fix(blocks): Address review feedback for video editing blocks

- Add start_time < end_time validation in VideoClipBlock and VideoTextOverlayBlock
- Fix resource leaks: close AudioFileClip in narration.py, TextClip in text_overlay.py
- Fix concat.py: proper resource cleanup in finally block, load clips individually
- Implement proper crossfade using crossfadein/crossfadeout
- Implement ducking mode with stronger attenuation (0.3x original_volume)
- Remove unused start_time/end_time params from VideoDownloadBlock
- Fix None handling for duration/title in download.py (use 'or' instead of 'get' default)
- Add exception chaining with 'from e' in all blocks
- Add minimum clips validation in VideoConcatBlock
- Sort __all__ in __init__.py
- Increase ElevenLabs API timeout to 120s for longer scripts

Co-authored-by: Nicholas Tindle <ntindle@users.noreply.github.com>

.github/workflows/claude-ci-failure-auto-fix.yml

@@ -93,5 +93,5 @@ jobs:
 
             Error logs:
             ${{ toJSON(fromJSON(steps.failure_details.outputs.result).errorLogs) }}
-          claude_code_oauth_token: ${{ secrets.CLAUDE_CODE_OAUTH_TOKEN }}
+          anthropic_api_key: ${{ secrets.ANTHROPIC_API_KEY }}
           claude_args: "--allowedTools 'Edit,MultiEdit,Write,Read,Glob,Grep,LS,Bash(git:*),Bash(bun:*),Bash(npm:*),Bash(npx:*),Bash(gh:*)'"

.github/workflows/claude-dependabot.yml

@@ -7,7 +7,7 @@
 # - Provide actionable recommendations for the development team
 #
 # Triggered on: Dependabot PRs (opened, synchronize)
-# Requirements: CLAUDE_CODE_OAUTH_TOKEN secret must be configured
+# Requirements: ANTHROPIC_API_KEY secret must be configured
 
 name: Claude Dependabot PR Review
 
@@ -308,7 +308,7 @@ jobs:
         id: claude_review
         uses: anthropics/claude-code-action@v1
         with:
-          claude_code_oauth_token: ${{ secrets.CLAUDE_CODE_OAUTH_TOKEN }}
+          anthropic_api_key: ${{ secrets.ANTHROPIC_API_KEY }}
           claude_args: |
             --allowedTools "Bash(npm:*),Bash(pnpm:*),Bash(poetry:*),Bash(git:*),Edit,Replace,NotebookEditCell,mcp__github_inline_comment__create_inline_comment,Bash(gh pr comment:*), Bash(gh pr diff:*), Bash(gh pr view:*)"
           prompt: |

.github/workflows/claude.yml

@@ -323,7 +323,7 @@ jobs:
         id: claude
         uses: anthropics/claude-code-action@v1
         with:
-          claude_code_oauth_token: ${{ secrets.CLAUDE_CODE_OAUTH_TOKEN }}
+          anthropic_api_key: ${{ secrets.ANTHROPIC_API_KEY }}
           claude_args: |
             --allowedTools "Bash(npm:*),Bash(pnpm:*),Bash(poetry:*),Bash(git:*),Edit,Replace,NotebookEditCell,mcp__github_inline_comment__create_inline_comment,Bash(gh pr comment:*), Bash(gh pr diff:*), Bash(gh pr view:*), Bash(gh pr edit:*)"
             --model opus

.github/workflows/docs-block-sync.yml

@@ -1,78 +0,0 @@
-name: Block Documentation Sync Check
-
-on:
-  push:
-    branches: [master, dev]
-    paths:
-      - "autogpt_platform/backend/backend/blocks/**"
-      - "docs/integrations/**"
-      - "autogpt_platform/backend/scripts/generate_block_docs.py"
-      - ".github/workflows/docs-block-sync.yml"
-  pull_request:
-    branches: [master, dev]
-    paths:
-      - "autogpt_platform/backend/backend/blocks/**"
-      - "docs/integrations/**"
-      - "autogpt_platform/backend/scripts/generate_block_docs.py"
-      - ".github/workflows/docs-block-sync.yml"
-
-jobs:
-  check-docs-sync:
-    runs-on: ubuntu-latest
-    timeout-minutes: 15
-
-    steps:
-      - name: Checkout code
-        uses: actions/checkout@v4
-        with:
-          fetch-depth: 1
-
-      - name: Set up Python
-        uses: actions/setup-python@v5
-        with:
-          python-version: "3.11"
-
-      - name: Set up Python dependency cache
-        uses: actions/cache@v4
-        with:
-          path: ~/.cache/pypoetry
-          key: poetry-${{ runner.os }}-${{ hashFiles('autogpt_platform/backend/poetry.lock') }}
-          restore-keys: |
-            poetry-${{ runner.os }}-
-
-      - name: Install Poetry
-        run: |
-          cd autogpt_platform/backend
-          HEAD_POETRY_VERSION=$(python3 ../../.github/workflows/scripts/get_package_version_from_lockfile.py poetry)
-          echo "Found Poetry version ${HEAD_POETRY_VERSION} in backend/poetry.lock"
-          curl -sSL https://install.python-poetry.org | POETRY_VERSION=$HEAD_POETRY_VERSION python3 -
-          echo "$HOME/.local/bin" >> $GITHUB_PATH
-
-      - name: Install dependencies
-        working-directory: autogpt_platform/backend
-        run: |
-          poetry install --only main
-          poetry run prisma generate
-
-      - name: Check block documentation is in sync
-        working-directory: autogpt_platform/backend
-        run: |
-          echo "Checking if block documentation is in sync with code..."
-          poetry run python scripts/generate_block_docs.py --check
-
-      - name: Show diff if out of sync
-        if: failure()
-        working-directory: autogpt_platform/backend
-        run: |
-          echo "::error::Block documentation is out of sync with code!"
-          echo ""
-          echo "To fix this, run the following command locally:"
-          echo "  cd autogpt_platform/backend && poetry run python scripts/generate_block_docs.py"
-          echo ""
-          echo "Then commit the updated documentation files."
-          echo ""
-          echo "Regenerating docs to show diff..."
-          poetry run python scripts/generate_block_docs.py
-          echo ""
-          echo "Changes detected:"
-          git diff ../../docs/integrations/ || true

.github/workflows/docs-claude-review.yml

@@ -1,95 +0,0 @@
-name: Claude Block Docs Review
-
-on:
-  pull_request:
-    types: [opened, synchronize]
-    paths:
-      - "docs/integrations/**"
-      - "autogpt_platform/backend/backend/blocks/**"
-
-jobs:
-  claude-review:
-    # Only run for PRs from members/collaborators
-    if: |
-      github.event.pull_request.author_association == 'OWNER' ||
-      github.event.pull_request.author_association == 'MEMBER' ||
-      github.event.pull_request.author_association == 'COLLABORATOR'
-    runs-on: ubuntu-latest
-    timeout-minutes: 15
-    permissions:
-      contents: read
-      pull-requests: write
-      id-token: write
-
-    steps:
-      - name: Checkout code
-        uses: actions/checkout@v4
-        with:
-          fetch-depth: 0
-
-      - name: Set up Python
-        uses: actions/setup-python@v5
-        with:
-          python-version: "3.11"
-
-      - name: Set up Python dependency cache
-        uses: actions/cache@v4
-        with:
-          path: ~/.cache/pypoetry
-          key: poetry-${{ runner.os }}-${{ hashFiles('autogpt_platform/backend/poetry.lock') }}
-          restore-keys: |
-            poetry-${{ runner.os }}-
-
-      - name: Install Poetry
-        run: |
-          cd autogpt_platform/backend
-          HEAD_POETRY_VERSION=$(python3 ../../.github/workflows/scripts/get_package_version_from_lockfile.py poetry)
-          curl -sSL https://install.python-poetry.org | POETRY_VERSION=$HEAD_POETRY_VERSION python3 -
-          echo "$HOME/.local/bin" >> $GITHUB_PATH
-
-      - name: Install dependencies
-        working-directory: autogpt_platform/backend
-        run: |
-          poetry install --only main
-          poetry run prisma generate
-
-      - name: Run Claude Code Review
-        uses: anthropics/claude-code-action@v1
-        with:
-          claude_code_oauth_token: ${{ secrets.CLAUDE_CODE_OAUTH_TOKEN }}
-          claude_args: |
-            --allowedTools "Read,Glob,Grep,Bash(gh pr comment:*),Bash(gh pr diff:*),Bash(gh pr view:*)"
-          prompt: |
-            You are reviewing a PR that modifies block documentation or block code for AutoGPT.
-
-            ## Your Task
-            Review the changes in this PR and provide constructive feedback. Focus on:
-
-            1. **Documentation Accuracy**: For any block code changes, verify that:
-               - Input/output tables in docs match the actual block schemas
-               - Description text accurately reflects what the block does
-               - Any new blocks have corresponding documentation
-
-            2. **Manual Content Quality**: Check manual sections (marked with `<!-- MANUAL: -->` markers):
-               - "How it works" sections should have clear technical explanations
-               - "Possible use case" sections should have practical, real-world examples
-               - Content should be helpful for users trying to understand the blocks
-
-            3. **Template Compliance**: Ensure docs follow the standard template:
-               - What it is (brief intro)
-               - What it does (description)
-               - How it works (technical explanation)
-               - Inputs table
-               - Outputs table
-               - Possible use case
-
-            4. **Cross-references**: Check that links and anchors are correct
-
-            ## Review Process
-            1. First, get the PR diff to see what changed: `gh pr diff ${{ github.event.pull_request.number }}`
-            2. Read any modified block files to understand the implementation
-            3. Read corresponding documentation files to verify accuracy
-            4. Provide your feedback as a PR comment
-
-            Be constructive and specific. If everything looks good, say so!
-            If there are issues, explain what's wrong and suggest how to fix it.

.github/workflows/docs-enhance.yml

@@ -1,194 +0,0 @@
-name: Enhance Block Documentation
-
-on:
-  workflow_dispatch:
-    inputs:
-      block_pattern:
-        description: 'Block file pattern to enhance (e.g., "google/*.md" or "*" for all blocks)'
-        required: true
-        default: '*'
-        type: string
-      dry_run:
-        description: 'Dry run mode - show proposed changes without committing'
-        type: boolean
-        default: true
-      max_blocks:
-        description: 'Maximum number of blocks to process (0 for unlimited)'
-        type: number
-        default: 10
-
-jobs:
-  enhance-docs:
-    runs-on: ubuntu-latest
-    timeout-minutes: 45
-    permissions:
-      contents: write
-      pull-requests: write
-      id-token: write
-
-    steps:
-      - name: Checkout code
-        uses: actions/checkout@v4
-        with:
-          fetch-depth: 1
-
-      - name: Set up Python
-        uses: actions/setup-python@v5
-        with:
-          python-version: "3.11"
-
-      - name: Set up Python dependency cache
-        uses: actions/cache@v4
-        with:
-          path: ~/.cache/pypoetry
-          key: poetry-${{ runner.os }}-${{ hashFiles('autogpt_platform/backend/poetry.lock') }}
-          restore-keys: |
-            poetry-${{ runner.os }}-
-
-      - name: Install Poetry
-        run: |
-          cd autogpt_platform/backend
-          HEAD_POETRY_VERSION=$(python3 ../../.github/workflows/scripts/get_package_version_from_lockfile.py poetry)
-          curl -sSL https://install.python-poetry.org | POETRY_VERSION=$HEAD_POETRY_VERSION python3 -
-          echo "$HOME/.local/bin" >> $GITHUB_PATH
-
-      - name: Install dependencies
-        working-directory: autogpt_platform/backend
-        run: |
-          poetry install --only main
-          poetry run prisma generate
-
-      - name: Run Claude Enhancement
-        uses: anthropics/claude-code-action@v1
-        with:
-          claude_code_oauth_token: ${{ secrets.CLAUDE_CODE_OAUTH_TOKEN }}
-          claude_args: |
-            --allowedTools "Read,Edit,Glob,Grep,Write,Bash(git:*),Bash(gh:*),Bash(find:*),Bash(ls:*)"
-          prompt: |
-            You are enhancing block documentation for AutoGPT. Your task is to improve the MANUAL sections
-            of block documentation files by reading the actual block implementations and writing helpful content.
-
-            ## Configuration
-            - Block pattern: ${{ inputs.block_pattern }}
-            - Dry run: ${{ inputs.dry_run }}
-            - Max blocks to process: ${{ inputs.max_blocks }}
-
-            ## Your Task
-
-            1. **Find Documentation Files**
-               Find block documentation files matching the pattern in `docs/integrations/`
-               Pattern: ${{ inputs.block_pattern }}
-
-               Use: `find docs/integrations -name "*.md" -type f`
-
-            2. **For Each Documentation File** (up to ${{ inputs.max_blocks }} files):
-
-               a. Read the documentation file
-
-               b. Identify which block(s) it documents (look for the block class name)
-
-               c. Find and read the corresponding block implementation in `autogpt_platform/backend/backend/blocks/`
-
-               d. Improve the MANUAL sections:
-
-                  **"How it works" section** (within `<!-- MANUAL: how_it_works -->` markers):
-                  - Explain the technical flow of the block
-                  - Describe what APIs or services it connects to
-                  - Note any important configuration or prerequisites
-                  - Keep it concise but informative (2-4 paragraphs)
-
-                  **"Possible use case" section** (within `<!-- MANUAL: use_case -->` markers):
-                  - Provide 2-3 practical, real-world examples
-                  - Make them specific and actionable
-                  - Show how this block could be used in an automation workflow
-
-            3. **Important Rules**
-               - ONLY modify content within `<!-- MANUAL: -->` and `<!-- END MANUAL -->` markers
-               - Do NOT modify auto-generated sections (inputs/outputs tables, descriptions)
-               - Keep content accurate based on the actual block implementation
-               - Write for users who may not be technical experts
-
-            4. **Output**
-               ${{ inputs.dry_run == true && 'DRY RUN MODE: Show proposed changes for each file but do NOT actually edit the files. Describe what you would change.' || 'LIVE MODE: Actually edit the files to improve the documentation.' }}
-
-            ## Example Improvements
-
-            **Before (How it works):**
-            ```
-            _Add technical explanation here._
-            ```
-
-            **After (How it works):**
-            ```
-            This block connects to the GitHub API to retrieve issue information. When executed,
-            it authenticates using your GitHub credentials and fetches issue details including
-            title, body, labels, and assignees.
-
-            The block requires a valid GitHub OAuth connection with repository access permissions.
-            It supports both public and private repositories you have access to.
-            ```
-
-            **Before (Possible use case):**
-            ```
-            _Add practical use case examples here._
-            ```
-
-            **After (Possible use case):**
-            ```
-            **Customer Support Automation**: Monitor a GitHub repository for new issues with
-            the "bug" label, then automatically create a ticket in your support system and
-            notify the on-call engineer via Slack.
-
-            **Release Notes Generation**: When a new release is published, gather all closed
-            issues since the last release and generate a summary for your changelog.
-            ```
-
-            Begin by finding and listing the documentation files to process.
-
-      - name: Create PR with enhanced documentation
-        if: ${{ inputs.dry_run == false }}
-        env:
-          GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-        run: |
-          # Check if there are changes
-          if git diff --quiet docs/integrations/; then
-            echo "No changes to commit"
-            exit 0
-          fi
-
-          # Configure git
-          git config user.name "github-actions[bot]"
-          git config user.email "github-actions[bot]@users.noreply.github.com"
-
-          # Create branch and commit
-          BRANCH_NAME="docs/enhance-blocks-$(date +%Y%m%d-%H%M%S)"
-          git checkout -b "$BRANCH_NAME"
-          git add docs/integrations/
-          git commit -m "docs: enhance block documentation with LLM-generated content
-
-          Pattern: ${{ inputs.block_pattern }}
-          Max blocks: ${{ inputs.max_blocks }}
-
-          🤖 Generated with [Claude Code](https://claude.com/claude-code)
-
-          Co-Authored-By: Claude <noreply@anthropic.com>"
-
-          # Push and create PR
-          git push -u origin "$BRANCH_NAME"
-          gh pr create \
-            --title "docs: LLM-enhanced block documentation" \
-            --body "## Summary
-          This PR contains LLM-enhanced documentation for block files matching pattern: \`${{ inputs.block_pattern }}\`
-
-          The following manual sections were improved:
-          - **How it works**: Technical explanations based on block implementations
-          - **Possible use case**: Practical, real-world examples
-
-          ## Review Checklist
-          - [ ] Content is accurate based on block implementations
-          - [ ] Examples are practical and helpful
-          - [ ] No auto-generated sections were modified
-
-          ---
-          🤖 Generated with [Claude Code](https://claude.com/claude-code)" \
-            --base dev

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

@@ -4,9 +4,14 @@ from collections.abc import AsyncGenerator
 from typing import Any
 
 import orjson
-from langfuse import get_client, propagate_attributes
-from langfuse.openai import openai  # type: ignore
-from openai import APIConnectionError, APIError, APIStatusError, RateLimitError
+from langfuse import Langfuse
+from openai import (
+    APIConnectionError,
+    APIError,
+    APIStatusError,
+    AsyncOpenAI,
+    RateLimitError,
+)
 from openai.types.chat import ChatCompletionChunk, ChatCompletionToolParam
 
 from backend.data.understanding import (
@@ -16,6 +21,7 @@ from backend.data.understanding import (
 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,
@@ -44,10 +50,10 @@ logger = logging.getLogger(__name__)
 
 config = ChatConfig()
 settings = Settings()
-client = openai.AsyncOpenAI(api_key=config.api_key, base_url=config.base_url)
+client = AsyncOpenAI(api_key=config.api_key, base_url=config.base_url)
 
-
-langfuse = get_client()
+# Langfuse client (lazy initialization)
+_langfuse_client: Langfuse | None = None
 
 
 class LangfuseNotConfiguredError(Exception):
@@ -63,6 +69,65 @@ def _is_langfuse_configured() -> bool:
     )
 
 
+def _get_langfuse_client() -> Langfuse:
+    """Get or create the Langfuse client for prompt management and tracing."""
+    global _langfuse_client
+    if _langfuse_client is None:
+        if not _is_langfuse_configured():
+            raise LangfuseNotConfiguredError(
+                "Langfuse is not configured. The chat feature requires Langfuse for prompt management. "
+                "Please set the 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_environment() -> str:
+    """Get the current environment name for Langfuse tagging."""
+    return settings.config.app_env.value
+
+
+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.
+
+    Returns True if the user has 1 or fewer sessions (meaning this is their first).
+    """
+    try:
+        session_count = await chat_db.get_user_session_count(user_id)
+        return session_count <= 1
+    except Exception as e:
+        logger.warning(f"Failed to check session count for user {user_id}: {e}")
+        return False  # Default to non-onboarding if we can't check
+
+
 async def _build_system_prompt(user_id: str | None) -> tuple[str, Any]:
     """Build the full system prompt including business understanding if available.
 
@@ -74,6 +139,8 @@ async def _build_system_prompt(user_id: str | None) -> tuple[str, Any]:
         Tuple of (compiled prompt string, Langfuse prompt object for tracing)
     """
 
+    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)
 
@@ -91,7 +158,7 @@ async def _build_system_prompt(user_id: str | None) -> tuple[str, Any]:
         context = "This is the first time you are meeting the user. Greet them and introduce them to the platform"
 
     compiled = prompt.compile(users_information=context)
-    return compiled, understanding
+    return compiled, prompt
 
 
 async def _generate_session_title(message: str) -> str | None:
@@ -150,7 +217,6 @@ async def assign_user_to_session(
 async def stream_chat_completion(
     session_id: str,
     message: str | None = None,
-    tool_call_response: str | None = None,
     is_user_message: bool = True,
     user_id: str | None = None,
     retry_count: int = 0,
@@ -190,6 +256,11 @@ async def stream_chat_completion(
         yield StreamFinish()
         return
 
+    # Langfuse observations will be created after session is loaded (need messages for input)
+    # Initialize to None so finally block can safely check and end them
+    trace = None
+    generation = None
+
     # Only fetch from Redis if session not provided (initial call)
     if session is None:
         session = await get_chat_session(session_id, user_id)
@@ -265,259 +336,297 @@ async def stream_chat_completion(
             asyncio.create_task(_update_title())
 
     # Build system prompt with business understanding
-    system_prompt, understanding = await _build_system_prompt(user_id)
+    system_prompt, langfuse_prompt = await _build_system_prompt(user_id)
+
+    # Build input messages including system prompt for complete Langfuse logging
+    trace_input_messages = [{"role": "system", "content": system_prompt}] + [
+        m.model_dump() for m in session.messages
+    ]
 
     # Create Langfuse trace for this LLM call (each call gets its own trace, grouped by session_id)
     # Using v3 SDK: start_observation creates a root span, update_trace sets trace-level attributes
-    input = message
-    if not message and tool_call_response:
-        input = tool_call_response
-
-    langfuse = get_client()
-    with langfuse.start_as_current_observation(
-        as_type="span",
-        name="user-copilot-request",
-        input=input,
-    ) as span:
-        with propagate_attributes(
+    try:
+        langfuse = _get_langfuse_client()
+        env = _get_environment()
+        trace = langfuse.start_observation(
+            name="chat_completion",
+            input={"messages": trace_input_messages},
+            metadata={
+                "environment": env,
+                "model": config.model,
+                "message_count": len(session.messages),
+                "prompt_name": langfuse_prompt.name if langfuse_prompt else None,
+                "prompt_version": langfuse_prompt.version if langfuse_prompt else None,
+            },
+        )
+        # Set trace-level attributes (session_id, user_id, tags)
+        trace.update_trace(
             session_id=session_id,
             user_id=user_id,
-            tags=["copilot"],
-            metadata={
-                "users_information": format_understanding_for_prompt(understanding)[
-                    :200
-                ]  # langfuse only accepts upto to 200 chars
-            },
-        ):
+            tags=[env, "copilot"],
+        )
+    except Exception as e:
+        logger.warning(f"Failed to create Langfuse trace: {e}")
 
-            # Initialize variables that will be used in finally block (must be defined before try)
-            assistant_response = ChatMessage(
-                role="assistant",
-                content="",
+    # Initialize variables that will be used in finally block (must be defined before try)
+    assistant_response = ChatMessage(
+        role="assistant",
+        content="",
+    )
+    accumulated_tool_calls: list[dict[str, Any]] = []
+
+    # Wrap main logic in try/finally to ensure Langfuse observations are always ended
+    try:
+        has_yielded_end = False
+        has_yielded_error = False
+        has_done_tool_call = False
+        has_received_text = False
+        text_streaming_ended = False
+        tool_response_messages: list[ChatMessage] = []
+        should_retry = False
+
+        # Generate unique IDs for AI SDK protocol
+        import uuid as uuid_module
+
+        message_id = str(uuid_module.uuid4())
+        text_block_id = str(uuid_module.uuid4())
+
+        # Yield message start
+        yield StreamStart(messageId=message_id)
+
+        # Create Langfuse generation for each LLM call, linked to the prompt
+        # Using v3 SDK: start_observation with as_type="generation"
+        generation = (
+            trace.start_observation(
+                as_type="generation",
+                name="llm_call",
+                model=config.model,
+                input={"messages": trace_input_messages},
+                prompt=langfuse_prompt,
             )
-            accumulated_tool_calls: list[dict[str, Any]] = []
+            if trace
+            else None
+        )
 
-            # Wrap main logic in try/finally to ensure Langfuse observations are always ended
-            has_yielded_end = False
-            has_yielded_error = False
-            has_done_tool_call = False
-            has_received_text = False
-            text_streaming_ended = False
-            tool_response_messages: list[ChatMessage] = []
-            should_retry = False
+        try:
+            async for chunk in _stream_chat_chunks(
+                session=session,
+                tools=tools,
+                system_prompt=system_prompt,
+                text_block_id=text_block_id,
+            ):
 
-            # Generate unique IDs for AI SDK protocol
-            import uuid as uuid_module
-
-            message_id = str(uuid_module.uuid4())
-            text_block_id = str(uuid_module.uuid4())
-
-            # Yield message start
-            yield StreamStart(messageId=message_id)
-
-            try:
-                async for chunk in _stream_chat_chunks(
-                    session=session,
-                    tools=tools,
-                    system_prompt=system_prompt,
-                    text_block_id=text_block_id,
-                ):
-
-                    if isinstance(chunk, StreamTextStart):
-                        # Emit text-start before first text delta
-                        if not has_received_text:
-                            yield chunk
-                    elif isinstance(chunk, StreamTextDelta):
-                        delta = chunk.delta or ""
-                        assert assistant_response.content is not None
-                        assistant_response.content += delta
-                        has_received_text = True
+                if isinstance(chunk, StreamTextStart):
+                    # Emit text-start before first text delta
+                    if not has_received_text:
                         yield chunk
-                    elif isinstance(chunk, StreamTextEnd):
-                        # Emit text-end after text completes
-                        if has_received_text and not text_streaming_ended:
-                            text_streaming_ended = True
-                            if assistant_response.content:
-                                logger.warn(
-                                    f"StreamTextEnd: Attempting to set output {assistant_response.content}"
-                                )
-                                span.update_trace(output=assistant_response.content)
-                                span.update(output=assistant_response.content)
-                            yield chunk
-                    elif isinstance(chunk, StreamToolInputStart):
-                        # Emit text-end before first tool call, but only if we've received text
+                elif isinstance(chunk, StreamTextDelta):
+                    delta = chunk.delta or ""
+                    assert assistant_response.content is not None
+                    assistant_response.content += delta
+                    has_received_text = True
+                    yield chunk
+                elif isinstance(chunk, StreamTextEnd):
+                    # Emit text-end after text completes
+                    if has_received_text and not text_streaming_ended:
+                        text_streaming_ended = True
+                        yield chunk
+                elif isinstance(chunk, StreamToolInputStart):
+                    # Emit text-end before first tool call, but only if we've received text
+                    if has_received_text and not text_streaming_ended:
+                        yield StreamTextEnd(id=text_block_id)
+                        text_streaming_ended = True
+                    yield chunk
+                elif isinstance(chunk, StreamToolInputAvailable):
+                    # Accumulate tool calls in OpenAI format
+                    accumulated_tool_calls.append(
+                        {
+                            "id": chunk.toolCallId,
+                            "type": "function",
+                            "function": {
+                                "name": chunk.toolName,
+                                "arguments": orjson.dumps(chunk.input).decode("utf-8"),
+                            },
+                        }
+                    )
+                elif isinstance(chunk, StreamToolOutputAvailable):
+                    result_content = (
+                        chunk.output
+                        if isinstance(chunk.output, str)
+                        else orjson.dumps(chunk.output).decode("utf-8")
+                    )
+                    tool_response_messages.append(
+                        ChatMessage(
+                            role="tool",
+                            content=result_content,
+                            tool_call_id=chunk.toolCallId,
+                        )
+                    )
+                    has_done_tool_call = True
+                    # Track if any tool execution failed
+                    if not chunk.success:
+                        logger.warning(
+                            f"Tool {chunk.toolName} (ID: {chunk.toolCallId}) execution failed"
+                        )
+                    yield chunk
+                elif isinstance(chunk, StreamFinish):
+                    if not has_done_tool_call:
+                        # Emit text-end before finish if we received text but haven't closed it
                         if has_received_text and not text_streaming_ended:
                             yield StreamTextEnd(id=text_block_id)
                             text_streaming_ended = True
+                        has_yielded_end = True
                         yield chunk
-                    elif isinstance(chunk, StreamToolInputAvailable):
-                        # Accumulate tool calls in OpenAI format
-                        accumulated_tool_calls.append(
-                            {
-                                "id": chunk.toolCallId,
-                                "type": "function",
-                                "function": {
-                                    "name": chunk.toolName,
-                                    "arguments": orjson.dumps(chunk.input).decode(
-                                        "utf-8"
-                                    ),
-                                },
-                            }
+                elif isinstance(chunk, StreamError):
+                    has_yielded_error = True
+                elif isinstance(chunk, StreamUsage):
+                    session.usage.append(
+                        Usage(
+                            prompt_tokens=chunk.promptTokens,
+                            completion_tokens=chunk.completionTokens,
+                            total_tokens=chunk.totalTokens,
                         )
-                    elif isinstance(chunk, StreamToolOutputAvailable):
-                        result_content = (
-                            chunk.output
-                            if isinstance(chunk.output, str)
-                            else orjson.dumps(chunk.output).decode("utf-8")
-                        )
-                        tool_response_messages.append(
-                            ChatMessage(
-                                role="tool",
-                                content=result_content,
-                                tool_call_id=chunk.toolCallId,
-                            )
-                        )
-                        has_done_tool_call = True
-                        # Track if any tool execution failed
-                        if not chunk.success:
-                            logger.warning(
-                                f"Tool {chunk.toolName} (ID: {chunk.toolCallId}) execution failed"
-                            )
-                        yield chunk
-                    elif isinstance(chunk, StreamFinish):
-                        if not has_done_tool_call:
-                            # Emit text-end before finish if we received text but haven't closed it
-                            if has_received_text and not text_streaming_ended:
-                                yield StreamTextEnd(id=text_block_id)
-                                text_streaming_ended = True
-                            has_yielded_end = True
-                            yield chunk
-                    elif isinstance(chunk, StreamError):
-                        has_yielded_error = True
-                    elif isinstance(chunk, StreamUsage):
-                        session.usage.append(
-                            Usage(
-                                prompt_tokens=chunk.promptTokens,
-                                completion_tokens=chunk.completionTokens,
-                                total_tokens=chunk.totalTokens,
-                            )
-                        )
-                    else:
-                        logger.error(
-                            f"Unknown chunk type: {type(chunk)}", exc_info=True
-                        )
-                if assistant_response.content:
-                    langfuse.update_current_trace(output=assistant_response.content)
-                    langfuse.update_current_span(output=assistant_response.content)
-                elif tool_response_messages:
-                    langfuse.update_current_trace(output=str(tool_response_messages))
-                    langfuse.update_current_span(output=str(tool_response_messages))
-
-            except Exception as e:
-                logger.error(f"Error during stream: {e!s}", exc_info=True)
-
-                # Check if this is a retryable error (JSON parsing, incomplete tool calls, etc.)
-                is_retryable = isinstance(
-                    e, (orjson.JSONDecodeError, KeyError, TypeError)
-                )
-
-                if is_retryable and retry_count < config.max_retries:
-                    logger.info(
-                        f"Retryable error encountered. Attempt {retry_count + 1}/{config.max_retries}"
                     )
-                    should_retry = True
                 else:
-                    # Non-retryable error or max retries exceeded
-                    # Save any partial progress before reporting error
-                    messages_to_save: list[ChatMessage] = []
+                    logger.error(f"Unknown chunk type: {type(chunk)}", exc_info=True)
+        except Exception as e:
+            logger.error(f"Error during stream: {e!s}", exc_info=True)
 
-                    # Add assistant message if it has content or tool calls
-                    if accumulated_tool_calls:
-                        assistant_response.tool_calls = accumulated_tool_calls
-                    if assistant_response.content or assistant_response.tool_calls:
-                        messages_to_save.append(assistant_response)
+            # Check if this is a retryable error (JSON parsing, incomplete tool calls, etc.)
+            is_retryable = isinstance(e, (orjson.JSONDecodeError, KeyError, TypeError))
 
-                    # Add tool response messages after assistant message
-                    messages_to_save.extend(tool_response_messages)
-
-                    session.messages.extend(messages_to_save)
-                    await upsert_chat_session(session)
-
-                    if not has_yielded_error:
-                        error_message = str(e)
-                        if not is_retryable:
-                            error_message = f"Non-retryable error: {error_message}"
-                        elif retry_count >= config.max_retries:
-                            error_message = f"Max retries ({config.max_retries}) exceeded: {error_message}"
-
-                        error_response = StreamError(errorText=error_message)
-                        yield error_response
-                    if not has_yielded_end:
-                        yield StreamFinish()
-                    return
-
-            # Handle retry outside of exception handler to avoid nesting
-            if should_retry and retry_count < config.max_retries:
+            if is_retryable and retry_count < config.max_retries:
                 logger.info(
-                    f"Retrying stream_chat_completion for session {session_id}, attempt {retry_count + 1}"
+                    f"Retryable error encountered. Attempt {retry_count + 1}/{config.max_retries}"
                 )
-                async for chunk in stream_chat_completion(
-                    session_id=session.session_id,
-                    user_id=user_id,
-                    retry_count=retry_count + 1,
-                    session=session,
-                    context=context,
-                ):
-                    yield chunk
-                return  # Exit after retry to avoid double-saving in finally block
+                should_retry = True
+            else:
+                # Non-retryable error or max retries exceeded
+                # Save any partial progress before reporting error
+                messages_to_save: list[ChatMessage] = []
 
-            # Normal completion path - save session and handle tool call continuation
+                # Add assistant message if it has content or tool calls
+                if accumulated_tool_calls:
+                    assistant_response.tool_calls = accumulated_tool_calls
+                if assistant_response.content or assistant_response.tool_calls:
+                    messages_to_save.append(assistant_response)
+
+                # Add tool response messages after assistant message
+                messages_to_save.extend(tool_response_messages)
+
+                session.messages.extend(messages_to_save)
+                await upsert_chat_session(session)
+
+                if not has_yielded_error:
+                    error_message = str(e)
+                    if not is_retryable:
+                        error_message = f"Non-retryable error: {error_message}"
+                    elif retry_count >= config.max_retries:
+                        error_message = f"Max retries ({config.max_retries}) exceeded: {error_message}"
+
+                    error_response = StreamError(errorText=error_message)
+                    yield error_response
+                if not has_yielded_end:
+                    yield StreamFinish()
+                return
+
+        # Handle retry outside of exception handler to avoid nesting
+        if should_retry and retry_count < config.max_retries:
             logger.info(
-                f"Normal completion path: session={session.session_id}, "
-                f"current message_count={len(session.messages)}"
+                f"Retrying stream_chat_completion for session {session_id}, attempt {retry_count + 1}"
+            )
+            async for chunk in stream_chat_completion(
+                session_id=session.session_id,
+                user_id=user_id,
+                retry_count=retry_count + 1,
+                session=session,
+                context=context,
+            ):
+                yield chunk
+            return  # Exit after retry to avoid double-saving in finally block
+
+        # Normal completion path - save session and handle tool call continuation
+        logger.info(
+            f"Normal completion path: session={session.session_id}, "
+            f"current message_count={len(session.messages)}"
+        )
+
+        # Build the messages list in the correct order
+        messages_to_save: list[ChatMessage] = []
+
+        # Add assistant message with tool_calls if any
+        if accumulated_tool_calls:
+            assistant_response.tool_calls = accumulated_tool_calls
+            logger.info(
+                f"Added {len(accumulated_tool_calls)} tool calls to assistant message"
+            )
+        if assistant_response.content or assistant_response.tool_calls:
+            messages_to_save.append(assistant_response)
+            logger.info(
+                f"Saving assistant message with content_len={len(assistant_response.content or '')}, tool_calls={len(assistant_response.tool_calls or [])}"
             )
 
-            # Build the messages list in the correct order
-            messages_to_save: list[ChatMessage] = []
+        # Add tool response messages after assistant message
+        messages_to_save.extend(tool_response_messages)
+        logger.info(
+            f"Saving {len(tool_response_messages)} tool response messages, "
+            f"total_to_save={len(messages_to_save)}"
+        )
 
-            # Add assistant message with tool_calls if any
-            if accumulated_tool_calls:
-                assistant_response.tool_calls = accumulated_tool_calls
-                logger.info(
-                    f"Added {len(accumulated_tool_calls)} tool calls to assistant message"
-                )
-            if assistant_response.content or assistant_response.tool_calls:
-                messages_to_save.append(assistant_response)
-                logger.info(
-                    f"Saving assistant message with content_len={len(assistant_response.content or '')}, tool_calls={len(assistant_response.tool_calls or [])}"
-                )
+        session.messages.extend(messages_to_save)
+        logger.info(
+            f"Extended session messages, new message_count={len(session.messages)}"
+        )
+        await upsert_chat_session(session)
 
-            # Add tool response messages after assistant message
-            messages_to_save.extend(tool_response_messages)
+        # If we did a tool call, stream the chat completion again to get the next response
+        if has_done_tool_call:
             logger.info(
-                f"Saving {len(tool_response_messages)} tool response messages, "
-                f"total_to_save={len(messages_to_save)}"
+                "Tool call executed, streaming chat completion again to get assistant response"
             )
+            async for chunk in stream_chat_completion(
+                session_id=session.session_id,
+                user_id=user_id,
+                session=session,  # Pass session object to avoid Redis refetch
+                context=context,
+            ):
+                yield chunk
 
-            session.messages.extend(messages_to_save)
-            logger.info(
-                f"Extended session messages, new message_count={len(session.messages)}"
-            )
-            await upsert_chat_session(session)
-
-            # If we did a tool call, stream the chat completion again to get the next response
-            if has_done_tool_call:
-                logger.info(
-                    "Tool call executed, streaming chat completion again to get assistant response"
+    finally:
+        # Always end Langfuse observations to prevent resource leaks
+        # Guard against None and catch errors to avoid masking original exceptions
+        if generation is not None:
+            try:
+                latest_usage = session.usage[-1] if session.usage else None
+                generation.update(
+                    model=config.model,
+                    output={
+                        "content": assistant_response.content,
+                        "tool_calls": accumulated_tool_calls or None,
+                    },
+                    usage_details=(
+                        {
+                            "input": latest_usage.prompt_tokens,
+                            "output": latest_usage.completion_tokens,
+                            "total": latest_usage.total_tokens,
+                        }
+                        if latest_usage
+                        else None
+                    ),
                 )
-                async for chunk in stream_chat_completion(
-                    session_id=session.session_id,
-                    user_id=user_id,
-                    session=session,  # Pass session object to avoid Redis refetch
-                    context=context,
-                    tool_call_response=str(tool_response_messages),
-                ):
-                    yield chunk
+                generation.end()
+            except Exception as e:
+                logger.warning(f"Failed to end Langfuse generation: {e}")
+
+        if trace is not None:
+            try:
+                if accumulated_tool_calls:
+                    trace.update_trace(output={"tool_calls": accumulated_tool_calls})
+                else:
+                    trace.update_trace(output={"response": assistant_response.content})
+                trace.end()
+            except Exception as e:
+                logger.warning(f"Failed to end Langfuse trace: {e}")
 
 
 # Retry configuration for OpenAI API calls
@@ -791,4 +900,5 @@ async def _yield_tool_call(
         session=session,
     )
 
+    logger.info(f"Yielding Tool execution response: {tool_execution_response}")
     yield tool_execution_response

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

@@ -30,7 +30,7 @@ TOOL_REGISTRY: dict[str, BaseTool] = {
     "find_library_agent": FindLibraryAgentTool(),
     "run_agent": RunAgentTool(),
     "run_block": RunBlockTool(),
-    "view_agent_output": AgentOutputTool(),
+    "agent_output": AgentOutputTool(),
     "search_docs": SearchDocsTool(),
     "get_doc_page": GetDocPageTool(),
 }

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

@@ -3,8 +3,6 @@
 import logging
 from typing import Any
 
-from langfuse import observe
-
 from backend.api.features.chat.model import ChatSession
 from backend.data.understanding import (
     BusinessUnderstandingInput,
@@ -61,7 +59,6 @@ and automations for the user's specific needs."""
         """Requires authentication to store user-specific data."""
         return True
 
-    @observe(as_type="tool", name="add_understanding")
     async def _execute(
         self,
         user_id: str | None,

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

@@ -1,28 +1,29 @@
 """Agent generator package - Creates agents from natural language."""
 
 from .core import (
-    AgentGeneratorNotConfiguredError,
+    apply_agent_patch,
     decompose_goal,
     generate_agent,
     generate_agent_patch,
     get_agent_as_json,
-    json_to_graph,
     save_agent_to_library,
 )
-from .service import health_check as check_external_service_health
-from .service import is_external_service_configured
+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",
-    "json_to_graph",
-    # Exceptions
-    "AgentGeneratorNotConfiguredError",
-    # Service
-    "is_external_service_configured",
-    "check_external_service_health",
+    # Fixer
+    "apply_all_fixes",
+    # Validator
+    "validate_agent",
+    # Utils
+    "get_blocks_info",
 ]

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

@@ -0,0 +1,25 @@
+"""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")
+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

@@ -1,5 +1,7 @@
 """Core agent generation functions."""
 
+import copy
+import json
 import logging
 import uuid
 from typing import Any
@@ -7,35 +9,13 @@ 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 .service import (
-    decompose_goal_external,
-    generate_agent_external,
-    generate_agent_patch_external,
-    is_external_service_configured,
-)
+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__)
 
 
-class AgentGeneratorNotConfiguredError(Exception):
-    """Raised when the external Agent Generator service is not configured."""
-
-    pass
-
-
-def _check_service_configured() -> None:
-    """Check if the external Agent Generator service is configured.
-
-    Raises:
-        AgentGeneratorNotConfiguredError: If the service is not configured.
-    """
-    if not is_external_service_configured():
-        raise AgentGeneratorNotConfiguredError(
-            "Agent Generator service is not configured. "
-            "Set AGENTGENERATOR_HOST environment variable to enable agent generation."
-        )
-
-
 async def decompose_goal(description: str, context: str = "") -> dict[str, Any] | None:
     """Break down a goal into steps or return clarifying questions.
 
@@ -48,13 +28,40 @@ async def decompose_goal(description: str, context: str = "") -> dict[str, Any]
         - {"type": "clarifying_questions", "questions": [...]}
         - {"type": "instructions", "steps": [...]}
         Or None on error
-
-    Raises:
-        AgentGeneratorNotConfiguredError: If the external service is not configured.
     """
-    _check_service_configured()
-    logger.info("Calling external Agent Generator service for decompose_goal")
-    return await decompose_goal_external(description, context)
+    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:
@@ -65,14 +72,31 @@ async def generate_agent(instructions: dict[str, Any]) -> dict[str, Any] | None:
 
     Returns:
         Agent JSON dict or None on error
-
-    Raises:
-        AgentGeneratorNotConfiguredError: If the external service is not configured.
     """
-    _check_service_configured()
-    logger.info("Calling external Agent Generator service for generate_agent")
-    result = await generate_agent_external(instructions)
-    if result:
+    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())
@@ -80,7 +104,12 @@ async def generate_agent(instructions: dict[str, Any]) -> dict[str, Any] | None:
             result["version"] = 1
         if "is_active" not in result:
             result["is_active"] = True
-    return result
+
+        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:
@@ -189,7 +218,6 @@ async def save_agent_to_library(
     library_agents = await library_db.create_library_agent(
         graph=created_graph,
         user_id=user_id,
-        sensitive_action_safe_mode=True,
         create_library_agents_for_sub_graphs=False,
     )
 
@@ -255,23 +283,108 @@ async def get_agent_as_json(
 async def generate_agent_patch(
     update_request: str, current_agent: dict[str, Any]
 ) -> dict[str, Any] | None:
-    """Update an existing agent using natural language.
-
-    The external Agent Generator service handles:
-    - Generating the patch
-    - Applying the patch
-    - Fixing and validating the result
+    """Generate a patch to update an existing agent.
 
     Args:
         update_request: Natural language description of changes
         current_agent: Current agent JSON
 
     Returns:
-        Updated agent JSON, clarifying questions dict, or None on error
-
-    Raises:
-        AgentGeneratorNotConfiguredError: If the external service is not configured.
+        Patch dict or clarifying questions, or None on error
     """
-    _check_service_configured()
-    logger.info("Calling external Agent Generator service for generate_agent_patch")
-    return await generate_agent_patch_external(update_request, current_agent)
+    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

@@ -0,0 +1,606 @@
+"""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

@@ -0,0 +1,225 @@
+"""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/service.py

@@ -1,269 +0,0 @@
-"""External Agent Generator service client.
-
-This module provides a client for communicating with the external Agent Generator
-microservice. When AGENTGENERATOR_HOST is configured, the agent generation functions
-will delegate to the external service instead of using the built-in LLM-based implementation.
-"""
-
-import logging
-from typing import Any
-
-import httpx
-
-from backend.util.settings import Settings
-
-logger = logging.getLogger(__name__)
-
-_client: httpx.AsyncClient | None = None
-_settings: Settings | None = None
-
-
-def _get_settings() -> Settings:
-    """Get or create settings singleton."""
-    global _settings
-    if _settings is None:
-        _settings = Settings()
-    return _settings
-
-
-def is_external_service_configured() -> bool:
-    """Check if external Agent Generator service is configured."""
-    settings = _get_settings()
-    return bool(settings.config.agentgenerator_host)
-
-
-def _get_base_url() -> str:
-    """Get the base URL for the external service."""
-    settings = _get_settings()
-    host = settings.config.agentgenerator_host
-    port = settings.config.agentgenerator_port
-    return f"http://{host}:{port}"
-
-
-def _get_client() -> httpx.AsyncClient:
-    """Get or create the HTTP client for the external service."""
-    global _client
-    if _client is None:
-        settings = _get_settings()
-        _client = httpx.AsyncClient(
-            base_url=_get_base_url(),
-            timeout=httpx.Timeout(settings.config.agentgenerator_timeout),
-        )
-    return _client
-
-
-async def decompose_goal_external(
-    description: str, context: str = ""
-) -> dict[str, Any] | None:
-    """Call the external service to decompose a goal.
-
-    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": [...]}
-        - {"type": "unachievable_goal", ...}
-        - {"type": "vague_goal", ...}
-        Or None on error
-    """
-    client = _get_client()
-
-    # Build the request payload
-    payload: dict[str, Any] = {"description": description}
-    if context:
-        # The external service uses user_instruction for additional context
-        payload["user_instruction"] = context
-
-    try:
-        response = await client.post("/api/decompose-description", json=payload)
-        response.raise_for_status()
-        data = response.json()
-
-        if not data.get("success"):
-            logger.error(f"External service returned error: {data.get('error')}")
-            return None
-
-        # Map the response to the expected format
-        response_type = data.get("type")
-        if response_type == "instructions":
-            return {"type": "instructions", "steps": data.get("steps", [])}
-        elif response_type == "clarifying_questions":
-            return {
-                "type": "clarifying_questions",
-                "questions": data.get("questions", []),
-            }
-        elif response_type == "unachievable_goal":
-            return {
-                "type": "unachievable_goal",
-                "reason": data.get("reason"),
-                "suggested_goal": data.get("suggested_goal"),
-            }
-        elif response_type == "vague_goal":
-            return {
-                "type": "vague_goal",
-                "suggested_goal": data.get("suggested_goal"),
-            }
-        else:
-            logger.error(
-                f"Unknown response type from external service: {response_type}"
-            )
-            return None
-
-    except httpx.HTTPStatusError as e:
-        logger.error(f"HTTP error calling external agent generator: {e}")
-        return None
-    except httpx.RequestError as e:
-        logger.error(f"Request error calling external agent generator: {e}")
-        return None
-    except Exception as e:
-        logger.error(f"Unexpected error calling external agent generator: {e}")
-        return None
-
-
-async def generate_agent_external(
-    instructions: dict[str, Any]
-) -> dict[str, Any] | None:
-    """Call the external service to generate an agent from instructions.
-
-    Args:
-        instructions: Structured instructions from decompose_goal
-
-    Returns:
-        Agent JSON dict or None on error
-    """
-    client = _get_client()
-
-    try:
-        response = await client.post(
-            "/api/generate-agent", json={"instructions": instructions}
-        )
-        response.raise_for_status()
-        data = response.json()
-
-        if not data.get("success"):
-            logger.error(f"External service returned error: {data.get('error')}")
-            return None
-
-        return data.get("agent_json")
-
-    except httpx.HTTPStatusError as e:
-        logger.error(f"HTTP error calling external agent generator: {e}")
-        return None
-    except httpx.RequestError as e:
-        logger.error(f"Request error calling external agent generator: {e}")
-        return None
-    except Exception as e:
-        logger.error(f"Unexpected error calling external agent generator: {e}")
-        return None
-
-
-async def generate_agent_patch_external(
-    update_request: str, current_agent: dict[str, Any]
-) -> dict[str, Any] | None:
-    """Call the external service to generate a patch for an existing agent.
-
-    Args:
-        update_request: Natural language description of changes
-        current_agent: Current agent JSON
-
-    Returns:
-        Updated agent JSON, clarifying questions dict, or None on error
-    """
-    client = _get_client()
-
-    try:
-        response = await client.post(
-            "/api/update-agent",
-            json={
-                "update_request": update_request,
-                "current_agent_json": current_agent,
-            },
-        )
-        response.raise_for_status()
-        data = response.json()
-
-        if not data.get("success"):
-            logger.error(f"External service returned error: {data.get('error')}")
-            return None
-
-        # Check if it's clarifying questions
-        if data.get("type") == "clarifying_questions":
-            return {
-                "type": "clarifying_questions",
-                "questions": data.get("questions", []),
-            }
-
-        # Otherwise return the updated agent JSON
-        return data.get("agent_json")
-
-    except httpx.HTTPStatusError as e:
-        logger.error(f"HTTP error calling external agent generator: {e}")
-        return None
-    except httpx.RequestError as e:
-        logger.error(f"Request error calling external agent generator: {e}")
-        return None
-    except Exception as e:
-        logger.error(f"Unexpected error calling external agent generator: {e}")
-        return None
-
-
-async def get_blocks_external() -> list[dict[str, Any]] | None:
-    """Get available blocks from the external service.
-
-    Returns:
-        List of block info dicts or None on error
-    """
-    client = _get_client()
-
-    try:
-        response = await client.get("/api/blocks")
-        response.raise_for_status()
-        data = response.json()
-
-        if not data.get("success"):
-            logger.error("External service returned error getting blocks")
-            return None
-
-        return data.get("blocks", [])
-
-    except httpx.HTTPStatusError as e:
-        logger.error(f"HTTP error getting blocks from external service: {e}")
-        return None
-    except httpx.RequestError as e:
-        logger.error(f"Request error getting blocks from external service: {e}")
-        return None
-    except Exception as e:
-        logger.error(f"Unexpected error getting blocks from external service: {e}")
-        return None
-
-
-async def health_check() -> bool:
-    """Check if the external service is healthy.
-
-    Returns:
-        True if healthy, False otherwise
-    """
-    if not is_external_service_configured():
-        return False
-
-    client = _get_client()
-
-    try:
-        response = await client.get("/health")
-        response.raise_for_status()
-        data = response.json()
-        return data.get("status") == "healthy" and data.get("blocks_loaded", False)
-    except Exception as e:
-        logger.warning(f"External agent generator health check failed: {e}")
-        return False
-
-
-async def close_client() -> None:
-    """Close the HTTP client."""
-    global _client
-    if _client is not None:
-        await _client.aclose()
-        _client = None

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

@@ -0,0 +1,213 @@
+"""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

@@ -0,0 +1,279 @@
+"""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/agent_output.py

@@ -5,7 +5,6 @@ import re
 from datetime import datetime, timedelta, timezone
 from typing import Any
 
-from langfuse import observe
 from pydantic import BaseModel, field_validator
 
 from backend.api.features.chat.model import ChatSession
@@ -104,7 +103,7 @@ class AgentOutputTool(BaseTool):
 
     @property
     def name(self) -> str:
-        return "view_agent_output"
+        return "agent_output"
 
     @property
     def description(self) -> str:
@@ -329,7 +328,6 @@ class AgentOutputTool(BaseTool):
             total_executions=len(available_executions) if available_executions else 1,
         )
 
-    @observe(as_type="tool", name="view_agent_output")
     async def _execute(
         self,
         user_id: str | None,

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

@@ -3,15 +3,15 @@
 import logging
 from typing import Any
 
-from langfuse import observe
-
 from backend.api.features.chat.model import ChatSession
 
 from .agent_generator import (
-    AgentGeneratorNotConfiguredError,
+    apply_all_fixes,
     decompose_goal,
     generate_agent,
+    get_blocks_info,
     save_agent_to_library,
+    validate_agent,
 )
 from .base import BaseTool
 from .models import (
@@ -25,6 +25,9 @@ from .models import (
 
 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."""
@@ -75,7 +78,6 @@ class CreateAgentTool(BaseTool):
             "required": ["description"],
         }
 
-    @observe(as_type="tool", name="create_agent")
     async def _execute(
         self,
         user_id: str | None,
@@ -86,8 +88,9 @@ class CreateAgentTool(BaseTool):
 
         Flow:
         1. Decompose the description into steps (may return clarifying questions)
-        2. Generate agent JSON (external service handles fixing and validation)
-        3. Preview or save based on the save parameter
+        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", "")
@@ -104,13 +107,11 @@ class CreateAgentTool(BaseTool):
         # Step 1: Decompose goal into steps
         try:
             decomposition_result = await decompose_goal(description, context)
-        except AgentGeneratorNotConfiguredError:
+        except ValueError as e:
+            # Handle missing API key or configuration errors
             return ErrorResponse(
-                message=(
-                    "Agent generation is not available. "
-                    "The Agent Generator service is not configured."
-                ),
-                error="service_not_configured",
+                message=f"Agent generation is not configured: {str(e)}",
+                error="configuration_error",
                 session_id=session_id,
             )
 
@@ -167,32 +168,72 @@ class CreateAgentTool(BaseTool):
                 session_id=session_id,
             )
 
-        # Step 2: Generate agent JSON (external service handles fixing and validation)
-        try:
-            agent_json = await generate_agent(decomposition_result)
-        except AgentGeneratorNotConfiguredError:
-            return ErrorResponse(
-                message=(
-                    "Agent generation is not available. "
-                    "The Agent Generator service is not configured."
-                ),
-                error="service_not_configured",
-                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 agent_json is None:
-            return ErrorResponse(
-                message="Failed to generate the agent. Please try again.",
-                error="Generation failed",
-                session_id=session_id,
-            )
+            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 3: Preview or save
+        # Step 4: Preview or save
         if not save:
             return AgentPreviewResponse(
                 message=(

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

@@ -3,15 +3,16 @@
 import logging
 from typing import Any
 
-from langfuse import observe
-
 from backend.api.features.chat.model import ChatSession
 
 from .agent_generator import (
-    AgentGeneratorNotConfiguredError,
+    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 (
@@ -25,6 +26,9 @@ from .models import (
 
 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."""
@@ -37,7 +41,7 @@ class EditAgentTool(BaseTool):
     def description(self) -> str:
         return (
             "Edit an existing agent from the user's library using natural language. "
-            "Generates updates to the agent while preserving unchanged parts."
+            "Generates a patch to update the agent while preserving unchanged parts."
         )
 
     @property
@@ -81,7 +85,6 @@ class EditAgentTool(BaseTool):
             "required": ["agent_id", "changes"],
         }
 
-    @observe(as_type="tool", name="edit_agent")
     async def _execute(
         self,
         user_id: str | None,
@@ -92,8 +95,9 @@ class EditAgentTool(BaseTool):
 
         Flow:
         1. Fetch the current agent
-        2. Generate updated agent (external service handles fixing and validation)
-        3. Preview or save based on the save parameter
+        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()
@@ -130,58 +134,121 @@ class EditAgentTool(BaseTool):
         if context:
             update_request = f"{changes}\n\nAdditional context:\n{context}"
 
-        # Step 2: Generate updated agent (external service handles fixing and validation)
-        try:
-            result = await generate_agent_patch(update_request, current_agent)
-        except AgentGeneratorNotConfiguredError:
-            return ErrorResponse(
-                message=(
-                    "Agent editing is not available. "
-                    "The Agent Generator service is not configured."
-                ),
-                error="service_not_configured",
-                session_id=session_id,
-            )
+        # Step 2: Generate patch with retry on validation failure
+        blocks_info = get_blocks_info()
+        updated_agent = None
+        validation_errors = None
+        intent = "Applied requested changes"
 
-        if result is None:
-            return ErrorResponse(
-                message="Failed to generate changes. Please try rephrasing.",
-                error="Update generation failed",
-                session_id=session_id,
-            )
-
-        # Check if LLM returned clarifying questions
-        if result.get("type") == "clarifying_questions":
-            questions = 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 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
                     )
-                    for q in questions
-                ],
-                session_id=session_id,
+                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}"
             )
 
-        # Result is the updated agent JSON
-        updated_agent = result
+            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 3: Preview or save
+        # Step 5: Preview or save
         if not save:
             return AgentPreviewResponse(
                 message=(
-                    f"I've updated the agent. "
+                    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."
                 ),
@@ -207,7 +274,10 @@ class EditAgentTool(BaseTool):
             )
 
             return AgentSavedResponse(
-                message=f"Updated agent '{created_graph.name}' has been saved to your library!",
+                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,

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

@@ -2,8 +2,6 @@
 
 from typing import Any
 
-from langfuse import observe
-
 from backend.api.features.chat.model import ChatSession
 
 from .agent_search import search_agents
@@ -37,7 +35,6 @@ class FindAgentTool(BaseTool):
             "required": ["query"],
         }
 
-    @observe(as_type="tool", name="find_agent")
     async def _execute(
         self, user_id: str | None, session: ChatSession, **kwargs
     ) -> ToolResponseBase:

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

@@ -1,7 +1,6 @@
 import logging
 from typing import Any
 
-from langfuse import observe
 from prisma.enums import ContentType
 
 from backend.api.features.chat.model import ChatSession
@@ -56,7 +55,6 @@ class FindBlockTool(BaseTool):
     def requires_auth(self) -> bool:
         return True
 
-    @observe(as_type="tool", name="find_block")
     async def _execute(
         self,
         user_id: str | None,

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

@@ -2,8 +2,6 @@
 
 from typing import Any
 
-from langfuse import observe
-
 from backend.api.features.chat.model import ChatSession
 
 from .agent_search import search_agents
@@ -43,7 +41,6 @@ class FindLibraryAgentTool(BaseTool):
     def requires_auth(self) -> bool:
         return True
 
-    @observe(as_type="tool", name="find_library_agent")
     async def _execute(
         self, user_id: str | None, session: ChatSession, **kwargs
     ) -> ToolResponseBase:

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

@@ -4,8 +4,6 @@ import logging
 from pathlib import Path
 from typing import Any
 
-from langfuse import observe
-
 from backend.api.features.chat.model import ChatSession
 from backend.api.features.chat.tools.base import BaseTool
 from backend.api.features.chat.tools.models import (
@@ -73,7 +71,6 @@ class GetDocPageTool(BaseTool):
         url_path = path.rsplit(".", 1)[0] if "." in path else path
         return f"{DOCS_BASE_URL}/{url_path}"
 
-    @observe(as_type="tool", name="get_doc_page")
     async def _execute(
         self,
         user_id: str | None,

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

@@ -3,7 +3,6 @@
 import logging
 from typing import Any
 
-from langfuse import observe
 from pydantic import BaseModel, Field, field_validator
 
 from backend.api.features.chat.config import ChatConfig
@@ -33,7 +32,7 @@ from .models import (
     UserReadiness,
 )
 from .utils import (
-    build_missing_credentials_from_graph,
+    check_user_has_required_credentials,
     extract_credentials_from_schema,
     fetch_graph_from_store_slug,
     get_or_create_library_agent,
@@ -155,7 +154,6 @@ class RunAgentTool(BaseTool):
         """All operations require authentication."""
         return True
 
-    @observe(as_type="tool", name="run_agent")
     async def _execute(
         self,
         user_id: str | None,
@@ -237,13 +235,15 @@ class RunAgentTool(BaseTool):
                 # Return credentials needed response with input data info
                 # The UI handles credential setup automatically, so the message
                 # focuses on asking about input data
-                requirements_creds_dict = build_missing_credentials_from_graph(
-                    graph, None
+                credentials = extract_credentials_from_schema(
+                    graph.credentials_input_schema
                 )
-                missing_credentials_dict = build_missing_credentials_from_graph(
-                    graph, graph_credentials
+                missing_creds_check = await check_user_has_required_credentials(
+                    user_id, credentials
                 )
-                requirements_creds_list = list(requirements_creds_dict.values())
+                missing_credentials_dict = {
+                    c.id: c.model_dump() for c in missing_creds_check
+                }
 
                 return SetupRequirementsResponse(
                     message=self._build_inputs_message(graph, MSG_WHAT_VALUES_TO_USE),
@@ -257,7 +257,7 @@ class RunAgentTool(BaseTool):
                             ready_to_run=False,
                         ),
                         requirements={
-                            "credentials": requirements_creds_list,
+                            "credentials": [c.model_dump() for c in credentials],
                             "inputs": self._get_inputs_list(graph.input_schema),
                             "execution_modes": self._get_execution_modes(graph),
                         },

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

@@ -4,8 +4,6 @@ import logging
 from collections import defaultdict
 from typing import Any
 
-from langfuse import observe
-
 from backend.api.features.chat.model import ChatSession
 from backend.data.block import get_block
 from backend.data.execution import ExecutionContext
@@ -22,7 +20,6 @@ from .models import (
     ToolResponseBase,
     UserReadiness,
 )
-from .utils import build_missing_credentials_from_field_info
 
 logger = logging.getLogger(__name__)
 
@@ -130,7 +127,6 @@ class RunBlockTool(BaseTool):
 
         return matched_credentials, missing_credentials
 
-    @observe(as_type="tool", name="run_block")
     async def _execute(
         self,
         user_id: str | None,
@@ -190,11 +186,7 @@ class RunBlockTool(BaseTool):
 
         if missing_credentials:
             # Return setup requirements response with missing credentials
-            credentials_fields_info = block.input_schema.get_credentials_fields_info()
-            missing_creds_dict = build_missing_credentials_from_field_info(
-                credentials_fields_info, set(matched_credentials.keys())
-            )
-            missing_creds_list = list(missing_creds_dict.values())
+            missing_creds_dict = {c.id: c.model_dump() for c in missing_credentials}
 
             return SetupRequirementsResponse(
                 message=(
@@ -211,7 +203,7 @@ class RunBlockTool(BaseTool):
                         ready_to_run=False,
                     ),
                     requirements={
-                        "credentials": missing_creds_list,
+                        "credentials": [c.model_dump() for c in missing_credentials],
                         "inputs": self._get_inputs_list(block),
                         "execution_modes": ["immediate"],
                     },

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

@@ -3,7 +3,6 @@
 import logging
 from typing import Any
 
-from langfuse import observe
 from prisma.enums import ContentType
 
 from backend.api.features.chat.model import ChatSession
@@ -88,7 +87,6 @@ class SearchDocsTool(BaseTool):
         url_path = path.rsplit(".", 1)[0] if "." in path else path
         return f"{DOCS_BASE_URL}/{url_path}"
 
-    @observe(as_type="tool", name="search_docs")
     async def _execute(
         self,
         user_id: str | None,

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

@@ -8,7 +8,7 @@ from backend.api.features.library import model as library_model
 from backend.api.features.store import db as store_db
 from backend.data import graph as graph_db
 from backend.data.graph import GraphModel
-from backend.data.model import CredentialsFieldInfo, CredentialsMetaInput
+from backend.data.model import CredentialsMetaInput
 from backend.integrations.creds_manager import IntegrationCredentialsManager
 from backend.util.exceptions import NotFoundError
 
@@ -89,59 +89,6 @@ def extract_credentials_from_schema(
     return credentials
 
 
-def _serialize_missing_credential(
-    field_key: str, field_info: CredentialsFieldInfo
-) -> dict[str, Any]:
-    """
-    Convert credential field info into a serializable dict that preserves all supported
-    credential types (e.g., api_key + oauth2) so the UI can offer multiple options.
-    """
-    supported_types = sorted(field_info.supported_types)
-    provider = next(iter(field_info.provider), "unknown")
-    scopes = sorted(field_info.required_scopes or [])
-
-    return {
-        "id": field_key,
-        "title": field_key.replace("_", " ").title(),
-        "provider": provider,
-        "provider_name": provider.replace("_", " ").title(),
-        "type": supported_types[0] if supported_types else "api_key",
-        "types": supported_types,
-        "scopes": scopes,
-    }
-
-
-def build_missing_credentials_from_graph(
-    graph: GraphModel, matched_credentials: dict[str, CredentialsMetaInput] | None
-) -> dict[str, Any]:
-    """
-    Build a missing_credentials mapping from a graph's aggregated credentials inputs,
-    preserving all supported credential types for each field.
-    """
-    matched_keys = set(matched_credentials.keys()) if matched_credentials else set()
-    aggregated_fields = graph.aggregate_credentials_inputs()
-
-    return {
-        field_key: _serialize_missing_credential(field_key, field_info)
-        for field_key, (field_info, _node_fields) in aggregated_fields.items()
-        if field_key not in matched_keys
-    }
-
-
-def build_missing_credentials_from_field_info(
-    credential_fields: dict[str, CredentialsFieldInfo],
-    matched_keys: set[str],
-) -> dict[str, Any]:
-    """
-    Build missing_credentials mapping from a simple credentials field info dictionary.
-    """
-    return {
-        field_key: _serialize_missing_credential(field_key, field_info)
-        for field_key, field_info in credential_fields.items()
-        if field_key not in matched_keys
-    }
-
-
 def extract_credentials_as_dict(
     credentials_input_schema: dict[str, Any] | None,
 ) -> dict[str, CredentialsMetaInput]:

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

@@ -401,11 +401,27 @@ async def add_generated_agent_image(
     )
 
 
+def _initialize_graph_settings(graph: graph_db.GraphModel) -> GraphSettings:
+    """
+    Initialize GraphSettings based on graph content.
+
+    Args:
+        graph: The graph to analyze
+
+    Returns:
+        GraphSettings with appropriate human_in_the_loop_safe_mode value
+    """
+    if graph.has_human_in_the_loop:
+        # Graph has HITL blocks - set safe mode to True by default
+        return GraphSettings(human_in_the_loop_safe_mode=True)
+    else:
+        # Graph has no HITL blocks - keep None
+        return GraphSettings(human_in_the_loop_safe_mode=None)
+
+
 async def create_library_agent(
     graph: graph_db.GraphModel,
     user_id: str,
-    hitl_safe_mode: bool = True,
-    sensitive_action_safe_mode: bool = False,
     create_library_agents_for_sub_graphs: bool = True,
 ) -> list[library_model.LibraryAgent]:
     """
@@ -414,8 +430,6 @@ async def create_library_agent(
     Args:
         agent: The agent/Graph to add to the library.
         user_id: The user to whom the agent will be added.
-        hitl_safe_mode: Whether HITL blocks require manual review (default True).
-        sensitive_action_safe_mode: Whether sensitive action blocks require review.
         create_library_agents_for_sub_graphs: If True, creates LibraryAgent records for sub-graphs as well.
 
     Returns:
@@ -451,11 +465,7 @@ async def create_library_agent(
                             }
                         },
                         settings=SafeJson(
-                            GraphSettings.from_graph(
-                                graph_entry,
-                                hitl_safe_mode=hitl_safe_mode,
-                                sensitive_action_safe_mode=sensitive_action_safe_mode,
-                            ).model_dump()
+                            _initialize_graph_settings(graph_entry).model_dump()
                         ),
                     ),
                     include=library_agent_include(
@@ -617,6 +627,33 @@ async def update_library_agent(
         raise DatabaseError("Failed to update library agent") from e
 
 
+async def update_library_agent_settings(
+    user_id: str,
+    agent_id: str,
+    settings: GraphSettings,
+) -> library_model.LibraryAgent:
+    """
+    Updates the settings for a specific LibraryAgent.
+
+    Args:
+        user_id: The owner of the LibraryAgent.
+        agent_id: The ID of the LibraryAgent to update.
+        settings: New GraphSettings to apply.
+
+    Returns:
+        The updated LibraryAgent.
+
+    Raises:
+        NotFoundError: If the specified LibraryAgent does not exist.
+        DatabaseError: If there's an error in the update operation.
+    """
+    return await update_library_agent(
+        library_agent_id=agent_id,
+        user_id=user_id,
+        settings=settings,
+    )
+
+
 async def delete_library_agent(
     library_agent_id: str, user_id: str, soft_delete: bool = True
 ) -> None:
@@ -801,7 +838,7 @@ async def add_store_agent_to_library(
                 "isCreatedByUser": False,
                 "useGraphIsActiveVersion": False,
                 "settings": SafeJson(
-                    GraphSettings.from_graph(graph_model).model_dump()
+                    _initialize_graph_settings(graph_model).model_dump()
                 ),
             },
             include=library_agent_include(
@@ -1191,15 +1228,8 @@ async def fork_library_agent(
         )
         new_graph = await on_graph_activate(new_graph, user_id=user_id)
 
-        # Create a library agent for the new graph, preserving safe mode settings
-        return (
-            await create_library_agent(
-                new_graph,
-                user_id,
-                hitl_safe_mode=original_agent.settings.human_in_the_loop_safe_mode,
-                sensitive_action_safe_mode=original_agent.settings.sensitive_action_safe_mode,
-            )
-        )[0]
+        # Create a library agent for the new graph
+        return (await create_library_agent(new_graph, user_id))[0]
     except prisma.errors.PrismaError as e:
         logger.error(f"Database error cloning library agent: {e}")
         raise DatabaseError("Failed to fork library agent") from e

autogpt_platform/backend/backend/api/features/library/model.py

@@ -73,12 +73,6 @@ class LibraryAgent(pydantic.BaseModel):
     has_external_trigger: bool = pydantic.Field(
         description="Whether the agent has an external trigger (e.g. webhook) node"
     )
-    has_human_in_the_loop: bool = pydantic.Field(
-        description="Whether the agent has human-in-the-loop blocks"
-    )
-    has_sensitive_action: bool = pydantic.Field(
-        description="Whether the agent has sensitive action blocks"
-    )
     trigger_setup_info: Optional[GraphTriggerInfo] = None
 
     # Indicates whether there's a new output (based on recent runs)
@@ -186,8 +180,6 @@ class LibraryAgent(pydantic.BaseModel):
                 graph.credentials_input_schema if sub_graphs is not None else None
             ),
             has_external_trigger=graph.has_external_trigger,
-            has_human_in_the_loop=graph.has_human_in_the_loop,
-            has_sensitive_action=graph.has_sensitive_action,
             trigger_setup_info=graph.trigger_setup_info,
             new_output=new_output,
             can_access_graph=can_access_graph,

autogpt_platform/backend/backend/api/features/library/routes_test.py

@@ -52,8 +52,6 @@ async def test_get_library_agents_success(
                 output_schema={"type": "object", "properties": {}},
                 credentials_input_schema={"type": "object", "properties": {}},
                 has_external_trigger=False,
-                has_human_in_the_loop=False,
-                has_sensitive_action=False,
                 status=library_model.LibraryAgentStatus.COMPLETED,
                 recommended_schedule_cron=None,
                 new_output=False,
@@ -77,8 +75,6 @@ async def test_get_library_agents_success(
                 output_schema={"type": "object", "properties": {}},
                 credentials_input_schema={"type": "object", "properties": {}},
                 has_external_trigger=False,
-                has_human_in_the_loop=False,
-                has_sensitive_action=False,
                 status=library_model.LibraryAgentStatus.COMPLETED,
                 recommended_schedule_cron=None,
                 new_output=False,
@@ -154,8 +150,6 @@ async def test_get_favorite_library_agents_success(
                 output_schema={"type": "object", "properties": {}},
                 credentials_input_schema={"type": "object", "properties": {}},
                 has_external_trigger=False,
-                has_human_in_the_loop=False,
-                has_sensitive_action=False,
                 status=library_model.LibraryAgentStatus.COMPLETED,
                 recommended_schedule_cron=None,
                 new_output=False,
@@ -224,8 +218,6 @@ def test_add_agent_to_library_success(
         output_schema={"type": "object", "properties": {}},
         credentials_input_schema={"type": "object", "properties": {}},
         has_external_trigger=False,
-        has_human_in_the_loop=False,
-        has_sensitive_action=False,
         status=library_model.LibraryAgentStatus.COMPLETED,
         new_output=False,
         can_access_graph=True,

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

@@ -154,7 +154,6 @@ async def store_content_embedding(
 
         # Upsert the embedding
         # WHERE clause in DO UPDATE prevents PostgreSQL 15 bug with NULLS NOT DISTINCT
-        # Use unqualified ::vector - pgvector is in search_path on all environments
         await execute_raw_with_schema(
             """
             INSERT INTO {schema_prefix}"UnifiedContentEmbedding" (
@@ -178,6 +177,7 @@ async def store_content_embedding(
             searchable_text,
             metadata_json,
             client=client,
+            set_public_search_path=True,
         )
 
         logger.info(f"Stored embedding for {content_type}:{content_id}")
@@ -236,6 +236,7 @@ async def get_content_embedding(
             content_type,
             content_id,
             user_id,
+            set_public_search_path=True,
         )
 
         if result and len(result) > 0:
@@ -870,45 +871,31 @@ async def semantic_search(
         # Add content type parameters and build placeholders dynamically
         content_type_start_idx = len(params) + 1
         content_type_placeholders = ", ".join(
-            "$" + str(content_type_start_idx + i) + '::{schema_prefix}"ContentType"'
+            f'${content_type_start_idx + i}::{{{{schema_prefix}}}}"ContentType"'
             for i in range(len(content_types))
         )
         params.extend([ct.value for ct in content_types])
 
-        # Build min_similarity param index before appending
-        min_similarity_idx = len(params) + 1
-        params.append(min_similarity)
-
-        # Use unqualified ::vector and <=> operator - pgvector is in search_path on all environments
-        sql = (
-            """
+        sql = f"""
             SELECT
                 "contentId" as content_id,
                 "contentType" as content_type,
                 "searchableText" as searchable_text,
                 metadata,
-                1 - (embedding <=> '"""
-            + embedding_str
-            + """'::vector) as similarity
-            FROM {schema_prefix}"UnifiedContentEmbedding"
-            WHERE "contentType" IN ("""
-            + content_type_placeholders
-            + """)
-            """
-            + user_filter
-            + """
-            AND 1 - (embedding <=> '"""
-            + embedding_str
-            + """'::vector) >= $"""
-            + str(min_similarity_idx)
-            + """
+                1 - (embedding <=> '{embedding_str}'::vector) as similarity
+            FROM {{{{schema_prefix}}}}"UnifiedContentEmbedding"
+            WHERE "contentType" IN ({content_type_placeholders})
+            {user_filter}
+            AND 1 - (embedding <=> '{embedding_str}'::vector) >= ${len(params) + 1}
             ORDER BY similarity DESC
             LIMIT $1
         """
-        )
+        params.append(min_similarity)
 
         try:
-            results = await query_raw_with_schema(sql, *params)
+            results = await query_raw_with_schema(
+                sql, *params, set_public_search_path=True
+            )
             return [
                 {
                     "content_id": row["content_id"],
@@ -935,41 +922,31 @@ async def semantic_search(
     # Add content type parameters and build placeholders dynamically
     content_type_start_idx = len(params_lexical) + 1
     content_type_placeholders_lexical = ", ".join(
-        "$" + str(content_type_start_idx + i) + '::{schema_prefix}"ContentType"'
+        f'${content_type_start_idx + i}::{{{{schema_prefix}}}}"ContentType"'
         for i in range(len(content_types))
     )
     params_lexical.extend([ct.value for ct in content_types])
 
-    # Build query param index before appending
-    query_param_idx = len(params_lexical) + 1
-    params_lexical.append(f"%{query}%")
-
-    # Use regular string (not f-string) for template to preserve {schema_prefix} placeholders
-    sql_lexical = (
-        """
+    sql_lexical = f"""
         SELECT
             "contentId" as content_id,
             "contentType" as content_type,
             "searchableText" as searchable_text,
             metadata,
             0.0 as similarity
-        FROM {schema_prefix}"UnifiedContentEmbedding"
-        WHERE "contentType" IN ("""
-        + content_type_placeholders_lexical
-        + """)
-        """
-        + user_filter
-        + """
-        AND "searchableText" ILIKE $"""
-        + str(query_param_idx)
-        + """
+        FROM {{{{schema_prefix}}}}"UnifiedContentEmbedding"
+        WHERE "contentType" IN ({content_type_placeholders_lexical})
+        {user_filter}
+        AND "searchableText" ILIKE ${len(params_lexical) + 1}
         ORDER BY "updatedAt" DESC
         LIMIT $1
     """
-    )
+    params_lexical.append(f"%{query}%")
 
     try:
-        results = await query_raw_with_schema(sql_lexical, *params_lexical)
+        results = await query_raw_with_schema(
+            sql_lexical, *params_lexical, set_public_search_path=True
+        )
         return [
             {
                 "content_id": row["content_id"],

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

@@ -155,14 +155,18 @@ async def test_store_embedding_success(mocker):
     )
 
     assert result is True
-    # execute_raw is called once for INSERT (no separate SET search_path needed)
-    assert mock_client.execute_raw.call_count == 1
+    # execute_raw is called twice: once for SET search_path, once for INSERT
+    assert mock_client.execute_raw.call_count == 2
 
-    # Verify the INSERT query with the actual data
-    call_args = mock_client.execute_raw.call_args_list[0][0]
-    assert "test-version-id" in call_args
-    assert "[0.1,0.2,0.3]" in call_args
-    assert None in call_args  # userId should be None for store agents
+    # First call: SET search_path
+    first_call_args = mock_client.execute_raw.call_args_list[0][0]
+    assert "SET search_path" in first_call_args[0]
+
+    # Second call: INSERT query with the actual data
+    second_call_args = mock_client.execute_raw.call_args_list[1][0]
+    assert "test-version-id" in second_call_args
+    assert "[0.1,0.2,0.3]" in second_call_args
+    assert None in second_call_args  # userId should be None for store agents
 
 
 @pytest.mark.asyncio(loop_scope="session")

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

@@ -12,7 +12,7 @@ from dataclasses import dataclass
 from typing import Any, Literal
 
 from prisma.enums import ContentType
-from rank_bm25 import BM25Okapi  # type: ignore[import-untyped]
+from rank_bm25 import BM25Okapi
 
 from backend.api.features.store.embeddings import (
     EMBEDDING_DIM,
@@ -363,7 +363,9 @@ async def unified_hybrid_search(
         LIMIT {limit_param} OFFSET {offset_param}
     """
 
-    results = await query_raw_with_schema(sql_query, *params)
+    results = await query_raw_with_schema(
+        sql_query, *params, set_public_search_path=True
+    )
 
     total = results[0]["total_count"] if results else 0
     # Apply BM25 reranking
@@ -686,7 +688,9 @@ async def hybrid_search(
         LIMIT {limit_param} OFFSET {offset_param}
     """
 
-    results = await query_raw_with_schema(sql_query, *params)
+    results = await query_raw_with_schema(
+        sql_query, *params, set_public_search_path=True
+    )
 
     total = results[0]["total_count"] if results else 0
 

autogpt_platform/backend/backend/api/features/v1.py

@@ -761,8 +761,10 @@ async def create_new_graph(
     graph.reassign_ids(user_id=user_id, reassign_graph_id=True)
     graph.validate_graph(for_run=False)
 
+    # The return value of the create graph & library function is intentionally not used here,
+    # as the graph already valid and no sub-graphs are returned back.
     await graph_db.create_graph(graph, user_id=user_id)
-    await library_db.create_library_agent(graph, user_id)
+    await library_db.create_library_agent(graph, user_id=user_id)
     activated_graph = await on_graph_activate(graph, user_id=user_id)
 
     if create_graph.source == "builder":
@@ -886,19 +888,21 @@ async def set_graph_active_version(
 async def _update_library_agent_version_and_settings(
     user_id: str, agent_graph: graph_db.GraphModel
 ) -> library_model.LibraryAgent:
+    # Keep the library agent up to date with the new active version
     library = await library_db.update_agent_version_in_library(
         user_id, agent_graph.id, agent_graph.version
     )
-    updated_settings = GraphSettings.from_graph(
-        graph=agent_graph,
-        hitl_safe_mode=library.settings.human_in_the_loop_safe_mode,
-        sensitive_action_safe_mode=library.settings.sensitive_action_safe_mode,
-    )
-    if updated_settings != library.settings:
-        library = await library_db.update_library_agent(
-            library_agent_id=library.id,
+    # If the graph has HITL node, initialize the setting if it's not already set.
+    if (
+        agent_graph.has_human_in_the_loop
+        and library.settings.human_in_the_loop_safe_mode is None
+    ):
+        await library_db.update_library_agent_settings(
             user_id=user_id,
-            settings=updated_settings,
+            agent_id=library.id,
+            settings=library.settings.model_copy(
+                update={"human_in_the_loop_safe_mode": True}
+            ),
         )
     return library
 
@@ -915,18 +919,21 @@ async def update_graph_settings(
     user_id: Annotated[str, Security(get_user_id)],
 ) -> GraphSettings:
     """Update graph settings for the user's library agent."""
+    # Get the library agent for this graph
     library_agent = await library_db.get_library_agent_by_graph_id(
         graph_id=graph_id, user_id=user_id
     )
     if not library_agent:
         raise HTTPException(404, f"Graph #{graph_id} not found in user's library")
 
-    updated_agent = await library_db.update_library_agent(
-        library_agent_id=library_agent.id,
+    # Update the library agent settings
+    updated_agent = await library_db.update_library_agent_settings(
         user_id=user_id,
+        agent_id=library_agent.id,
         settings=settings,
     )
 
+    # Return the updated settings
     return GraphSettings.model_validate(updated_agent.settings)
 
 

autogpt_platform/backend/backend/blocks/ai_shortform_video_block.py

@@ -174,7 +174,7 @@ class AIShortformVideoCreatorBlock(Block):
         )
         frame_rate: int = SchemaField(description="Frame rate of the video", default=60)
         generation_preset: GenerationPreset = SchemaField(
-            description="Generation preset for visual style - only affects AI-generated visuals",
+            description="Generation preset for visual style - only effects AI generated visuals",
             default=GenerationPreset.LEONARDO,
             placeholder=GenerationPreset.LEONARDO,
         )

autogpt_platform/backend/backend/blocks/apollo/models.py

@@ -381,7 +381,7 @@ Each range you add needs to be a string, with the upper and lower numbers of the
     organization_locations: Optional[list[str]] = SchemaField(
         description="""The location of the company headquarters. You can search across cities, US states, and countries.
 
-If a company has several office locations, results are still based on the headquarters location. For example, if you search chicago but a company's HQ location is in boston, any Boston-based companies will not appear in your search results, even if they match other parameters.
+If a company has several office locations, results are still based on the headquarters location. For example, if you search chicago but a company's HQ location is in boston, any Boston-based companies will not appearch in your search results, even if they match other parameters.
 
 To exclude companies based on location, use the organization_not_locations parameter.
 """,

autogpt_platform/backend/backend/blocks/apollo/organization.py

@@ -34,7 +34,7 @@ Each range you add needs to be a string, with the upper and lower numbers of the
         organization_locations: list[str] = SchemaField(
             description="""The location of the company headquarters. You can search across cities, US states, and countries.
 
-If a company has several office locations, results are still based on the headquarters location. For example, if you search chicago but a company's HQ location is in boston, any Boston-based companies will not appear in your search results, even if they match other parameters.
+If a company has several office locations, results are still based on the headquarters location. For example, if you search chicago but a company's HQ location is in boston, any Boston-based companies will not appearch in your search results, even if they match other parameters.
 
 To exclude companies based on location, use the organization_not_locations parameter.
 """,

autogpt_platform/backend/backend/blocks/basic.py

@@ -81,7 +81,7 @@ class StoreValueBlock(Block):
     def __init__(self):
         super().__init__(
             id="1ff065e9-88e8-4358-9d82-8dc91f622ba9",
-            description="A basic block that stores and forwards a value throughout workflows, allowing it to be reused without changes across multiple blocks.",
+            description="This block forwards an input value as output, allowing reuse without change.",
             categories={BlockCategory.BASIC},
             input_schema=StoreValueBlock.Input,
             output_schema=StoreValueBlock.Output,
@@ -111,7 +111,7 @@ class PrintToConsoleBlock(Block):
     def __init__(self):
         super().__init__(
             id="f3b1c1b2-4c4f-4f0d-8d2f-4c4f0d8d2f4c",
-            description="A debugging block that outputs text to the console for monitoring and troubleshooting workflow execution.",
+            description="Print the given text to the console, this is used for a debugging purpose.",
             categories={BlockCategory.BASIC},
             input_schema=PrintToConsoleBlock.Input,
             output_schema=PrintToConsoleBlock.Output,
@@ -137,7 +137,7 @@ class NoteBlock(Block):
     def __init__(self):
         super().__init__(
             id="cc10ff7b-7753-4ff2-9af6-9399b1a7eddc",
-            description="A visual annotation block that displays a sticky note in the workflow editor for documentation and organization purposes.",
+            description="This block is used to display a sticky note with the given text.",
             categories={BlockCategory.BASIC},
             input_schema=NoteBlock.Input,
             output_schema=NoteBlock.Output,

autogpt_platform/backend/backend/blocks/data_manipulation.py

@@ -159,7 +159,7 @@ class FindInDictionaryBlock(Block):
     def __init__(self):
         super().__init__(
             id="0e50422c-6dee-4145-83d6-3a5a392f65de",
-            description="A block that looks up a value in a dictionary, list, or object by key or index and returns the corresponding value.",
+            description="Lookup the given key in the input dictionary/object/list and return the value.",
             input_schema=FindInDictionaryBlock.Input,
             output_schema=FindInDictionaryBlock.Output,
             test_input=[
@@ -680,58 +680,3 @@ class ListIsEmptyBlock(Block):
 
     async def run(self, input_data: Input, **kwargs) -> BlockOutput:
         yield "is_empty", len(input_data.list) == 0
-
-
-class ConcatenateListsBlock(Block):
-    class Input(BlockSchemaInput):
-        lists: List[List[Any]] = SchemaField(
-            description="A list of lists to concatenate together. All lists will be combined in order into a single list.",
-            placeholder="e.g., [[1, 2], [3, 4], [5, 6]]",
-        )
-
-    class Output(BlockSchemaOutput):
-        concatenated_list: List[Any] = SchemaField(
-            description="The concatenated list containing all elements from all input lists in order."
-        )
-        error: str = SchemaField(
-            description="Error message if concatenation failed due to invalid input types."
-        )
-
-    def __init__(self):
-        super().__init__(
-            id="3cf9298b-5817-4141-9d80-7c2cc5199c8e",
-            description="Concatenates multiple lists into a single list. All elements from all input lists are combined in order.",
-            categories={BlockCategory.BASIC},
-            input_schema=ConcatenateListsBlock.Input,
-            output_schema=ConcatenateListsBlock.Output,
-            test_input=[
-                {"lists": [[1, 2, 3], [4, 5, 6]]},
-                {"lists": [["a", "b"], ["c"], ["d", "e", "f"]]},
-                {"lists": [[1, 2], []]},
-                {"lists": []},
-            ],
-            test_output=[
-                ("concatenated_list", [1, 2, 3, 4, 5, 6]),
-                ("concatenated_list", ["a", "b", "c", "d", "e", "f"]),
-                ("concatenated_list", [1, 2]),
-                ("concatenated_list", []),
-            ],
-        )
-
-    async def run(self, input_data: Input, **kwargs) -> BlockOutput:
-        concatenated = []
-        for idx, lst in enumerate(input_data.lists):
-            if lst is None:
-                # Skip None values to avoid errors
-                continue
-            if not isinstance(lst, list):
-                # Type validation: each item must be a list
-                # Strings are iterable and would cause extend() to iterate character-by-character
-                # Non-iterable types would raise TypeError
-                yield "error", (
-                    f"Invalid input at index {idx}: expected a list, got {type(lst).__name__}. "
-                    f"All items in 'lists' must be lists (e.g., [[1, 2], [3, 4]])."
-                )
-                return
-            concatenated.extend(lst)
-        yield "concatenated_list", concatenated

autogpt_platform/backend/backend/blocks/github/issues.py

@@ -51,7 +51,7 @@ class GithubCommentBlock(Block):
     def __init__(self):
         super().__init__(
             id="a8db4d8d-db1c-4a25-a1b0-416a8c33602b",
-            description="A block that posts comments on GitHub issues or pull requests using the GitHub API.",
+            description="This block posts a comment on a specified GitHub issue or pull request.",
             categories={BlockCategory.DEVELOPER_TOOLS},
             input_schema=GithubCommentBlock.Input,
             output_schema=GithubCommentBlock.Output,
@@ -151,7 +151,7 @@ class GithubUpdateCommentBlock(Block):
     def __init__(self):
         super().__init__(
             id="b3f4d747-10e3-4e69-8c51-f2be1d99c9a7",
-            description="A block that updates an existing comment on a GitHub issue or pull request.",
+            description="This block updates a comment on a specified GitHub issue or pull request.",
             categories={BlockCategory.DEVELOPER_TOOLS},
             input_schema=GithubUpdateCommentBlock.Input,
             output_schema=GithubUpdateCommentBlock.Output,
@@ -249,7 +249,7 @@ class GithubListCommentsBlock(Block):
     def __init__(self):
         super().__init__(
             id="c4b5fb63-0005-4a11-b35a-0c2467bd6b59",
-            description="A block that retrieves all comments from a GitHub issue or pull request, including comment metadata and content.",
+            description="This block lists all comments for a specified GitHub issue or pull request.",
             categories={BlockCategory.DEVELOPER_TOOLS},
             input_schema=GithubListCommentsBlock.Input,
             output_schema=GithubListCommentsBlock.Output,
@@ -363,7 +363,7 @@ class GithubMakeIssueBlock(Block):
     def __init__(self):
         super().__init__(
             id="691dad47-f494-44c3-a1e8-05b7990f2dab",
-            description="A block that creates new issues on GitHub repositories with a title and body content.",
+            description="This block creates a new issue on a specified GitHub repository.",
             categories={BlockCategory.DEVELOPER_TOOLS},
             input_schema=GithubMakeIssueBlock.Input,
             output_schema=GithubMakeIssueBlock.Output,
@@ -433,7 +433,7 @@ class GithubReadIssueBlock(Block):
     def __init__(self):
         super().__init__(
             id="6443c75d-032a-4772-9c08-230c707c8acc",
-            description="A block that retrieves information about a specific GitHub issue, including its title, body content, and creator.",
+            description="This block reads the body, title, and user of a specified GitHub issue.",
             categories={BlockCategory.DEVELOPER_TOOLS},
             input_schema=GithubReadIssueBlock.Input,
             output_schema=GithubReadIssueBlock.Output,
@@ -510,7 +510,7 @@ class GithubListIssuesBlock(Block):
     def __init__(self):
         super().__init__(
             id="c215bfd7-0e57-4573-8f8c-f7d4963dcd74",
-            description="A block that retrieves a list of issues from a GitHub repository with their titles and URLs.",
+            description="This block lists all issues for a specified GitHub repository.",
             categories={BlockCategory.DEVELOPER_TOOLS},
             input_schema=GithubListIssuesBlock.Input,
             output_schema=GithubListIssuesBlock.Output,
@@ -597,7 +597,7 @@ class GithubAddLabelBlock(Block):
     def __init__(self):
         super().__init__(
             id="98bd6b77-9506-43d5-b669-6b9733c4b1f1",
-            description="A block that adds a label to a GitHub issue or pull request for categorization and organization.",
+            description="This block adds a label to a specified GitHub issue or pull request.",
             categories={BlockCategory.DEVELOPER_TOOLS},
             input_schema=GithubAddLabelBlock.Input,
             output_schema=GithubAddLabelBlock.Output,
@@ -657,7 +657,7 @@ class GithubRemoveLabelBlock(Block):
     def __init__(self):
         super().__init__(
             id="78f050c5-3e3a-48c0-9e5b-ef1ceca5589c",
-            description="A block that removes a label from a GitHub issue or pull request.",
+            description="This block removes a label from a specified GitHub issue or pull request.",
             categories={BlockCategory.DEVELOPER_TOOLS},
             input_schema=GithubRemoveLabelBlock.Input,
             output_schema=GithubRemoveLabelBlock.Output,
@@ -720,7 +720,7 @@ class GithubAssignIssueBlock(Block):
     def __init__(self):
         super().__init__(
             id="90507c72-b0ff-413a-886a-23bbbd66f542",
-            description="A block that assigns a GitHub user to an issue for task ownership and tracking.",
+            description="This block assigns a user to a specified GitHub issue.",
             categories={BlockCategory.DEVELOPER_TOOLS},
             input_schema=GithubAssignIssueBlock.Input,
             output_schema=GithubAssignIssueBlock.Output,
@@ -786,7 +786,7 @@ class GithubUnassignIssueBlock(Block):
     def __init__(self):
         super().__init__(
             id="d154002a-38f4-46c2-962d-2488f2b05ece",
-            description="A block that removes a user's assignment from a GitHub issue.",
+            description="This block unassigns a user from a specified GitHub issue.",
             categories={BlockCategory.DEVELOPER_TOOLS},
             input_schema=GithubUnassignIssueBlock.Input,
             output_schema=GithubUnassignIssueBlock.Output,

autogpt_platform/backend/backend/blocks/google/gmail.py

@@ -353,7 +353,7 @@ class GmailReadBlock(GmailBase):
     def __init__(self):
         super().__init__(
             id="25310c70-b89b-43ba-b25c-4dfa7e2a481c",
-            description="A block that retrieves and reads emails from a Gmail account based on search criteria, returning detailed message information including subject, sender, body, and attachments.",
+            description="This block reads emails from Gmail.",
             categories={BlockCategory.COMMUNICATION},
             disabled=not GOOGLE_OAUTH_IS_CONFIGURED,
             input_schema=GmailReadBlock.Input,
@@ -743,7 +743,7 @@ class GmailListLabelsBlock(GmailBase):
     def __init__(self):
         super().__init__(
             id="3e1c2c1c-c689-4520-b956-1f3bf4e02bb7",
-            description="A block that retrieves all labels (categories) from a Gmail account for organizing and categorizing emails.",
+            description="This block lists all labels in Gmail.",
             categories={BlockCategory.COMMUNICATION},
             input_schema=GmailListLabelsBlock.Input,
             output_schema=GmailListLabelsBlock.Output,
@@ -807,7 +807,7 @@ class GmailAddLabelBlock(GmailBase):
     def __init__(self):
         super().__init__(
             id="f884b2fb-04f4-4265-9658-14f433926ac9",
-            description="A block that adds a label to a specific email message in Gmail, creating the label if it doesn't exist.",
+            description="This block adds a label to a Gmail message.",
             categories={BlockCategory.COMMUNICATION},
             input_schema=GmailAddLabelBlock.Input,
             output_schema=GmailAddLabelBlock.Output,
@@ -893,7 +893,7 @@ class GmailRemoveLabelBlock(GmailBase):
     def __init__(self):
         super().__init__(
             id="0afc0526-aba1-4b2b-888e-a22b7c3f359d",
-            description="A block that removes a label from a specific email message in a Gmail account.",
+            description="This block removes a label from a Gmail message.",
             categories={BlockCategory.COMMUNICATION},
             input_schema=GmailRemoveLabelBlock.Input,
             output_schema=GmailRemoveLabelBlock.Output,
@@ -961,7 +961,7 @@ class GmailGetThreadBlock(GmailBase):
     def __init__(self):
         super().__init__(
             id="21a79166-9df7-4b5f-9f36-96f639d86112",
-            description="A block that retrieves an entire Gmail thread (email conversation) by ID, returning all messages with decoded bodies for reading complete conversations.",
+            description="Get a full Gmail thread by ID",
             categories={BlockCategory.COMMUNICATION},
             input_schema=GmailGetThreadBlock.Input,
             output_schema=GmailGetThreadBlock.Output,

autogpt_platform/backend/backend/blocks/google/sheets.py

@@ -282,7 +282,7 @@ class GoogleSheetsReadBlock(Block):
     def __init__(self):
         super().__init__(
             id="5724e902-3635-47e9-a108-aaa0263a4988",
-            description="A block that reads data from a Google Sheets spreadsheet using A1 notation range selection.",
+            description="This block reads data from a Google Sheets spreadsheet.",
             categories={BlockCategory.DATA},
             input_schema=GoogleSheetsReadBlock.Input,
             output_schema=GoogleSheetsReadBlock.Output,
@@ -409,7 +409,7 @@ class GoogleSheetsWriteBlock(Block):
     def __init__(self):
         super().__init__(
             id="d9291e87-301d-47a8-91fe-907fb55460e5",
-            description="A block that writes data to a Google Sheets spreadsheet at a specified A1 notation range.",
+            description="This block writes data to a Google Sheets spreadsheet.",
             categories={BlockCategory.DATA},
             input_schema=GoogleSheetsWriteBlock.Input,
             output_schema=GoogleSheetsWriteBlock.Output,

autogpt_platform/backend/backend/blocks/helpers/review.py

@@ -84,7 +84,7 @@ class HITLReviewHelper:
             Exception: If review creation or status update fails
         """
         # Skip review if safe mode is disabled - return auto-approved result
-        if not execution_context.human_in_the_loop_safe_mode:
+        if not execution_context.safe_mode:
             logger.info(
                 f"Block {block_name} skipping review for node {node_exec_id} - safe mode disabled"
             )

autogpt_platform/backend/backend/blocks/human_in_the_loop.py

@@ -104,7 +104,7 @@ class HumanInTheLoopBlock(Block):
         execution_context: ExecutionContext,
         **_kwargs,
     ) -> BlockOutput:
-        if not execution_context.human_in_the_loop_safe_mode:
+        if not execution_context.safe_mode:
             logger.info(
                 f"HITL block skipping review for node {node_exec_id} - safe mode disabled"
             )

autogpt_platform/backend/backend/blocks/io.py

@@ -76,7 +76,7 @@ class AgentInputBlock(Block):
         super().__init__(
             **{
                 "id": "c0a8e994-ebf1-4a9c-a4d8-89d09c86741b",
-                "description": "A block that accepts and processes user input values within a workflow, supporting various input types and validation.",
+                "description": "Base block for user inputs.",
                 "input_schema": AgentInputBlock.Input,
                 "output_schema": AgentInputBlock.Output,
                 "test_input": [
@@ -168,7 +168,7 @@ class AgentOutputBlock(Block):
     def __init__(self):
         super().__init__(
             id="363ae599-353e-4804-937e-b2ee3cef3da4",
-            description="A block that records and formats workflow results for display to users, with optional Jinja2 template formatting support.",
+            description="Stores the output of the graph for users to see.",
             input_schema=AgentOutputBlock.Input,
             output_schema=AgentOutputBlock.Output,
             test_input=[

autogpt_platform/backend/backend/blocks/llm.py

@@ -79,10 +79,6 @@ class ModelMetadata(NamedTuple):
     provider: str
     context_window: int
     max_output_tokens: int | None
-    display_name: str
-    provider_name: str
-    creator_name: str
-    price_tier: Literal[1, 2, 3]
 
 
 class LlmModelMeta(EnumMeta):
@@ -175,26 +171,6 @@ class LlmModel(str, Enum, metaclass=LlmModelMeta):
     V0_1_5_LG = "v0-1.5-lg"
     V0_1_0_MD = "v0-1.0-md"
 
-    @classmethod
-    def __get_pydantic_json_schema__(cls, schema, handler):
-        json_schema = handler(schema)
-        llm_model_metadata = {}
-        for model in cls:
-            model_name = model.value
-            metadata = model.metadata
-            llm_model_metadata[model_name] = {
-                "creator": metadata.creator_name,
-                "creator_name": metadata.creator_name,
-                "title": metadata.display_name,
-                "provider": metadata.provider,
-                "provider_name": metadata.provider_name,
-                "name": model_name,
-                "price_tier": metadata.price_tier,
-            }
-        json_schema["llm_model"] = True
-        json_schema["llm_model_metadata"] = llm_model_metadata
-        return json_schema
-
     @property
     def metadata(self) -> ModelMetadata:
         return MODEL_METADATA[self]
@@ -214,291 +190,119 @@ class LlmModel(str, Enum, metaclass=LlmModelMeta):
 
 MODEL_METADATA = {
     # https://platform.openai.com/docs/models
-    LlmModel.O3: ModelMetadata("openai", 200000, 100000, "O3", "OpenAI", "OpenAI", 2),
-    LlmModel.O3_MINI: ModelMetadata(
-        "openai", 200000, 100000, "O3 Mini", "OpenAI", "OpenAI", 1
-    ),  # o3-mini-2025-01-31
-    LlmModel.O1: ModelMetadata(
-        "openai", 200000, 100000, "O1", "OpenAI", "OpenAI", 3
-    ),  # o1-2024-12-17
-    LlmModel.O1_MINI: ModelMetadata(
-        "openai", 128000, 65536, "O1 Mini", "OpenAI", "OpenAI", 2
-    ),  # o1-mini-2024-09-12
+    LlmModel.O3: ModelMetadata("openai", 200000, 100000),
+    LlmModel.O3_MINI: ModelMetadata("openai", 200000, 100000),  # o3-mini-2025-01-31
+    LlmModel.O1: ModelMetadata("openai", 200000, 100000),  # o1-2024-12-17
+    LlmModel.O1_MINI: ModelMetadata("openai", 128000, 65536),  # o1-mini-2024-09-12
     # GPT-5 models
-    LlmModel.GPT5_2: ModelMetadata(
-        "openai", 400000, 128000, "GPT-5.2", "OpenAI", "OpenAI", 3
-    ),
-    LlmModel.GPT5_1: ModelMetadata(
-        "openai", 400000, 128000, "GPT-5.1", "OpenAI", "OpenAI", 2
-    ),
-    LlmModel.GPT5: ModelMetadata(
-        "openai", 400000, 128000, "GPT-5", "OpenAI", "OpenAI", 1
-    ),
-    LlmModel.GPT5_MINI: ModelMetadata(
-        "openai", 400000, 128000, "GPT-5 Mini", "OpenAI", "OpenAI", 1
-    ),
-    LlmModel.GPT5_NANO: ModelMetadata(
-        "openai", 400000, 128000, "GPT-5 Nano", "OpenAI", "OpenAI", 1
-    ),
-    LlmModel.GPT5_CHAT: ModelMetadata(
-        "openai", 400000, 16384, "GPT-5 Chat Latest", "OpenAI", "OpenAI", 2
-    ),
-    LlmModel.GPT41: ModelMetadata(
-        "openai", 1047576, 32768, "GPT-4.1", "OpenAI", "OpenAI", 1
-    ),
-    LlmModel.GPT41_MINI: ModelMetadata(
-        "openai", 1047576, 32768, "GPT-4.1 Mini", "OpenAI", "OpenAI", 1
-    ),
+    LlmModel.GPT5_2: ModelMetadata("openai", 400000, 128000),
+    LlmModel.GPT5_1: ModelMetadata("openai", 400000, 128000),
+    LlmModel.GPT5: ModelMetadata("openai", 400000, 128000),
+    LlmModel.GPT5_MINI: ModelMetadata("openai", 400000, 128000),
+    LlmModel.GPT5_NANO: ModelMetadata("openai", 400000, 128000),
+    LlmModel.GPT5_CHAT: ModelMetadata("openai", 400000, 16384),
+    LlmModel.GPT41: ModelMetadata("openai", 1047576, 32768),
+    LlmModel.GPT41_MINI: ModelMetadata("openai", 1047576, 32768),
     LlmModel.GPT4O_MINI: ModelMetadata(
-        "openai", 128000, 16384, "GPT-4o Mini", "OpenAI", "OpenAI", 1
+        "openai", 128000, 16384
     ),  # gpt-4o-mini-2024-07-18
-    LlmModel.GPT4O: ModelMetadata(
-        "openai", 128000, 16384, "GPT-4o", "OpenAI", "OpenAI", 2
-    ),  # gpt-4o-2024-08-06
+    LlmModel.GPT4O: ModelMetadata("openai", 128000, 16384),  # gpt-4o-2024-08-06
     LlmModel.GPT4_TURBO: ModelMetadata(
-        "openai", 128000, 4096, "GPT-4 Turbo", "OpenAI", "OpenAI", 3
+        "openai", 128000, 4096
     ),  # gpt-4-turbo-2024-04-09
-    LlmModel.GPT3_5_TURBO: ModelMetadata(
-        "openai", 16385, 4096, "GPT-3.5 Turbo", "OpenAI", "OpenAI", 1
-    ),  # gpt-3.5-turbo-0125
+    LlmModel.GPT3_5_TURBO: ModelMetadata("openai", 16385, 4096),  # gpt-3.5-turbo-0125
     # https://docs.anthropic.com/en/docs/about-claude/models
     LlmModel.CLAUDE_4_1_OPUS: ModelMetadata(
-        "anthropic", 200000, 32000, "Claude Opus 4.1", "Anthropic", "Anthropic", 3
+        "anthropic", 200000, 32000
     ),  # claude-opus-4-1-20250805
     LlmModel.CLAUDE_4_OPUS: ModelMetadata(
-        "anthropic", 200000, 32000, "Claude Opus 4", "Anthropic", "Anthropic", 3
+        "anthropic", 200000, 32000
     ),  # claude-4-opus-20250514
     LlmModel.CLAUDE_4_SONNET: ModelMetadata(
-        "anthropic", 200000, 64000, "Claude Sonnet 4", "Anthropic", "Anthropic", 2
+        "anthropic", 200000, 64000
     ),  # claude-4-sonnet-20250514
     LlmModel.CLAUDE_4_5_OPUS: ModelMetadata(
-        "anthropic", 200000, 64000, "Claude Opus 4.5", "Anthropic", "Anthropic", 3
+        "anthropic", 200000, 64000
     ),  # claude-opus-4-5-20251101
     LlmModel.CLAUDE_4_5_SONNET: ModelMetadata(
-        "anthropic", 200000, 64000, "Claude Sonnet 4.5", "Anthropic", "Anthropic", 3
+        "anthropic", 200000, 64000
     ),  # claude-sonnet-4-5-20250929
     LlmModel.CLAUDE_4_5_HAIKU: ModelMetadata(
-        "anthropic", 200000, 64000, "Claude Haiku 4.5", "Anthropic", "Anthropic", 2
+        "anthropic", 200000, 64000
     ),  # claude-haiku-4-5-20251001
     LlmModel.CLAUDE_3_7_SONNET: ModelMetadata(
-        "anthropic", 200000, 64000, "Claude 3.7 Sonnet", "Anthropic", "Anthropic", 2
+        "anthropic", 200000, 64000
     ),  # claude-3-7-sonnet-20250219
     LlmModel.CLAUDE_3_HAIKU: ModelMetadata(
-        "anthropic", 200000, 4096, "Claude 3 Haiku", "Anthropic", "Anthropic", 1
+        "anthropic", 200000, 4096
     ),  # claude-3-haiku-20240307
     # https://docs.aimlapi.com/api-overview/model-database/text-models
-    LlmModel.AIML_API_QWEN2_5_72B: ModelMetadata(
-        "aiml_api", 32000, 8000, "Qwen 2.5 72B Instruct Turbo", "AI/ML", "Qwen", 1
-    ),
-    LlmModel.AIML_API_LLAMA3_1_70B: ModelMetadata(
-        "aiml_api",
-        128000,
-        40000,
-        "Llama 3.1 Nemotron 70B Instruct",
-        "AI/ML",
-        "Nvidia",
-        1,
-    ),
-    LlmModel.AIML_API_LLAMA3_3_70B: ModelMetadata(
-        "aiml_api", 128000, None, "Llama 3.3 70B Instruct Turbo", "AI/ML", "Meta", 1
-    ),
-    LlmModel.AIML_API_META_LLAMA_3_1_70B: ModelMetadata(
-        "aiml_api", 131000, 2000, "Llama 3.1 70B Instruct Turbo", "AI/ML", "Meta", 1
-    ),
-    LlmModel.AIML_API_LLAMA_3_2_3B: ModelMetadata(
-        "aiml_api", 128000, None, "Llama 3.2 3B Instruct Turbo", "AI/ML", "Meta", 1
-    ),
+    LlmModel.AIML_API_QWEN2_5_72B: ModelMetadata("aiml_api", 32000, 8000),
+    LlmModel.AIML_API_LLAMA3_1_70B: ModelMetadata("aiml_api", 128000, 40000),
+    LlmModel.AIML_API_LLAMA3_3_70B: ModelMetadata("aiml_api", 128000, None),
+    LlmModel.AIML_API_META_LLAMA_3_1_70B: ModelMetadata("aiml_api", 131000, 2000),
+    LlmModel.AIML_API_LLAMA_3_2_3B: ModelMetadata("aiml_api", 128000, None),
     # https://console.groq.com/docs/models
-    LlmModel.LLAMA3_3_70B: ModelMetadata(
-        "groq", 128000, 32768, "Llama 3.3 70B Versatile", "Groq", "Meta", 1
-    ),
-    LlmModel.LLAMA3_1_8B: ModelMetadata(
-        "groq", 128000, 8192, "Llama 3.1 8B Instant", "Groq", "Meta", 1
-    ),
+    LlmModel.LLAMA3_3_70B: ModelMetadata("groq", 128000, 32768),
+    LlmModel.LLAMA3_1_8B: ModelMetadata("groq", 128000, 8192),
     # https://ollama.com/library
-    LlmModel.OLLAMA_LLAMA3_3: ModelMetadata(
-        "ollama", 8192, None, "Llama 3.3", "Ollama", "Meta", 1
-    ),
-    LlmModel.OLLAMA_LLAMA3_2: ModelMetadata(
-        "ollama", 8192, None, "Llama 3.2", "Ollama", "Meta", 1
-    ),
-    LlmModel.OLLAMA_LLAMA3_8B: ModelMetadata(
-        "ollama", 8192, None, "Llama 3", "Ollama", "Meta", 1
-    ),
-    LlmModel.OLLAMA_LLAMA3_405B: ModelMetadata(
-        "ollama", 8192, None, "Llama 3.1 405B", "Ollama", "Meta", 1
-    ),
-    LlmModel.OLLAMA_DOLPHIN: ModelMetadata(
-        "ollama", 32768, None, "Dolphin Mistral Latest", "Ollama", "Mistral AI", 1
-    ),
+    LlmModel.OLLAMA_LLAMA3_3: ModelMetadata("ollama", 8192, None),
+    LlmModel.OLLAMA_LLAMA3_2: ModelMetadata("ollama", 8192, None),
+    LlmModel.OLLAMA_LLAMA3_8B: ModelMetadata("ollama", 8192, None),
+    LlmModel.OLLAMA_LLAMA3_405B: ModelMetadata("ollama", 8192, None),
+    LlmModel.OLLAMA_DOLPHIN: ModelMetadata("ollama", 32768, None),
     # https://openrouter.ai/models
-    LlmModel.GEMINI_2_5_PRO: ModelMetadata(
-        "open_router",
-        1050000,
-        8192,
-        "Gemini 2.5 Pro Preview 03.25",
-        "OpenRouter",
-        "Google",
-        2,
-    ),
-    LlmModel.GEMINI_3_PRO_PREVIEW: ModelMetadata(
-        "open_router", 1048576, 65535, "Gemini 3 Pro Preview", "OpenRouter", "Google", 2
-    ),
-    LlmModel.GEMINI_2_5_FLASH: ModelMetadata(
-        "open_router", 1048576, 65535, "Gemini 2.5 Flash", "OpenRouter", "Google", 1
-    ),
-    LlmModel.GEMINI_2_0_FLASH: ModelMetadata(
-        "open_router", 1048576, 8192, "Gemini 2.0 Flash 001", "OpenRouter", "Google", 1
-    ),
+    LlmModel.GEMINI_2_5_PRO: ModelMetadata("open_router", 1050000, 8192),
+    LlmModel.GEMINI_3_PRO_PREVIEW: ModelMetadata("open_router", 1048576, 65535),
+    LlmModel.GEMINI_2_5_FLASH: ModelMetadata("open_router", 1048576, 65535),
+    LlmModel.GEMINI_2_0_FLASH: ModelMetadata("open_router", 1048576, 8192),
     LlmModel.GEMINI_2_5_FLASH_LITE_PREVIEW: ModelMetadata(
-        "open_router",
-        1048576,
-        65535,
-        "Gemini 2.5 Flash Lite Preview 06.17",
-        "OpenRouter",
-        "Google",
-        1,
-    ),
-    LlmModel.GEMINI_2_0_FLASH_LITE: ModelMetadata(
-        "open_router",
-        1048576,
-        8192,
-        "Gemini 2.0 Flash Lite 001",
-        "OpenRouter",
-        "Google",
-        1,
-    ),
-    LlmModel.MISTRAL_NEMO: ModelMetadata(
-        "open_router", 128000, 4096, "Mistral Nemo", "OpenRouter", "Mistral AI", 1
-    ),
-    LlmModel.COHERE_COMMAND_R_08_2024: ModelMetadata(
-        "open_router", 128000, 4096, "Command R 08.2024", "OpenRouter", "Cohere", 1
-    ),
-    LlmModel.COHERE_COMMAND_R_PLUS_08_2024: ModelMetadata(
-        "open_router", 128000, 4096, "Command R Plus 08.2024", "OpenRouter", "Cohere", 2
-    ),
-    LlmModel.DEEPSEEK_CHAT: ModelMetadata(
-        "open_router", 64000, 2048, "DeepSeek Chat", "OpenRouter", "DeepSeek", 1
-    ),
-    LlmModel.DEEPSEEK_R1_0528: ModelMetadata(
-        "open_router", 163840, 163840, "DeepSeek R1 0528", "OpenRouter", "DeepSeek", 1
-    ),
-    LlmModel.PERPLEXITY_SONAR: ModelMetadata(
-        "open_router", 127000, 8000, "Sonar", "OpenRouter", "Perplexity", 1
-    ),
-    LlmModel.PERPLEXITY_SONAR_PRO: ModelMetadata(
-        "open_router", 200000, 8000, "Sonar Pro", "OpenRouter", "Perplexity", 2
+        "open_router", 1048576, 65535
     ),
+    LlmModel.GEMINI_2_0_FLASH_LITE: ModelMetadata("open_router", 1048576, 8192),
+    LlmModel.MISTRAL_NEMO: ModelMetadata("open_router", 128000, 4096),
+    LlmModel.COHERE_COMMAND_R_08_2024: ModelMetadata("open_router", 128000, 4096),
+    LlmModel.COHERE_COMMAND_R_PLUS_08_2024: ModelMetadata("open_router", 128000, 4096),
+    LlmModel.DEEPSEEK_CHAT: ModelMetadata("open_router", 64000, 2048),
+    LlmModel.DEEPSEEK_R1_0528: ModelMetadata("open_router", 163840, 163840),
+    LlmModel.PERPLEXITY_SONAR: ModelMetadata("open_router", 127000, 8000),
+    LlmModel.PERPLEXITY_SONAR_PRO: ModelMetadata("open_router", 200000, 8000),
     LlmModel.PERPLEXITY_SONAR_DEEP_RESEARCH: ModelMetadata(
         "open_router",
         128000,
         16000,
-        "Sonar Deep Research",
-        "OpenRouter",
-        "Perplexity",
-        3,
     ),
     LlmModel.NOUSRESEARCH_HERMES_3_LLAMA_3_1_405B: ModelMetadata(
-        "open_router",
-        131000,
-        4096,
-        "Hermes 3 Llama 3.1 405B",
-        "OpenRouter",
-        "Nous Research",
-        1,
+        "open_router", 131000, 4096
     ),
     LlmModel.NOUSRESEARCH_HERMES_3_LLAMA_3_1_70B: ModelMetadata(
-        "open_router",
-        12288,
-        12288,
-        "Hermes 3 Llama 3.1 70B",
-        "OpenRouter",
-        "Nous Research",
-        1,
-    ),
-    LlmModel.OPENAI_GPT_OSS_120B: ModelMetadata(
-        "open_router", 131072, 131072, "GPT-OSS 120B", "OpenRouter", "OpenAI", 1
-    ),
-    LlmModel.OPENAI_GPT_OSS_20B: ModelMetadata(
-        "open_router", 131072, 32768, "GPT-OSS 20B", "OpenRouter", "OpenAI", 1
-    ),
-    LlmModel.AMAZON_NOVA_LITE_V1: ModelMetadata(
-        "open_router", 300000, 5120, "Nova Lite V1", "OpenRouter", "Amazon", 1
-    ),
-    LlmModel.AMAZON_NOVA_MICRO_V1: ModelMetadata(
-        "open_router", 128000, 5120, "Nova Micro V1", "OpenRouter", "Amazon", 1
-    ),
-    LlmModel.AMAZON_NOVA_PRO_V1: ModelMetadata(
-        "open_router", 300000, 5120, "Nova Pro V1", "OpenRouter", "Amazon", 1
-    ),
-    LlmModel.MICROSOFT_WIZARDLM_2_8X22B: ModelMetadata(
-        "open_router", 65536, 4096, "WizardLM 2 8x22B", "OpenRouter", "Microsoft", 1
-    ),
-    LlmModel.GRYPHE_MYTHOMAX_L2_13B: ModelMetadata(
-        "open_router", 4096, 4096, "MythoMax L2 13B", "OpenRouter", "Gryphe", 1
-    ),
-    LlmModel.META_LLAMA_4_SCOUT: ModelMetadata(
-        "open_router", 131072, 131072, "Llama 4 Scout", "OpenRouter", "Meta", 1
-    ),
-    LlmModel.META_LLAMA_4_MAVERICK: ModelMetadata(
-        "open_router", 1048576, 1000000, "Llama 4 Maverick", "OpenRouter", "Meta", 1
-    ),
-    LlmModel.GROK_4: ModelMetadata(
-        "open_router", 256000, 256000, "Grok 4", "OpenRouter", "xAI", 3
-    ),
-    LlmModel.GROK_4_FAST: ModelMetadata(
-        "open_router", 2000000, 30000, "Grok 4 Fast", "OpenRouter", "xAI", 1
-    ),
-    LlmModel.GROK_4_1_FAST: ModelMetadata(
-        "open_router", 2000000, 30000, "Grok 4.1 Fast", "OpenRouter", "xAI", 1
-    ),
-    LlmModel.GROK_CODE_FAST_1: ModelMetadata(
-        "open_router", 256000, 10000, "Grok Code Fast 1", "OpenRouter", "xAI", 1
-    ),
-    LlmModel.KIMI_K2: ModelMetadata(
-        "open_router", 131000, 131000, "Kimi K2", "OpenRouter", "Moonshot AI", 1
-    ),
-    LlmModel.QWEN3_235B_A22B_THINKING: ModelMetadata(
-        "open_router",
-        262144,
-        262144,
-        "Qwen 3 235B A22B Thinking 2507",
-        "OpenRouter",
-        "Qwen",
-        1,
-    ),
-    LlmModel.QWEN3_CODER: ModelMetadata(
-        "open_router", 262144, 262144, "Qwen 3 Coder", "OpenRouter", "Qwen", 3
+        "open_router", 12288, 12288
     ),
+    LlmModel.OPENAI_GPT_OSS_120B: ModelMetadata("open_router", 131072, 131072),
+    LlmModel.OPENAI_GPT_OSS_20B: ModelMetadata("open_router", 131072, 32768),
+    LlmModel.AMAZON_NOVA_LITE_V1: ModelMetadata("open_router", 300000, 5120),
+    LlmModel.AMAZON_NOVA_MICRO_V1: ModelMetadata("open_router", 128000, 5120),
+    LlmModel.AMAZON_NOVA_PRO_V1: ModelMetadata("open_router", 300000, 5120),
+    LlmModel.MICROSOFT_WIZARDLM_2_8X22B: ModelMetadata("open_router", 65536, 4096),
+    LlmModel.GRYPHE_MYTHOMAX_L2_13B: ModelMetadata("open_router", 4096, 4096),
+    LlmModel.META_LLAMA_4_SCOUT: ModelMetadata("open_router", 131072, 131072),
+    LlmModel.META_LLAMA_4_MAVERICK: ModelMetadata("open_router", 1048576, 1000000),
+    LlmModel.GROK_4: ModelMetadata("open_router", 256000, 256000),
+    LlmModel.GROK_4_FAST: ModelMetadata("open_router", 2000000, 30000),
+    LlmModel.GROK_4_1_FAST: ModelMetadata("open_router", 2000000, 30000),
+    LlmModel.GROK_CODE_FAST_1: ModelMetadata("open_router", 256000, 10000),
+    LlmModel.KIMI_K2: ModelMetadata("open_router", 131000, 131000),
+    LlmModel.QWEN3_235B_A22B_THINKING: ModelMetadata("open_router", 262144, 262144),
+    LlmModel.QWEN3_CODER: ModelMetadata("open_router", 262144, 262144),
     # Llama API models
-    LlmModel.LLAMA_API_LLAMA_4_SCOUT: ModelMetadata(
-        "llama_api",
-        128000,
-        4028,
-        "Llama 4 Scout 17B 16E Instruct FP8",
-        "Llama API",
-        "Meta",
-        1,
-    ),
-    LlmModel.LLAMA_API_LLAMA4_MAVERICK: ModelMetadata(
-        "llama_api",
-        128000,
-        4028,
-        "Llama 4 Maverick 17B 128E Instruct FP8",
-        "Llama API",
-        "Meta",
-        1,
-    ),
-    LlmModel.LLAMA_API_LLAMA3_3_8B: ModelMetadata(
-        "llama_api", 128000, 4028, "Llama 3.3 8B Instruct", "Llama API", "Meta", 1
-    ),
-    LlmModel.LLAMA_API_LLAMA3_3_70B: ModelMetadata(
-        "llama_api", 128000, 4028, "Llama 3.3 70B Instruct", "Llama API", "Meta", 1
-    ),
+    LlmModel.LLAMA_API_LLAMA_4_SCOUT: ModelMetadata("llama_api", 128000, 4028),
+    LlmModel.LLAMA_API_LLAMA4_MAVERICK: ModelMetadata("llama_api", 128000, 4028),
+    LlmModel.LLAMA_API_LLAMA3_3_8B: ModelMetadata("llama_api", 128000, 4028),
+    LlmModel.LLAMA_API_LLAMA3_3_70B: ModelMetadata("llama_api", 128000, 4028),
     # v0 by Vercel models
-    LlmModel.V0_1_5_MD: ModelMetadata("v0", 128000, 64000, "v0 1.5 MD", "V0", "V0", 1),
-    LlmModel.V0_1_5_LG: ModelMetadata("v0", 512000, 64000, "v0 1.5 LG", "V0", "V0", 1),
-    LlmModel.V0_1_0_MD: ModelMetadata("v0", 128000, 64000, "v0 1.0 MD", "V0", "V0", 1),
+    LlmModel.V0_1_5_MD: ModelMetadata("v0", 128000, 64000),
+    LlmModel.V0_1_5_LG: ModelMetadata("v0", 512000, 64000),
+    LlmModel.V0_1_0_MD: ModelMetadata("v0", 128000, 64000),
 }
 
 DEFAULT_LLM_MODEL = LlmModel.GPT5_2
@@ -1050,7 +854,7 @@ class AIStructuredResponseGeneratorBlock(AIBlockBase):
     def __init__(self):
         super().__init__(
             id="ed55ac19-356e-4243-a6cb-bc599e9b716f",
-            description="A block that generates structured JSON responses using a Large Language Model (LLM), with schema validation and format enforcement.",
+            description="Call a Large Language Model (LLM) to generate formatted object based on the given prompt.",
             categories={BlockCategory.AI},
             input_schema=AIStructuredResponseGeneratorBlock.Input,
             output_schema=AIStructuredResponseGeneratorBlock.Output,
@@ -1461,7 +1265,7 @@ class AITextGeneratorBlock(AIBlockBase):
     def __init__(self):
         super().__init__(
             id="1f292d4a-41a4-4977-9684-7c8d560b9f91",
-            description="A block that produces text responses using a Large Language Model (LLM) based on customizable prompts and system instructions.",
+            description="Call a Large Language Model (LLM) to generate a string based on the given prompt.",
             categories={BlockCategory.AI},
             input_schema=AITextGeneratorBlock.Input,
             output_schema=AITextGeneratorBlock.Output,
@@ -1557,7 +1361,7 @@ class AITextSummarizerBlock(AIBlockBase):
     def __init__(self):
         super().__init__(
             id="a0a69be1-4528-491c-a85a-a4ab6873e3f0",
-            description="A block that summarizes long texts using a Large Language Model (LLM), with configurable focus topics and summary styles.",
+            description="Utilize a Large Language Model (LLM) to summarize a long text.",
             categories={BlockCategory.AI, BlockCategory.TEXT},
             input_schema=AITextSummarizerBlock.Input,
             output_schema=AITextSummarizerBlock.Output,
@@ -1758,7 +1562,7 @@ class AIConversationBlock(AIBlockBase):
     def __init__(self):
         super().__init__(
             id="32a87eab-381e-4dd4-bdb8-4c47151be35a",
-            description="A block that facilitates multi-turn conversations with a Large Language Model (LLM), maintaining context across message exchanges.",
+            description="Advanced LLM call that takes a list of messages and sends them to the language model.",
             categories={BlockCategory.AI},
             input_schema=AIConversationBlock.Input,
             output_schema=AIConversationBlock.Output,
@@ -1878,7 +1682,7 @@ class AIListGeneratorBlock(AIBlockBase):
     def __init__(self):
         super().__init__(
             id="9c0b0450-d199-458b-a731-072189dd6593",
-            description="A block that creates lists of items based on prompts using a Large Language Model (LLM), with optional source data for context.",
+            description="Generate a list of values based on the given prompt using a Large Language Model (LLM).",
             categories={BlockCategory.AI, BlockCategory.TEXT},
             input_schema=AIListGeneratorBlock.Input,
             output_schema=AIListGeneratorBlock.Output,

autogpt_platform/backend/backend/blocks/medium.py

@@ -46,7 +46,7 @@ class PublishToMediumBlock(Block):
     class Input(BlockSchemaInput):
         author_id: BlockSecret = SecretField(
             key="medium_author_id",
-            description="""The Medium AuthorID of the user. You can get this by calling the /me endpoint of the Medium API.\n\ncurl -H "Authorization: Bearer YOUR_ACCESS_TOKEN" https://api.medium.com/v1/me\n\nThe response will contain the authorId field.""",
+            description="""The Medium AuthorID of the user. You can get this by calling the /me endpoint of the Medium API.\n\ncurl -H "Authorization: Bearer YOUR_ACCESS_TOKEN" https://api.medium.com/v1/me" the response will contain the authorId field.""",
             placeholder="Enter the author's Medium AuthorID",
         )
         title: str = SchemaField(

autogpt_platform/backend/backend/blocks/talking_head.py

@@ -50,7 +50,7 @@ class CreateTalkingAvatarVideoBlock(Block):
             description="The voice provider to use", default="microsoft"
         )
         voice_id: str = SchemaField(
-            description="The voice ID to use, see [available voice IDs](https://agpt.co/docs/platform/using-ai-services/d_id)",
+            description="The voice ID to use, get list of voices [here](https://docs.agpt.co/server/d_id)",
             default="en-US-JennyNeural",
         )
         presenter_id: str = SchemaField(

autogpt_platform/backend/backend/blocks/test/test_smart_decision_maker.py

@@ -242,7 +242,7 @@ async def test_smart_decision_maker_tracks_llm_stats():
         outputs = {}
         # Create execution context
 
-        mock_execution_context = ExecutionContext(human_in_the_loop_safe_mode=False)
+        mock_execution_context = ExecutionContext(safe_mode=False)
 
         # Create a mock execution processor for tests
 
@@ -343,7 +343,7 @@ async def test_smart_decision_maker_parameter_validation():
 
         # Create execution context
 
-        mock_execution_context = ExecutionContext(human_in_the_loop_safe_mode=False)
+        mock_execution_context = ExecutionContext(safe_mode=False)
 
         # Create a mock execution processor for tests
 
@@ -409,7 +409,7 @@ async def test_smart_decision_maker_parameter_validation():
 
         # Create execution context
 
-        mock_execution_context = ExecutionContext(human_in_the_loop_safe_mode=False)
+        mock_execution_context = ExecutionContext(safe_mode=False)
 
         # Create a mock execution processor for tests
 
@@ -471,7 +471,7 @@ async def test_smart_decision_maker_parameter_validation():
         outputs = {}
         # Create execution context
 
-        mock_execution_context = ExecutionContext(human_in_the_loop_safe_mode=False)
+        mock_execution_context = ExecutionContext(safe_mode=False)
 
         # Create a mock execution processor for tests
 
@@ -535,7 +535,7 @@ async def test_smart_decision_maker_parameter_validation():
         outputs = {}
         # Create execution context
 
-        mock_execution_context = ExecutionContext(human_in_the_loop_safe_mode=False)
+        mock_execution_context = ExecutionContext(safe_mode=False)
 
         # Create a mock execution processor for tests
 
@@ -658,7 +658,7 @@ async def test_smart_decision_maker_raw_response_conversion():
         outputs = {}
         # Create execution context
 
-        mock_execution_context = ExecutionContext(human_in_the_loop_safe_mode=False)
+        mock_execution_context = ExecutionContext(safe_mode=False)
 
         # Create a mock execution processor for tests
 
@@ -730,7 +730,7 @@ async def test_smart_decision_maker_raw_response_conversion():
         outputs = {}
         # Create execution context
 
-        mock_execution_context = ExecutionContext(human_in_the_loop_safe_mode=False)
+        mock_execution_context = ExecutionContext(safe_mode=False)
 
         # Create a mock execution processor for tests
 
@@ -786,7 +786,7 @@ async def test_smart_decision_maker_raw_response_conversion():
         outputs = {}
         # Create execution context
 
-        mock_execution_context = ExecutionContext(human_in_the_loop_safe_mode=False)
+        mock_execution_context = ExecutionContext(safe_mode=False)
 
         # Create a mock execution processor for tests
 
@@ -905,7 +905,7 @@ async def test_smart_decision_maker_agent_mode():
         # Create a mock execution context
 
         mock_execution_context = ExecutionContext(
-            human_in_the_loop_safe_mode=False,
+            safe_mode=False,
         )
 
         # Create a mock execution processor for agent mode tests
@@ -1027,7 +1027,7 @@ async def test_smart_decision_maker_traditional_mode_default():
 
         # Create execution context
 
-        mock_execution_context = ExecutionContext(human_in_the_loop_safe_mode=False)
+        mock_execution_context = ExecutionContext(safe_mode=False)
 
         # Create a mock execution processor for tests
 

autogpt_platform/backend/backend/blocks/test/test_smart_decision_maker_dynamic_fields.py

@@ -386,7 +386,7 @@ async def test_output_yielding_with_dynamic_fields():
             outputs = {}
             from backend.data.execution import ExecutionContext
 
-            mock_execution_context = ExecutionContext(human_in_the_loop_safe_mode=False)
+            mock_execution_context = ExecutionContext(safe_mode=False)
             mock_execution_processor = MagicMock()
 
             async for output_name, output_value in block.run(
@@ -609,9 +609,7 @@ async def test_validation_errors_dont_pollute_conversation():
                 outputs = {}
                 from backend.data.execution import ExecutionContext
 
-                mock_execution_context = ExecutionContext(
-                    human_in_the_loop_safe_mode=False
-                )
+                mock_execution_context = ExecutionContext(safe_mode=False)
 
                 # Create a proper mock execution processor for agent mode
                 from collections import defaultdict

autogpt_platform/backend/backend/data/block.py

@@ -474,7 +474,7 @@ class Block(ABC, Generic[BlockSchemaInputType, BlockSchemaOutputType]):
         self.block_type = block_type
         self.webhook_config = webhook_config
         self.execution_stats: NodeExecutionStats = NodeExecutionStats()
-        self.is_sensitive_action: bool = False
+        self.requires_human_review: bool = False
 
         if self.webhook_config:
             if isinstance(self.webhook_config, BlockWebhookConfig):
@@ -637,9 +637,8 @@ class Block(ABC, Generic[BlockSchemaInputType, BlockSchemaOutputType]):
             - should_pause: True if execution should be paused for review
             - input_data_to_use: The input data to use (may be modified by reviewer)
         """
-        if not (
-            self.is_sensitive_action and execution_context.sensitive_action_safe_mode
-        ):
+        # Skip review if not required or safe mode is disabled
+        if not self.requires_human_review or not execution_context.safe_mode:
             return False, input_data
 
         from backend.blocks.helpers.review import HITLReviewHelper

autogpt_platform/backend/backend/data/block_cost_config.py

@@ -99,15 +99,10 @@ MODEL_COST: dict[LlmModel, int] = {
     LlmModel.OPENAI_GPT_OSS_20B: 1,
     LlmModel.GEMINI_2_5_PRO: 4,
     LlmModel.GEMINI_3_PRO_PREVIEW: 5,
-    LlmModel.GEMINI_2_5_FLASH: 1,
-    LlmModel.GEMINI_2_0_FLASH: 1,
-    LlmModel.GEMINI_2_5_FLASH_LITE_PREVIEW: 1,
-    LlmModel.GEMINI_2_0_FLASH_LITE: 1,
     LlmModel.MISTRAL_NEMO: 1,
     LlmModel.COHERE_COMMAND_R_08_2024: 1,
     LlmModel.COHERE_COMMAND_R_PLUS_08_2024: 3,
     LlmModel.DEEPSEEK_CHAT: 2,
-    LlmModel.DEEPSEEK_R1_0528: 1,
     LlmModel.PERPLEXITY_SONAR: 1,
     LlmModel.PERPLEXITY_SONAR_PRO: 5,
     LlmModel.PERPLEXITY_SONAR_DEEP_RESEARCH: 10,
@@ -131,6 +126,11 @@ MODEL_COST: dict[LlmModel, int] = {
     LlmModel.KIMI_K2: 1,
     LlmModel.QWEN3_235B_A22B_THINKING: 1,
     LlmModel.QWEN3_CODER: 9,
+    LlmModel.GEMINI_2_5_FLASH: 1,
+    LlmModel.GEMINI_2_0_FLASH: 1,
+    LlmModel.GEMINI_2_5_FLASH_LITE_PREVIEW: 1,
+    LlmModel.GEMINI_2_0_FLASH_LITE: 1,
+    LlmModel.DEEPSEEK_R1_0528: 1,
     # v0 by Vercel models
     LlmModel.V0_1_5_MD: 1,
     LlmModel.V0_1_5_LG: 2,

autogpt_platform/backend/backend/data/db.py

@@ -38,6 +38,20 @@ POOL_TIMEOUT = os.getenv("DB_POOL_TIMEOUT")
 if POOL_TIMEOUT:
     DATABASE_URL = add_param(DATABASE_URL, "pool_timeout", POOL_TIMEOUT)
 
+# Add public schema to search_path for pgvector type access
+# The vector extension is in public schema, but search_path is determined by schema parameter
+# Extract the schema from DATABASE_URL or default to 'public' (matching get_database_schema())
+parsed_url = urlparse(DATABASE_URL)
+url_params = dict(parse_qsl(parsed_url.query))
+db_schema = url_params.get("schema", "public")
+# Build search_path, avoiding duplicates if db_schema is already 'public'
+search_path_schemas = list(
+    dict.fromkeys([db_schema, "public"])
+)  # Preserves order, removes duplicates
+search_path = ",".join(search_path_schemas)
+# This allows using ::vector without schema qualification
+DATABASE_URL = add_param(DATABASE_URL, "options", f"-c search_path={search_path}")
+
 HTTP_TIMEOUT = int(POOL_TIMEOUT) if POOL_TIMEOUT else None
 
 prisma = Prisma(
@@ -113,48 +127,38 @@ async def _raw_with_schema(
     *args,
     execute: bool = False,
     client: Prisma | None = None,
+    set_public_search_path: bool = False,
 ) -> list[dict] | int:
     """Internal: Execute raw SQL with proper schema handling.
 
     Use query_raw_with_schema() or execute_raw_with_schema() instead.
 
-    Supports placeholders:
-        - {schema_prefix}: Table/type prefix (e.g., "platform".)
-        - {schema}: Raw schema name for application tables (e.g., platform)
-
-    Note on pgvector types:
-        Use unqualified ::vector and <=> operator in queries. PostgreSQL resolves
-        these via search_path, which includes the schema where pgvector is installed
-        on all environments (local, CI, dev).
-
     Args:
-        query_template: SQL query with {schema_prefix} and/or {schema} placeholders
+        query_template: SQL query with {schema_prefix} placeholder
         *args: Query parameters
         execute: If False, executes SELECT query. If True, executes INSERT/UPDATE/DELETE.
         client: Optional Prisma client for transactions (only used when execute=True).
+        set_public_search_path: If True, sets search_path to include public schema.
+                                Needed for pgvector types and other public schema objects.
 
     Returns:
         - list[dict] if execute=False (query results)
         - int if execute=True (number of affected rows)
-
-    Example with vector type:
-        await execute_raw_with_schema(
-            'INSERT INTO {schema_prefix}"Embedding" (vec) VALUES ($1::vector)',
-            embedding_data
-        )
     """
     schema = get_database_schema()
     schema_prefix = f'"{schema}".' if schema != "public" else ""
-
-    formatted_query = query_template.format(
-        schema_prefix=schema_prefix,
-        schema=schema,
-    )
+    formatted_query = query_template.format(schema_prefix=schema_prefix)
 
     import prisma as prisma_module
 
     db_client = client if client else prisma_module.get_client()
 
+    # Set search_path to include public schema if requested
+    # Prisma doesn't support the 'options' connection parameter, so we set it per-session
+    # This is idempotent and safe to call multiple times
+    if set_public_search_path:
+        await db_client.execute_raw(f"SET search_path = {schema}, public")  # type: ignore
+
     if execute:
         result = await db_client.execute_raw(formatted_query, *args)  # type: ignore
     else:
@@ -163,12 +167,16 @@ async def _raw_with_schema(
     return result
 
 
-async def query_raw_with_schema(query_template: str, *args) -> list[dict]:
+async def query_raw_with_schema(
+    query_template: str, *args, set_public_search_path: bool = False
+) -> list[dict]:
     """Execute raw SQL SELECT query with proper schema handling.
 
     Args:
-        query_template: SQL query with {schema_prefix} and/or {schema} placeholders
+        query_template: SQL query with {schema_prefix} placeholder
         *args: Query parameters
+        set_public_search_path: If True, sets search_path to include public schema.
+                                Needed for pgvector types and other public schema objects.
 
     Returns:
         List of result rows as dictionaries
@@ -179,20 +187,23 @@ async def query_raw_with_schema(query_template: str, *args) -> list[dict]:
             user_id
         )
     """
-    return await _raw_with_schema(query_template, *args, execute=False)  # type: ignore
+    return await _raw_with_schema(query_template, *args, execute=False, set_public_search_path=set_public_search_path)  # type: ignore
 
 
 async def execute_raw_with_schema(
     query_template: str,
     *args,
     client: Prisma | None = None,
+    set_public_search_path: bool = False,
 ) -> int:
     """Execute raw SQL command (INSERT/UPDATE/DELETE) with proper schema handling.
 
     Args:
-        query_template: SQL query with {schema_prefix} and/or {schema} placeholders
+        query_template: SQL query with {schema_prefix} placeholder
         *args: Query parameters
         client: Optional Prisma client for transactions
+        set_public_search_path: If True, sets search_path to include public schema.
+                                Needed for pgvector types and other public schema objects.
 
     Returns:
         Number of affected rows
@@ -204,7 +215,7 @@ async def execute_raw_with_schema(
             client=tx  # Optional transaction client
         )
     """
-    return await _raw_with_schema(query_template, *args, execute=True, client=client)  # type: ignore
+    return await _raw_with_schema(query_template, *args, execute=True, client=client, set_public_search_path=set_public_search_path)  # type: ignore
 
 
 class BaseDbModel(BaseModel):

autogpt_platform/backend/backend/data/event_bus.py

@@ -103,18 +103,8 @@ class RedisEventBus(BaseRedisEventBus[M], ABC):
         return redis.get_redis()
 
     def publish_event(self, event: M, channel_key: str):
-        """
-        Publish an event to Redis. Gracefully handles connection failures
-        by logging the error instead of raising exceptions.
-        """
-        try:
-            message, full_channel_name = self._serialize_message(event, channel_key)
-            self.connection.publish(full_channel_name, message)
-        except Exception:
-            logger.exception(
-                f"Failed to publish event to Redis channel {channel_key}. "
-                "Event bus operation will continue without Redis connectivity."
-            )
+        message, full_channel_name = self._serialize_message(event, channel_key)
+        self.connection.publish(full_channel_name, message)
 
     def listen_events(self, channel_key: str) -> Generator[M, None, None]:
         pubsub, full_channel_name = self._get_pubsub_channel(
@@ -138,19 +128,9 @@ class AsyncRedisEventBus(BaseRedisEventBus[M], ABC):
         return await redis.get_redis_async()
 
     async def publish_event(self, event: M, channel_key: str):
-        """
-        Publish an event to Redis. Gracefully handles connection failures
-        by logging the error instead of raising exceptions.
-        """
-        try:
-            message, full_channel_name = self._serialize_message(event, channel_key)
-            connection = await self.connection
-            await connection.publish(full_channel_name, message)
-        except Exception:
-            logger.exception(
-                f"Failed to publish event to Redis channel {channel_key}. "
-                "Event bus operation will continue without Redis connectivity."
-            )
+        message, full_channel_name = self._serialize_message(event, channel_key)
+        connection = await self.connection
+        await connection.publish(full_channel_name, message)
 
     async def listen_events(self, channel_key: str) -> AsyncGenerator[M, None]:
         pubsub, full_channel_name = self._get_pubsub_channel(

autogpt_platform/backend/backend/data/event_bus_test.py

@@ -1,56 +0,0 @@
-"""
-Tests for event_bus graceful degradation when Redis is unavailable.
-"""
-
-from unittest.mock import AsyncMock, patch
-
-import pytest
-from pydantic import BaseModel
-
-from backend.data.event_bus import AsyncRedisEventBus
-
-
-class TestEvent(BaseModel):
-    """Test event model."""
-
-    message: str
-
-
-class TestNotificationBus(AsyncRedisEventBus[TestEvent]):
-    """Test implementation of AsyncRedisEventBus."""
-
-    Model = TestEvent
-
-    @property
-    def event_bus_name(self) -> str:
-        return "test_event_bus"
-
-
-@pytest.mark.asyncio
-async def test_publish_event_handles_connection_failure_gracefully():
-    """Test that publish_event logs exception instead of raising when Redis is unavailable."""
-    bus = TestNotificationBus()
-    event = TestEvent(message="test message")
-
-    # Mock get_redis_async to raise connection error
-    with patch(
-        "backend.data.event_bus.redis.get_redis_async",
-        side_effect=ConnectionError("Authentication required."),
-    ):
-        # Should not raise exception
-        await bus.publish_event(event, "test_channel")
-
-
-@pytest.mark.asyncio
-async def test_publish_event_works_with_redis_available():
-    """Test that publish_event works normally when Redis is available."""
-    bus = TestNotificationBus()
-    event = TestEvent(message="test message")
-
-    # Mock successful Redis connection
-    mock_redis = AsyncMock()
-    mock_redis.publish = AsyncMock()
-
-    with patch("backend.data.event_bus.redis.get_redis_async", return_value=mock_redis):
-        await bus.publish_event(event, "test_channel")
-        mock_redis.publish.assert_called_once()

autogpt_platform/backend/backend/data/execution.py

@@ -81,10 +81,7 @@ class ExecutionContext(BaseModel):
     This includes information needed by blocks, sub-graphs, and execution management.
     """
 
-    model_config = {"extra": "ignore"}
-
-    human_in_the_loop_safe_mode: bool = True
-    sensitive_action_safe_mode: bool = False
+    safe_mode: bool = True
     user_timezone: str = "UTC"
     root_execution_id: Optional[str] = None
     parent_execution_id: Optional[str] = None

autogpt_platform/backend/backend/data/graph.py

@@ -3,7 +3,7 @@ import logging
 import uuid
 from collections import defaultdict
 from datetime import datetime, timezone
-from typing import TYPE_CHECKING, Annotated, Any, Literal, Optional, cast
+from typing import TYPE_CHECKING, Any, Literal, Optional, cast
 
 from prisma.enums import SubmissionStatus
 from prisma.models import (
@@ -20,7 +20,7 @@ from prisma.types import (
     AgentNodeLinkCreateInput,
     StoreListingVersionWhereInput,
 )
-from pydantic import BaseModel, BeforeValidator, Field, create_model
+from pydantic import BaseModel, Field, create_model
 from pydantic.fields import computed_field
 
 from backend.blocks.agent import AgentExecutorBlock
@@ -62,31 +62,7 @@ logger = logging.getLogger(__name__)
 
 
 class GraphSettings(BaseModel):
-    # Use Annotated with BeforeValidator to coerce None to default values.
-    # This handles cases where the database has null values for these fields.
-    model_config = {"extra": "ignore"}
-
-    human_in_the_loop_safe_mode: Annotated[
-        bool, BeforeValidator(lambda v: v if v is not None else True)
-    ] = True
-    sensitive_action_safe_mode: Annotated[
-        bool, BeforeValidator(lambda v: v if v is not None else False)
-    ] = False
-
-    @classmethod
-    def from_graph(
-        cls,
-        graph: "GraphModel",
-        hitl_safe_mode: bool | None = None,
-        sensitive_action_safe_mode: bool = False,
-    ) -> "GraphSettings":
-        # Default to True if not explicitly set
-        if hitl_safe_mode is None:
-            hitl_safe_mode = True
-        return cls(
-            human_in_the_loop_safe_mode=hitl_safe_mode,
-            sensitive_action_safe_mode=sensitive_action_safe_mode,
-        )
+    human_in_the_loop_safe_mode: bool | None = None
 
 
 class Link(BaseDbModel):
@@ -268,14 +244,10 @@ class BaseGraph(BaseDbModel):
         return any(
             node.block_id
             for node in self.nodes
-            if node.block.block_type == BlockType.HUMAN_IN_THE_LOOP
-        )
-
-    @computed_field
-    @property
-    def has_sensitive_action(self) -> bool:
-        return any(
-            node.block_id for node in self.nodes if node.block.is_sensitive_action
+            if (
+                node.block.block_type == BlockType.HUMAN_IN_THE_LOOP
+                or node.block.requires_human_review
+            )
         )
 
     @property

autogpt_platform/backend/backend/data/understanding.py

@@ -328,8 +328,6 @@ async def clear_business_understanding(user_id: str) -> bool:
 
 def format_understanding_for_prompt(understanding: BusinessUnderstanding) -> str:
     """Format business understanding as text for system prompt injection."""
-    if not understanding:
-        return ""
     sections = []
 
     # User info section

autogpt_platform/backend/backend/executor/scheduler.py

@@ -309,7 +309,7 @@ def ensure_embeddings_coverage():
 
         # Process in batches until no more missing embeddings
         while True:
-            result = db_client.backfill_missing_embeddings(batch_size=100)
+            result = db_client.backfill_missing_embeddings(batch_size=10)
 
             total_processed += result["processed"]
             total_success += result["success"]

autogpt_platform/backend/backend/executor/utils.py

@@ -873,8 +873,11 @@ async def add_graph_execution(
         settings = await gdb.get_graph_settings(user_id=user_id, graph_id=graph_id)
 
         execution_context = ExecutionContext(
-            human_in_the_loop_safe_mode=settings.human_in_the_loop_safe_mode,
-            sensitive_action_safe_mode=settings.sensitive_action_safe_mode,
+            safe_mode=(
+                settings.human_in_the_loop_safe_mode
+                if settings.human_in_the_loop_safe_mode is not None
+                else True
+            ),
             user_timezone=(
                 user.timezone if user.timezone != USER_TIMEZONE_NOT_SET else "UTC"
             ),

autogpt_platform/backend/backend/executor/utils_test.py

@@ -386,7 +386,6 @@ async def test_add_graph_execution_is_repeatable(mocker: MockerFixture):
     mock_user.timezone = "UTC"
     mock_settings = mocker.MagicMock()
     mock_settings.human_in_the_loop_safe_mode = True
-    mock_settings.sensitive_action_safe_mode = False
 
     mock_udb.get_user_by_id = mocker.AsyncMock(return_value=mock_user)
     mock_gdb.get_graph_settings = mocker.AsyncMock(return_value=mock_settings)
@@ -652,7 +651,6 @@ async def test_add_graph_execution_with_nodes_to_skip(mocker: MockerFixture):
     mock_user.timezone = "UTC"
     mock_settings = mocker.MagicMock()
     mock_settings.human_in_the_loop_safe_mode = True
-    mock_settings.sensitive_action_safe_mode = False
 
     mock_udb.get_user_by_id = mocker.AsyncMock(return_value=mock_user)
     mock_gdb.get_graph_settings = mocker.AsyncMock(return_value=mock_settings)

autogpt_platform/backend/backend/util/settings.py

@@ -350,19 +350,6 @@ class Config(UpdateTrackingModel["Config"], BaseSettings):
         description="Whether to mark failed scans as clean or not",
     )
 
-    agentgenerator_host: str = Field(
-        default="",
-        description="The host for the Agent Generator service (empty to use built-in)",
-    )
-    agentgenerator_port: int = Field(
-        default=8000,
-        description="The port for the Agent Generator service",
-    )
-    agentgenerator_timeout: int = Field(
-        default=120,
-        description="The timeout in seconds for Agent Generator service requests",
-    )
-
     enable_example_blocks: bool = Field(
         default=False,
         description="Whether to enable example blocks in production",

autogpt_platform/backend/migrations/20260109181714_add_docs_embedding/migration.sql

@@ -1,10 +1,9 @@
 -- CreateExtension
 -- Supabase: pgvector must be enabled via Dashboard → Database → Extensions first
--- Creates extension in current schema (determined by search_path from DATABASE_URL ?schema= param)
--- This ensures vector type is in the same schema as tables, making ::vector work without explicit qualification
+-- Create in public schema so vector type is available across all schemas
 DO $$
 BEGIN
-    CREATE EXTENSION IF NOT EXISTS "vector";
+    CREATE EXTENSION IF NOT EXISTS "vector" WITH SCHEMA "public";
 EXCEPTION WHEN OTHERS THEN
     RAISE NOTICE 'vector extension not available or already exists, skipping';
 END $$;
@@ -20,7 +19,7 @@ CREATE TABLE "UnifiedContentEmbedding" (
     "contentType" "ContentType" NOT NULL,
     "contentId" TEXT NOT NULL,
     "userId" TEXT,
-    "embedding" vector(1536) NOT NULL,
+    "embedding" public.vector(1536) NOT NULL,
     "searchableText" TEXT NOT NULL,
     "metadata" JSONB NOT NULL DEFAULT '{}',
 
@@ -46,4 +45,4 @@ CREATE UNIQUE INDEX "UnifiedContentEmbedding_contentType_contentId_userId_key" O
 -- Uses cosine distance operator (<=>), which matches the query in hybrid_search.py
 -- Note: Drop first in case Prisma created a btree index (Prisma doesn't support HNSW)
 DROP INDEX IF EXISTS "UnifiedContentEmbedding_embedding_idx";
-CREATE INDEX "UnifiedContentEmbedding_embedding_idx" ON "UnifiedContentEmbedding" USING hnsw ("embedding" vector_cosine_ops);
+CREATE INDEX "UnifiedContentEmbedding_embedding_idx" ON "UnifiedContentEmbedding" USING hnsw ("embedding" public.vector_cosine_ops);

autogpt_platform/backend/scripts/generate_block_docs.py

@@ -1,864 +0,0 @@
-#!/usr/bin/env python3
-"""
-Block Documentation Generator
-
-Generates markdown documentation for all blocks from code introspection.
-Preserves manually-written content between marker comments.
-
-Usage:
-    # Generate all docs
-    poetry run python scripts/generate_block_docs.py
-
-    # Check mode for CI (exits 1 if stale)
-    poetry run python scripts/generate_block_docs.py --check
-
-    # Verbose output
-    poetry run python scripts/generate_block_docs.py -v
-"""
-
-import argparse
-import inspect
-import logging
-import re
-import sys
-from collections import defaultdict
-from dataclasses import dataclass, field
-from pathlib import Path
-from typing import Any
-
-# Add backend to path for imports
-backend_dir = Path(__file__).parent.parent
-sys.path.insert(0, str(backend_dir))
-
-logger = logging.getLogger(__name__)
-
-# Default output directory relative to repo root
-DEFAULT_OUTPUT_DIR = (
-    Path(__file__).parent.parent.parent.parent / "docs" / "integrations"
-)
-
-
-@dataclass
-class FieldDoc:
-    """Documentation for a single input/output field."""
-
-    name: str
-    description: str
-    type_str: str
-    required: bool
-    default: Any = None
-    advanced: bool = False
-    hidden: bool = False
-    placeholder: str | None = None
-
-
-@dataclass
-class BlockDoc:
-    """Documentation data extracted from a block."""
-
-    id: str
-    name: str
-    class_name: str
-    description: str
-    categories: list[str]
-    category_descriptions: dict[str, str]
-    inputs: list[FieldDoc]
-    outputs: list[FieldDoc]
-    block_type: str
-    source_file: str
-    contributors: list[str] = field(default_factory=list)
-
-
-# Category to human-readable name mapping
-CATEGORY_DISPLAY_NAMES = {
-    "AI": "AI and Language Models",
-    "BASIC": "Basic Operations",
-    "TEXT": "Text Processing",
-    "SEARCH": "Search and Information Retrieval",
-    "SOCIAL": "Social Media and Content",
-    "DEVELOPER_TOOLS": "Developer Tools",
-    "DATA": "Data Processing",
-    "LOGIC": "Logic and Control Flow",
-    "COMMUNICATION": "Communication",
-    "INPUT": "Input/Output",
-    "OUTPUT": "Input/Output",
-    "MULTIMEDIA": "Media Generation",
-    "PRODUCTIVITY": "Productivity",
-    "HARDWARE": "Hardware",
-    "AGENT": "Agent Integration",
-    "CRM": "CRM Services",
-    "SAFETY": "AI Safety",
-    "ISSUE_TRACKING": "Issue Tracking",
-    "MARKETING": "Marketing",
-}
-
-# Category to doc file mapping (for grouping related blocks)
-CATEGORY_FILE_MAP = {
-    "BASIC": "basic",
-    "TEXT": "text",
-    "AI": "llm",
-    "SEARCH": "search",
-    "DATA": "data",
-    "LOGIC": "logic",
-    "COMMUNICATION": "communication",
-    "MULTIMEDIA": "multimedia",
-    "PRODUCTIVITY": "productivity",
-}
-
-
-def class_name_to_display_name(class_name: str) -> str:
-    """Convert BlockClassName to 'Block Class Name'."""
-    # Remove 'Block' suffix (only at the end, not all occurrences)
-    name = class_name.removesuffix("Block")
-    # Insert space before capitals
-    name = re.sub(r"([a-z])([A-Z])", r"\1 \2", name)
-    # Handle consecutive capitals (e.g., 'HTTPRequest' -> 'HTTP Request')
-    name = re.sub(r"([A-Z]+)([A-Z][a-z])", r"\1 \2", name)
-    return name.strip()
-
-
-def type_to_readable(type_schema: dict[str, Any] | Any) -> str:
-    """Convert JSON schema type to human-readable string."""
-    if not isinstance(type_schema, dict):
-        return str(type_schema) if type_schema else "Any"
-
-    if "anyOf" in type_schema:
-        # Union type - show options
-        any_of = type_schema["anyOf"]
-        if not isinstance(any_of, list):
-            return "Any"
-        options = []
-        for opt in any_of:
-            if isinstance(opt, dict) and opt.get("type") == "null":
-                continue
-            options.append(type_to_readable(opt))
-        if not options:
-            return "None"
-        if len(options) == 1:
-            return options[0]
-        return " | ".join(options)
-
-    if "allOf" in type_schema:
-        all_of = type_schema["allOf"]
-        if not isinstance(all_of, list) or not all_of:
-            return "Any"
-        return type_to_readable(all_of[0])
-
-    schema_type = type_schema.get("type")
-
-    if schema_type == "array":
-        items = type_schema.get("items", {})
-        item_type = type_to_readable(items)
-        return f"List[{item_type}]"
-
-    if schema_type == "object":
-        if "additionalProperties" in type_schema:
-            additional_props = type_schema["additionalProperties"]
-            # additionalProperties: true means any value type is allowed
-            if additional_props is True:
-                return "Dict[str, Any]"
-            value_type = type_to_readable(additional_props)
-            return f"Dict[str, {value_type}]"
-        # Check if it's a specific model
-        title = type_schema.get("title", "Object")
-        return title
-
-    if schema_type == "string":
-        if "enum" in type_schema:
-            return " | ".join(f'"{v}"' for v in type_schema["enum"])
-        if "format" in type_schema:
-            return f"str ({type_schema['format']})"
-        return "str"
-
-    if schema_type == "integer":
-        return "int"
-
-    if schema_type == "number":
-        return "float"
-
-    if schema_type == "boolean":
-        return "bool"
-
-    if schema_type == "null":
-        return "None"
-
-    # Fallback
-    return type_schema.get("title", schema_type or "Any")
-
-
-def safe_get(d: Any, key: str, default: Any = None) -> Any:
-    """Safely get a value from a dict-like object."""
-    if isinstance(d, dict):
-        return d.get(key, default)
-    return default
-
-
-def file_path_to_title(file_path: str) -> str:
-    """Convert file path to a readable title.
-
-    Examples:
-        "github/issues.md" -> "GitHub Issues"
-        "basic.md" -> "Basic"
-        "llm.md" -> "LLM"
-        "google/sheets.md" -> "Google Sheets"
-    """
-    # Special case replacements (applied after title casing)
-    TITLE_FIXES = {
-        "Llm": "LLM",
-        "Github": "GitHub",
-        "Api": "API",
-        "Ai": "AI",
-        "Oauth": "OAuth",
-        "Url": "URL",
-        "Ci": "CI",
-        "Pr": "PR",
-        "Gmb": "GMB",  # Google My Business
-        "Hubspot": "HubSpot",
-        "Linkedin": "LinkedIn",
-        "Tiktok": "TikTok",
-        "Youtube": "YouTube",
-    }
-
-    def apply_fixes(text: str) -> str:
-        # Split into words, fix each word, rejoin
-        words = text.split()
-        fixed_words = [TITLE_FIXES.get(word, word) for word in words]
-        return " ".join(fixed_words)
-
-    path = Path(file_path)
-    name = path.stem  # e.g., "issues" or "sheets"
-
-    # Get parent dir if exists
-    parent = path.parent.name if path.parent.name != "." else None
-
-    # Title case and apply fixes
-    if parent:
-        parent_title = apply_fixes(parent.replace("_", " ").title())
-        name_title = apply_fixes(name.replace("_", " ").title())
-        return f"{parent_title} {name_title}"
-    return apply_fixes(name.replace("_", " ").title())
-
-
-def extract_block_doc(block_cls: type) -> BlockDoc:
-    """Extract documentation data from a block class."""
-    block = block_cls.create()
-
-    # Get source file
-    try:
-        source_file = inspect.getfile(block_cls)
-        # Make relative to blocks directory
-        blocks_dir = Path(source_file).parent
-        while blocks_dir.name != "blocks" and blocks_dir.parent != blocks_dir:
-            blocks_dir = blocks_dir.parent
-        source_file = str(Path(source_file).relative_to(blocks_dir.parent))
-    except (TypeError, ValueError):
-        source_file = "unknown"
-
-    # Extract input fields
-    input_schema = block.input_schema.jsonschema()
-    input_properties = safe_get(input_schema, "properties", {})
-    if not isinstance(input_properties, dict):
-        input_properties = {}
-    required_raw = safe_get(input_schema, "required", [])
-    # Handle edge cases where required might not be a list
-    if isinstance(required_raw, (list, set, tuple)):
-        required_inputs = set(required_raw)
-    else:
-        required_inputs = set()
-
-    inputs = []
-    for field_name, field_schema in input_properties.items():
-        if not isinstance(field_schema, dict):
-            continue
-        # Skip credentials fields in docs (they're auto-handled)
-        if "credentials" in field_name.lower():
-            continue
-
-        inputs.append(
-            FieldDoc(
-                name=field_name,
-                description=safe_get(field_schema, "description", ""),
-                type_str=type_to_readable(field_schema),
-                required=field_name in required_inputs,
-                default=safe_get(field_schema, "default"),
-                advanced=safe_get(field_schema, "advanced", False) or False,
-                hidden=safe_get(field_schema, "hidden", False) or False,
-                placeholder=safe_get(field_schema, "placeholder"),
-            )
-        )
-
-    # Extract output fields
-    output_schema = block.output_schema.jsonschema()
-    output_properties = safe_get(output_schema, "properties", {})
-    if not isinstance(output_properties, dict):
-        output_properties = {}
-
-    outputs = []
-    for field_name, field_schema in output_properties.items():
-        if not isinstance(field_schema, dict):
-            continue
-        outputs.append(
-            FieldDoc(
-                name=field_name,
-                description=safe_get(field_schema, "description", ""),
-                type_str=type_to_readable(field_schema),
-                required=True,  # Outputs are always produced
-                hidden=safe_get(field_schema, "hidden", False) or False,
-            )
-        )
-
-    # Get category info (sort for deterministic ordering since it's a set)
-    categories = []
-    category_descriptions = {}
-    for cat in sorted(block.categories, key=lambda c: c.name):
-        categories.append(cat.name)
-        category_descriptions[cat.name] = cat.value
-
-    # Get contributors
-    contributors = []
-    for contrib in block.contributors:
-        contributors.append(contrib.name if hasattr(contrib, "name") else str(contrib))
-
-    return BlockDoc(
-        id=block.id,
-        name=class_name_to_display_name(block.name),
-        class_name=block.name,
-        description=block.description,
-        categories=categories,
-        category_descriptions=category_descriptions,
-        inputs=inputs,
-        outputs=outputs,
-        block_type=block.block_type.value,
-        source_file=source_file,
-        contributors=contributors,
-    )
-
-
-def generate_anchor(name: str) -> str:
-    """Generate markdown anchor from block name."""
-    return name.lower().replace(" ", "-").replace("(", "").replace(")", "")
-
-
-def extract_manual_content(existing_content: str) -> dict[str, str]:
-    """Extract content between MANUAL markers from existing file."""
-    manual_sections = {}
-
-    # Pattern: <!-- MANUAL: section_name -->content<!-- END MANUAL -->
-    pattern = r"<!-- MANUAL: (\w+) -->\s*(.*?)\s*<!-- END MANUAL -->"
-    matches = re.findall(pattern, existing_content, re.DOTALL)
-
-    for section_name, content in matches:
-        manual_sections[section_name] = content.strip()
-
-    return manual_sections
-
-
-def generate_block_markdown(
-    block: BlockDoc,
-    manual_content: dict[str, str] | None = None,
-) -> str:
-    """Generate markdown documentation for a single block."""
-    manual_content = manual_content or {}
-    lines = []
-
-    # All blocks use ## heading, sections use ### (consistent siblings)
-    lines.append(f"## {block.name}")
-    lines.append("")
-
-    # What it is (full description)
-    lines.append("### What it is")
-    lines.append(block.description or "No description available.")
-    lines.append("")
-
-    # How it works (manual section)
-    lines.append("### How it works")
-    how_it_works = manual_content.get(
-        "how_it_works", "_Add technical explanation here._"
-    )
-    lines.append("<!-- MANUAL: how_it_works -->")
-    lines.append(how_it_works)
-    lines.append("<!-- END MANUAL -->")
-    lines.append("")
-
-    # Inputs table (auto-generated)
-    visible_inputs = [f for f in block.inputs if not f.hidden]
-    if visible_inputs:
-        lines.append("### Inputs")
-        lines.append("")
-        lines.append("| Input | Description | Type | Required |")
-        lines.append("|-------|-------------|------|----------|")
-        for inp in visible_inputs:
-            required = "Yes" if inp.required else "No"
-            desc = inp.description or "-"
-            type_str = inp.type_str or "-"
-            # Normalize newlines and escape pipes for valid table syntax
-            desc = desc.replace("\n", " ").replace("|", "\\|")
-            type_str = type_str.replace("|", "\\|")
-            lines.append(f"| {inp.name} | {desc} | {type_str} | {required} |")
-        lines.append("")
-
-    # Outputs table (auto-generated)
-    visible_outputs = [f for f in block.outputs if not f.hidden]
-    if visible_outputs:
-        lines.append("### Outputs")
-        lines.append("")
-        lines.append("| Output | Description | Type |")
-        lines.append("|--------|-------------|------|")
-        for out in visible_outputs:
-            desc = out.description or "-"
-            type_str = out.type_str or "-"
-            # Normalize newlines and escape pipes for valid table syntax
-            desc = desc.replace("\n", " ").replace("|", "\\|")
-            type_str = type_str.replace("|", "\\|")
-            lines.append(f"| {out.name} | {desc} | {type_str} |")
-        lines.append("")
-
-    # Possible use case (manual section)
-    lines.append("### Possible use case")
-    use_case = manual_content.get("use_case", "_Add practical use case examples here._")
-    lines.append("<!-- MANUAL: use_case -->")
-    lines.append(use_case)
-    lines.append("<!-- END MANUAL -->")
-    lines.append("")
-
-    lines.append("---")
-    lines.append("")
-
-    return "\n".join(lines)
-
-
-def get_block_file_mapping(blocks: list[BlockDoc]) -> dict[str, list[BlockDoc]]:
-    """
-    Map blocks to their documentation files.
-
-    Returns dict of {relative_file_path: [blocks]}
-    """
-    file_mapping = defaultdict(list)
-
-    for block in blocks:
-        # Determine file path based on source file or category
-        source_path = Path(block.source_file)
-
-        # If source is in a subdirectory (e.g., google/gmail.py), use that structure
-        if len(source_path.parts) > 2:  # blocks/subdir/file.py
-            subdir = source_path.parts[1]  # e.g., "google"
-            # Use the Python filename as the md filename
-            md_file = source_path.stem + ".md"  # e.g., "gmail.md"
-            file_path = f"{subdir}/{md_file}"
-        else:
-            # Use category-based grouping for top-level blocks
-            primary_category = block.categories[0] if block.categories else "BASIC"
-            file_name = CATEGORY_FILE_MAP.get(primary_category, "misc")
-            file_path = f"{file_name}.md"
-
-        file_mapping[file_path].append(block)
-
-    return dict(file_mapping)
-
-
-def generate_overview_table(blocks: list[BlockDoc]) -> str:
-    """Generate the overview table markdown (blocks.md)."""
-    lines = []
-
-    lines.append("# AutoGPT Blocks Overview")
-    lines.append("")
-    lines.append(
-        'AutoGPT uses a modular approach with various "blocks" to handle different tasks. These blocks are the building blocks of AutoGPT workflows, allowing users to create complex automations by combining simple, specialized components.'
-    )
-    lines.append("")
-    lines.append('!!! info "Creating Your Own Blocks"')
-    lines.append("    Want to create your own custom blocks? Check out our guides:")
-    lines.append("    ")
-    lines.append(
-        "    - [Build your own Blocks](https://docs.agpt.co/platform/new_blocks/) - Step-by-step tutorial with examples"
-    )
-    lines.append(
-        "    - [Block SDK Guide](https://docs.agpt.co/platform/block-sdk-guide/) - Advanced SDK patterns with OAuth, webhooks, and provider configuration"
-    )
-    lines.append("")
-    lines.append(
-        "Below is a comprehensive list of all available blocks, categorized by their primary function. Click on any block name to view its detailed documentation."
-    )
-    lines.append("")
-
-    # Group blocks by category
-    by_category = defaultdict(list)
-    for block in blocks:
-        primary_cat = block.categories[0] if block.categories else "BASIC"
-        by_category[primary_cat].append(block)
-
-    # Sort categories
-    category_order = [
-        "BASIC",
-        "DATA",
-        "TEXT",
-        "AI",
-        "SEARCH",
-        "SOCIAL",
-        "COMMUNICATION",
-        "DEVELOPER_TOOLS",
-        "MULTIMEDIA",
-        "PRODUCTIVITY",
-        "LOGIC",
-        "INPUT",
-        "OUTPUT",
-        "AGENT",
-        "CRM",
-        "SAFETY",
-        "ISSUE_TRACKING",
-        "HARDWARE",
-        "MARKETING",
-    ]
-
-    # Track emitted display names to avoid duplicate headers
-    # (e.g., INPUT and OUTPUT both map to "Input/Output")
-    emitted_display_names: set[str] = set()
-
-    for category in category_order:
-        if category not in by_category:
-            continue
-
-        display_name = CATEGORY_DISPLAY_NAMES.get(category, category)
-
-        # Collect all blocks for this display name (may span multiple categories)
-        if display_name in emitted_display_names:
-            # Already emitted header, just add rows to existing table
-            # Find the position before the last empty line and insert rows
-            cat_blocks = sorted(by_category[category], key=lambda b: b.name)
-            # Remove the trailing empty line, add rows, then re-add empty line
-            lines.pop()
-            for block in cat_blocks:
-                file_mapping = get_block_file_mapping([block])
-                file_path = list(file_mapping.keys())[0]
-                anchor = generate_anchor(block.name)
-                short_desc = (
-                    block.description.split(".")[0]
-                    if block.description
-                    else "No description"
-                )
-                short_desc = short_desc.replace("\n", " ").replace("|", "\\|")
-                lines.append(f"| [{block.name}]({file_path}#{anchor}) | {short_desc} |")
-            lines.append("")
-            continue
-
-        emitted_display_names.add(display_name)
-        cat_blocks = sorted(by_category[category], key=lambda b: b.name)
-
-        lines.append(f"## {display_name}")
-        lines.append("")
-        lines.append("| Block Name | Description |")
-        lines.append("|------------|-------------|")
-
-        for block in cat_blocks:
-            # Determine link path
-            file_mapping = get_block_file_mapping([block])
-            file_path = list(file_mapping.keys())[0]
-            anchor = generate_anchor(block.name)
-
-            # Short description (first sentence)
-            short_desc = (
-                block.description.split(".")[0]
-                if block.description
-                else "No description"
-            )
-            short_desc = short_desc.replace("\n", " ").replace("|", "\\|")
-
-            lines.append(f"| [{block.name}]({file_path}#{anchor}) | {short_desc} |")
-
-        lines.append("")
-
-    return "\n".join(lines)
-
-
-def load_all_blocks_for_docs() -> list[BlockDoc]:
-    """Load all blocks and extract documentation."""
-    from backend.blocks import load_all_blocks
-
-    block_classes = load_all_blocks()
-    blocks = []
-
-    for _block_id, block_cls in block_classes.items():
-        try:
-            block_doc = extract_block_doc(block_cls)
-            blocks.append(block_doc)
-        except Exception as e:
-            logger.warning(f"Failed to extract docs for {block_cls.__name__}: {e}")
-
-    return blocks
-
-
-def write_block_docs(
-    output_dir: Path,
-    blocks: list[BlockDoc],
-    verbose: bool = False,
-) -> dict[str, str]:
-    """
-    Write block documentation files.
-
-    Returns dict of {file_path: content} for all generated files.
-    """
-    output_dir = Path(output_dir)
-    output_dir.mkdir(parents=True, exist_ok=True)
-
-    file_mapping = get_block_file_mapping(blocks)
-    generated_files = {}
-
-    for file_path, file_blocks in file_mapping.items():
-        full_path = output_dir / file_path
-
-        # Create subdirectories if needed
-        full_path.parent.mkdir(parents=True, exist_ok=True)
-
-        # Load existing content for manual section preservation
-        existing_content = ""
-        if full_path.exists():
-            existing_content = full_path.read_text()
-
-        # Always generate title from file path (with fixes applied)
-        file_title = file_path_to_title(file_path)
-
-        # Extract existing file description if present (preserve manual content)
-        file_header_pattern = (
-            r"^# .+?\n<!-- MANUAL: file_description -->\n(.*?)\n<!-- END MANUAL -->"
-        )
-        file_header_match = re.search(file_header_pattern, existing_content, re.DOTALL)
-
-        if file_header_match:
-            file_description = file_header_match.group(1)
-        else:
-            file_description = "_Add a description of this category of blocks._"
-
-        # Generate file header
-        file_header = f"# {file_title}\n"
-        file_header += "<!-- MANUAL: file_description -->\n"
-        file_header += f"{file_description}\n"
-        file_header += "<!-- END MANUAL -->\n"
-
-        # Generate content for each block
-        content_parts = []
-        for block in sorted(file_blocks, key=lambda b: b.name):
-            # Extract manual content specific to this block
-            # Match block heading (h2) and capture until --- separator
-            block_pattern = rf"(?:^|\n)## {re.escape(block.name)}\s*\n(.*?)(?=\n---|\Z)"
-            block_match = re.search(block_pattern, existing_content, re.DOTALL)
-            if block_match:
-                manual_content = extract_manual_content(block_match.group(1))
-            else:
-                manual_content = {}
-
-            content_parts.append(
-                generate_block_markdown(
-                    block,
-                    manual_content,
-                )
-            )
-
-        full_content = file_header + "\n" + "\n".join(content_parts)
-        generated_files[str(file_path)] = full_content
-
-        if verbose:
-            print(f"  Writing {file_path} ({len(file_blocks)} blocks)")
-
-        full_path.write_text(full_content)
-
-    # Generate overview file
-    overview_content = generate_overview_table(blocks)
-    overview_path = output_dir / "README.md"
-    generated_files["README.md"] = overview_content
-    overview_path.write_text(overview_content)
-
-    if verbose:
-        print("  Writing README.md (overview)")
-
-    return generated_files
-
-
-def check_docs_in_sync(output_dir: Path, blocks: list[BlockDoc]) -> bool:
-    """
-    Check if generated docs match existing docs.
-
-    Returns True if in sync, False otherwise.
-    """
-    output_dir = Path(output_dir)
-    file_mapping = get_block_file_mapping(blocks)
-
-    all_match = True
-    out_of_sync_details: list[tuple[str, list[str]]] = []
-
-    for file_path, file_blocks in file_mapping.items():
-        full_path = output_dir / file_path
-
-        if not full_path.exists():
-            block_names = [b.name for b in sorted(file_blocks, key=lambda b: b.name)]
-            print(f"MISSING: {file_path}")
-            print(f"  Blocks: {', '.join(block_names)}")
-            out_of_sync_details.append((file_path, block_names))
-            all_match = False
-            continue
-
-        existing_content = full_path.read_text()
-
-        # Always generate title from file path (with fixes applied)
-        file_title = file_path_to_title(file_path)
-
-        # Extract existing file description if present (preserve manual content)
-        file_header_pattern = (
-            r"^# .+?\n<!-- MANUAL: file_description -->\n(.*?)\n<!-- END MANUAL -->"
-        )
-        file_header_match = re.search(file_header_pattern, existing_content, re.DOTALL)
-
-        if file_header_match:
-            file_description = file_header_match.group(1)
-        else:
-            file_description = "_Add a description of this category of blocks._"
-
-        # Generate expected file header
-        file_header = f"# {file_title}\n"
-        file_header += "<!-- MANUAL: file_description -->\n"
-        file_header += f"{file_description}\n"
-        file_header += "<!-- END MANUAL -->\n"
-
-        # Extract manual content from existing file
-        manual_sections_by_block = {}
-        for block in file_blocks:
-            block_pattern = rf"(?:^|\n)## {re.escape(block.name)}\s*\n(.*?)(?=\n---|\Z)"
-            block_match = re.search(block_pattern, existing_content, re.DOTALL)
-            if block_match:
-                manual_sections_by_block[block.name] = extract_manual_content(
-                    block_match.group(1)
-                )
-
-        # Generate expected content and check each block individually
-        content_parts = []
-        mismatched_blocks = []
-        for block in sorted(file_blocks, key=lambda b: b.name):
-            manual_content = manual_sections_by_block.get(block.name, {})
-            expected_block_content = generate_block_markdown(
-                block,
-                manual_content,
-            )
-            content_parts.append(expected_block_content)
-
-            # Check if this specific block's section exists and matches
-            # Include the --- separator to match generate_block_markdown output
-            block_pattern = rf"(?:^|\n)(## {re.escape(block.name)}\s*\n.*?\n---\n)"
-            block_match = re.search(block_pattern, existing_content, re.DOTALL)
-            if not block_match:
-                mismatched_blocks.append(f"{block.name} (missing)")
-            elif block_match.group(1).strip() != expected_block_content.strip():
-                mismatched_blocks.append(block.name)
-
-        expected_content = file_header + "\n" + "\n".join(content_parts)
-
-        if existing_content.strip() != expected_content.strip():
-            print(f"OUT OF SYNC: {file_path}")
-            if mismatched_blocks:
-                print(f"  Affected blocks: {', '.join(mismatched_blocks)}")
-            out_of_sync_details.append((file_path, mismatched_blocks))
-            all_match = False
-
-    # Check overview
-    overview_path = output_dir / "README.md"
-    if overview_path.exists():
-        existing_overview = overview_path.read_text()
-        expected_overview = generate_overview_table(blocks)
-        if existing_overview.strip() != expected_overview.strip():
-            print("OUT OF SYNC: README.md (overview)")
-            print("  The blocks overview table needs regeneration")
-            out_of_sync_details.append(("README.md", ["overview table"]))
-            all_match = False
-    else:
-        print("MISSING: README.md (overview)")
-        out_of_sync_details.append(("README.md", ["overview table"]))
-        all_match = False
-
-    # Check for unfilled manual sections
-    unfilled_patterns = [
-        "_Add a description of this category of blocks._",
-        "_Add technical explanation here._",
-        "_Add practical use case examples here._",
-    ]
-    files_with_unfilled = []
-    for file_path in file_mapping.keys():
-        full_path = output_dir / file_path
-        if full_path.exists():
-            content = full_path.read_text()
-            unfilled_count = sum(1 for p in unfilled_patterns if p in content)
-            if unfilled_count > 0:
-                files_with_unfilled.append((file_path, unfilled_count))
-
-    if files_with_unfilled:
-        print("\nWARNING: Files with unfilled manual sections:")
-        for file_path, count in sorted(files_with_unfilled):
-            print(f"  {file_path}: {count} unfilled section(s)")
-        print(
-            f"\nTotal: {len(files_with_unfilled)} files with unfilled manual sections"
-        )
-
-    return all_match
-
-
-def main():
-    parser = argparse.ArgumentParser(
-        description="Generate block documentation from code introspection"
-    )
-    parser.add_argument(
-        "--output-dir",
-        type=Path,
-        default=DEFAULT_OUTPUT_DIR,
-        help="Output directory for generated docs",
-    )
-    parser.add_argument(
-        "--check",
-        action="store_true",
-        help="Check if docs are in sync (for CI), exit 1 if not",
-    )
-    parser.add_argument(
-        "-v",
-        "--verbose",
-        action="store_true",
-        help="Verbose output",
-    )
-
-    args = parser.parse_args()
-
-    logging.basicConfig(
-        level=logging.DEBUG if args.verbose else logging.INFO,
-        format="%(levelname)s: %(message)s",
-    )
-
-    print("Loading blocks...")
-    blocks = load_all_blocks_for_docs()
-    print(f"Found {len(blocks)} blocks")
-
-    if args.check:
-        print(f"Checking docs in {args.output_dir}...")
-        in_sync = check_docs_in_sync(args.output_dir, blocks)
-        if in_sync:
-            print("All documentation is in sync!")
-            sys.exit(0)
-        else:
-            print("\n" + "=" * 60)
-            print("Documentation is out of sync!")
-            print("=" * 60)
-            print("\nTo fix this, run one of the following:")
-            print("\n  Option 1 - Run locally:")
-            print(
-                "    cd autogpt_platform/backend && poetry run python scripts/generate_block_docs.py"
-            )
-            print("\n  Option 2 - Ask Claude Code to run it:")
-            print('    "Run the block docs generator script to sync documentation"')
-            print("\n" + "=" * 60)
-            sys.exit(1)
-    else:
-        print(f"Generating docs to {args.output_dir}...")
-        write_block_docs(
-            args.output_dir,
-            blocks,
-            verbose=args.verbose,
-        )
-        print("Done!")
-
-
-if __name__ == "__main__":
-    main()

autogpt_platform/backend/scripts/test_generate_block_docs.py

@@ -1,208 +0,0 @@
-#!/usr/bin/env python3
-"""Tests for the block documentation generator."""
-import pytest
-
-from scripts.generate_block_docs import (
-    class_name_to_display_name,
-    extract_manual_content,
-    generate_anchor,
-    type_to_readable,
-)
-
-
-class TestClassNameToDisplayName:
-    """Tests for class_name_to_display_name function."""
-
-    def test_simple_block_name(self):
-        assert class_name_to_display_name("PrintBlock") == "Print"
-
-    def test_multi_word_block_name(self):
-        assert class_name_to_display_name("GetWeatherBlock") == "Get Weather"
-
-    def test_consecutive_capitals(self):
-        assert class_name_to_display_name("HTTPRequestBlock") == "HTTP Request"
-
-    def test_ai_prefix(self):
-        assert class_name_to_display_name("AIConditionBlock") == "AI Condition"
-
-    def test_no_block_suffix(self):
-        assert class_name_to_display_name("SomeClass") == "Some Class"
-
-
-class TestTypeToReadable:
-    """Tests for type_to_readable function."""
-
-    def test_string_type(self):
-        assert type_to_readable({"type": "string"}) == "str"
-
-    def test_integer_type(self):
-        assert type_to_readable({"type": "integer"}) == "int"
-
-    def test_number_type(self):
-        assert type_to_readable({"type": "number"}) == "float"
-
-    def test_boolean_type(self):
-        assert type_to_readable({"type": "boolean"}) == "bool"
-
-    def test_array_type(self):
-        result = type_to_readable({"type": "array", "items": {"type": "string"}})
-        assert result == "List[str]"
-
-    def test_object_type(self):
-        result = type_to_readable({"type": "object", "title": "MyModel"})
-        assert result == "MyModel"
-
-    def test_anyof_with_null(self):
-        result = type_to_readable({"anyOf": [{"type": "string"}, {"type": "null"}]})
-        assert result == "str"
-
-    def test_anyof_multiple_types(self):
-        result = type_to_readable({"anyOf": [{"type": "string"}, {"type": "integer"}]})
-        assert result == "str | int"
-
-    def test_enum_type(self):
-        result = type_to_readable(
-            {"type": "string", "enum": ["option1", "option2", "option3"]}
-        )
-        assert result == '"option1" | "option2" | "option3"'
-
-    def test_none_input(self):
-        assert type_to_readable(None) == "Any"
-
-    def test_non_dict_input(self):
-        assert type_to_readable("string") == "string"
-
-
-class TestExtractManualContent:
-    """Tests for extract_manual_content function."""
-
-    def test_extract_how_it_works(self):
-        content = """
-### How it works
-<!-- MANUAL: how_it_works -->
-This is how it works.
-<!-- END MANUAL -->
-"""
-        result = extract_manual_content(content)
-        assert result == {"how_it_works": "This is how it works."}
-
-    def test_extract_use_case(self):
-        content = """
-### Possible use case
-<!-- MANUAL: use_case -->
-Example use case here.
-<!-- END MANUAL -->
-"""
-        result = extract_manual_content(content)
-        assert result == {"use_case": "Example use case here."}
-
-    def test_extract_multiple_sections(self):
-        content = """
-<!-- MANUAL: how_it_works -->
-How it works content.
-<!-- END MANUAL -->
-
-<!-- MANUAL: use_case -->
-Use case content.
-<!-- END MANUAL -->
-"""
-        result = extract_manual_content(content)
-        assert result == {
-            "how_it_works": "How it works content.",
-            "use_case": "Use case content.",
-        }
-
-    def test_empty_content(self):
-        result = extract_manual_content("")
-        assert result == {}
-
-    def test_no_markers(self):
-        result = extract_manual_content("Some content without markers")
-        assert result == {}
-
-
-class TestGenerateAnchor:
-    """Tests for generate_anchor function."""
-
-    def test_simple_name(self):
-        assert generate_anchor("Print") == "print"
-
-    def test_multi_word_name(self):
-        assert generate_anchor("Get Weather") == "get-weather"
-
-    def test_name_with_parentheses(self):
-        assert generate_anchor("Something (Optional)") == "something-optional"
-
-    def test_already_lowercase(self):
-        assert generate_anchor("already lowercase") == "already-lowercase"
-
-
-class TestIntegration:
-    """Integration tests that require block loading."""
-
-    def test_load_blocks(self):
-        """Test that blocks can be loaded successfully."""
-        import logging
-        import sys
-        from pathlib import Path
-
-        logging.disable(logging.CRITICAL)
-        sys.path.insert(0, str(Path(__file__).parent.parent))
-
-        from scripts.generate_block_docs import load_all_blocks_for_docs
-
-        blocks = load_all_blocks_for_docs()
-        assert len(blocks) > 0, "Should load at least one block"
-
-    def test_block_doc_has_required_fields(self):
-        """Test that extracted block docs have required fields."""
-        import logging
-        import sys
-        from pathlib import Path
-
-        logging.disable(logging.CRITICAL)
-        sys.path.insert(0, str(Path(__file__).parent.parent))
-
-        from scripts.generate_block_docs import load_all_blocks_for_docs
-
-        blocks = load_all_blocks_for_docs()
-        block = blocks[0]
-
-        assert hasattr(block, "id")
-        assert hasattr(block, "name")
-        assert hasattr(block, "description")
-        assert hasattr(block, "categories")
-        assert hasattr(block, "inputs")
-        assert hasattr(block, "outputs")
-
-    def test_file_mapping_is_deterministic(self):
-        """Test that file mapping produces consistent results."""
-        import logging
-        import sys
-        from pathlib import Path
-
-        logging.disable(logging.CRITICAL)
-        sys.path.insert(0, str(Path(__file__).parent.parent))
-
-        from scripts.generate_block_docs import (
-            get_block_file_mapping,
-            load_all_blocks_for_docs,
-        )
-
-        # Load blocks twice and compare mappings
-        blocks1 = load_all_blocks_for_docs()
-        blocks2 = load_all_blocks_for_docs()
-
-        mapping1 = get_block_file_mapping(blocks1)
-        mapping2 = get_block_file_mapping(blocks2)
-
-        # Check same files are generated
-        assert set(mapping1.keys()) == set(mapping2.keys())
-
-        # Check same block counts per file
-        for file_path in mapping1:
-            assert len(mapping1[file_path]) == len(mapping2[file_path])
-
-
-if __name__ == "__main__":
-    pytest.main([__file__, "-v"])

autogpt_platform/backend/snapshots/grph_single

@@ -11,7 +11,6 @@
   "forked_from_version": null,
   "has_external_trigger": false,
   "has_human_in_the_loop": false,
-  "has_sensitive_action": false,
   "id": "graph-123",
   "input_schema": {
     "properties": {},

autogpt_platform/backend/snapshots/grphs_all

@@ -11,7 +11,6 @@
     "forked_from_version": null,
     "has_external_trigger": false,
     "has_human_in_the_loop": false,
-    "has_sensitive_action": false,
     "id": "graph-123",
     "input_schema": {
       "properties": {},

autogpt_platform/backend/snapshots/lib_agts_search

@@ -27,8 +27,6 @@
         "properties": {}
       },
       "has_external_trigger": false,
-      "has_human_in_the_loop": false,
-      "has_sensitive_action": false,
       "trigger_setup_info": null,
       "new_output": false,
       "can_access_graph": true,
@@ -36,8 +34,7 @@
       "is_favorite": false,
       "recommended_schedule_cron": null,
       "settings": {
-        "human_in_the_loop_safe_mode": true,
-        "sensitive_action_safe_mode": false
+        "human_in_the_loop_safe_mode": null
       },
       "marketplace_listing": null
     },
@@ -68,8 +65,6 @@
         "properties": {}
       },
       "has_external_trigger": false,
-      "has_human_in_the_loop": false,
-      "has_sensitive_action": false,
       "trigger_setup_info": null,
       "new_output": false,
       "can_access_graph": false,
@@ -77,8 +72,7 @@
       "is_favorite": false,
       "recommended_schedule_cron": null,
       "settings": {
-        "human_in_the_loop_safe_mode": true,
-        "sensitive_action_safe_mode": false
+        "human_in_the_loop_safe_mode": null
       },
       "marketplace_listing": null
     }

autogpt_platform/backend/test/agent_generator/__init__.py

@@ -1 +0,0 @@
-"""Tests for agent generator module."""

autogpt_platform/backend/test/agent_generator/test_core_integration.py

@@ -1,273 +0,0 @@
-"""
-Tests for the Agent Generator core module.
-
-This test suite verifies that the core functions correctly delegate to
-the external Agent Generator service.
-"""
-
-from unittest.mock import AsyncMock, patch
-
-import pytest
-
-from backend.api.features.chat.tools.agent_generator import core
-from backend.api.features.chat.tools.agent_generator.core import (
-    AgentGeneratorNotConfiguredError,
-)
-
-
-class TestServiceNotConfigured:
-    """Test that functions raise AgentGeneratorNotConfiguredError when service is not configured."""
-
-    @pytest.mark.asyncio
-    async def test_decompose_goal_raises_when_not_configured(self):
-        """Test that decompose_goal raises error when service not configured."""
-        with patch.object(core, "is_external_service_configured", return_value=False):
-            with pytest.raises(AgentGeneratorNotConfiguredError):
-                await core.decompose_goal("Build a chatbot")
-
-    @pytest.mark.asyncio
-    async def test_generate_agent_raises_when_not_configured(self):
-        """Test that generate_agent raises error when service not configured."""
-        with patch.object(core, "is_external_service_configured", return_value=False):
-            with pytest.raises(AgentGeneratorNotConfiguredError):
-                await core.generate_agent({"steps": []})
-
-    @pytest.mark.asyncio
-    async def test_generate_agent_patch_raises_when_not_configured(self):
-        """Test that generate_agent_patch raises error when service not configured."""
-        with patch.object(core, "is_external_service_configured", return_value=False):
-            with pytest.raises(AgentGeneratorNotConfiguredError):
-                await core.generate_agent_patch("Add a node", {"nodes": []})
-
-
-class TestDecomposeGoal:
-    """Test decompose_goal function service delegation."""
-
-    @pytest.mark.asyncio
-    async def test_calls_external_service(self):
-        """Test that decompose_goal calls the external service."""
-        expected_result = {"type": "instructions", "steps": ["Step 1"]}
-
-        with patch.object(
-            core, "is_external_service_configured", return_value=True
-        ), patch.object(
-            core, "decompose_goal_external", new_callable=AsyncMock
-        ) as mock_external:
-            mock_external.return_value = expected_result
-
-            result = await core.decompose_goal("Build a chatbot")
-
-            mock_external.assert_called_once_with("Build a chatbot", "")
-            assert result == expected_result
-
-    @pytest.mark.asyncio
-    async def test_passes_context_to_external_service(self):
-        """Test that decompose_goal passes context to external service."""
-        expected_result = {"type": "instructions", "steps": ["Step 1"]}
-
-        with patch.object(
-            core, "is_external_service_configured", return_value=True
-        ), patch.object(
-            core, "decompose_goal_external", new_callable=AsyncMock
-        ) as mock_external:
-            mock_external.return_value = expected_result
-
-            await core.decompose_goal("Build a chatbot", "Use Python")
-
-            mock_external.assert_called_once_with("Build a chatbot", "Use Python")
-
-    @pytest.mark.asyncio
-    async def test_returns_none_on_service_failure(self):
-        """Test that decompose_goal returns None when external service fails."""
-        with patch.object(
-            core, "is_external_service_configured", return_value=True
-        ), patch.object(
-            core, "decompose_goal_external", new_callable=AsyncMock
-        ) as mock_external:
-            mock_external.return_value = None
-
-            result = await core.decompose_goal("Build a chatbot")
-
-            assert result is None
-
-
-class TestGenerateAgent:
-    """Test generate_agent function service delegation."""
-
-    @pytest.mark.asyncio
-    async def test_calls_external_service(self):
-        """Test that generate_agent calls the external service."""
-        expected_result = {"name": "Test Agent", "nodes": [], "links": []}
-
-        with patch.object(
-            core, "is_external_service_configured", return_value=True
-        ), patch.object(
-            core, "generate_agent_external", new_callable=AsyncMock
-        ) as mock_external:
-            mock_external.return_value = expected_result
-
-            instructions = {"type": "instructions", "steps": ["Step 1"]}
-            result = await core.generate_agent(instructions)
-
-            mock_external.assert_called_once_with(instructions)
-            # Result should have id, version, is_active added if not present
-            assert result is not None
-            assert result["name"] == "Test Agent"
-            assert "id" in result
-            assert result["version"] == 1
-            assert result["is_active"] is True
-
-    @pytest.mark.asyncio
-    async def test_preserves_existing_id_and_version(self):
-        """Test that external service result preserves existing id and version."""
-        expected_result = {
-            "id": "existing-id",
-            "version": 3,
-            "is_active": False,
-            "name": "Test Agent",
-        }
-
-        with patch.object(
-            core, "is_external_service_configured", return_value=True
-        ), patch.object(
-            core, "generate_agent_external", new_callable=AsyncMock
-        ) as mock_external:
-            mock_external.return_value = expected_result.copy()
-
-            result = await core.generate_agent({"steps": []})
-
-            assert result is not None
-            assert result["id"] == "existing-id"
-            assert result["version"] == 3
-            assert result["is_active"] is False
-
-    @pytest.mark.asyncio
-    async def test_returns_none_when_external_service_fails(self):
-        """Test that generate_agent returns None when external service fails."""
-        with patch.object(
-            core, "is_external_service_configured", return_value=True
-        ), patch.object(
-            core, "generate_agent_external", new_callable=AsyncMock
-        ) as mock_external:
-            mock_external.return_value = None
-
-            result = await core.generate_agent({"steps": []})
-
-            assert result is None
-
-
-class TestGenerateAgentPatch:
-    """Test generate_agent_patch function service delegation."""
-
-    @pytest.mark.asyncio
-    async def test_calls_external_service(self):
-        """Test that generate_agent_patch calls the external service."""
-        expected_result = {"name": "Updated Agent", "nodes": [], "links": []}
-
-        with patch.object(
-            core, "is_external_service_configured", return_value=True
-        ), patch.object(
-            core, "generate_agent_patch_external", new_callable=AsyncMock
-        ) as mock_external:
-            mock_external.return_value = expected_result
-
-            current_agent = {"nodes": [], "links": []}
-            result = await core.generate_agent_patch("Add a node", current_agent)
-
-            mock_external.assert_called_once_with("Add a node", current_agent)
-            assert result == expected_result
-
-    @pytest.mark.asyncio
-    async def test_returns_clarifying_questions(self):
-        """Test that generate_agent_patch returns clarifying questions."""
-        expected_result = {
-            "type": "clarifying_questions",
-            "questions": [{"question": "What type of node?"}],
-        }
-
-        with patch.object(
-            core, "is_external_service_configured", return_value=True
-        ), patch.object(
-            core, "generate_agent_patch_external", new_callable=AsyncMock
-        ) as mock_external:
-            mock_external.return_value = expected_result
-
-            result = await core.generate_agent_patch("Add a node", {"nodes": []})
-
-            assert result == expected_result
-
-    @pytest.mark.asyncio
-    async def test_returns_none_when_external_service_fails(self):
-        """Test that generate_agent_patch returns None when service fails."""
-        with patch.object(
-            core, "is_external_service_configured", return_value=True
-        ), patch.object(
-            core, "generate_agent_patch_external", new_callable=AsyncMock
-        ) as mock_external:
-            mock_external.return_value = None
-
-            result = await core.generate_agent_patch("Add a node", {"nodes": []})
-
-            assert result is None
-
-
-class TestJsonToGraph:
-    """Test json_to_graph function."""
-
-    def test_converts_agent_json_to_graph(self):
-        """Test conversion of agent JSON to Graph model."""
-        agent_json = {
-            "id": "test-id",
-            "version": 2,
-            "is_active": True,
-            "name": "Test Agent",
-            "description": "A test agent",
-            "nodes": [
-                {
-                    "id": "node1",
-                    "block_id": "block1",
-                    "input_default": {"key": "value"},
-                    "metadata": {"x": 100},
-                }
-            ],
-            "links": [
-                {
-                    "id": "link1",
-                    "source_id": "node1",
-                    "sink_id": "output",
-                    "source_name": "result",
-                    "sink_name": "input",
-                    "is_static": False,
-                }
-            ],
-        }
-
-        graph = core.json_to_graph(agent_json)
-
-        assert graph.id == "test-id"
-        assert graph.version == 2
-        assert graph.is_active is True
-        assert graph.name == "Test Agent"
-        assert graph.description == "A test agent"
-        assert len(graph.nodes) == 1
-        assert graph.nodes[0].id == "node1"
-        assert graph.nodes[0].block_id == "block1"
-        assert len(graph.links) == 1
-        assert graph.links[0].source_id == "node1"
-
-    def test_generates_ids_if_missing(self):
-        """Test that missing IDs are generated."""
-        agent_json = {
-            "name": "Test Agent",
-            "nodes": [{"block_id": "block1"}],
-            "links": [],
-        }
-
-        graph = core.json_to_graph(agent_json)
-
-        assert graph.id is not None
-        assert graph.nodes[0].id is not None
-
-
-if __name__ == "__main__":
-    pytest.main([__file__, "-v"])

autogpt_platform/backend/test/agent_generator/test_service.py

@@ -1,422 +0,0 @@
-"""
-Tests for the Agent Generator external service client.
-
-This test suite verifies the external Agent Generator service integration,
-including service detection, API calls, and error handling.
-"""
-
-from unittest.mock import AsyncMock, MagicMock, patch
-
-import httpx
-import pytest
-
-from backend.api.features.chat.tools.agent_generator import service
-
-
-class TestServiceConfiguration:
-    """Test service configuration detection."""
-
-    def setup_method(self):
-        """Reset settings singleton before each test."""
-        service._settings = None
-        service._client = None
-
-    def test_external_service_not_configured_when_host_empty(self):
-        """Test that external service is not configured when host is empty."""
-        mock_settings = MagicMock()
-        mock_settings.config.agentgenerator_host = ""
-
-        with patch.object(service, "_get_settings", return_value=mock_settings):
-            assert service.is_external_service_configured() is False
-
-    def test_external_service_configured_when_host_set(self):
-        """Test that external service is configured when host is set."""
-        mock_settings = MagicMock()
-        mock_settings.config.agentgenerator_host = "agent-generator.local"
-
-        with patch.object(service, "_get_settings", return_value=mock_settings):
-            assert service.is_external_service_configured() is True
-
-    def test_get_base_url(self):
-        """Test base URL construction."""
-        mock_settings = MagicMock()
-        mock_settings.config.agentgenerator_host = "agent-generator.local"
-        mock_settings.config.agentgenerator_port = 8000
-
-        with patch.object(service, "_get_settings", return_value=mock_settings):
-            url = service._get_base_url()
-            assert url == "http://agent-generator.local:8000"
-
-
-class TestDecomposeGoalExternal:
-    """Test decompose_goal_external function."""
-
-    def setup_method(self):
-        """Reset client singleton before each test."""
-        service._settings = None
-        service._client = None
-
-    @pytest.mark.asyncio
-    async def test_decompose_goal_returns_instructions(self):
-        """Test successful decomposition returning instructions."""
-        mock_response = MagicMock()
-        mock_response.json.return_value = {
-            "success": True,
-            "type": "instructions",
-            "steps": ["Step 1", "Step 2"],
-        }
-        mock_response.raise_for_status = MagicMock()
-
-        mock_client = AsyncMock()
-        mock_client.post.return_value = mock_response
-
-        with patch.object(service, "_get_client", return_value=mock_client):
-            result = await service.decompose_goal_external("Build a chatbot")
-
-        assert result == {"type": "instructions", "steps": ["Step 1", "Step 2"]}
-        mock_client.post.assert_called_once_with(
-            "/api/decompose-description", json={"description": "Build a chatbot"}
-        )
-
-    @pytest.mark.asyncio
-    async def test_decompose_goal_returns_clarifying_questions(self):
-        """Test decomposition returning clarifying questions."""
-        mock_response = MagicMock()
-        mock_response.json.return_value = {
-            "success": True,
-            "type": "clarifying_questions",
-            "questions": ["What platform?", "What language?"],
-        }
-        mock_response.raise_for_status = MagicMock()
-
-        mock_client = AsyncMock()
-        mock_client.post.return_value = mock_response
-
-        with patch.object(service, "_get_client", return_value=mock_client):
-            result = await service.decompose_goal_external("Build something")
-
-        assert result == {
-            "type": "clarifying_questions",
-            "questions": ["What platform?", "What language?"],
-        }
-
-    @pytest.mark.asyncio
-    async def test_decompose_goal_with_context(self):
-        """Test decomposition with additional context."""
-        mock_response = MagicMock()
-        mock_response.json.return_value = {
-            "success": True,
-            "type": "instructions",
-            "steps": ["Step 1"],
-        }
-        mock_response.raise_for_status = MagicMock()
-
-        mock_client = AsyncMock()
-        mock_client.post.return_value = mock_response
-
-        with patch.object(service, "_get_client", return_value=mock_client):
-            await service.decompose_goal_external(
-                "Build a chatbot", context="Use Python"
-            )
-
-        mock_client.post.assert_called_once_with(
-            "/api/decompose-description",
-            json={"description": "Build a chatbot", "user_instruction": "Use Python"},
-        )
-
-    @pytest.mark.asyncio
-    async def test_decompose_goal_returns_unachievable_goal(self):
-        """Test decomposition returning unachievable goal response."""
-        mock_response = MagicMock()
-        mock_response.json.return_value = {
-            "success": True,
-            "type": "unachievable_goal",
-            "reason": "Cannot do X",
-            "suggested_goal": "Try Y instead",
-        }
-        mock_response.raise_for_status = MagicMock()
-
-        mock_client = AsyncMock()
-        mock_client.post.return_value = mock_response
-
-        with patch.object(service, "_get_client", return_value=mock_client):
-            result = await service.decompose_goal_external("Do something impossible")
-
-        assert result == {
-            "type": "unachievable_goal",
-            "reason": "Cannot do X",
-            "suggested_goal": "Try Y instead",
-        }
-
-    @pytest.mark.asyncio
-    async def test_decompose_goal_handles_http_error(self):
-        """Test decomposition handles HTTP errors gracefully."""
-        mock_client = AsyncMock()
-        mock_client.post.side_effect = httpx.HTTPStatusError(
-            "Server error", request=MagicMock(), response=MagicMock()
-        )
-
-        with patch.object(service, "_get_client", return_value=mock_client):
-            result = await service.decompose_goal_external("Build a chatbot")
-
-        assert result is None
-
-    @pytest.mark.asyncio
-    async def test_decompose_goal_handles_request_error(self):
-        """Test decomposition handles request errors gracefully."""
-        mock_client = AsyncMock()
-        mock_client.post.side_effect = httpx.RequestError("Connection failed")
-
-        with patch.object(service, "_get_client", return_value=mock_client):
-            result = await service.decompose_goal_external("Build a chatbot")
-
-        assert result is None
-
-    @pytest.mark.asyncio
-    async def test_decompose_goal_handles_service_error(self):
-        """Test decomposition handles service returning error."""
-        mock_response = MagicMock()
-        mock_response.json.return_value = {
-            "success": False,
-            "error": "Internal error",
-        }
-        mock_response.raise_for_status = MagicMock()
-
-        mock_client = AsyncMock()
-        mock_client.post.return_value = mock_response
-
-        with patch.object(service, "_get_client", return_value=mock_client):
-            result = await service.decompose_goal_external("Build a chatbot")
-
-        assert result is None
-
-
-class TestGenerateAgentExternal:
-    """Test generate_agent_external function."""
-
-    def setup_method(self):
-        """Reset client singleton before each test."""
-        service._settings = None
-        service._client = None
-
-    @pytest.mark.asyncio
-    async def test_generate_agent_success(self):
-        """Test successful agent generation."""
-        agent_json = {
-            "name": "Test Agent",
-            "nodes": [],
-            "links": [],
-        }
-        mock_response = MagicMock()
-        mock_response.json.return_value = {
-            "success": True,
-            "agent_json": agent_json,
-        }
-        mock_response.raise_for_status = MagicMock()
-
-        mock_client = AsyncMock()
-        mock_client.post.return_value = mock_response
-
-        instructions = {"type": "instructions", "steps": ["Step 1"]}
-
-        with patch.object(service, "_get_client", return_value=mock_client):
-            result = await service.generate_agent_external(instructions)
-
-        assert result == agent_json
-        mock_client.post.assert_called_once_with(
-            "/api/generate-agent", json={"instructions": instructions}
-        )
-
-    @pytest.mark.asyncio
-    async def test_generate_agent_handles_error(self):
-        """Test agent generation handles errors gracefully."""
-        mock_client = AsyncMock()
-        mock_client.post.side_effect = httpx.RequestError("Connection failed")
-
-        with patch.object(service, "_get_client", return_value=mock_client):
-            result = await service.generate_agent_external({"steps": []})
-
-        assert result is None
-
-
-class TestGenerateAgentPatchExternal:
-    """Test generate_agent_patch_external function."""
-
-    def setup_method(self):
-        """Reset client singleton before each test."""
-        service._settings = None
-        service._client = None
-
-    @pytest.mark.asyncio
-    async def test_generate_patch_returns_updated_agent(self):
-        """Test successful patch generation returning updated agent."""
-        updated_agent = {
-            "name": "Updated Agent",
-            "nodes": [{"id": "1", "block_id": "test"}],
-            "links": [],
-        }
-        mock_response = MagicMock()
-        mock_response.json.return_value = {
-            "success": True,
-            "agent_json": updated_agent,
-        }
-        mock_response.raise_for_status = MagicMock()
-
-        mock_client = AsyncMock()
-        mock_client.post.return_value = mock_response
-
-        current_agent = {"name": "Old Agent", "nodes": [], "links": []}
-
-        with patch.object(service, "_get_client", return_value=mock_client):
-            result = await service.generate_agent_patch_external(
-                "Add a new node", current_agent
-            )
-
-        assert result == updated_agent
-        mock_client.post.assert_called_once_with(
-            "/api/update-agent",
-            json={
-                "update_request": "Add a new node",
-                "current_agent_json": current_agent,
-            },
-        )
-
-    @pytest.mark.asyncio
-    async def test_generate_patch_returns_clarifying_questions(self):
-        """Test patch generation returning clarifying questions."""
-        mock_response = MagicMock()
-        mock_response.json.return_value = {
-            "success": True,
-            "type": "clarifying_questions",
-            "questions": ["What type of node?"],
-        }
-        mock_response.raise_for_status = MagicMock()
-
-        mock_client = AsyncMock()
-        mock_client.post.return_value = mock_response
-
-        with patch.object(service, "_get_client", return_value=mock_client):
-            result = await service.generate_agent_patch_external(
-                "Add something", {"nodes": []}
-            )
-
-        assert result == {
-            "type": "clarifying_questions",
-            "questions": ["What type of node?"],
-        }
-
-
-class TestHealthCheck:
-    """Test health_check function."""
-
-    def setup_method(self):
-        """Reset singletons before each test."""
-        service._settings = None
-        service._client = None
-
-    @pytest.mark.asyncio
-    async def test_health_check_returns_false_when_not_configured(self):
-        """Test health check returns False when service not configured."""
-        with patch.object(
-            service, "is_external_service_configured", return_value=False
-        ):
-            result = await service.health_check()
-            assert result is False
-
-    @pytest.mark.asyncio
-    async def test_health_check_returns_true_when_healthy(self):
-        """Test health check returns True when service is healthy."""
-        mock_response = MagicMock()
-        mock_response.json.return_value = {
-            "status": "healthy",
-            "blocks_loaded": True,
-        }
-        mock_response.raise_for_status = MagicMock()
-
-        mock_client = AsyncMock()
-        mock_client.get.return_value = mock_response
-
-        with patch.object(service, "is_external_service_configured", return_value=True):
-            with patch.object(service, "_get_client", return_value=mock_client):
-                result = await service.health_check()
-
-        assert result is True
-        mock_client.get.assert_called_once_with("/health")
-
-    @pytest.mark.asyncio
-    async def test_health_check_returns_false_when_not_healthy(self):
-        """Test health check returns False when service is not healthy."""
-        mock_response = MagicMock()
-        mock_response.json.return_value = {
-            "status": "unhealthy",
-            "blocks_loaded": False,
-        }
-        mock_response.raise_for_status = MagicMock()
-
-        mock_client = AsyncMock()
-        mock_client.get.return_value = mock_response
-
-        with patch.object(service, "is_external_service_configured", return_value=True):
-            with patch.object(service, "_get_client", return_value=mock_client):
-                result = await service.health_check()
-
-        assert result is False
-
-    @pytest.mark.asyncio
-    async def test_health_check_returns_false_on_error(self):
-        """Test health check returns False on connection error."""
-        mock_client = AsyncMock()
-        mock_client.get.side_effect = httpx.RequestError("Connection failed")
-
-        with patch.object(service, "is_external_service_configured", return_value=True):
-            with patch.object(service, "_get_client", return_value=mock_client):
-                result = await service.health_check()
-
-        assert result is False
-
-
-class TestGetBlocksExternal:
-    """Test get_blocks_external function."""
-
-    def setup_method(self):
-        """Reset client singleton before each test."""
-        service._settings = None
-        service._client = None
-
-    @pytest.mark.asyncio
-    async def test_get_blocks_success(self):
-        """Test successful blocks retrieval."""
-        blocks = [
-            {"id": "block1", "name": "Block 1"},
-            {"id": "block2", "name": "Block 2"},
-        ]
-        mock_response = MagicMock()
-        mock_response.json.return_value = {
-            "success": True,
-            "blocks": blocks,
-        }
-        mock_response.raise_for_status = MagicMock()
-
-        mock_client = AsyncMock()
-        mock_client.get.return_value = mock_response
-
-        with patch.object(service, "_get_client", return_value=mock_client):
-            result = await service.get_blocks_external()
-
-        assert result == blocks
-        mock_client.get.assert_called_once_with("/api/blocks")
-
-    @pytest.mark.asyncio
-    async def test_get_blocks_handles_error(self):
-        """Test blocks retrieval handles errors gracefully."""
-        mock_client = AsyncMock()
-        mock_client.get.side_effect = httpx.RequestError("Connection failed")
-
-        with patch.object(service, "_get_client", return_value=mock_client):
-            result = await service.get_blocks_external()
-
-        assert result is None
-
-
-if __name__ == "__main__":
-    pytest.main([__file__, "-v"])

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

@@ -5,11 +5,10 @@ import {
   TooltipContent,
   TooltipTrigger,
 } from "@/components/atoms/Tooltip/BaseTooltip";
-import { CircleNotchIcon, PlayIcon, StopIcon } from "@phosphor-icons/react";
+import { PlayIcon, StopIcon } from "@phosphor-icons/react";
 import { useShallow } from "zustand/react/shallow";
 import { RunInputDialog } from "../RunInputDialog/RunInputDialog";
 import { useRunGraph } from "./useRunGraph";
-import { cn } from "@/lib/utils";
 
 export const RunGraph = ({ flowID }: { flowID: string | null }) => {
   const {
@@ -25,31 +24,6 @@ export const RunGraph = ({ flowID }: { flowID: string | null }) => {
     useShallow((state) => state.isGraphRunning),
   );
 
-  const isLoading = isExecutingGraph || isTerminatingGraph || isSaving;
-
-  // Determine which icon to show with proper animation
-  const renderIcon = () => {
-    const iconClass = cn(
-      "size-4 transition-transform duration-200 ease-out",
-      !isLoading && "group-hover:scale-110",
-    );
-
-    if (isLoading) {
-      return (
-        <CircleNotchIcon
-          className={cn(iconClass, "animate-spin")}
-          weight="bold"
-        />
-      );
-    }
-
-    if (isGraphRunning) {
-      return <StopIcon className={iconClass} weight="fill" />;
-    }
-
-    return <PlayIcon className={iconClass} weight="fill" />;
-  };
-
   return (
     <>
       <Tooltip>
@@ -59,18 +33,18 @@ export const RunGraph = ({ flowID }: { flowID: string | null }) => {
             variant={isGraphRunning ? "destructive" : "primary"}
             data-id={isGraphRunning ? "stop-graph-button" : "run-graph-button"}
             onClick={isGraphRunning ? handleStopGraph : handleRunGraph}
-            disabled={!flowID || isLoading}
-            className="group"
+            disabled={!flowID || isExecutingGraph || isTerminatingGraph}
+            loading={isExecutingGraph || isTerminatingGraph || isSaving}
           >
-            {renderIcon()}
+            {!isGraphRunning ? (
+              <PlayIcon className="size-4" />
+            ) : (
+              <StopIcon className="size-4" />
+            )}
           </Button>
         </TooltipTrigger>
         <TooltipContent>
-          {isLoading
-            ? "Processing..."
-            : isGraphRunning
-              ? "Stop agent"
-              : "Run agent"}
+          {isGraphRunning ? "Stop agent" : "Run agent"}
         </TooltipContent>
       </Tooltip>
       <RunInputDialog

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

@@ -10,7 +10,6 @@ import { useRunInputDialog } from "./useRunInputDialog";
 import { CronSchedulerDialog } from "../CronSchedulerDialog/CronSchedulerDialog";
 import { useTutorialStore } from "@/app/(platform)/build/stores/tutorialStore";
 import { useEffect } from "react";
-import { CredentialsGroupedView } from "@/components/contextual/CredentialsInput/components/CredentialsGroupedView/CredentialsGroupedView";
 
 export const RunInputDialog = ({
   isOpen,
@@ -24,17 +23,19 @@ export const RunInputDialog = ({
   const hasInputs = useGraphStore((state) => state.hasInputs);
   const hasCredentials = useGraphStore((state) => state.hasCredentials);
   const inputSchema = useGraphStore((state) => state.inputSchema);
+  const credentialsSchema = useGraphStore(
+    (state) => state.credentialsInputSchema,
+  );
 
   const {
-    credentialFields,
-    requiredCredentials,
+    credentialsUiSchema,
     handleManualRun,
     handleInputChange,
     openCronSchedulerDialog,
     setOpenCronSchedulerDialog,
     inputValues,
     credentialValues,
-    handleCredentialFieldChange,
+    handleCredentialChange,
     isExecutingGraph,
   } = useRunInputDialog({ setIsOpen });
 
@@ -61,67 +62,67 @@ export const RunInputDialog = ({
           isOpen,
           set: setIsOpen,
         }}
-        styling={{ maxWidth: "700px", minWidth: "700px" }}
+        styling={{ maxWidth: "600px", minWidth: "600px" }}
       >
         <Dialog.Content>
-          <div
-            className="grid grid-cols-[1fr_auto] gap-10 p-1"
-            data-id="run-input-dialog-content"
-          >
-            <div className="space-y-6">
-              {/* Credentials Section */}
-              {hasCredentials() && credentialFields.length > 0 && (
-                <div data-id="run-input-credentials-section">
-                  <div className="mb-4">
-                    <Text variant="h4" className="text-gray-900">
-                      Credentials
-                    </Text>
-                  </div>
-                  <div className="px-2" data-id="run-input-credentials-form">
-                    <CredentialsGroupedView
-                      credentialFields={credentialFields}
-                      requiredCredentials={requiredCredentials}
-                      inputCredentials={credentialValues}
-                      inputValues={inputValues}
-                      onCredentialChange={handleCredentialFieldChange}
-                    />
-                  </div>
+          <div className="space-y-6 p-1" data-id="run-input-dialog-content">
+            {/* Credentials Section */}
+            {hasCredentials() && (
+              <div data-id="run-input-credentials-section">
+                <div className="mb-4">
+                  <Text variant="h4" className="text-gray-900">
+                    Credentials
+                  </Text>
                 </div>
-              )}
-
-              {/* Inputs Section */}
-              {hasInputs() && (
-                <div data-id="run-input-inputs-section">
-                  <div className="mb-4">
-                    <Text variant="h4" className="text-gray-900">
-                      Inputs
-                    </Text>
-                  </div>
-                  <div data-id="run-input-inputs-form">
-                    <FormRenderer
-                      jsonSchema={inputSchema as RJSFSchema}
-                      handleChange={(v) => handleInputChange(v.formData)}
-                      uiSchema={uiSchema}
-                      initialValues={{}}
-                      formContext={{
-                        showHandles: false,
-                        size: "large",
-                      }}
-                    />
-                  </div>
+                <div className="px-2" data-id="run-input-credentials-form">
+                  <FormRenderer
+                    jsonSchema={credentialsSchema as RJSFSchema}
+                    handleChange={(v) => handleCredentialChange(v.formData)}
+                    uiSchema={credentialsUiSchema}
+                    initialValues={{}}
+                    formContext={{
+                      showHandles: false,
+                      size: "large",
+                      showOptionalToggle: false,
+                    }}
+                  />
                 </div>
-              )}
-            </div>
+              </div>
+            )}
 
+            {/* Inputs Section */}
+            {hasInputs() && (
+              <div data-id="run-input-inputs-section">
+                <div className="mb-4">
+                  <Text variant="h4" className="text-gray-900">
+                    Inputs
+                  </Text>
+                </div>
+                <div data-id="run-input-inputs-form">
+                  <FormRenderer
+                    jsonSchema={inputSchema as RJSFSchema}
+                    handleChange={(v) => handleInputChange(v.formData)}
+                    uiSchema={uiSchema}
+                    initialValues={{}}
+                    formContext={{
+                      showHandles: false,
+                      size: "large",
+                    }}
+                  />
+                </div>
+              </div>
+            )}
+
+            {/* Action Button */}
             <div
-              className="flex flex-col items-end justify-start"
+              className="flex justify-end pt-2"
               data-id="run-input-actions-section"
             >
               {purpose === "run" && (
                 <Button
                   variant="primary"
                   size="large"
-                  className="group h-fit min-w-0 gap-2 px-10"
+                  className="group h-fit min-w-0 gap-2"
                   onClick={handleManualRun}
                   loading={isExecutingGraph}
                   data-id="run-input-manual-run-button"
@@ -136,7 +137,7 @@ export const RunInputDialog = ({
                 <Button
                   variant="primary"
                   size="large"
-                  className="group h-fit min-w-0 gap-2 px-10"
+                  className="group h-fit min-w-0 gap-2"
                   onClick={() => setOpenCronSchedulerDialog(true)}
                   data-id="run-input-schedule-button"
                 >

autogpt_platform/frontend/src/app/(platform)/build/components/BuilderActions/components/RunInputDialog/useRunInputDialog.ts

@@ -7,11 +7,12 @@ import {
   GraphExecutionMeta,
 } from "@/lib/autogpt-server-api";
 import { parseAsInteger, parseAsString, useQueryStates } from "nuqs";
-import { useCallback, useMemo, useState } from "react";
+import { useMemo, useState } from "react";
+import { uiSchema } from "../../../FlowEditor/nodes/uiSchema";
+import { isCredentialFieldSchema } from "@/components/renderers/InputRenderer/custom/CredentialField/helpers";
 import { useNodeStore } from "@/app/(platform)/build/stores/nodeStore";
 import { useToast } from "@/components/molecules/Toast/use-toast";
 import { useReactFlow } from "@xyflow/react";
-import type { CredentialField } from "@/components/contextual/CredentialsInput/components/CredentialsGroupedView/helpers";
 
 export const useRunInputDialog = ({
   setIsOpen,
@@ -119,32 +120,27 @@ export const useRunInputDialog = ({
       },
     });
 
-  // Convert credentials schema to credential fields array for CredentialsGroupedView
-  const credentialFields: CredentialField[] = useMemo(() => {
-    if (!credentialsSchema?.properties) return [];
-    return Object.entries(credentialsSchema.properties);
-  }, [credentialsSchema]);
+  // We are rendering the credentials field differently compared to other fields.
+  // In the node, we have the field name as "credential" - so our library catches it and renders it differently.
+  // But here we have a different name, something like `Firecrawl credentials`, so here we are telling the library that this field is a credential field type.
 
-  // Get required credentials as a Set
-  const requiredCredentials = useMemo(() => {
-    return new Set<string>(credentialsSchema?.required || []);
-  }, [credentialsSchema]);
+  const credentialsUiSchema = useMemo(() => {
+    const dynamicUiSchema: any = { ...uiSchema };
 
-  // Handler for individual credential changes
-  const handleCredentialFieldChange = useCallback(
-    (key: string, value?: CredentialsMetaInput) => {
-      setCredentialValues((prev) => {
-        if (value) {
-          return { ...prev, [key]: value };
-        } else {
-          const next = { ...prev };
-          delete next[key];
-          return next;
+    if (credentialsSchema?.properties) {
+      Object.keys(credentialsSchema.properties).forEach((fieldName) => {
+        const fieldSchema = credentialsSchema.properties[fieldName];
+        if (isCredentialFieldSchema(fieldSchema)) {
+          dynamicUiSchema[fieldName] = {
+            ...dynamicUiSchema[fieldName],
+            "ui:field": "custom/credential_field",
+          };
         }
       });
-    },
-    [],
-  );
+    }
+
+    return dynamicUiSchema;
+  }, [credentialsSchema]);
 
   const handleManualRun = async () => {
     // Filter out incomplete credentials (those without a valid id)
@@ -177,14 +173,12 @@ export const useRunInputDialog = ({
   };
 
   return {
-    credentialFields,
-    requiredCredentials,
+    credentialsUiSchema,
     inputValues,
     credentialValues,
     isExecutingGraph,
     handleInputChange,
     handleCredentialChange,
-    handleCredentialFieldChange,
     handleManualRun,
     openCronSchedulerDialog,
     setOpenCronSchedulerDialog,

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

@@ -18,118 +18,69 @@ interface Props {
   fullWidth?: boolean;
 }
 
-interface SafeModeButtonProps {
-  isEnabled: boolean;
-  label: string;
-  tooltipEnabled: string;
-  tooltipDisabled: string;
-  onToggle: () => void;
-  isPending: boolean;
-  fullWidth?: boolean;
-}
-
-function SafeModeButton({
-  isEnabled,
-  label,
-  tooltipEnabled,
-  tooltipDisabled,
-  onToggle,
-  isPending,
-  fullWidth = false,
-}: SafeModeButtonProps) {
-  return (
-    <Tooltip delayDuration={100}>
-      <TooltipTrigger asChild>
-        <Button
-          variant={isEnabled ? "primary" : "outline"}
-          size="small"
-          onClick={onToggle}
-          disabled={isPending}
-          className={cn("justify-start", fullWidth ? "w-full" : "")}
-        >
-          {isEnabled ? (
-            <>
-              <ShieldCheckIcon weight="bold" size={16} />
-              <Text variant="body" className="text-zinc-200">
-                {label}: ON
-              </Text>
-            </>
-          ) : (
-            <>
-              <ShieldIcon weight="bold" size={16} />
-              <Text variant="body" className="text-zinc-600">
-                {label}: OFF
-              </Text>
-            </>
-          )}
-        </Button>
-      </TooltipTrigger>
-      <TooltipContent>
-        <div className="text-center">
-          <div className="font-medium">
-            {label}: {isEnabled ? "ON" : "OFF"}
-          </div>
-          <div className="mt-1 text-xs text-muted-foreground">
-            {isEnabled ? tooltipEnabled : tooltipDisabled}
-          </div>
-        </div>
-      </TooltipContent>
-    </Tooltip>
-  );
-}
-
 export function FloatingSafeModeToggle({
   graph,
   className,
   fullWidth = false,
 }: Props) {
   const {
-    currentHITLSafeMode,
-    showHITLToggle,
-    isHITLStateUndetermined,
-    handleHITLToggle,
-    currentSensitiveActionSafeMode,
-    showSensitiveActionToggle,
-    handleSensitiveActionToggle,
+    currentSafeMode,
     isPending,
     shouldShowToggle,
+    isStateUndetermined,
+    handleToggle,
   } = useAgentSafeMode(graph);
 
-  if (!shouldShowToggle || isPending) {
-    return null;
-  }
-
-  const showHITL = showHITLToggle && !isHITLStateUndetermined;
-  const showSensitive = showSensitiveActionToggle;
-
-  if (!showHITL && !showSensitive) {
+  if (!shouldShowToggle || isStateUndetermined || isPending) {
     return null;
   }
 
   return (
-    <div className={cn("fixed z-50 flex flex-col gap-2", className)}>
-      {showHITL && (
-        <SafeModeButton
-          isEnabled={currentHITLSafeMode}
-          label="Human in the loop block approval"
-          tooltipEnabled="The agent will pause at human-in-the-loop blocks and wait for your approval"
-          tooltipDisabled="Human in the loop blocks will proceed automatically"
-          onToggle={handleHITLToggle}
-          isPending={isPending}
-          fullWidth={fullWidth}
-        />
-      )}
-      {showSensitive && (
-        <SafeModeButton
-          isEnabled={currentSensitiveActionSafeMode}
-          label="Sensitive actions blocks approval"
-          tooltipEnabled="The agent will pause at sensitive action blocks and wait for your approval"
-          tooltipDisabled="Sensitive action blocks will proceed automatically"
-          onToggle={handleSensitiveActionToggle}
-          isPending={isPending}
-          fullWidth={fullWidth}
-        />
-      )}
+    <div className={cn("fixed z-50", className)}>
+      <Tooltip delayDuration={100}>
+        <TooltipTrigger asChild>
+          <Button
+            variant={currentSafeMode! ? "primary" : "outline"}
+            key={graph.id}
+            size="small"
+            title={
+              currentSafeMode!
+                ? "Safe Mode: ON. Human in the loop blocks require manual review"
+                : "Safe Mode: OFF. Human in the loop blocks proceed automatically"
+            }
+            onClick={handleToggle}
+            className={cn(fullWidth ? "w-full" : "")}
+          >
+            {currentSafeMode! ? (
+              <>
+                <ShieldCheckIcon weight="bold" size={16} />
+                <Text variant="body" className="text-zinc-200">
+                  Safe Mode: ON
+                </Text>
+              </>
+            ) : (
+              <>
+                <ShieldIcon weight="bold" size={16} />
+                <Text variant="body" className="text-zinc-600">
+                  Safe Mode: OFF
+                </Text>
+              </>
+            )}
+          </Button>
+        </TooltipTrigger>
+        <TooltipContent>
+          <div className="text-center">
+            <div className="font-medium">
+              Safe Mode: {currentSafeMode! ? "ON" : "OFF"}
+            </div>
+            <div className="mt-1 text-xs text-muted-foreground">
+              {currentSafeMode!
+                ? "Human in the loop blocks require manual review"
+                : "Human in the loop blocks proceed automatically"}
+            </div>
+          </div>
+        </TooltipContent>
+      </Tooltip>
     </div>
   );
 }

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

@@ -53,14 +53,14 @@ export const CustomControls = memo(
     const controls = [
       {
         id: "zoom-in-button",
-        icon: <PlusIcon className="size-3.5 text-zinc-600" />,
+        icon: <PlusIcon className="size-4" />,
         label: "Zoom In",
         onClick: () => zoomIn(),
         className: "h-10 w-10 border-none",
       },
       {
         id: "zoom-out-button",
-        icon: <MinusIcon className="size-3.5 text-zinc-600" />,
+        icon: <MinusIcon className="size-4" />,
         label: "Zoom Out",
         onClick: () => zoomOut(),
         className: "h-10 w-10 border-none",
@@ -68,9 +68,9 @@ export const CustomControls = memo(
       {
         id: "tutorial-button",
         icon: isTutorialLoading ? (
-          <CircleNotchIcon className="size-3.5 animate-spin text-zinc-600" />
+          <CircleNotchIcon className="size-4 animate-spin" />
         ) : (
-          <ChalkboardIcon className="size-3.5 text-zinc-600" />
+          <ChalkboardIcon className="size-4" />
         ),
         label: isTutorialLoading ? "Loading Tutorial..." : "Start Tutorial",
         onClick: handleTutorialClick,
@@ -79,7 +79,7 @@ export const CustomControls = memo(
       },
       {
         id: "fit-view-button",
-        icon: <FrameCornersIcon className="size-3.5 text-zinc-600" />,
+        icon: <FrameCornersIcon className="size-4" />,
         label: "Fit View",
         onClick: () => fitView({ padding: 0.2, duration: 800, maxZoom: 1 }),
         className: "h-10 w-10 border-none",
@@ -87,9 +87,9 @@ export const CustomControls = memo(
       {
         id: "lock-button",
         icon: !isLocked ? (
-          <LockOpenIcon className="size-3.5 text-zinc-600" />
+          <LockOpenIcon className="size-4" />
         ) : (
-          <LockIcon className="size-3.5 text-zinc-600" />
+          <LockIcon className="size-4" />
         ),
         label: "Toggle Lock",
         onClick: () => setIsLocked(!isLocked),

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

@@ -139,6 +139,14 @@ export const useFlow = () => {
       useNodeStore.getState().setNodes([]);
       useNodeStore.getState().clearResolutionState();
       addNodes(customNodes);
+
+      // Sync hardcoded values with handle IDs.
+      // If a key–value field has a key without a value, the backend omits it from hardcoded values.
+      // But if a handleId exists for that key, it causes inconsistency.
+      // This ensures hardcoded values stay in sync with handle IDs.
+      customNodes.forEach((node) => {
+        useNodeStore.getState().syncHardcodedValuesWithHandleIds(node.id);
+      });
     }
   }, [customNodes, addNodes]);
 
@@ -150,14 +158,6 @@ export const useFlow = () => {
     }
   }, [graph?.links, addLinks]);
 
-  useEffect(() => {
-    if (customNodes.length > 0 && graph?.links) {
-      customNodes.forEach((node) => {
-        useNodeStore.getState().syncHardcodedValuesWithHandleIds(node.id);
-      });
-    }
-  }, [customNodes, graph?.links]);
-
   // update node execution status in nodes
   useEffect(() => {
     if (

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

@@ -19,8 +19,6 @@ export type CustomEdgeData = {
   beadUp?: number;
   beadDown?: number;
   beadData?: Map<string, NodeExecutionResult["status"]>;
-  edgeColorClass?: string;
-  edgeHexColor?: string;
 };
 
 export type CustomEdge = XYEdge<CustomEdgeData, "custom">;
@@ -38,6 +36,7 @@ const CustomEdge = ({
   selected,
 }: EdgeProps<CustomEdge>) => {
   const removeConnection = useEdgeStore((state) => state.removeEdge);
+  // Subscribe to the brokenEdgeIDs map and check if this edge is broken across any node
   const isBroken = useNodeStore((state) => state.isEdgeBroken(id));
   const [isHovered, setIsHovered] = useState(false);
 
@@ -53,7 +52,6 @@ const CustomEdge = ({
   const isStatic = data?.isStatic ?? false;
   const beadUp = data?.beadUp ?? 0;
   const beadDown = data?.beadDown ?? 0;
-  const edgeColorClass = data?.edgeColorClass;
 
   const handleRemoveEdge = () => {
     removeConnection(id);
@@ -72,9 +70,7 @@ const CustomEdge = ({
             ? "!stroke-red-500 !stroke-[2px] [stroke-dasharray:4]"
             : selected
               ? "stroke-zinc-800"
-              : edgeColorClass
-                ? cn(edgeColorClass, "opacity-70 hover:opacity-100")
-                : "stroke-zinc-500/50 hover:stroke-zinc-500",
+              : "stroke-zinc-500/50 hover:stroke-zinc-500",
         )}
       />
       <JSBeads

autogpt_platform/frontend/src/app/(platform)/build/components/FlowEditor/edges/useCustomEdge.ts

@@ -8,7 +8,6 @@ import { useCallback } from "react";
 import { useNodeStore } from "../../../stores/nodeStore";
 import { useHistoryStore } from "../../../stores/historyStore";
 import { CustomEdge } from "./CustomEdge";
-import { getEdgeColorFromOutputType } from "../nodes/helpers";
 
 export const useCustomEdge = () => {
   const edges = useEdgeStore((s) => s.edges);
@@ -35,13 +34,8 @@ export const useCustomEdge = () => {
       if (exists) return;
 
       const nodes = useNodeStore.getState().nodes;
-      const sourceNode = nodes.find((n) => n.id === conn.source);
-      const isStatic = sourceNode?.data?.staticOutput;
-
-      const { colorClass, hexColor } = getEdgeColorFromOutputType(
-        sourceNode?.data?.outputSchema,
-        conn.sourceHandle,
-      );
+      const isStatic = nodes.find((n) => n.id === conn.source)?.data
+        ?.staticOutput;
 
       addEdge({
         source: conn.source,
@@ -50,8 +44,6 @@ export const useCustomEdge = () => {
         targetHandle: conn.targetHandle,
         data: {
           isStatic,
-          edgeColorClass: colorClass,
-          edgeHexColor: hexColor,
         },
       });
     },

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

@@ -1,21 +1,22 @@
 import { Button } from "@/components/atoms/Button/Button";
 import { Text } from "@/components/atoms/Text/Text";
-import {
-  Accordion,
-  AccordionContent,
-  AccordionItem,
-  AccordionTrigger,
-} from "@/components/molecules/Accordion/Accordion";
 import { beautifyString, cn } from "@/lib/utils";
-import { CopyIcon, CheckIcon } from "@phosphor-icons/react";
+import { CaretDownIcon, CopyIcon, CheckIcon } from "@phosphor-icons/react";
 import { NodeDataViewer } from "./components/NodeDataViewer/NodeDataViewer";
 import { ContentRenderer } from "./components/ContentRenderer";
 import { useNodeOutput } from "./useNodeOutput";
 import { ViewMoreData } from "./components/ViewMoreData";
 
 export const NodeDataRenderer = ({ nodeId }: { nodeId: string }) => {
-  const { outputData, copiedKey, handleCopy, executionResultId, inputData } =
-    useNodeOutput(nodeId);
+  const {
+    outputData,
+    isExpanded,
+    setIsExpanded,
+    copiedKey,
+    handleCopy,
+    executionResultId,
+    inputData,
+  } = useNodeOutput(nodeId);
 
   if (Object.keys(outputData).length === 0) {
     return null;
@@ -24,117 +25,122 @@ export const NodeDataRenderer = ({ nodeId }: { nodeId: string }) => {
   return (
     <div
       data-tutorial-id={`node-output`}
-      className="rounded-b-xl border-t border-zinc-200 px-4 py-2"
+      className="flex flex-col gap-3 rounded-b-xl border-t border-zinc-200 px-4 py-4"
     >
-      <Accordion type="single" collapsible defaultValue="node-output">
-        <AccordionItem value="node-output" className="border-none">
-          <AccordionTrigger className="py-2 hover:no-underline">
-            <Text
-              variant="body-medium"
-              className="!font-semibold text-slate-700"
-            >
-              Node Output
-            </Text>
-          </AccordionTrigger>
-          <AccordionContent className="pt-2">
-            <div className="flex max-w-[350px] flex-col gap-4">
-              <div className="space-y-2">
-                <Text variant="small-medium">Input</Text>
+      <div className="flex items-center justify-between">
+        <Text variant="body-medium" className="!font-semibold text-slate-700">
+          Node Output
+        </Text>
+        <Button
+          variant="ghost"
+          size="small"
+          onClick={() => setIsExpanded(!isExpanded)}
+          className="h-fit min-w-0 p-1 text-slate-600 hover:text-slate-900"
+        >
+          <CaretDownIcon
+            size={16}
+            weight="bold"
+            className={`transition-transform ${isExpanded ? "rotate-180" : ""}`}
+          />
+        </Button>
+      </div>
 
-                <ContentRenderer value={inputData} shortContent={false} />
+      {isExpanded && (
+        <>
+          <div className="flex max-w-[350px] flex-col gap-4">
+            <div className="space-y-2">
+              <Text variant="small-medium">Input</Text>
 
-                <div className="mt-1 flex justify-end gap-1">
-                  <NodeDataViewer
-                    data={inputData}
-                    pinName="Input"
-                    execId={executionResultId}
-                  />
-                  <Button
-                    variant="secondary"
-                    size="small"
-                    onClick={() => handleCopy("input", inputData)}
-                    className={cn(
-                      "h-fit min-w-0 gap-1.5 border border-zinc-200 p-2 text-black hover:text-slate-900",
-                      copiedKey === "input" &&
-                        "border-green-400 bg-green-100 hover:border-green-400 hover:bg-green-200",
-                    )}
-                  >
-                    {copiedKey === "input" ? (
-                      <CheckIcon size={12} className="text-green-600" />
-                    ) : (
-                      <CopyIcon size={12} />
-                    )}
-                  </Button>
-                </div>
+              <ContentRenderer value={inputData} shortContent={false} />
+
+              <div className="mt-1 flex justify-end gap-1">
+                <NodeDataViewer
+                  data={inputData}
+                  pinName="Input"
+                  execId={executionResultId}
+                />
+                <Button
+                  variant="secondary"
+                  size="small"
+                  onClick={() => handleCopy("input", inputData)}
+                  className={cn(
+                    "h-fit min-w-0 gap-1.5 border border-zinc-200 p-2 text-black hover:text-slate-900",
+                    copiedKey === "input" &&
+                      "border-green-400 bg-green-100 hover:border-green-400 hover:bg-green-200",
+                  )}
+                >
+                  {copiedKey === "input" ? (
+                    <CheckIcon size={12} className="text-green-600" />
+                  ) : (
+                    <CopyIcon size={12} />
+                  )}
+                </Button>
               </div>
+            </div>
 
-              {Object.entries(outputData)
-                .slice(0, 2)
-                .map(([key, value]) => (
-                  <div key={key} className="flex flex-col gap-2">
-                    <div className="flex items-center gap-2">
-                      <Text
-                        variant="small-medium"
-                        className="!font-semibold text-slate-600"
-                      >
-                        Pin:
-                      </Text>
-                      <Text variant="small" className="text-slate-700">
-                        {beautifyString(key)}
-                      </Text>
-                    </div>
-                    <div className="w-full space-y-2">
-                      <Text
-                        variant="small"
-                        className="!font-semibold text-slate-600"
-                      >
-                        Data:
-                      </Text>
-                      <div className="relative space-y-2">
-                        {value.map((item, index) => (
-                          <div key={index}>
-                            <ContentRenderer value={item} shortContent={true} />
-                          </div>
-                        ))}
-
-                        <div className="mt-1 flex justify-end gap-1">
-                          <NodeDataViewer
-                            data={value}
-                            pinName={key}
-                            execId={executionResultId}
-                          />
-                          <Button
-                            variant="secondary"
-                            size="small"
-                            onClick={() => handleCopy(key, value)}
-                            className={cn(
-                              "h-fit min-w-0 gap-1.5 border border-zinc-200 p-2 text-black hover:text-slate-900",
-                              copiedKey === key &&
-                                "border-green-400 bg-green-100 hover:border-green-400 hover:bg-green-200",
-                            )}
-                          >
-                            {copiedKey === key ? (
-                              <CheckIcon size={12} className="text-green-600" />
-                            ) : (
-                              <CopyIcon size={12} />
-                            )}
-                          </Button>
+            {Object.entries(outputData)
+              .slice(0, 2)
+              .map(([key, value]) => (
+                <div key={key} className="flex flex-col gap-2">
+                  <div className="flex items-center gap-2">
+                    <Text
+                      variant="small-medium"
+                      className="!font-semibold text-slate-600"
+                    >
+                      Pin:
+                    </Text>
+                    <Text variant="small" className="text-slate-700">
+                      {beautifyString(key)}
+                    </Text>
+                  </div>
+                  <div className="w-full space-y-2">
+                    <Text
+                      variant="small"
+                      className="!font-semibold text-slate-600"
+                    >
+                      Data:
+                    </Text>
+                    <div className="relative space-y-2">
+                      {value.map((item, index) => (
+                        <div key={index}>
+                          <ContentRenderer value={item} shortContent={true} />
                         </div>
+                      ))}
+
+                      <div className="mt-1 flex justify-end gap-1">
+                        <NodeDataViewer
+                          data={value}
+                          pinName={key}
+                          execId={executionResultId}
+                        />
+                        <Button
+                          variant="secondary"
+                          size="small"
+                          onClick={() => handleCopy(key, value)}
+                          className={cn(
+                            "h-fit min-w-0 gap-1.5 border border-zinc-200 p-2 text-black hover:text-slate-900",
+                            copiedKey === key &&
+                              "border-green-400 bg-green-100 hover:border-green-400 hover:bg-green-200",
+                          )}
+                        >
+                          {copiedKey === key ? (
+                            <CheckIcon size={12} className="text-green-600" />
+                          ) : (
+                            <CopyIcon size={12} />
+                          )}
+                        </Button>
                       </div>
                     </div>
                   </div>
-                ))}
-            </div>
+                </div>
+              ))}
+          </div>
 
-            {Object.keys(outputData).length > 2 && (
-              <ViewMoreData
-                outputData={outputData}
-                execId={executionResultId}
-              />
-            )}
-          </AccordionContent>
-        </AccordionItem>
-      </Accordion>
+          {Object.keys(outputData).length > 2 && (
+            <ViewMoreData outputData={outputData} execId={executionResultId} />
+          )}
+        </>
+      )}
     </div>
   );
 };

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

@@ -4,6 +4,7 @@ import { useShallow } from "zustand/react/shallow";
 import { useState } from "react";
 
 export const useNodeOutput = (nodeId: string) => {
+  const [isExpanded, setIsExpanded] = useState(true);
   const [copiedKey, setCopiedKey] = useState<string | null>(null);
   const { toast } = useToast();
 
@@ -36,10 +37,13 @@ export const useNodeOutput = (nodeId: string) => {
     }
   };
   return {
-    outputData,
-    inputData,
-    copiedKey,
-    handleCopy,
+    outputData: outputData,
+    inputData: inputData,
+    isExpanded: isExpanded,
+    setIsExpanded: setIsExpanded,
+    copiedKey: copiedKey,
+    setCopiedKey: setCopiedKey,
+    handleCopy: handleCopy,
     executionResultId: nodeExecutionResult?.node_exec_id,
   };
 };

autogpt_platform/frontend/src/app/(platform)/build/components/FlowEditor/nodes/helpers.ts

@@ -187,38 +187,3 @@ export const getTypeDisplayInfo = (schema: any) => {
     hexColor,
   };
 };
-
-export function getEdgeColorFromOutputType(
-  outputSchema: RJSFSchema | undefined,
-  sourceHandle: string,
-): { colorClass: string; hexColor: string } {
-  const defaultColor = {
-    colorClass: "stroke-zinc-500/50",
-    hexColor: "#6b7280",
-  };
-
-  if (!outputSchema?.properties) return defaultColor;
-
-  const properties = outputSchema.properties as Record<string, unknown>;
-  const handleParts = sourceHandle.split("_#_");
-  let currentSchema: Record<string, unknown> = properties;
-
-  for (let i = 0; i < handleParts.length; i++) {
-    const part = handleParts[i];
-    const fieldSchema = currentSchema[part] as Record<string, unknown>;
-    if (!fieldSchema) return defaultColor;
-
-    if (i === handleParts.length - 1) {
-      const { hexColor, colorClass } = getTypeDisplayInfo(fieldSchema);
-      return { colorClass: colorClass.replace("!text-", "stroke-"), hexColor };
-    }
-
-    if (fieldSchema.properties) {
-      currentSchema = fieldSchema.properties as Record<string, unknown>;
-    } else {
-      return defaultColor;
-    }
-  }
-
-  return defaultColor;
-}

autogpt_platform/frontend/src/app/(platform)/build/components/FlowEditor/tutorial/icons.ts

@@ -1,32 +1,7 @@
-type IconOptions = {
-  size?: number;
-  color?: string;
-};
-
-const DEFAULT_SIZE = 16;
-const DEFAULT_COLOR = "#52525b"; // zinc-600
-
-const iconPaths = {
-  ClickIcon: `M88,24V16a8,8,0,0,1,16,0v8a8,8,0,0,1-16,0ZM16,104h8a8,8,0,0,0,0-16H16a8,8,0,0,0,0,16ZM124.42,39.16a8,8,0,0,0,10.74-3.58l8-16a8,8,0,0,0-14.31-7.16l-8,16A8,8,0,0,0,124.42,39.16Zm-96,81.69-16,8a8,8,0,0,0,7.16,14.31l16-8a8,8,0,1,0-7.16-14.31ZM219.31,184a16,16,0,0,1,0,22.63l-12.68,12.68a16,16,0,0,1-22.63,0L132.7,168,115,214.09c0,.1-.08.21-.13.32a15.83,15.83,0,0,1-14.6,9.59l-.79,0a15.83,15.83,0,0,1-14.41-11L32.8,52.92A16,16,0,0,1,52.92,32.8L213,85.07a16,16,0,0,1,1.41,29.8l-.32.13L168,132.69ZM208,195.31,156.69,144h0a16,16,0,0,1,4.93-26l.32-.14,45.95-17.64L48,48l52.2,159.86,17.65-46c0-.11.08-.22.13-.33a16,16,0,0,1,11.69-9.34,16.72,16.72,0,0,1,3-.28,16,16,0,0,1,11.3,4.69L195.31,208Z`,
-  Keyboard: `M224,48H32A16,16,0,0,0,16,64V192a16,16,0,0,0,16,16H224a16,16,0,0,0,16-16V64A16,16,0,0,0,224,48Zm0,144H32V64H224V192Zm-16-64a8,8,0,0,1-8,8H56a8,8,0,0,1,0-16H200A8,8,0,0,1,208,128Zm0-32a8,8,0,0,1-8,8H56a8,8,0,0,1,0-16H200A8,8,0,0,1,208,96ZM72,160a8,8,0,0,1-8,8H56a8,8,0,0,1,0-16h8A8,8,0,0,1,72,160Zm96,0a8,8,0,0,1-8,8H96a8,8,0,0,1,0-16h64A8,8,0,0,1,168,160Zm40,0a8,8,0,0,1-8,8h-8a8,8,0,0,1,0-16h8A8,8,0,0,1,208,160Z`,
-  Drag: `M188,80a27.79,27.79,0,0,0-13.36,3.4,28,28,0,0,0-46.64-11A28,28,0,0,0,80,92v20H68a28,28,0,0,0-28,28v12a88,88,0,0,0,176,0V108A28,28,0,0,0,188,80Zm12,72a72,72,0,0,1-144,0V140a12,12,0,0,1,12-12H80v24a8,8,0,0,0,16,0V92a12,12,0,0,1,24,0v28a8,8,0,0,0,16,0V92a12,12,0,0,1,24,0v28a8,8,0,0,0,16,0V108a12,12,0,0,1,24,0Z`,
-};
-
-function createIcon(path: string, options: IconOptions = {}): string {
-  const size = options.size ?? DEFAULT_SIZE;
-  const color = options.color ?? DEFAULT_COLOR;
-  return `<svg xmlns="http://www.w3.org/2000/svg" width="${size}" height="${size}" fill="${color}" viewBox="0 0 256 256"><path d="${path}"></path></svg>`;
-}
+// These are SVG Phosphor icons
 
 export const ICONS = {
-  ClickIcon: createIcon(iconPaths.ClickIcon),
-  Keyboard: createIcon(iconPaths.Keyboard),
-  Drag: createIcon(iconPaths.Drag),
+  ClickIcon: `<svg xmlns="http://www.w3.org/2000/svg" width="20" height="20" fill="#000000" viewBox="0 0 256 256"><path d="M88,24V16a8,8,0,0,1,16,0v8a8,8,0,0,1-16,0ZM16,104h8a8,8,0,0,0,0-16H16a8,8,0,0,0,0,16ZM124.42,39.16a8,8,0,0,0,10.74-3.58l8-16a8,8,0,0,0-14.31-7.16l-8,16A8,8,0,0,0,124.42,39.16Zm-96,81.69-16,8a8,8,0,0,0,7.16,14.31l16-8a8,8,0,1,0-7.16-14.31ZM219.31,184a16,16,0,0,1,0,22.63l-12.68,12.68a16,16,0,0,1-22.63,0L132.7,168,115,214.09c0,.1-.08.21-.13.32a15.83,15.83,0,0,1-14.6,9.59l-.79,0a15.83,15.83,0,0,1-14.41-11L32.8,52.92A16,16,0,0,1,52.92,32.8L213,85.07a16,16,0,0,1,1.41,29.8l-.32.13L168,132.69ZM208,195.31,156.69,144h0a16,16,0,0,1,4.93-26l.32-.14,45.95-17.64L48,48l52.2,159.86,17.65-46c0-.11.08-.22.13-.33a16,16,0,0,1,11.69-9.34,16.72,16.72,0,0,1,3-.28,16,16,0,0,1,11.3,4.69L195.31,208Z"></path></svg>`,
+  Keyboard: `<svg xmlns="http://www.w3.org/2000/svg" width="20" height="20" fill="#000000" viewBox="0 0 256 256"><path d="M224,48H32A16,16,0,0,0,16,64V192a16,16,0,0,0,16,16H224a16,16,0,0,0,16-16V64A16,16,0,0,0,224,48Zm0,144H32V64H224V192Zm-16-64a8,8,0,0,1-8,8H56a8,8,0,0,1,0-16H200A8,8,0,0,1,208,128Zm0-32a8,8,0,0,1-8,8H56a8,8,0,0,1,0-16H200A8,8,0,0,1,208,96ZM72,160a8,8,0,0,1-8,8H56a8,8,0,0,1,0-16h8A8,8,0,0,1,72,160Zm96,0a8,8,0,0,1-8,8H96a8,8,0,0,1,0-16h64A8,8,0,0,1,168,160Zm40,0a8,8,0,0,1-8,8h-8a8,8,0,0,1,0-16h8A8,8,0,0,1,208,160Z"></path></svg>`,
+  Drag: `<svg xmlns="http://www.w3.org/2000/svg" width="20" height="20" fill="#000000" viewBox="0 0 256 256"><path d="M188,80a27.79,27.79,0,0,0-13.36,3.4,28,28,0,0,0-46.64-11A28,28,0,0,0,80,92v20H68a28,28,0,0,0-28,28v12a88,88,0,0,0,176,0V108A28,28,0,0,0,188,80Zm12,72a72,72,0,0,1-144,0V140a12,12,0,0,1,12-12H80v24a8,8,0,0,0,16,0V92a12,12,0,0,1,24,0v28a8,8,0,0,0,16,0V92a12,12,0,0,1,24,0v28a8,8,0,0,0,16,0V108a12,12,0,0,1,24,0Z"></path></svg>`,
 };
-
-export function getIcon(
-  name: keyof typeof iconPaths,
-  options?: IconOptions,
-): string {
-  return createIcon(iconPaths[name], options);
-}

autogpt_platform/frontend/src/app/(platform)/build/components/FlowEditor/tutorial/index.ts

@@ -11,7 +11,6 @@ import {
 } from "./helpers";
 import { useNodeStore } from "../../../stores/nodeStore";
 import { useEdgeStore } from "../../../stores/edgeStore";
-import { useTutorialStore } from "../../../stores/tutorialStore";
 
 let isTutorialLoading = false;
 let tutorialLoadingCallback: ((loading: boolean) => void) | null = null;
@@ -61,14 +60,12 @@ export const startTutorial = async () => {
     handleTutorialComplete();
     removeTutorialStyles();
     clearPrefetchedBlocks();
-    useTutorialStore.getState().setIsTutorialRunning(false);
   });
 
   tour.on("cancel", () => {
     handleTutorialCancel(tour);
     removeTutorialStyles();
     clearPrefetchedBlocks();
-    useTutorialStore.getState().setIsTutorialRunning(false);
   });
 
   for (const step of tour.steps) {

autogpt_platform/frontend/src/app/(platform)/build/components/helper.ts

@@ -61,18 +61,12 @@ export const convertNodesPlusBlockInfoIntoCustomNodes = (
   return customNode;
 };
 
-const isToolSourceName = (sourceName: string): boolean =>
-  sourceName.startsWith("tools_^_");
-
-const cleanupSourceName = (sourceName: string): string =>
-  isToolSourceName(sourceName) ? "tools" : sourceName;
-
 export const linkToCustomEdge = (link: Link): CustomEdge => ({
   id: link.id ?? "",
   type: "custom" as const,
   source: link.source_id,
   target: link.sink_id,
-  sourceHandle: cleanupSourceName(link.source_name),
+  sourceHandle: link.source_name,
   targetHandle: link.sink_name,
   data: {
     isStatic: link.is_static,

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

@@ -267,34 +267,23 @@ export function extractCredentialsNeeded(
       | undefined;
     if (missingCreds && Object.keys(missingCreds).length > 0) {
       const agentName = (setupInfo?.agent_name as string) || "this block";
-      const credentials = Object.values(missingCreds).map((credInfo) => {
-        // Normalize to array at boundary - prefer 'types' array, fall back to single 'type'
-        const typesArray = credInfo.types as
-          | Array<"api_key" | "oauth2" | "user_password" | "host_scoped">
-          | undefined;
-        const singleType =
+      const credentials = Object.values(missingCreds).map((credInfo) => ({
+        provider: (credInfo.provider as string) || "unknown",
+        providerName:
+          (credInfo.provider_name as string) ||
+          (credInfo.provider as string) ||
+          "Unknown Provider",
+        credentialType:
           (credInfo.type as
             | "api_key"
             | "oauth2"
             | "user_password"
-            | "host_scoped"
-            | undefined) || "api_key";
-        const credentialTypes =
-          typesArray && typesArray.length > 0 ? typesArray : [singleType];
-
-        return {
-          provider: (credInfo.provider as string) || "unknown",
-          providerName:
-            (credInfo.provider_name as string) ||
-            (credInfo.provider as string) ||
-            "Unknown Provider",
-          credentialTypes,
-          title:
-            (credInfo.title as string) ||
-            `${(credInfo.provider_name as string) || (credInfo.provider as string)} credentials`,
-          scopes: credInfo.scopes as string[] | undefined,
-        };
-      });
+            | "host_scoped") || "api_key",
+        title:
+          (credInfo.title as string) ||
+          `${(credInfo.provider_name as string) || (credInfo.provider as string)} credentials`,
+        scopes: credInfo.scopes as string[] | undefined,
+      }));
       return {
         type: "credentials_needed",
         toolName,
@@ -369,14 +358,11 @@ export function extractInputsNeeded(
       credentials.forEach((cred) => {
         const id = cred.id as string;
         if (id) {
-          const credentialTypes = Array.isArray(cred.types)
-            ? cred.types
-            : [(cred.type as string) || "api_key"];
           credentialsSchema[id] = {
             type: "object",
             properties: {},
             credentials_provider: [cred.provider as string],
-            credentials_types: credentialTypes,
+            credentials_types: [(cred.type as string) || "api_key"],
             credentials_scopes: cred.scopes as string[] | undefined,
           };
         }