-4">
+              <div className="flex items-center gap-3">
+                <div className="flex h-10 w-10 items-center justify-center rounded-full bg-green-600">
+                  <CheckCircleIcon
+                    size={20}
+                    weight="fill"
+                    className="text-white"
+                  />
+                </div>
+                <div>
+                  <h3 className="text-lg font-semibold text-neutral-900 dark:text-neutral-100">
+                    Successfully Authenticated
+                  </h3>
+                  <p className="text-sm text-neutral-600 dark:text-neutral-400">
+                    You&apos;re now signed in and ready to continue
+                  </p>
+                </div>
+              </div>
+            </div>
+          </div>
+        </div>
+      );
+    }
+
+    // Show auth prompt if not logged in
+    return (
+      <div className={cn("px-4 py-2", className)}>
+        <AuthPromptWidget
+          message={message.message}
+          sessionId={message.sessionId}
+          agentInfo={message.agentInfo}
+          returnUrl="/chat"
+        />
+      </div>
+    );
+  }
+
+  // Render tool call messages
+  if (isToolCall && message.type === "tool_call") {
+    return (
+      <div className={cn("px-4 py-2", className)}>
+        <ToolCallMessage toolName={message.toolName} />
+      </div>
+    );
+  }
+
+  // Render tool response messages
+  if (
+    (isToolResponse && message.type === "tool_response") ||
+    message.type === "no_results" ||
+    message.type === "agent_carousel" ||
+    message.type === "execution_started"
+  ) {
+    return (
+      <div className={cn("px-4 py-2", className)}>
+        <ToolResponseMessage toolName={getToolActionPhrase(message.toolName)} />
+      </div>
+    );
+  }
+
+  // Render regular chat messages
+  if (message.type === "message") {
+    return (
+      <div
+        className={cn(
+          "flex gap-3 px-4 py-4",
+          isUser && "flex-row-reverse",
+          className,
+        )}
+      >
+        {/* Avatar */}
+        <div className="flex-shrink-0">
+          <div
+            className={cn(
+              "flex h-8 w-8 items-center justify-center rounded-full",
+              isUser && "bg-zinc-200 dark:bg-zinc-700",
+              isAssistant && "bg-purple-600 dark:bg-purple-500",
+            )}
+          >
+            {isUser ? (
+              <UserIcon className="h-5 w-5 text-zinc-700 dark:text-zinc-200" />
+            ) : (
+              <RobotIcon className="h-5 w-5 text-white" />
+            )}
+          </div>
+        </div>
+
+        {/* Message Content */}
+        <div className={cn("flex max-w-[70%] flex-col", isUser && "items-end")}>
+          <MessageBubble variant={isUser ? "user" : "assistant"}>
+            <MarkdownContent content={message.content} />
+          </MessageBubble>
+
+          {/* Timestamp */}
+          <span
+            className={cn(
+              "mt-1 text-xs text-zinc-500 dark:text-zinc-400",
+              isUser && "text-right",
+            )}
+          >
+            {formattedTimestamp}
+          </span>
+        </div>
+      </div>
+    );
+  }
+
+  // Fallback for unknown message types
+  return null;
+}

autogpt_platform/frontend/src/app/(platform)/chat/components/ChatMessage/useChatMessage.ts

@@ -1,5 +1,5 @@
-import type { ToolArguments, ToolResult } from "@/types/chat";
 import { formatDistanceToNow } from "date-fns";
+import type { ToolArguments, ToolResult } from "@/types/chat";
 
 export type ChatMessageData =
   | {
@@ -65,7 +65,6 @@ export type ChatMessageData =
         name: string;
         description: string;
         version?: number;
-        image_url?: string;
       }>;
       totalCount?: number;
       timestamp?: string | Date;
@@ -78,17 +77,6 @@ export type ChatMessageData =
       message?: string;
       libraryAgentLink?: string;
       timestamp?: string | Date;
-    }
-  | {
-      type: "inputs_needed";
-      toolName: string;
-      agentName?: string;
-      agentId?: string;
-      graphVersion?: number;
-      inputSchema: Record<string, any>;
-      credentialsSchema?: Record<string, any>;
-      message: string;
-      timestamp?: string | Date;
     };
 
 export function useChatMessage(message: ChatMessageData) {
@@ -108,6 +96,5 @@ export function useChatMessage(message: ChatMessageData) {
     isNoResults: message.type === "no_results",
     isAgentCarousel: message.type === "agent_carousel",
     isExecutionStarted: message.type === "execution_started",
-    isInputsNeeded: message.type === "inputs_needed",
   };
 }

autogpt_platform/frontend/src/app/(platform)/chat/components/ExecutionStartedMessage/ExecutionStartedMessage.tsx

@@ -1,7 +1,8 @@
-import { Button } from "@/components/atoms/Button/Button";
+import React from "react";
 import { Text } from "@/components/atoms/Text/Text";
+import { Button } from "@/components/atoms/Button/Button";
+import { CheckCircle, Play, ArrowSquareOut } from "@phosphor-icons/react";
 import { cn } from "@/lib/utils";
-import { ArrowSquareOut, CheckCircle, Play } from "@phosphor-icons/react";
 
 export interface ExecutionStartedMessageProps {
   executionId: string;
@@ -21,7 +22,7 @@ export function ExecutionStartedMessage({
   return (
     <div
       className={cn(
-        "mx-4 my-2 flex flex-col gap-4 rounded-lg border border-green-200 bg-green-50 p-6",
+        "mx-4 my-2 flex flex-col gap-4 rounded-lg border border-green-200 bg-green-50 p-6 dark:border-green-900 dark:bg-green-950",
         className,
       )}
     >
@@ -31,33 +32,48 @@ export function ExecutionStartedMessage({
           <CheckCircle size={24} weight="bold" className="text-white" />
         </div>
         <div className="flex-1">
-          <Text variant="h3" className="mb-1 text-green-900">
+          <Text
+            variant="h3"
+            className="mb-1 text-green-900 dark:text-green-100"
+          >
             Execution Started
           </Text>
-          <Text variant="body" className="text-green-700">
+          <Text variant="body" className="text-green-700 dark:text-green-300">
             {message}
           </Text>
         </div>
       </div>
 
       {/* Details */}
-      <div className="rounded-md bg-green-100 p-4">
+      <div className="rounded-md bg-green-100 p-4 dark:bg-green-900">
         <div className="space-y-2">
           {agentName && (
             <div className="flex items-center justify-between">
-              <Text variant="small" className="font-semibold text-green-900">
+              <Text
+                variant="small"
+                className="font-semibold text-green-900 dark:text-green-100"
+              >
                 Agent:
               </Text>
-              <Text variant="body" className="text-green-800">
+              <Text
+                variant="body"
+                className="text-green-800 dark:text-green-200"
+              >
                 {agentName}
               </Text>
             </div>
           )}
           <div className="flex items-center justify-between">
-            <Text variant="small" className="font-semibold text-green-900">
+            <Text
+              variant="small"
+              className="font-semibold text-green-900 dark:text-green-100"
+            >
               Execution ID:
             </Text>
-            <Text variant="small" className="font-mono text-green-800">
+            <Text
+              variant="small"
+              className="font-mono text-green-800 dark:text-green-200"
+            >
               {executionId.slice(0, 16)}...
             </Text>
           </div>
@@ -78,7 +94,7 @@ export function ExecutionStartedMessage({
         </div>
       )}
 
-      <div className="flex items-center gap-2 text-green-600">
+      <div className="flex items-center gap-2 text-green-600 dark:text-green-400">
         <Play size={16} weight="fill" />
         <Text variant="small">
           Your agent is now running. You can monitor its progress in the monitor

autogpt_platform/frontend/src/app/(platform)/chat/components/MarkdownContent/MarkdownContent.tsx

@@ -1,9 +1,9 @@
 "use client";
 
-import { cn } from "@/lib/utils";
 import React from "react";
 import ReactMarkdown from "react-markdown";
 import remarkGfm from "remark-gfm";
+import { cn } from "@/lib/utils";
 
 interface MarkdownContentProps {
   content: string;
@@ -41,7 +41,7 @@ export function MarkdownContent({ content, className }: MarkdownContentProps) {
             if (isInline) {
               return (
                 <code
-                  className="rounded bg-zinc-100 px-1.5 py-0.5 font-mono text-sm text-zinc-800"
+                  className="rounded bg-zinc-100 px-1.5 py-0.5 font-mono text-sm text-zinc-800 dark:bg-zinc-800 dark:text-zinc-200"
                   {...props}
                 >
                   {children}
@@ -49,14 +49,17 @@ export function MarkdownContent({ content, className }: MarkdownContentProps) {
               );
             }
             return (
-              <code className="font-mono text-sm text-zinc-100" {...props}>
+              <code
+                className="font-mono text-sm text-zinc-100 dark:text-zinc-200"
+                {...props}
+              >
                 {children}
               </code>
             );
           },
           pre: ({ children, ...props }) => (
             <pre
-              className="my-2 overflow-x-auto rounded-md bg-zinc-900 p-3"
+              className="my-2 overflow-x-auto rounded-md bg-zinc-900 p-3 dark:bg-zinc-950"
               {...props}
             >
               {children}
@@ -67,7 +70,7 @@ export function MarkdownContent({ content, className }: MarkdownContentProps) {
               href={href}
               target="_blank"
               rel="noopener noreferrer"
-              className="text-purple-600 underline decoration-1 underline-offset-2 hover:text-purple-700"
+              className="text-purple-600 underline decoration-1 underline-offset-2 hover:text-purple-700 dark:text-purple-400 dark:hover:text-purple-300"
               {...props}
             >
               {children}
@@ -123,7 +126,7 @@ export function MarkdownContent({ content, className }: MarkdownContentProps) {
               return (
                 <input
                   type="checkbox"
-                  className="mr-2 h-4 w-4 rounded border-zinc-300 text-purple-600 focus:ring-purple-500 disabled:cursor-not-allowed disabled:opacity-70"
+                  className="mr-2 h-4 w-4 rounded border-zinc-300 text-purple-600 focus:ring-purple-500 disabled:cursor-not-allowed disabled:opacity-70 dark:border-zinc-600"
                   disabled
                   {...props}
                 />
@@ -133,42 +136,57 @@ export function MarkdownContent({ content, className }: MarkdownContentProps) {
           },
           blockquote: ({ children, ...props }) => (
             <blockquote
-              className="my-2 border-l-4 border-zinc-300 pl-3 italic text-zinc-700"
+              className="my-2 border-l-4 border-zinc-300 pl-3 italic text-zinc-700 dark:border-zinc-600 dark:text-zinc-300"
               {...props}
             >
               {children}
             </blockquote>
           ),
           h1: ({ children, ...props }) => (
-            <h1 className="my-2 text-xl font-bold text-zinc-900" {...props}>
+            <h1
+              className="my-2 text-xl font-bold text-zinc-900 dark:text-zinc-100"
+              {...props}
+            >
               {children}
             </h1>
           ),
           h2: ({ children, ...props }) => (
-            <h2 className="my-2 text-lg font-semibold text-zinc-800" {...props}>
+            <h2
+              className="my-2 text-lg font-semibold text-zinc-800 dark:text-zinc-200"
+              {...props}
+            >
               {children}
             </h2>
           ),
           h3: ({ children, ...props }) => (
             <h3
-              className="my-1 text-base font-semibold text-zinc-800"
+              className="my-1 text-base font-semibold text-zinc-800 dark:text-zinc-200"
               {...props}
             >
               {children}
             </h3>
           ),
           h4: ({ children, ...props }) => (
-            <h4 className="my-1 text-sm font-medium text-zinc-700" {...props}>
+            <h4
+              className="my-1 text-sm font-medium text-zinc-700 dark:text-zinc-300"
+              {...props}
+            >
               {children}
             </h4>
           ),
           h5: ({ children, ...props }) => (
-            <h5 className="my-1 text-sm font-medium text-zinc-700" {...props}>
+            <h5
+              className="my-1 text-sm font-medium text-zinc-700 dark:text-zinc-300"
+              {...props}
+            >
               {children}
             </h5>
           ),
           h6: ({ children, ...props }) => (
-            <h6 className="my-1 text-xs font-medium text-zinc-600" {...props}>
+            <h6
+              className="my-1 text-xs font-medium text-zinc-600 dark:text-zinc-400"
+              {...props}
+            >
               {children}
             </h6>
           ),
@@ -178,12 +196,15 @@ export function MarkdownContent({ content, className }: MarkdownContentProps) {
             </p>
           ),
           hr: ({ ...props }) => (
-            <hr className="my-3 border-zinc-300" {...props} />
+            <hr
+              className="my-3 border-zinc-300 dark:border-zinc-700"
+              {...props}
+            />
           ),
           table: ({ children, ...props }) => (
             <div className="my-2 overflow-x-auto">
               <table
-                className="min-w-full divide-y divide-zinc-200 rounded border border-zinc-200"
+                className="min-w-full divide-y divide-zinc-200 rounded border border-zinc-200 dark:divide-zinc-700 dark:border-zinc-700"
                 {...props}
               >
                 {children}
@@ -192,7 +213,7 @@ export function MarkdownContent({ content, className }: MarkdownContentProps) {
           ),
           th: ({ children, ...props }) => (
             <th
-              className="bg-zinc-50 px-3 py-2 text-left text-xs font-semibold text-zinc-700"
+              className="bg-zinc-50 px-3 py-2 text-left text-xs font-semibold text-zinc-700 dark:bg-zinc-800 dark:text-zinc-300"
               {...props}
             >
               {children}
@@ -200,7 +221,7 @@ export function MarkdownContent({ content, className }: MarkdownContentProps) {
           ),
           td: ({ children, ...props }) => (
             <td
-              className="border-t border-zinc-200 px-3 py-2 text-sm"
+              className="border-t border-zinc-200 px-3 py-2 text-sm dark:border-zinc-700"
               {...props}
             >
               {children}

autogpt_platform/frontend/src/app/(platform)/chat/components/MessageBubble/MessageBubble.tsx

@@ -0,0 +1,28 @@
+import { cn } from "@/lib/utils";
+import { ReactNode } from "react";
+
+export interface MessageBubbleProps {
+  children: ReactNode;
+  variant: "user" | "assistant";
+  className?: string;
+}
+
+export function MessageBubble({
+  children,
+  variant,
+  className,
+}: MessageBubbleProps) {
+  return (
+    <div
+      className={cn(
+        "rounded-lg px-4 py-3 text-sm",
+        variant === "user" && "bg-violet-600 text-white dark:bg-violet-500",
+        variant === "assistant" &&
+          "border border-neutral-200 bg-white dark:border-neutral-700 dark:bg-neutral-900 dark:text-neutral-100",
+        className,
+      )}
+    >
+      {children}
+    </div>
+  );
+}

autogpt_platform/frontend/src/app/(platform)/chat/components/MessageList/MessageList.tsx

@@ -0,0 +1,61 @@
+import { cn } from "@/lib/utils";
+import { ChatMessage } from "../ChatMessage/ChatMessage";
+import type { ChatMessageData } from "../ChatMessage/useChatMessage";
+import { StreamingMessage } from "../StreamingMessage/StreamingMessage";
+import { useMessageList } from "./useMessageList";
+
+export interface MessageListProps {
+  messages: ChatMessageData[];
+  streamingChunks?: string[];
+  isStreaming?: boolean;
+  className?: string;
+  onStreamComplete?: () => void;
+  onSendMessage?: (content: string) => void;
+}
+
+export function MessageList({
+  messages,
+  streamingChunks = [],
+  isStreaming = false,
+  className,
+  onStreamComplete,
+  onSendMessage,
+}: MessageListProps) {
+  const { messagesEndRef, messagesContainerRef } = useMessageList({
+    messageCount: messages.length,
+    isStreaming,
+  });
+
+  return (
+    <div
+      ref={messagesContainerRef}
+      className={cn(
+        "flex-1 overflow-y-auto",
+        "scrollbar-thin scrollbar-track-transparent scrollbar-thumb-zinc-300 dark:scrollbar-thumb-zinc-700",
+        className,
+      )}
+    >
+      <div className="space-y-0">
+        {/* Render all persisted messages */}
+        {messages.map((message, index) => (
+          <ChatMessage
+            key={index}
+            message={message}
+            onSendMessage={onSendMessage}
+          />
+        ))}
+
+        {/* Render streaming message if active */}
+        {isStreaming && streamingChunks.length > 0 && (
+          <StreamingMessage
+            chunks={streamingChunks}
+            onComplete={onStreamComplete}
+          />
+        )}
+
+        {/* Invisible div to scroll to */}
+        <div ref={messagesEndRef} />
+      </div>
+    </div>
+  );
+}

autogpt_platform/frontend/src/app/(platform)/chat/components/NoResultsMessage/NoResultsMessage.tsx

@@ -1,6 +1,7 @@
+import React from "react";
 import { Text } from "@/components/atoms/Text/Text";
-import { cn } from "@/lib/utils";
 import { MagnifyingGlass, X } from "@phosphor-icons/react";
+import { cn } from "@/lib/utils";
 
 export interface NoResultsMessageProps {
   message: string;
@@ -16,26 +17,26 @@ export function NoResultsMessage({
   return (
     <div
       className={cn(
-        "mx-4 my-2 flex flex-col items-center gap-4 rounded-lg border border-gray-200 bg-gray-50 p-6",
+        "mx-4 my-2 flex flex-col items-center gap-4 rounded-lg border border-gray-200 bg-gray-50 p-6 dark:border-gray-800 dark:bg-gray-900",
         className,
       )}
     >
       {/* Icon */}
       <div className="relative flex h-16 w-16 items-center justify-center">
-        <div className="flex h-16 w-16 items-center justify-center rounded-full bg-gray-200">
+        <div className="flex h-16 w-16 items-center justify-center rounded-full bg-gray-200 dark:bg-gray-700">
           <MagnifyingGlass size={32} weight="bold" className="text-gray-500" />
         </div>
-        <div className="absolute -right-1 -top-1 flex h-8 w-8 items-center justify-center rounded-full bg-gray-400">
+        <div className="absolute -right-1 -top-1 flex h-8 w-8 items-center justify-center rounded-full bg-gray-400 dark:bg-gray-600">
           <X size={20} weight="bold" className="text-white" />
         </div>
       </div>
 
       {/* Content */}
       <div className="text-center">
-        <Text variant="h3" className="mb-2 text-gray-900">
+        <Text variant="h3" className="mb-2 text-gray-900 dark:text-gray-100">
           No Results Found
         </Text>
-        <Text variant="body" className="text-gray-700">
+        <Text variant="body" className="text-gray-700 dark:text-gray-300">
           {message}
         </Text>
       </div>
@@ -43,14 +44,17 @@ export function NoResultsMessage({
       {/* Suggestions */}
       {suggestions.length > 0 && (
         <div className="w-full space-y-2">
-          <Text variant="small" className="font-semibold text-gray-900">
+          <Text
+            variant="small"
+            className="font-semibold text-gray-900 dark:text-gray-100"
+          >
             Try these suggestions:
           </Text>
-          <ul className="space-y-1 rounded-md bg-gray-100 p-4">
+          <ul className="space-y-1 rounded-md bg-gray-100 p-4 dark:bg-gray-800">
             {suggestions.map((suggestion, index) => (
               <li
                 key={index}
-                className="flex items-start gap-2 text-sm text-gray-700"
+                className="flex items-start gap-2 text-sm text-gray-700 dark:text-gray-300"
               >
                 <span className="mt-1 text-gray-500">•</span>
                 <span>{suggestion}</span>

autogpt_platform/frontend/src/app/(platform)/chat/components/QuickActionsWelcome/QuickActionsWelcome.tsx

@@ -0,0 +1,51 @@
+import React from "react";
+import { Text } from "@/components/atoms/Text/Text";
+import { cn } from "@/lib/utils";
+
+export interface QuickActionsWelcomeProps {
+  title: string;
+  description: string;
+  actions: string[];
+  onActionClick: (action: string) => void;
+  disabled?: boolean;
+  className?: string;
+}
+
+export function QuickActionsWelcome({
+  title,
+  description,
+  actions,
+  onActionClick,
+  disabled = false,
+  className,
+}: QuickActionsWelcomeProps) {
+  return (
+    <div
+      className={cn("flex flex-1 items-center justify-center p-4", className)}
+    >
+      <div className="max-w-2xl text-center">
+        <Text
+          variant="h2"
+          className="mb-4 text-3xl font-bold text-zinc-900 dark:text-zinc-100"
+        >
+          {title}
+        </Text>
+        <Text variant="body" className="mb-8 text-zinc-600 dark:text-zinc-400">
+          {description}
+        </Text>
+        <div className="grid gap-2 sm:grid-cols-2">
+          {actions.map((action) => (
+            <button
+              key={action}
+              onClick={() => onActionClick(action)}
+              disabled={disabled}
+              className="rounded-lg border border-zinc-200 bg-white p-4 text-left text-sm hover:bg-zinc-50 disabled:cursor-not-allowed disabled:opacity-50 dark:border-zinc-800 dark:bg-zinc-900 dark:hover:bg-zinc-800"
+            >
+              {action}
+            </button>
+          ))}
+        </div>
+      </div>
+    </div>
+  );
+}

autogpt_platform/frontend/src/app/(platform)/chat/components/StreamingMessage/StreamingMessage.tsx

@@ -0,0 +1,42 @@
+import { cn } from "@/lib/utils";
+import { Robot } from "@phosphor-icons/react";
+import { MessageBubble } from "@/app/(platform)/chat/components/MessageBubble/MessageBubble";
+import { MarkdownContent } from "@/app/(platform)/chat/components/MarkdownContent/MarkdownContent";
+import { useStreamingMessage } from "./useStreamingMessage";
+
+export interface StreamingMessageProps {
+  chunks: string[];
+  className?: string;
+  onComplete?: () => void;
+}
+
+export function StreamingMessage({
+  chunks,
+  className,
+  onComplete,
+}: StreamingMessageProps) {
+  const { displayText } = useStreamingMessage({ chunks, onComplete });
+
+  return (
+    <div className={cn("flex gap-3 px-4 py-4", className)}>
+      {/* Avatar */}
+      <div className="flex-shrink-0">
+        <div className="flex h-8 w-8 items-center justify-center rounded-full bg-purple-600 dark:bg-purple-500">
+          <Robot className="h-5 w-5 text-white" />
+        </div>
+      </div>
+
+      {/* Message Content */}
+      <div className="flex max-w-[70%] flex-col">
+        <MessageBubble variant="assistant">
+          <MarkdownContent content={displayText} />
+        </MessageBubble>
+
+        {/* Timestamp */}
+        <span className="mt-1 text-xs text-neutral-500 dark:text-neutral-400">
+          Typing...
+        </span>
+      </div>
+    </div>
+  );
+}

autogpt_platform/frontend/src/app/(platform)/chat/components/ToolCallMessage/ToolCallMessage.tsx

@@ -0,0 +1,49 @@
+import React from "react";
+import { WrenchIcon } from "@phosphor-icons/react";
+import { cn } from "@/lib/utils";
+import { getToolActionPhrase } from "@/app/(platform)/chat/helpers";
+
+export interface ToolCallMessageProps {
+  toolName: string;
+  className?: string;
+}
+
+export function ToolCallMessage({ toolName, className }: ToolCallMessageProps) {
+  return (
+    <div
+      className={cn(
+        "mx-10 max-w-[70%] overflow-hidden rounded-lg border transition-all duration-200",
+        "border-neutral-200 dark:border-neutral-700",
+        "bg-white dark:bg-neutral-900",
+        "animate-in fade-in-50 slide-in-from-top-1",
+        className,
+      )}
+    >
+      {/* Header */}
+      <div
+        className={cn(
+          "flex items-center justify-between px-3 py-2",
+          "bg-gradient-to-r from-neutral-50 to-neutral-100 dark:from-neutral-800/20 dark:to-neutral-700/20",
+        )}
+      >
+        <div className="flex items-center gap-2 overflow-hidden">
+          <WrenchIcon
+            size={16}
+            weight="bold"
+            className="flex-shrink-0 text-neutral-500 dark:text-neutral-400"
+          />
+          <span className="relative inline-block overflow-hidden text-sm font-medium text-neutral-700 dark:text-neutral-300">
+            {getToolActionPhrase(toolName)}...
+            <span
+              className={cn(
+                "absolute inset-0 bg-gradient-to-r from-transparent via-white/50 to-transparent",
+                "dark:via-white/20",
+                "animate-shimmer",
+              )}
+            />
+          </span>
+        </div>
+      </div>
+    </div>
+  );
+}

autogpt_platform/frontend/src/app/(platform)/chat/components/ToolResponseMessage/ToolResponseMessage.tsx

@@ -0,0 +1,52 @@
+import React from "react";
+import { WrenchIcon } from "@phosphor-icons/react";
+import { cn } from "@/lib/utils";
+import { getToolActionPhrase } from "@/app/(platform)/chat/helpers";
+
+export interface ToolResponseMessageProps {
+  toolName: string;
+  success?: boolean;
+  className?: string;
+}
+
+export function ToolResponseMessage({
+  toolName,
+  success = true,
+  className,
+}: ToolResponseMessageProps) {
+  return (
+    <div
+      className={cn(
+        "mx-10 max-w-[70%] overflow-hidden rounded-lg border transition-all duration-200",
+        success
+          ? "border-neutral-200 dark:border-neutral-700"
+          : "border-red-200 dark:border-red-800",
+        "bg-white dark:bg-neutral-900",
+        "animate-in fade-in-50 slide-in-from-top-1",
+        className,
+      )}
+    >
+      {/* Header */}
+      <div
+        className={cn(
+          "flex items-center justify-between px-3 py-2",
+          "bg-gradient-to-r",
+          success
+            ? "from-neutral-50 to-neutral-100 dark:from-neutral-800/20 dark:to-neutral-700/20"
+            : "from-red-50 to-red-100 dark:from-red-900/20 dark:to-red-800/20",
+        )}
+      >
+        <div className="flex items-center gap-2">
+          <WrenchIcon
+            size={16}
+            weight="bold"
+            className="text-neutral-500 dark:text-neutral-400"
+          />
+          <span className="text-sm font-medium text-neutral-700 dark:text-neutral-300">
+            {getToolActionPhrase(toolName)}...
+          </span>
+        </div>
+      </div>
+    </div>
+  );
+}

autogpt_platform/frontend/src/app/(platform)/chat/page.tsx

@@ -1,24 +1,16 @@
 "use client";
 
-import { Button } from "@/components/__legacy__/ui/button";
-import { scrollbarStyles } from "@/components/styles/scrollbars";
-import { cn } from "@/lib/utils";
-import { Flag, useGetFlag } from "@/services/feature-flags/use-get-flag";
-import { X } from "@phosphor-icons/react";
-import { usePathname, useRouter } from "next/navigation";
-import { useEffect } from "react";
-import { Drawer } from "vaul";
-
-import { ChatContainer } from "@/components/contextual/Chat/components/ChatContainer/ChatContainer";
-import { ChatErrorState } from "@/components/contextual/Chat/components/ChatErrorState/ChatErrorState";
-import { ChatLoadingState } from "@/components/contextual/Chat/components/ChatLoadingState/ChatLoadingState";
 import { useChatPage } from "./useChatPage";
+import { ChatContainer } from "./components/ChatContainer/ChatContainer";
+import { ChatErrorState } from "./components/ChatErrorState/ChatErrorState";
+import { ChatLoadingState } from "./components/ChatLoadingState/ChatLoadingState";
+import { useGetFlag, Flag } from "@/services/feature-flags/use-get-flag";
+import { useRouter } from "next/navigation";
+import { useEffect } from "react";
 
 export default function ChatPage() {
   const isChatEnabled = useGetFlag(Flag.CHAT);
   const router = useRouter();
-  const pathname = usePathname();
-  const isOpen = pathname === "/chat";
   const {
     messages,
     isLoading,
@@ -36,88 +28,56 @@ export default function ChatPage() {
     }
   }, [isChatEnabled, router]);
 
-  function handleOpenChange(open: boolean) {
-    if (!open) {
-      router.replace("/marketplace");
-    }
-  }
-
   if (isChatEnabled === null || isChatEnabled === false) {
     return null;
   }
 
   return (
-    <Drawer.Root
-      open={isOpen}
-      onOpenChange={handleOpenChange}
-      direction="right"
-      modal={false}
-    >
-      <Drawer.Portal>
-        <Drawer.Content
-          className={cn(
-            "fixed right-0 top-0 z-50 flex h-full w-1/2 flex-col border-l border-zinc-200 bg-white dark:border-zinc-800 dark:bg-zinc-900",
-            scrollbarStyles,
-          )}
-        >
-          {/* Header */}
-          <header className="shrink-0 border-b border-zinc-200 bg-white p-4 dark:border-zinc-800 dark:bg-zinc-900">
-            <div className="flex items-center justify-between">
-              <Drawer.Title className="text-xl font-semibold">
-                Chat
-              </Drawer.Title>
-              <div className="flex items-center gap-4">
-                {sessionId && (
-                  <>
-                    <span className="text-sm text-zinc-600 dark:text-zinc-400">
-                      Session: {sessionId.slice(0, 8)}...
-                    </span>
-                    <button
-                      onClick={clearSession}
-                      className="text-sm text-zinc-600 hover:text-zinc-900 dark:text-zinc-400 dark:hover:text-zinc-100"
-                    >
-                      New Chat
-                    </button>
-                  </>
-                )}
-                <Button
-                  variant="link"
-                  aria-label="Close"
-                  onClick={() => handleOpenChange(false)}
-                  className="!focus-visible:ring-0 p-0"
-                >
-                  <X width="1.5rem" />
-                </Button>
-              </div>
+    <div className="flex h-full flex-col">
+      {/* Header */}
+      <header className="border-b border-zinc-200 bg-white p-4 dark:border-zinc-800 dark:bg-zinc-900">
+        <div className="container mx-auto flex items-center justify-between">
+          <h1 className="text-xl font-semibold">Chat</h1>
+          {sessionId && (
+            <div className="flex items-center gap-4">
+              <span className="text-sm text-zinc-600 dark:text-zinc-400">
+                Session: {sessionId.slice(0, 8)}...
+              </span>
+              <button
+                onClick={clearSession}
+                className="text-sm text-zinc-600 hover:text-zinc-900 dark:text-zinc-400 dark:hover:text-zinc-100"
+              >
+                New Chat
+              </button>
             </div>
-          </header>
+          )}
+        </div>
+      </header>
 
-          {/* Main Content */}
-          <main className="flex min-h-0 flex-1 flex-col overflow-hidden">
-            {/* Loading State - show when explicitly loading/creating OR when we don't have a session yet and no error */}
-            {(isLoading || isCreating || (!sessionId && !error)) && (
-              <ChatLoadingState
-                message={isCreating ? "Creating session..." : "Loading..."}
-              />
-            )}
+      {/* Main Content */}
+      <main className="container mx-auto flex flex-1 flex-col overflow-hidden">
+        {/* Loading State - show when explicitly loading/creating OR when we don't have a session yet and no error */}
+        {(isLoading || isCreating || (!sessionId && !error)) && (
+          <ChatLoadingState
+            message={isCreating ? "Creating session..." : "Loading..."}
+          />
+        )}
 
-            {/* Error State */}
-            {error && !isLoading && (
-              <ChatErrorState error={error} onRetry={createSession} />
-            )}
+        {/* Error State */}
+        {error && !isLoading && (
+          <ChatErrorState error={error} onRetry={createSession} />
+        )}
 
-            {/* Session Content */}
-            {sessionId && !isLoading && !error && (
-              <ChatContainer
-                sessionId={sessionId}
-                initialMessages={messages}
-                onRefreshSession={refreshSession}
-                className="flex-1"
-              />
-            )}
-          </main>
-        </Drawer.Content>
-      </Drawer.Portal>
-    </Drawer.Root>
+        {/* Session Content */}
+        {sessionId && !isLoading && !error && (
+          <ChatContainer
+            sessionId={sessionId}
+            initialMessages={messages}
+            onRefreshSession={refreshSession}
+            className="flex-1"
+          />
+        )}
+      </main>
+    </div>
   );
 }

autogpt_platform/frontend/src/app/(platform)/chat/useChatPage.ts

@@ -1,11 +1,11 @@
 "use client";
 
-import { useChatSession } from "@/components/contextual/Chat/useChatSession";
-import { useChatStream } from "@/components/contextual/Chat/useChatStream";
-import { useSupabase } from "@/lib/supabase/hooks/useSupabase";
-import { useRouter, useSearchParams } from "next/navigation";
 import { useEffect, useRef } from "react";
+import { useRouter, useSearchParams } from "next/navigation";
 import { toast } from "sonner";
+import { useChatSession } from "@/app/(platform)/chat/useChatSession";
+import { useSupabase } from "@/lib/supabase/hooks/useSupabase";
+import { useChatStream } from "@/app/(platform)/chat/useChatStream";
 
 export function useChatPage() {
   const router = useRouter();

autogpt_platform/frontend/src/app/(platform)/chat/useChatSession.ts

@@ -1,18 +1,17 @@
+import { useCallback, useEffect, useState, useRef, useMemo } from "react";
+import { useQueryClient } from "@tanstack/react-query";
+import { toast } from "sonner";
 import {
-  getGetV2GetSessionQueryKey,
-  getGetV2GetSessionQueryOptions,
+  usePostV2CreateSession,
   postV2CreateSession,
   useGetV2GetSession,
   usePatchV2SessionAssignUser,
-  usePostV2CreateSession,
+  getGetV2GetSessionQueryKey,
 } from "@/app/api/__generated__/endpoints/chat/chat";
 import type { SessionDetailResponse } from "@/app/api/__generated__/models/sessionDetailResponse";
+import { storage, Key } from "@/services/storage/local-storage";
+import { isValidUUID } from "@/app/(platform)/chat/helpers";
 import { okData } from "@/app/api/helpers";
-import { Key, storage } from "@/services/storage/local-storage";
-import { useQueryClient } from "@tanstack/react-query";
-import { useCallback, useEffect, useMemo, useRef, useState } from "react";
-import { toast } from "sonner";
-import { isValidUUID } from "./helpers";
 
 interface UseChatSessionArgs {
   urlSessionId?: string | null;
@@ -156,22 +155,10 @@ export function useChatSession({
     async function loadSession(id: string) {
       try {
         setError(null);
-        // Invalidate the query cache for this session to force a fresh fetch
-        await queryClient.invalidateQueries({
-          queryKey: getGetV2GetSessionQueryKey(id),
-        });
-        // Set sessionId after invalidation to ensure the hook refetches
         setSessionId(id);
         storage.set(Key.CHAT_SESSION_ID, id);
-        // Force fetch with fresh data (bypass cache)
-        const queryOptions = getGetV2GetSessionQueryOptions(id, {
-          query: {
-            staleTime: 0, // Force fresh fetch
-            retry: 1,
-          },
-        });
-        const result = await queryClient.fetchQuery(queryOptions);
-        if (!result || ("status" in result && result.status !== 200)) {
+        const result = await refetch();
+        if (!result.data || result.isError) {
           console.warn("Session not found on server, clearing local state");
           storage.clean(Key.CHAT_SESSION_ID);
           setSessionId(null);
@@ -184,7 +171,7 @@ export function useChatSession({
         throw error;
       }
     },
-    [queryClient],
+    [refetch],
   );
 
   const refreshSession = useCallback(

autogpt_platform/frontend/src/app/(platform)/chat/useChatStream.ts

@@ -0,0 +1,204 @@
+import { useState, useCallback, useRef, useEffect } from "react";
+import { toast } from "sonner";
+import type { ToolArguments, ToolResult } from "@/types/chat";
+
+const MAX_RETRIES = 3;
+const INITIAL_RETRY_DELAY = 1000;
+
+export interface StreamChunk {
+  type:
+    | "text_chunk"
+    | "text_ended"
+    | "tool_call"
+    | "tool_call_start"
+    | "tool_response"
+    | "login_needed"
+    | "need_login"
+    | "credentials_needed"
+    | "error"
+    | "usage"
+    | "stream_end";
+  timestamp?: string;
+  content?: string;
+  message?: string;
+  tool_id?: string;
+  tool_name?: string;
+  arguments?: ToolArguments;
+  result?: ToolResult;
+  success?: boolean;
+  idx?: number;
+  session_id?: string;
+  agent_info?: {
+    graph_id: string;
+    name: string;
+    trigger_type: string;
+  };
+  provider?: string;
+  provider_name?: string;
+  credential_type?: string;
+  scopes?: string[];
+  title?: string;
+  [key: string]: unknown;
+}
+
+export function useChatStream() {
+  const [isStreaming, setIsStreaming] = useState(false);
+  const [error, setError] = useState<Error | null>(null);
+  const eventSourceRef = useRef<EventSource | null>(null);
+  const retryCountRef = useRef<number>(0);
+  const retryTimeoutRef = useRef<NodeJS.Timeout | null>(null);
+  const abortControllerRef = useRef<AbortController | null>(null);
+
+  const stopStreaming = useCallback(() => {
+    if (abortControllerRef.current) {
+      abortControllerRef.current.abort();
+      abortControllerRef.current = null;
+    }
+    if (eventSourceRef.current) {
+      eventSourceRef.current.close();
+      eventSourceRef.current = null;
+    }
+    if (retryTimeoutRef.current) {
+      clearTimeout(retryTimeoutRef.current);
+      retryTimeoutRef.current = null;
+    }
+    retryCountRef.current = 0;
+    setIsStreaming(false);
+  }, []);
+
+  useEffect(() => {
+    return () => {
+      stopStreaming();
+    };
+  }, [stopStreaming]);
+
+  const sendMessage = useCallback(
+    async (
+      sessionId: string,
+      message: string,
+      onChunk: (chunk: StreamChunk) => void,
+      isUserMessage: boolean = true,
+    ) => {
+      stopStreaming();
+
+      const abortController = new AbortController();
+      abortControllerRef.current = abortController;
+
+      if (abortController.signal.aborted) {
+        return Promise.reject(new Error("Request aborted"));
+      }
+
+      retryCountRef.current = 0;
+      setIsStreaming(true);
+      setError(null);
+
+      try {
+        const url = `/api/chat/sessions/${sessionId}/stream?message=${encodeURIComponent(
+          message,
+        )}&is_user_message=${isUserMessage}`;
+
+        const eventSource = new EventSource(url);
+        eventSourceRef.current = eventSource;
+
+        abortController.signal.addEventListener("abort", () => {
+          eventSource.close();
+          eventSourceRef.current = null;
+        });
+
+        return new Promise<void>((resolve, reject) => {
+          const cleanup = () => {
+            eventSource.removeEventListener("message", messageHandler);
+            eventSource.removeEventListener("error", errorHandler);
+          };
+
+          const messageHandler = (event: MessageEvent) => {
+            try {
+              const chunk = JSON.parse(event.data) as StreamChunk;
+
+              if (retryCountRef.current > 0) {
+                retryCountRef.current = 0;
+              }
+
+              // Call the chunk handler
+              onChunk(chunk);
+
+              // Handle stream lifecycle
+              if (chunk.type === "stream_end") {
+                cleanup();
+                stopStreaming();
+                resolve();
+              } else if (chunk.type === "error") {
+                cleanup();
+                reject(
+                  new Error(chunk.message || chunk.content || "Stream error"),
+                );
+              }
+            } catch (err) {
+              const parseError =
+                err instanceof Error
+                  ? err
+                  : new Error("Failed to parse stream chunk");
+              setError(parseError);
+              cleanup();
+              reject(parseError);
+            }
+          };
+
+          const errorHandler = () => {
+            if (eventSourceRef.current) {
+              eventSourceRef.current.close();
+              eventSourceRef.current = null;
+            }
+
+            if (retryCountRef.current < MAX_RETRIES) {
+              retryCountRef.current += 1;
+              const retryDelay =
+                INITIAL_RETRY_DELAY * Math.pow(2, retryCountRef.current - 1);
+
+              toast.info("Connection interrupted", {
+                description: `Retrying in ${retryDelay / 1000} seconds...`,
+              });
+
+              retryTimeoutRef.current = setTimeout(() => {
+                sendMessage(sessionId, message, onChunk, isUserMessage).catch(
+                  (_err) => {
+                    // Retry failed
+                  },
+                );
+              }, retryDelay);
+            } else {
+              const streamError = new Error(
+                "Stream connection failed after multiple retries",
+              );
+              setError(streamError);
+              toast.error("Connection Failed", {
+                description:
+                  "Unable to connect to chat service. Please try again.",
+              });
+              cleanup();
+              stopStreaming();
+              reject(streamError);
+            }
+          };
+
+          eventSource.addEventListener("message", messageHandler);
+          eventSource.addEventListener("error", errorHandler);
+        });
+      } catch (err) {
+        const streamError =
+          err instanceof Error ? err : new Error("Failed to start stream");
+        setError(streamError);
+        setIsStreaming(false);
+        throw streamError;
+      }
+    },
+    [stopStreaming],
+  );
+
+  return {
+    isStreaming,
+    error,
+    sendMessage,
+    stopStreaming,
+  };
+}

autogpt_platform/frontend/src/app/(platform)/layout.tsx

@@ -1,14 +1,13 @@
 import { Navbar } from "@/components/layout/Navbar/Navbar";
-import { ReactNode } from "react";
 import { AdminImpersonationBanner } from "./admin/components/AdminImpersonationBanner";
-import { PlatformLayoutContent } from "./PlatformLayoutContent";
+import { ReactNode } from "react";
 
 export default function PlatformLayout({ children }: { children: ReactNode }) {
   return (
-    <PlatformLayoutContent>
+    <main className="flex h-screen w-full flex-col">
       <Navbar />
       <AdminImpersonationBanner />
-      {children}
-    </PlatformLayoutContent>
+      <section className="flex-1">{children}</section>
+    </main>
   );
 }

autogpt_platform/frontend/src/app/(platform)/library/agents/[id]/components/NewAgentLibraryView/components/modals/AgentInputsReadOnly/AgentInputsReadOnly.tsx

@@ -3,8 +3,8 @@
 import type { LibraryAgent } from "@/app/api/__generated__/models/libraryAgent";
 import { Text } from "@/components/atoms/Text/Text";
 import type { CredentialsMetaInput } from "@/lib/autogpt-server-api/types";
-import { CredentialsInput } from "../../../../../../../../../../components/contextual/CredentialsInputs/CredentialsInputs";
-import { RunAgentInputs } from "../../../../../../../../../../components/contextual/RunAgentInputs/RunAgentInputs";
+import { CredentialsInput } from "../CredentialsInputs/CredentialsInputs";
+import { RunAgentInputs } from "../RunAgentInputs/RunAgentInputs";
 import { getAgentCredentialsFields, getAgentInputFields } from "./helpers";
 
 type Props = {