fix(frontend): Handle object values in FileInput component

Fixes #11800

The FileInput component crashed with 'TypeError: e.startsWith is not a function'
when the value was an object (from external API) instead of a string.

Changes:
- Updated getFileLabelFromValue() to handle object format: { name, type, size, data }
- Added type guards for string vs object values
- Graceful fallback for edge cases (null, undefined, empty object)

Test cases verified:
- Object with name: returns filename
- Object with type only: extracts and formats MIME type
- String data URI: parses correctly
- String file path: extracts extension
- Edge cases: returns 'File' fallback

README.md

@@ -54,7 +54,7 @@ Before proceeding with the installation, ensure your system meets the following
 ### Updated Setup Instructions:
 We've moved to a fully maintained and regularly updated documentation site.
 
-👉 [Follow the official self-hosting guide here](https://docs.agpt.co/platform/getting-started/)
+👉 [Follow the official self-hosting guide here](https://agpt.co/docs/platform/getting-started/getting-started)
 
 
 This tutorial assumes you have Docker, VSCode, git and npm installed.

autogpt_platform/backend/.env.default

@@ -152,7 +152,6 @@ REPLICATE_API_KEY=
 REVID_API_KEY=
 SCREENSHOTONE_API_KEY=
 UNREAL_SPEECH_API_KEY=
-ELEVENLABS_API_KEY=
 
 # Data & Search Services
 E2B_API_KEY=

autogpt_platform/backend/Dockerfile

@@ -62,11 +62,10 @@ ENV POETRY_HOME=/opt/poetry \
     DEBIAN_FRONTEND=noninteractive
 ENV PATH=/opt/poetry/bin:$PATH
 
-# Install Python and FFmpeg (required for video processing blocks)
+# Install Python without upgrading system-managed packages
 RUN apt-get update && apt-get install -y \
     python3.13 \
     python3-pip \
-    ffmpeg \
     && rm -rf /var/lib/apt/lists/*
 
 # Copy only necessary files from builder

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

@@ -3,9 +3,13 @@ import logging
 import time
 from asyncio import CancelledError
 from collections.abc import AsyncGenerator
-from typing import Any
+from typing import TYPE_CHECKING, Any, cast
 
 import openai
+
+if TYPE_CHECKING:
+    from backend.util.prompt import CompressResult
+
 import orjson
 from langfuse import get_client
 from openai import (
@@ -15,7 +19,13 @@ from openai import (
     PermissionDeniedError,
     RateLimitError,
 )
-from openai.types.chat import ChatCompletionChunk, ChatCompletionToolParam
+from openai.types.chat import (
+    ChatCompletionChunk,
+    ChatCompletionMessageParam,
+    ChatCompletionStreamOptionsParam,
+    ChatCompletionSystemMessageParam,
+    ChatCompletionToolParam,
+)
 
 from backend.data.redis_client import get_redis_async
 from backend.data.understanding import (
@@ -794,207 +804,58 @@ def _is_region_blocked_error(error: Exception) -> bool:
     return "not available in your region" in str(error).lower()
 
 
-async def _summarize_messages(
+async def _manage_context_window(
     messages: list,
     model: str,
     api_key: str | None = None,
     base_url: str | None = None,
-    timeout: float = 30.0,
-) -> str:
-    """Summarize a list of messages into concise context.
+) -> "CompressResult":
+    """
+    Manage context window using the unified compress_context function.
 
-    Uses the same model as the chat for higher quality summaries.
+    This is a thin wrapper that creates an OpenAI client for summarization
+    and delegates to the shared compression logic in prompt.py.
 
     Args:
-        messages: List of message dicts to summarize
-        model: Model to use for summarization (same as chat model)
-        api_key: API key for OpenAI client
-        base_url: Base URL for OpenAI client
-        timeout: Request timeout in seconds (default: 30.0)
+        messages: List of messages in OpenAI format
+        model: Model name for token counting and summarization
+        api_key: API key for summarization calls
+        base_url: Base URL for summarization calls
 
     Returns:
-        Summarized text
+        CompressResult with compacted messages and metadata
     """
-    # Format messages for summarization
-    conversation = []
-    for msg in messages:
-        role = msg.get("role", "")
-        content = msg.get("content", "")
-        # Include user, assistant, and tool messages (tool outputs are important context)
-        if content and role in ("user", "assistant", "tool"):
-            conversation.append(f"{role.upper()}: {content}")
-
-    conversation_text = "\n\n".join(conversation)
-
-    # Handle empty conversation
-    if not conversation_text:
-        return "No conversation history available."
-
-    # Truncate conversation to fit within summarization model's context
-    # gpt-4o-mini has 128k context, but we limit to ~25k tokens (~100k chars) for safety
-    MAX_CHARS = 100_000
-    if len(conversation_text) > MAX_CHARS:
-        conversation_text = conversation_text[:MAX_CHARS] + "\n\n[truncated]"
-
-    # Call LLM to summarize
     import openai
 
-    summarization_client = openai.AsyncOpenAI(
-        api_key=api_key, base_url=base_url, timeout=timeout
-    )
+    from backend.util.prompt import compress_context
 
-    response = await summarization_client.chat.completions.create(
-        model=model,
-        messages=[
-            {
-                "role": "system",
-                "content": (
-                    "Create a detailed summary of the conversation so far. "
-                    "This summary will be used as context when continuing the conversation.\n\n"
-                    "Before writing the summary, analyze each message chronologically to identify:\n"
-                    "- User requests and their explicit goals\n"
-                    "- Your approach and key decisions made\n"
-                    "- Technical specifics (file names, tool outputs, function signatures)\n"
-                    "- Errors encountered and resolutions applied\n\n"
-                    "You MUST include ALL of the following sections:\n\n"
-                    "## 1. Primary Request and Intent\n"
-                    "The user's explicit goals and what they are trying to accomplish.\n\n"
-                    "## 2. Key Technical Concepts\n"
-                    "Technologies, frameworks, tools, and patterns being used or discussed.\n\n"
-                    "## 3. Files and Resources Involved\n"
-                    "Specific files examined or modified, with relevant snippets and identifiers.\n\n"
-                    "## 4. Errors and Fixes\n"
-                    "Problems encountered, error messages, and their resolutions. "
-                    "Include any user feedback on fixes.\n\n"
-                    "## 5. Problem Solving\n"
-                    "Issues that have been resolved and how they were addressed.\n\n"
-                    "## 6. All User Messages\n"
-                    "A complete list of all user inputs (excluding tool outputs) to preserve their exact requests.\n\n"
-                    "## 7. Pending Tasks\n"
-                    "Work items the user explicitly requested that have not yet been completed.\n\n"
-                    "## 8. Current Work\n"
-                    "Precise description of what was being worked on most recently, including relevant context.\n\n"
-                    "## 9. Next Steps\n"
-                    "What should happen next, aligned with the user's most recent requests. "
-                    "Include verbatim quotes of recent instructions if relevant."
-                ),
-            },
-            {"role": "user", "content": f"Summarize:\n\n{conversation_text}"},
-        ],
-        max_tokens=1500,
-        temperature=0.3,
-    )
+    # Convert messages to dict format
+    messages_dict = []
+    for msg in messages:
+        if isinstance(msg, dict):
+            msg_dict = {k: v for k, v in msg.items() if v is not None}
+        else:
+            msg_dict = dict(msg)
+        messages_dict.append(msg_dict)
 
-    summary = response.choices[0].message.content
-    return summary or "No summary available."
-
-
-def _ensure_tool_pairs_intact(
-    recent_messages: list[dict],
-    all_messages: list[dict],
-    start_index: int,
-) -> list[dict]:
-    """
-    Ensure tool_call/tool_response pairs stay together after slicing.
-
-    When slicing messages for context compaction, a naive slice can separate
-    an assistant message containing tool_calls from its corresponding tool
-    response messages. This causes API validation errors (e.g., Anthropic's
-    "unexpected tool_use_id found in tool_result blocks").
-
-    This function checks for orphan tool responses in the slice and extends
-    backwards to include their corresponding assistant messages.
-
-    Args:
-        recent_messages: The sliced messages to validate
-        all_messages: The complete message list (for looking up missing assistants)
-        start_index: The index in all_messages where recent_messages begins
-
-    Returns:
-        A potentially extended list of messages with tool pairs intact
-    """
-    if not recent_messages:
-        return recent_messages
-
-    # Collect all tool_call_ids from assistant messages in the slice
-    available_tool_call_ids: set[str] = set()
-    for msg in recent_messages:
-        if msg.get("role") == "assistant" and msg.get("tool_calls"):
-            for tc in msg["tool_calls"]:
-                tc_id = tc.get("id")
-                if tc_id:
-                    available_tool_call_ids.add(tc_id)
-
-    # Find orphan tool responses (tool messages whose tool_call_id is missing)
-    orphan_tool_call_ids: set[str] = set()
-    for msg in recent_messages:
-        if msg.get("role") == "tool":
-            tc_id = msg.get("tool_call_id")
-            if tc_id and tc_id not in available_tool_call_ids:
-                orphan_tool_call_ids.add(tc_id)
-
-    if not orphan_tool_call_ids:
-        # No orphans, slice is valid
-        return recent_messages
-
-    # Find the assistant messages that contain the orphan tool_call_ids
-    # Search backwards from start_index in all_messages
-    messages_to_prepend: list[dict] = []
-    for i in range(start_index - 1, -1, -1):
-        msg = all_messages[i]
-        if msg.get("role") == "assistant" and msg.get("tool_calls"):
-            msg_tool_ids = {tc.get("id") for tc in msg["tool_calls"] if tc.get("id")}
-            if msg_tool_ids & orphan_tool_call_ids:
-                # This assistant message has tool_calls we need
-                # Also collect its contiguous tool responses that follow it
-                assistant_and_responses: list[dict] = [msg]
-
-                # Scan forward from this assistant to collect tool responses
-                for j in range(i + 1, start_index):
-                    following_msg = all_messages[j]
-                    if following_msg.get("role") == "tool":
-                        tool_id = following_msg.get("tool_call_id")
-                        if tool_id and tool_id in msg_tool_ids:
-                            assistant_and_responses.append(following_msg)
-                    else:
-                        # Stop at first non-tool message
-                        break
-
-                # Prepend the assistant and its tool responses (maintain order)
-                messages_to_prepend = assistant_and_responses + messages_to_prepend
-                # Mark these as found
-                orphan_tool_call_ids -= msg_tool_ids
-                # Also add this assistant's tool_call_ids to available set
-                available_tool_call_ids |= msg_tool_ids
-
-        if not orphan_tool_call_ids:
-            # Found all missing assistants
-            break
-
-    if orphan_tool_call_ids:
-        # Some tool_call_ids couldn't be resolved - remove those tool responses
-        # This shouldn't happen in normal operation but handles edge cases
-        logger.warning(
-            f"Could not find assistant messages for tool_call_ids: {orphan_tool_call_ids}. "
-            "Removing orphan tool responses."
-        )
-        recent_messages = [
-            msg
-            for msg in recent_messages
-            if not (
-                msg.get("role") == "tool"
-                and msg.get("tool_call_id") in orphan_tool_call_ids
+    # Only create client if api_key is provided (enables summarization)
+    # Use context manager to avoid socket leaks
+    if api_key:
+        async with openai.AsyncOpenAI(
+            api_key=api_key, base_url=base_url, timeout=30.0
+        ) as client:
+            return await compress_context(
+                messages=messages_dict,
+                model=model,
+                client=client,
             )
-        ]
-
-    if messages_to_prepend:
-        logger.info(
-            f"Extended recent messages by {len(messages_to_prepend)} to preserve "
-            f"tool_call/tool_response pairs"
+    else:
+        # No API key - use truncation-only mode
+        return await compress_context(
+            messages=messages_dict,
+            model=model,
+            client=None,
         )
-        return messages_to_prepend + recent_messages
-
-    return recent_messages
 
 
 async def _stream_chat_chunks(
@@ -1022,11 +883,8 @@ async def _stream_chat_chunks(
 
     logger.info("Starting pure chat stream")
 
-    # Build messages with system prompt prepended
     messages = session.to_openai_messages()
     if system_prompt:
-        from openai.types.chat import ChatCompletionSystemMessageParam
-
         system_message = ChatCompletionSystemMessageParam(
             role="system",
             content=system_prompt,
@@ -1034,314 +892,38 @@ async def _stream_chat_chunks(
         messages = [system_message] + messages
 
     # Apply context window management
-    token_count = 0  # Initialize for exception handler
-    try:
-        from backend.util.prompt import estimate_token_count
+    context_result = await _manage_context_window(
+        messages=messages,
+        model=model,
+        api_key=config.api_key,
+        base_url=config.base_url,
+    )
 
-        # Convert to dict for token counting
-        # OpenAI message types are TypedDicts, so they're already dict-like
-        messages_dict = []
-        for msg in messages:
-            # TypedDict objects are already dicts, just filter None values
-            if isinstance(msg, dict):
-                msg_dict = {k: v for k, v in msg.items() if v is not None}
-            else:
-                # Fallback for unexpected types
-                msg_dict = dict(msg)
-            messages_dict.append(msg_dict)
-
-        # Estimate tokens using appropriate tokenizer
-        # Normalize model name for token counting (tiktoken only supports OpenAI models)
-        token_count_model = model
-        if "/" in model:
-            # Strip provider prefix (e.g., "anthropic/claude-opus-4.5" -> "claude-opus-4.5")
-            token_count_model = model.split("/")[-1]
-
-        # For Claude and other non-OpenAI models, approximate with gpt-4o tokenizer
-        # Most modern LLMs have similar tokenization (~1 token per 4 chars)
-        if "claude" in token_count_model.lower() or not any(
-            known in token_count_model.lower()
-            for known in ["gpt", "o1", "chatgpt", "text-"]
-        ):
-            token_count_model = "gpt-4o"
-
-        # Attempt token counting with error handling
-        try:
-            token_count = estimate_token_count(messages_dict, model=token_count_model)
-        except Exception as token_error:
-            # If token counting fails, use gpt-4o as fallback approximation
-            logger.warning(
-                f"Token counting failed for model {token_count_model}: {token_error}. "
-                "Using gpt-4o approximation."
-            )
-            token_count = estimate_token_count(messages_dict, model="gpt-4o")
-
-        # If over threshold, summarize old messages
-        if token_count > 120_000:
-            KEEP_RECENT = 15
-
-            # Check if we have a system prompt at the start
-            has_system_prompt = (
-                len(messages) > 0 and messages[0].get("role") == "system"
-            )
-
-            # Always attempt mitigation when over limit, even with few messages
-            if messages:
-                # Split messages based on whether system prompt exists
-                # Calculate start index for the slice
-                slice_start = max(0, len(messages_dict) - KEEP_RECENT)
-                recent_messages = messages_dict[-KEEP_RECENT:]
-
-                # Ensure tool_call/tool_response pairs stay together
-                # This prevents API errors from orphan tool responses
-                recent_messages = _ensure_tool_pairs_intact(
-                    recent_messages, messages_dict, slice_start
-                )
-
-                if has_system_prompt:
-                    # Keep system prompt separate, summarize everything between system and recent
-                    system_msg = messages[0]
-                    old_messages_dict = messages_dict[1:-KEEP_RECENT]
-                else:
-                    # No system prompt, summarize everything except recent
-                    system_msg = None
-                    old_messages_dict = messages_dict[:-KEEP_RECENT]
-
-                # Summarize any non-empty old messages (no minimum threshold)
-                # If we're over the token limit, we need to compress whatever we can
-                if old_messages_dict:
-                    # Summarize old messages using the same model as chat
-                    summary_text = await _summarize_messages(
-                        old_messages_dict,
-                        model=model,
-                        api_key=config.api_key,
-                        base_url=config.base_url,
-                    )
-
-                    # Build new message list
-                    # Use assistant role (not system) to prevent privilege escalation
-                    # of user-influenced content to instruction-level authority
-                    from openai.types.chat import ChatCompletionAssistantMessageParam
-
-                    summary_msg = ChatCompletionAssistantMessageParam(
-                        role="assistant",
-                        content=(
-                            "[Previous conversation summary — for context only]: "
-                            f"{summary_text}"
-                        ),
-                    )
-
-                    # Rebuild messages based on whether we have a system prompt
-                    if has_system_prompt:
-                        # system_prompt + summary + recent_messages
-                        messages = [system_msg, summary_msg] + recent_messages
-                    else:
-                        # summary + recent_messages (no original system prompt)
-                        messages = [summary_msg] + recent_messages
-
-                    logger.info(
-                        f"Context summarized: {token_count} tokens, "
-                        f"summarized {len(old_messages_dict)} old messages, "
-                        f"kept last {KEEP_RECENT} messages"
-                    )
-
-                    # Fallback: If still over limit after summarization, progressively drop recent messages
-                    # This handles edge cases where recent messages are extremely large
-                    new_messages_dict = []
-                    for msg in messages:
-                        if isinstance(msg, dict):
-                            msg_dict = {k: v for k, v in msg.items() if v is not None}
-                        else:
-                            msg_dict = dict(msg)
-                        new_messages_dict.append(msg_dict)
-
-                    new_token_count = estimate_token_count(
-                        new_messages_dict, model=token_count_model
-                    )
-
-                    if new_token_count > 120_000:
-                        # Still over limit - progressively reduce KEEP_RECENT
-                        logger.warning(
-                            f"Still over limit after summarization: {new_token_count} tokens. "
-                            "Reducing number of recent messages kept."
-                        )
-
-                        for keep_count in [12, 10, 8, 5, 3, 2, 1, 0]:
-                            if keep_count == 0:
-                                # Try with just system prompt + summary (no recent messages)
-                                if has_system_prompt:
-                                    messages = [system_msg, summary_msg]
-                                else:
-                                    messages = [summary_msg]
-                                logger.info(
-                                    "Trying with 0 recent messages (system + summary only)"
-                                )
-                            else:
-                                # Slice from ORIGINAL recent_messages to avoid duplicating summary
-                                reduced_recent = (
-                                    recent_messages[-keep_count:]
-                                    if len(recent_messages) >= keep_count
-                                    else recent_messages
-                                )
-                                # Ensure tool pairs stay intact in the reduced slice
-                                reduced_slice_start = max(
-                                    0, len(recent_messages) - keep_count
-                                )
-                                reduced_recent = _ensure_tool_pairs_intact(
-                                    reduced_recent, recent_messages, reduced_slice_start
-                                )
-                                if has_system_prompt:
-                                    messages = [
-                                        system_msg,
-                                        summary_msg,
-                                    ] + reduced_recent
-                                else:
-                                    messages = [summary_msg] + reduced_recent
-
-                            new_messages_dict = []
-                            for msg in messages:
-                                if isinstance(msg, dict):
-                                    msg_dict = {
-                                        k: v for k, v in msg.items() if v is not None
-                                    }
-                                else:
-                                    msg_dict = dict(msg)
-                                new_messages_dict.append(msg_dict)
-
-                            new_token_count = estimate_token_count(
-                                new_messages_dict, model=token_count_model
-                            )
-
-                            if new_token_count <= 120_000:
-                                logger.info(
-                                    f"Reduced to {keep_count} recent messages, "
-                                    f"now {new_token_count} tokens"
-                                )
-                                break
-                        else:
-                            logger.error(
-                                f"Unable to reduce token count below threshold even with 0 messages. "
-                                f"Final count: {new_token_count} tokens"
-                            )
-                            # ABSOLUTE LAST RESORT: Drop system prompt
-                            # This should only happen if summary itself is massive
-                            if has_system_prompt and len(messages) > 1:
-                                messages = messages[1:]  # Drop system prompt
-                                logger.critical(
-                                    "CRITICAL: Dropped system prompt as absolute last resort. "
-                                    "Behavioral consistency may be affected."
-                                )
-                                # Yield error to user
-                                yield StreamError(
-                                    errorText=(
-                                        "Warning: System prompt dropped due to size constraints. "
-                                        "Assistant behavior may be affected."
-                                    )
-                                )
-                else:
-                    # No old messages to summarize - all messages are "recent"
-                    # Apply progressive truncation to reduce token count
-                    logger.warning(
-                        f"Token count {token_count} exceeds threshold but no old messages to summarize. "
-                        f"Applying progressive truncation to recent messages."
-                    )
-
-                    # Create a base list excluding system prompt to avoid duplication
-                    # This is the pool of messages we'll slice from in the loop
-                    # Use messages_dict for type consistency with _ensure_tool_pairs_intact
-                    base_msgs = (
-                        messages_dict[1:] if has_system_prompt else messages_dict
-                    )
-
-                    # Try progressively smaller keep counts
-                    new_token_count = token_count  # Initialize with current count
-                    for keep_count in [12, 10, 8, 5, 3, 2, 1, 0]:
-                        if keep_count == 0:
-                            # Try with just system prompt (no recent messages)
-                            if has_system_prompt:
-                                messages = [system_msg]
-                                logger.info(
-                                    "Trying with 0 recent messages (system prompt only)"
-                                )
-                            else:
-                                # No system prompt and no recent messages = empty messages list
-                                # This is invalid, skip this iteration
-                                continue
-                        else:
-                            if len(base_msgs) < keep_count:
-                                continue  # Skip if we don't have enough messages
-
-                            # Slice from base_msgs to get recent messages (without system prompt)
-                            recent_messages = base_msgs[-keep_count:]
-
-                            # Ensure tool pairs stay intact in the reduced slice
-                            reduced_slice_start = max(0, len(base_msgs) - keep_count)
-                            recent_messages = _ensure_tool_pairs_intact(
-                                recent_messages, base_msgs, reduced_slice_start
-                            )
-
-                            if has_system_prompt:
-                                messages = [system_msg] + recent_messages
-                            else:
-                                messages = recent_messages
-
-                        new_messages_dict = []
-                        for msg in messages:
-                            if msg is None:
-                                continue  # Skip None messages (type safety)
-                            if isinstance(msg, dict):
-                                msg_dict = {
-                                    k: v for k, v in msg.items() if v is not None
-                                }
-                            else:
-                                msg_dict = dict(msg)
-                            new_messages_dict.append(msg_dict)
-
-                        new_token_count = estimate_token_count(
-                            new_messages_dict, model=token_count_model
-                        )
-
-                        if new_token_count <= 120_000:
-                            logger.info(
-                                f"Reduced to {keep_count} recent messages, "
-                                f"now {new_token_count} tokens"
-                            )
-                            break
-                    else:
-                        # Even with 0 messages still over limit
-                        logger.error(
-                            f"Unable to reduce token count below threshold even with 0 messages. "
-                            f"Final count: {new_token_count} tokens. Messages may be extremely large."
-                        )
-                        # ABSOLUTE LAST RESORT: Drop system prompt
-                        if has_system_prompt and len(messages) > 1:
-                            messages = messages[1:]  # Drop system prompt
-                            logger.critical(
-                                "CRITICAL: Dropped system prompt as absolute last resort. "
-                                "Behavioral consistency may be affected."
-                            )
-                            # Yield error to user
-                            yield StreamError(
-                                errorText=(
-                                    "Warning: System prompt dropped due to size constraints. "
-                                    "Assistant behavior may be affected."
-                                )
-                            )
-
-    except Exception as e:
-        logger.error(f"Context summarization failed: {e}", exc_info=True)
-        # If we were over the token limit, yield error to user
-        # Don't silently continue with oversized messages that will fail
-        if token_count > 120_000:
+    if context_result.error:
+        if "System prompt dropped" in context_result.error:
+            # Warning only - continue with reduced context
             yield StreamError(
                 errorText=(
-                    f"Unable to manage context window (token limit exceeded: {token_count} tokens). "
-                    "Context summarization failed. Please start a new conversation."
+                    "Warning: System prompt dropped due to size constraints. "
+                    "Assistant behavior may be affected."
+                )
+            )
+        else:
+            # Any other error - abort to prevent failed LLM calls
+            yield StreamError(
+                errorText=(
+                    f"Context window management failed: {context_result.error}. "
+                    "Please start a new conversation."
                 )
             )
             yield StreamFinish()
             return
-        # Otherwise, continue with original messages (under limit)
+
+    messages = context_result.messages
+    if context_result.was_compacted:
+        logger.info(
+            f"Context compacted for streaming: {context_result.token_count} tokens"
+        )
 
     # Loop to handle tool calls and continue conversation
     while True:
@@ -1369,14 +951,6 @@ async def _stream_chat_chunks(
                         :128
                     ]  # OpenRouter limit
 
-                # Create the stream with proper types
-                from typing import cast
-
-                from openai.types.chat import (
-                    ChatCompletionMessageParam,
-                    ChatCompletionStreamOptionsParam,
-                )
-
                 stream = await client.chat.completions.create(
                     model=model,
                     messages=cast(list[ChatCompletionMessageParam], messages),
@@ -1834,6 +1408,11 @@ async def _execute_long_running_tool(
             tool_call_id=tool_call_id,
             result=error_response.model_dump_json(),
         )
+        # Generate LLM continuation so user sees explanation even for errors
+        try:
+            await _generate_llm_continuation(session_id=session_id, user_id=user_id)
+        except Exception as llm_err:
+            logger.warning(f"Failed to generate LLM continuation for error: {llm_err}")
     finally:
         await _mark_operation_completed(tool_call_id)
 
@@ -1895,17 +1474,36 @@ async def _generate_llm_continuation(
         # Build system prompt
         system_prompt, _ = await _build_system_prompt(user_id)
 
-        # Build messages in OpenAI format
         messages = session.to_openai_messages()
         if system_prompt:
-            from openai.types.chat import ChatCompletionSystemMessageParam
-
             system_message = ChatCompletionSystemMessageParam(
                 role="system",
                 content=system_prompt,
             )
             messages = [system_message] + messages
 
+        # Apply context window management to prevent oversized requests
+        context_result = await _manage_context_window(
+            messages=messages,
+            model=config.model,
+            api_key=config.api_key,
+            base_url=config.base_url,
+        )
+
+        if context_result.error and "System prompt dropped" not in context_result.error:
+            logger.error(
+                f"Context window management failed for session {session_id}: "
+                f"{context_result.error} (tokens={context_result.token_count})"
+            )
+            return
+
+        messages = context_result.messages
+        if context_result.was_compacted:
+            logger.info(
+                f"Context compacted for LLM continuation: "
+                f"{context_result.token_count} tokens"
+            )
+
         # Build extra_body for tracing
         extra_body: dict[str, Any] = {
             "posthogProperties": {
@@ -1918,19 +1516,54 @@ async def _generate_llm_continuation(
         if session_id:
             extra_body["session_id"] = session_id[:128]
 
-        # Make non-streaming LLM call (no tools - just text response)
-        from typing import cast
+        retry_count = 0
+        last_error: Exception | None = None
+        response = None
 
-        from openai.types.chat import ChatCompletionMessageParam
+        while retry_count <= MAX_RETRIES:
+            try:
+                logger.info(
+                    f"Generating LLM continuation for session {session_id}"
+                    f"{f' (retry {retry_count}/{MAX_RETRIES})' if retry_count > 0 else ''}"
+                )
 
-        # No tools parameter = text-only response (no tool calls)
-        response = await client.chat.completions.create(
-            model=config.model,
-            messages=cast(list[ChatCompletionMessageParam], messages),
-            extra_body=extra_body,
-        )
+                response = await client.chat.completions.create(
+                    model=config.model,
+                    messages=cast(list[ChatCompletionMessageParam], messages),
+                    extra_body=extra_body,
+                )
+                last_error = None  # Clear any previous error on success
+                break  # Success, exit retry loop
+            except Exception as e:
+                last_error = e
+                if _is_retryable_error(e) and retry_count < MAX_RETRIES:
+                    retry_count += 1
+                    delay = min(
+                        BASE_DELAY_SECONDS * (2 ** (retry_count - 1)),
+                        MAX_DELAY_SECONDS,
+                    )
+                    logger.warning(
+                        f"Retryable error in LLM continuation: {e!s}. "
+                        f"Retrying in {delay:.1f}s (attempt {retry_count}/{MAX_RETRIES})"
+                    )
+                    await asyncio.sleep(delay)
+                    continue
+                else:
+                    # Non-retryable error - log and exit gracefully
+                    logger.error(
+                        f"Non-retryable error in LLM continuation: {e!s}",
+                        exc_info=True,
+                    )
+                    return
 
-        if response.choices and response.choices[0].message.content:
+        if last_error:
+            logger.error(
+                f"Max retries ({MAX_RETRIES}) exceeded for LLM continuation. "
+                f"Last error: {last_error!s}"
+            )
+            return
+
+        if response and response.choices and response.choices[0].message.content:
             assistant_content = response.choices[0].message.content
 
             # Reload session from DB to avoid race condition with user messages

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

@@ -2,30 +2,54 @@
 
 from .core import (
     AgentGeneratorNotConfiguredError,
+    AgentJsonValidationError,
+    AgentSummary,
+    DecompositionResult,
+    DecompositionStep,
+    LibraryAgentSummary,
+    MarketplaceAgentSummary,
     decompose_goal,
+    enrich_library_agents_from_steps,
+    extract_search_terms_from_steps,
+    extract_uuids_from_text,
     generate_agent,
     generate_agent_patch,
     get_agent_as_json,
+    get_all_relevant_agents_for_generation,
+    get_library_agent_by_graph_id,
+    get_library_agent_by_id,
+    get_library_agents_for_generation,
     json_to_graph,
     save_agent_to_library,
+    search_marketplace_agents_for_generation,
 )
 from .errors import get_user_message_for_error
 from .service import health_check as check_external_service_health
 from .service import is_external_service_configured
 
 __all__ = [
-    # Core functions
+    "AgentGeneratorNotConfiguredError",
+    "AgentJsonValidationError",
+    "AgentSummary",
+    "DecompositionResult",
+    "DecompositionStep",
+    "LibraryAgentSummary",
+    "MarketplaceAgentSummary",
+    "check_external_service_health",
     "decompose_goal",
+    "enrich_library_agents_from_steps",
+    "extract_search_terms_from_steps",
+    "extract_uuids_from_text",
     "generate_agent",
     "generate_agent_patch",
-    "save_agent_to_library",
     "get_agent_as_json",
-    "json_to_graph",
-    # Exceptions
-    "AgentGeneratorNotConfiguredError",
-    # Service
-    "is_external_service_configured",
-    "check_external_service_health",
-    # Error handling
+    "get_all_relevant_agents_for_generation",
+    "get_library_agent_by_graph_id",
+    "get_library_agent_by_id",
+    "get_library_agents_for_generation",
     "get_user_message_for_error",
+    "is_external_service_configured",
+    "json_to_graph",
+    "save_agent_to_library",
+    "search_marketplace_agents_for_generation",
 ]

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

@@ -1,11 +1,22 @@
 """Core agent generation functions."""
 
 import logging
+import re
 import uuid
-from typing import Any
+from typing import Any, NotRequired, TypedDict
 
 from backend.api.features.library import db as library_db
-from backend.data.graph import Graph, Link, Node, create_graph
+from backend.api.features.store import db as store_db
+from backend.data.graph import (
+    Graph,
+    Link,
+    Node,
+    create_graph,
+    get_graph,
+    get_graph_all_versions,
+    get_store_listed_graphs,
+)
+from backend.util.exceptions import DatabaseError, NotFoundError
 
 from .service import (
     decompose_goal_external,
@@ -16,6 +27,74 @@ from .service import (
 
 logger = logging.getLogger(__name__)
 
+AGENT_EXECUTOR_BLOCK_ID = "e189baac-8c20-45a1-94a7-55177ea42565"
+
+
+class ExecutionSummary(TypedDict):
+    """Summary of a single execution for quality assessment."""
+
+    status: str
+    correctness_score: NotRequired[float]
+    activity_summary: NotRequired[str]
+
+
+class LibraryAgentSummary(TypedDict):
+    """Summary of a library agent for sub-agent composition.
+
+    Includes recent executions to help the LLM decide whether to use this agent.
+    Each execution shows status, correctness_score (0-1), and activity_summary.
+    """
+
+    graph_id: str
+    graph_version: int
+    name: str
+    description: str
+    input_schema: dict[str, Any]
+    output_schema: dict[str, Any]
+    recent_executions: NotRequired[list[ExecutionSummary]]
+
+
+class MarketplaceAgentSummary(TypedDict):
+    """Summary of a marketplace agent for sub-agent composition."""
+
+    name: str
+    description: str
+    sub_heading: str
+    creator: str
+    is_marketplace_agent: bool
+
+
+class DecompositionStep(TypedDict, total=False):
+    """A single step in decomposed instructions."""
+
+    description: str
+    action: str
+    block_name: str
+    tool: str
+    name: str
+
+
+class DecompositionResult(TypedDict, total=False):
+    """Result from decompose_goal - can be instructions, questions, or error."""
+
+    type: str
+    steps: list[DecompositionStep]
+    questions: list[dict[str, Any]]
+    error: str
+    error_type: str
+
+
+AgentSummary = LibraryAgentSummary | MarketplaceAgentSummary | dict[str, Any]
+
+
+def _to_dict_list(
+    agents: list[AgentSummary] | list[dict[str, Any]] | None,
+) -> list[dict[str, Any]] | None:
+    """Convert typed agent summaries to plain dicts for external service calls."""
+    if agents is None:
+        return None
+    return [dict(a) for a in agents]
+
 
 class AgentGeneratorNotConfiguredError(Exception):
     """Raised when the external Agent Generator service is not configured."""
@@ -36,15 +115,422 @@ def _check_service_configured() -> None:
         )
 
 
-async def decompose_goal(description: str, context: str = "") -> dict[str, Any] | None:
+_UUID_PATTERN = 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}",
+    re.IGNORECASE,
+)
+
+
+def extract_uuids_from_text(text: str) -> list[str]:
+    """Extract all UUID v4 strings from text.
+
+    Args:
+        text: Text that may contain UUIDs (e.g., user's goal description)
+
+    Returns:
+        List of unique UUIDs found in the text (lowercase)
+    """
+    matches = _UUID_PATTERN.findall(text)
+    return list({m.lower() for m in matches})
+
+
+async def get_library_agent_by_id(
+    user_id: str, agent_id: str
+) -> LibraryAgentSummary | None:
+    """Fetch a specific library agent by its ID (library agent ID or graph_id).
+
+    This function tries multiple lookup strategies:
+    1. First tries to find by graph_id (AgentGraph primary key)
+    2. If not found, tries to find by library agent ID (LibraryAgent primary key)
+
+    This handles both cases:
+    - User provides graph_id (e.g., from AgentExecutorBlock)
+    - User provides library agent ID (e.g., from library URL)
+
+    Args:
+        user_id: The user ID
+        agent_id: The ID to look up (can be graph_id or library agent ID)
+
+    Returns:
+        LibraryAgentSummary if found, None otherwise
+    """
+    try:
+        agent = await library_db.get_library_agent_by_graph_id(user_id, agent_id)
+        if agent:
+            logger.debug(f"Found library agent by graph_id: {agent.name}")
+            return LibraryAgentSummary(
+                graph_id=agent.graph_id,
+                graph_version=agent.graph_version,
+                name=agent.name,
+                description=agent.description,
+                input_schema=agent.input_schema,
+                output_schema=agent.output_schema,
+            )
+    except DatabaseError:
+        raise
+    except Exception as e:
+        logger.debug(f"Could not fetch library agent by graph_id {agent_id}: {e}")
+
+    try:
+        agent = await library_db.get_library_agent(agent_id, user_id)
+        if agent:
+            logger.debug(f"Found library agent by library_id: {agent.name}")
+            return LibraryAgentSummary(
+                graph_id=agent.graph_id,
+                graph_version=agent.graph_version,
+                name=agent.name,
+                description=agent.description,
+                input_schema=agent.input_schema,
+                output_schema=agent.output_schema,
+            )
+    except NotFoundError:
+        logger.debug(f"Library agent not found by library_id: {agent_id}")
+    except DatabaseError:
+        raise
+    except Exception as e:
+        logger.warning(
+            f"Could not fetch library agent by library_id {agent_id}: {e}",
+            exc_info=True,
+        )
+
+    return None
+
+
+get_library_agent_by_graph_id = get_library_agent_by_id
+
+
+async def get_library_agents_for_generation(
+    user_id: str,
+    search_query: str | None = None,
+    exclude_graph_id: str | None = None,
+    max_results: int = 15,
+) -> list[LibraryAgentSummary]:
+    """Fetch user's library agents formatted for Agent Generator.
+
+    Uses search-based fetching to return relevant agents instead of all agents.
+    This is more scalable for users with large libraries.
+
+    Includes recent_executions list to help the LLM assess agent quality:
+    - Each execution has status, correctness_score (0-1), and activity_summary
+    - This gives the LLM concrete examples of recent performance
+
+    Args:
+        user_id: The user ID
+        search_query: Optional search term to find relevant agents (user's goal/description)
+        exclude_graph_id: Optional graph ID to exclude (prevents circular references)
+        max_results: Maximum number of agents to return (default 15)
+
+    Returns:
+        List of LibraryAgentSummary with schemas and recent executions for sub-agent composition
+    """
+    try:
+        response = await library_db.list_library_agents(
+            user_id=user_id,
+            search_term=search_query,
+            page=1,
+            page_size=max_results,
+            include_executions=True,
+        )
+
+        results: list[LibraryAgentSummary] = []
+        for agent in response.agents:
+            if exclude_graph_id is not None and agent.graph_id == exclude_graph_id:
+                continue
+
+            summary = LibraryAgentSummary(
+                graph_id=agent.graph_id,
+                graph_version=agent.graph_version,
+                name=agent.name,
+                description=agent.description,
+                input_schema=agent.input_schema,
+                output_schema=agent.output_schema,
+            )
+            if agent.recent_executions:
+                exec_summaries: list[ExecutionSummary] = []
+                for ex in agent.recent_executions:
+                    exec_sum = ExecutionSummary(status=ex.status)
+                    if ex.correctness_score is not None:
+                        exec_sum["correctness_score"] = ex.correctness_score
+                    if ex.activity_summary:
+                        exec_sum["activity_summary"] = ex.activity_summary
+                    exec_summaries.append(exec_sum)
+                summary["recent_executions"] = exec_summaries
+            results.append(summary)
+        return results
+    except DatabaseError:
+        raise
+    except Exception as e:
+        logger.warning(f"Failed to fetch library agents: {e}")
+        return []
+
+
+async def search_marketplace_agents_for_generation(
+    search_query: str,
+    max_results: int = 10,
+) -> list[LibraryAgentSummary]:
+    """Search marketplace agents formatted for Agent Generator.
+
+    Fetches marketplace agents and their full schemas so they can be used
+    as sub-agents in generated workflows.
+
+    Args:
+        search_query: Search term to find relevant public agents
+        max_results: Maximum number of agents to return (default 10)
+
+    Returns:
+        List of LibraryAgentSummary with full input/output schemas
+    """
+    try:
+        response = await store_db.get_store_agents(
+            search_query=search_query,
+            page=1,
+            page_size=max_results,
+        )
+
+        agents_with_graphs = [
+            agent for agent in response.agents if agent.agent_graph_id
+        ]
+
+        if not agents_with_graphs:
+            return []
+
+        graph_ids = [agent.agent_graph_id for agent in agents_with_graphs]
+        graphs = await get_store_listed_graphs(*graph_ids)
+
+        results: list[LibraryAgentSummary] = []
+        for agent in agents_with_graphs:
+            graph_id = agent.agent_graph_id
+            if graph_id and graph_id in graphs:
+                graph = graphs[graph_id]
+                results.append(
+                    LibraryAgentSummary(
+                        graph_id=graph.id,
+                        graph_version=graph.version,
+                        name=agent.agent_name,
+                        description=agent.description,
+                        input_schema=graph.input_schema,
+                        output_schema=graph.output_schema,
+                    )
+                )
+        return results
+    except Exception as e:
+        logger.warning(f"Failed to search marketplace agents: {e}")
+        return []
+
+
+async def get_all_relevant_agents_for_generation(
+    user_id: str,
+    search_query: str | None = None,
+    exclude_graph_id: str | None = None,
+    include_library: bool = True,
+    include_marketplace: bool = True,
+    max_library_results: int = 15,
+    max_marketplace_results: int = 10,
+) -> list[AgentSummary]:
+    """Fetch relevant agents from library and/or marketplace.
+
+    Searches both user's library and marketplace by default.
+    Explicitly mentioned UUIDs in the search query are always looked up.
+
+    Args:
+        user_id: The user ID
+        search_query: Search term to find relevant agents (user's goal/description)
+        exclude_graph_id: Optional graph ID to exclude (prevents circular references)
+        include_library: Whether to search user's library (default True)
+        include_marketplace: Whether to also search marketplace (default True)
+        max_library_results: Max library agents to return (default 15)
+        max_marketplace_results: Max marketplace agents to return (default 10)
+
+    Returns:
+        List of AgentSummary with full schemas (both library and marketplace agents)
+    """
+    agents: list[AgentSummary] = []
+    seen_graph_ids: set[str] = set()
+
+    if search_query:
+        mentioned_uuids = extract_uuids_from_text(search_query)
+        for graph_id in mentioned_uuids:
+            if graph_id == exclude_graph_id:
+                continue
+            agent = await get_library_agent_by_graph_id(user_id, graph_id)
+            agent_graph_id = agent.get("graph_id") if agent else None
+            if agent and agent_graph_id and agent_graph_id not in seen_graph_ids:
+                agents.append(agent)
+                seen_graph_ids.add(agent_graph_id)
+                logger.debug(
+                    f"Found explicitly mentioned agent: {agent.get('name') or 'Unknown'}"
+                )
+
+    if include_library:
+        library_agents = await get_library_agents_for_generation(
+            user_id=user_id,
+            search_query=search_query,
+            exclude_graph_id=exclude_graph_id,
+            max_results=max_library_results,
+        )
+        for agent in library_agents:
+            graph_id = agent.get("graph_id")
+            if graph_id and graph_id not in seen_graph_ids:
+                agents.append(agent)
+                seen_graph_ids.add(graph_id)
+
+    if include_marketplace and search_query:
+        marketplace_agents = await search_marketplace_agents_for_generation(
+            search_query=search_query,
+            max_results=max_marketplace_results,
+        )
+        for agent in marketplace_agents:
+            graph_id = agent.get("graph_id")
+            if graph_id and graph_id not in seen_graph_ids:
+                agents.append(agent)
+                seen_graph_ids.add(graph_id)
+
+    return agents
+
+
+def extract_search_terms_from_steps(
+    decomposition_result: DecompositionResult | dict[str, Any],
+) -> list[str]:
+    """Extract search terms from decomposed instruction steps.
+
+    Analyzes the decomposition result to extract relevant keywords
+    for additional library agent searches.
+
+    Args:
+        decomposition_result: Result from decompose_goal containing steps
+
+    Returns:
+        List of unique search terms extracted from steps
+    """
+    search_terms: list[str] = []
+
+    if decomposition_result.get("type") != "instructions":
+        return search_terms
+
+    steps = decomposition_result.get("steps", [])
+    if not steps:
+        return search_terms
+
+    step_keys: list[str] = ["description", "action", "block_name", "tool", "name"]
+
+    for step in steps:
+        for key in step_keys:
+            value = step.get(key)  # type: ignore[union-attr]
+            if isinstance(value, str) and len(value) > 3:
+                search_terms.append(value)
+
+    seen: set[str] = set()
+    unique_terms: list[str] = []
+    for term in search_terms:
+        term_lower = term.lower()
+        if term_lower not in seen:
+            seen.add(term_lower)
+            unique_terms.append(term)
+
+    return unique_terms
+
+
+async def enrich_library_agents_from_steps(
+    user_id: str,
+    decomposition_result: DecompositionResult | dict[str, Any],
+    existing_agents: list[AgentSummary] | list[dict[str, Any]],
+    exclude_graph_id: str | None = None,
+    include_marketplace: bool = True,
+    max_additional_results: int = 10,
+) -> list[AgentSummary] | list[dict[str, Any]]:
+    """Enrich library agents list with additional searches based on decomposed steps.
+
+    This implements two-phase search: after decomposition, we search for additional
+    relevant agents based on the specific steps identified.
+
+    Args:
+        user_id: The user ID
+        decomposition_result: Result from decompose_goal containing steps
+        existing_agents: Already fetched library agents from initial search
+        exclude_graph_id: Optional graph ID to exclude
+        include_marketplace: Whether to also search marketplace
+        max_additional_results: Max additional agents per search term (default 10)
+
+    Returns:
+        Combined list of library agents (existing + newly discovered)
+    """
+    search_terms = extract_search_terms_from_steps(decomposition_result)
+
+    if not search_terms:
+        return existing_agents
+
+    existing_ids: set[str] = set()
+    existing_names: set[str] = set()
+
+    for agent in existing_agents:
+        agent_name = agent.get("name")
+        if agent_name and isinstance(agent_name, str):
+            existing_names.add(agent_name.lower())
+        graph_id = agent.get("graph_id")  # type: ignore[call-overload]
+        if graph_id and isinstance(graph_id, str):
+            existing_ids.add(graph_id)
+
+    all_agents: list[AgentSummary] | list[dict[str, Any]] = list(existing_agents)
+
+    for term in search_terms[:3]:
+        try:
+            additional_agents = await get_all_relevant_agents_for_generation(
+                user_id=user_id,
+                search_query=term,
+                exclude_graph_id=exclude_graph_id,
+                include_marketplace=include_marketplace,
+                max_library_results=max_additional_results,
+                max_marketplace_results=5,
+            )
+
+            for agent in additional_agents:
+                agent_name = agent.get("name")
+                if not agent_name or not isinstance(agent_name, str):
+                    continue
+                agent_name_lower = agent_name.lower()
+
+                if agent_name_lower in existing_names:
+                    continue
+
+                graph_id = agent.get("graph_id")  # type: ignore[call-overload]
+                if graph_id and graph_id in existing_ids:
+                    continue
+
+                all_agents.append(agent)
+                existing_names.add(agent_name_lower)
+                if graph_id and isinstance(graph_id, str):
+                    existing_ids.add(graph_id)
+
+        except DatabaseError:
+            logger.error(f"Database error searching for agents with term '{term}'")
+            raise
+        except Exception as e:
+            logger.warning(
+                f"Failed to search for additional agents with term '{term}': {e}"
+            )
+
+    logger.debug(
+        f"Enriched library agents: {len(existing_agents)} initial + "
+        f"{len(all_agents) - len(existing_agents)} additional = {len(all_agents)} total"
+    )
+
+    return all_agents
+
+
+async def decompose_goal(
+    description: str,
+    context: str = "",
+    library_agents: list[AgentSummary] | None = None,
+) -> DecompositionResult | None:
     """Break down a goal into steps or return clarifying questions.
 
     Args:
         description: Natural language goal description
         context: Additional context (e.g., answers to previous questions)
+        library_agents: User's library agents available for sub-agent composition
 
     Returns:
-        Dict with either:
+        DecompositionResult with either:
         - {"type": "clarifying_questions", "questions": [...]}
         - {"type": "instructions", "steps": [...]}
         Or None on error
@@ -54,14 +540,21 @@ async def decompose_goal(description: str, context: str = "") -> dict[str, Any]
     """
     _check_service_configured()
     logger.info("Calling external Agent Generator service for decompose_goal")
-    return await decompose_goal_external(description, context)
+    result = await decompose_goal_external(
+        description, context, _to_dict_list(library_agents)
+    )
+    return result  # type: ignore[return-value]
 
 
-async def generate_agent(instructions: dict[str, Any]) -> dict[str, Any] | None:
+async def generate_agent(
+    instructions: DecompositionResult | dict[str, Any],
+    library_agents: list[AgentSummary] | list[dict[str, Any]] | None = None,
+) -> dict[str, Any] | None:
     """Generate agent JSON from instructions.
 
     Args:
         instructions: Structured instructions from decompose_goal
+        library_agents: User's library agents available for sub-agent composition
 
     Returns:
         Agent JSON dict, error dict {"type": "error", ...}, or None on error
@@ -71,12 +564,12 @@ async def generate_agent(instructions: dict[str, Any]) -> dict[str, Any] | None:
     """
     _check_service_configured()
     logger.info("Calling external Agent Generator service for generate_agent")
-    result = await generate_agent_external(instructions)
+    result = await generate_agent_external(
+        dict(instructions), _to_dict_list(library_agents)
+    )
     if result:
-        # Check if it's an error response - pass through as-is
         if isinstance(result, dict) and result.get("type") == "error":
             return result
-        # Ensure required fields for successful agent generation
         if "id" not in result:
             result["id"] = str(uuid.uuid4())
         if "version" not in result:
@@ -86,6 +579,12 @@ async def generate_agent(instructions: dict[str, Any]) -> dict[str, Any] | None:
     return result
 
 
+class AgentJsonValidationError(Exception):
+    """Raised when agent JSON is invalid or missing required fields."""
+
+    pass
+
+
 def json_to_graph(agent_json: dict[str, Any]) -> Graph:
     """Convert agent JSON dict to Graph model.
 
@@ -94,25 +593,55 @@ def json_to_graph(agent_json: dict[str, Any]) -> Graph:
 
     Returns:
         Graph ready for saving
+
+    Raises:
+        AgentJsonValidationError: If required fields are missing from nodes or links
     """
     nodes = []
-    for n in agent_json.get("nodes", []):
+    for idx, n in enumerate(agent_json.get("nodes", [])):
+        block_id = n.get("block_id")
+        if not block_id:
+            node_id = n.get("id", f"index_{idx}")
+            raise AgentJsonValidationError(
+                f"Node '{node_id}' is missing required field 'block_id'"
+            )
         node = Node(
             id=n.get("id", str(uuid.uuid4())),
-            block_id=n["block_id"],
+            block_id=block_id,
             input_default=n.get("input_default", {}),
             metadata=n.get("metadata", {}),
         )
         nodes.append(node)
 
     links = []
-    for link_data in agent_json.get("links", []):
+    for idx, link_data in enumerate(agent_json.get("links", [])):
+        source_id = link_data.get("source_id")
+        sink_id = link_data.get("sink_id")
+        source_name = link_data.get("source_name")
+        sink_name = link_data.get("sink_name")
+
+        missing_fields = []
+        if not source_id:
+            missing_fields.append("source_id")
+        if not sink_id:
+            missing_fields.append("sink_id")
+        if not source_name:
+            missing_fields.append("source_name")
+        if not sink_name:
+            missing_fields.append("sink_name")
+
+        if missing_fields:
+            link_id = link_data.get("id", f"index_{idx}")
+            raise AgentJsonValidationError(
+                f"Link '{link_id}' is missing required fields: {', '.join(missing_fields)}"
+            )
+
         link = Link(
             id=link_data.get("id", str(uuid.uuid4())),
-            source_id=link_data["source_id"],
-            sink_id=link_data["sink_id"],
-            source_name=link_data["source_name"],
-            sink_name=link_data["sink_name"],
+            source_id=source_id,
+            sink_id=sink_id,
+            source_name=source_name,
+            sink_name=sink_name,
             is_static=link_data.get("is_static", False),
         )
         links.append(link)
@@ -133,22 +662,40 @@ def _reassign_node_ids(graph: Graph) -> None:
 
     This is needed when creating a new version to avoid unique constraint violations.
     """
-    # Create mapping from old node IDs to new UUIDs
     id_map = {node.id: str(uuid.uuid4()) for node in graph.nodes}
 
-    # Reassign node IDs
     for node in graph.nodes:
         node.id = id_map[node.id]
 
-    # Update link references to use new node IDs
     for link in graph.links:
-        link.id = str(uuid.uuid4())  # Also give links new IDs
+        link.id = str(uuid.uuid4())
         if link.source_id in id_map:
             link.source_id = id_map[link.source_id]
         if link.sink_id in id_map:
             link.sink_id = id_map[link.sink_id]
 
 
+def _populate_agent_executor_user_ids(agent_json: dict[str, Any], user_id: str) -> None:
+    """Populate user_id in AgentExecutorBlock nodes.
+
+    The external agent generator creates AgentExecutorBlock nodes with empty user_id.
+    This function fills in the actual user_id so sub-agents run with correct permissions.
+
+    Args:
+        agent_json: Agent JSON dict (modified in place)
+        user_id: User ID to set
+    """
+    for node in agent_json.get("nodes", []):
+        if node.get("block_id") == AGENT_EXECUTOR_BLOCK_ID:
+            input_default = node.get("input_default") or {}
+            if not input_default.get("user_id"):
+                input_default["user_id"] = user_id
+                node["input_default"] = input_default
+                logger.debug(
+                    f"Set user_id for AgentExecutorBlock node {node.get('id')}"
+                )
+
+
 async def save_agent_to_library(
     agent_json: dict[str, Any], user_id: str, is_update: bool = False
 ) -> tuple[Graph, Any]:
@@ -162,33 +709,27 @@ async def save_agent_to_library(
     Returns:
         Tuple of (created Graph, LibraryAgent)
     """
-    from backend.data.graph import get_graph_all_versions
+    # Populate user_id in AgentExecutorBlock nodes before conversion
+    _populate_agent_executor_user_ids(agent_json, user_id)
 
     graph = json_to_graph(agent_json)
 
     if is_update:
-        # For updates, keep the same graph ID but increment version
-        # and reassign node/link IDs to avoid conflicts
         if graph.id:
             existing_versions = await get_graph_all_versions(graph.id, user_id)
             if existing_versions:
                 latest_version = max(v.version for v in existing_versions)
                 graph.version = latest_version + 1
-                # Reassign node IDs (but keep graph ID the same)
                 _reassign_node_ids(graph)
                 logger.info(f"Updating agent {graph.id} to version {graph.version}")
     else:
-        # For new agents, always generate a fresh UUID to avoid collisions
         graph.id = str(uuid.uuid4())
         graph.version = 1
-        # Reassign all node IDs as well
         _reassign_node_ids(graph)
         logger.info(f"Creating new agent with ID {graph.id}")
 
-    # Save to database
     created_graph = await create_graph(graph, user_id)
 
-    # Add to user's library (or update existing library agent)
     library_agents = await library_db.create_library_agent(
         graph=created_graph,
         user_id=user_id,
@@ -200,25 +741,31 @@ async def save_agent_to_library(
 
 
 async def get_agent_as_json(
-    graph_id: str, user_id: str | None
+    agent_id: str, user_id: str | None
 ) -> dict[str, Any] | None:
     """Fetch an agent and convert to JSON format for editing.
 
     Args:
-        graph_id: Graph ID or library agent ID
+        agent_id: Graph ID or library agent ID
         user_id: User ID
 
     Returns:
         Agent as JSON dict or None if not found
     """
-    from backend.data.graph import get_graph
+    graph = await get_graph(agent_id, version=None, user_id=user_id)
+
+    if not graph and user_id:
+        try:
+            library_agent = await library_db.get_library_agent(agent_id, user_id)
+            graph = await get_graph(
+                library_agent.graph_id, version=None, user_id=user_id
+            )
+        except NotFoundError:
+            pass
 
-    # Try to get the graph (version=None gets the active version)
-    graph = await get_graph(graph_id, version=None, user_id=user_id)
     if not graph:
         return None
 
-    # Convert to JSON format
     nodes = []
     for node in graph.nodes:
         nodes.append(
@@ -256,7 +803,9 @@ async def get_agent_as_json(
 
 
 async def generate_agent_patch(
-    update_request: str, current_agent: dict[str, Any]
+    update_request: str,
+    current_agent: dict[str, Any],
+    library_agents: list[AgentSummary] | None = None,
 ) -> dict[str, Any] | None:
     """Update an existing agent using natural language.
 
@@ -268,6 +817,7 @@ async def generate_agent_patch(
     Args:
         update_request: Natural language description of changes
         current_agent: Current agent JSON
+        library_agents: User's library agents available for sub-agent composition
 
     Returns:
         Updated agent JSON, clarifying questions dict {"type": "clarifying_questions", ...},
@@ -278,4 +828,6 @@ async def generate_agent_patch(
     """
     _check_service_configured()
     logger.info("Calling external Agent Generator service for generate_agent_patch")
-    return await generate_agent_patch_external(update_request, current_agent)
+    return await generate_agent_patch_external(
+        update_request, current_agent, _to_dict_list(library_agents)
+    )

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

@@ -1,11 +1,43 @@
 """Error handling utilities for agent generator."""
 
+import re
+
+
+def _sanitize_error_details(details: str) -> str:
+    """Sanitize error details to remove sensitive information.
+
+    Strips common patterns that could expose internal system info:
+    - File paths (Unix and Windows)
+    - Database connection strings
+    - URLs with credentials
+    - Stack trace internals
+
+    Args:
+        details: Raw error details string
+
+    Returns:
+        Sanitized error details safe for user display
+    """
+    sanitized = re.sub(
+        r"/[a-zA-Z0-9_./\-]+\.(py|js|ts|json|yaml|yml)", "[path]", details
+    )
+    sanitized = re.sub(r"[A-Z]:\\[a-zA-Z0-9_\\.\\-]+", "[path]", sanitized)
+    sanitized = re.sub(
+        r"(postgres|mysql|mongodb|redis)://[^\s]+", "[database_url]", sanitized
+    )
+    sanitized = re.sub(r"https?://[^:]+:[^@]+@[^\s]+", "[url]", sanitized)
+    sanitized = re.sub(r", line \d+", "", sanitized)
+    sanitized = re.sub(r'File "[^"]+",?', "", sanitized)
+
+    return sanitized.strip()
+
 
 def get_user_message_for_error(
     error_type: str,
     operation: str = "process the request",
     llm_parse_message: str | None = None,
     validation_message: str | None = None,
+    error_details: str | None = None,
 ) -> str:
     """Get a user-friendly error message based on error type.
 
@@ -19,25 +51,45 @@ def get_user_message_for_error(
             message (e.g., "analyze the goal", "generate the agent")
         llm_parse_message: Custom message for llm_parse_error type
         validation_message: Custom message for validation_error type
+        error_details: Optional additional details about the error
 
     Returns:
         User-friendly error message suitable for display to the user
     """
+    base_message = ""
+
     if error_type == "llm_parse_error":
-        return (
+        base_message = (
             llm_parse_message
             or "The AI had trouble processing this request. Please try again."
         )
     elif error_type == "validation_error":
-        return (
+        base_message = (
             validation_message
-            or "The request failed validation. Please try rephrasing."
+            or "The generated agent failed validation. "
+            "This usually happens when the agent structure doesn't match "
+            "what the platform expects. Please try simplifying your goal "
+            "or breaking it into smaller parts."
         )
     elif error_type == "patch_error":
-        return "Failed to apply the changes. Please try a different approach."
+        base_message = (
+            "Failed to apply the changes. The modification couldn't be "
+            "validated. Please try a different approach or simplify the change."
+        )
     elif error_type in ("timeout", "llm_timeout"):
-        return "The request took too long. Please try again."
+        base_message = (
+            "The request took too long to process. This can happen with "
+            "complex agents. Please try again or simplify your goal."
+        )
     elif error_type in ("rate_limit", "llm_rate_limit"):
-        return "The service is currently busy. Please try again in a moment."
+        base_message = "The service is currently busy. Please try again in a moment."
     else:
-        return f"Failed to {operation}. Please try again."
+        base_message = f"Failed to {operation}. Please try again."
+
+    if error_details:
+        details = _sanitize_error_details(error_details)
+        if len(details) > 200:
+            details = details[:200] + "..."
+        base_message += f"\n\nTechnical details: {details}"
+
+    return base_message

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

@@ -117,13 +117,16 @@ def _get_client() -> httpx.AsyncClient:
 
 
 async def decompose_goal_external(
-    description: str, context: str = ""
+    description: str,
+    context: str = "",
+    library_agents: list[dict[str, Any]] | None = None,
 ) -> 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)
+        library_agents: User's library agents available for sub-agent composition
 
     Returns:
         Dict with either:
@@ -136,11 +139,12 @@ async def decompose_goal_external(
     """
     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
+        description = f"{description}\n\nAdditional context from user:\n{context}"
+
+    payload: dict[str, Any] = {"description": description}
+    if library_agents:
+        payload["library_agents"] = library_agents
 
     try:
         response = await client.post("/api/decompose-description", json=payload)
@@ -207,21 +211,25 @@ async def decompose_goal_external(
 
 async def generate_agent_external(
     instructions: dict[str, Any],
+    library_agents: list[dict[str, Any]] | None = None,
 ) -> dict[str, Any] | None:
     """Call the external service to generate an agent from instructions.
 
     Args:
         instructions: Structured instructions from decompose_goal
+        library_agents: User's library agents available for sub-agent composition
 
     Returns:
         Agent JSON dict on success, or error dict {"type": "error", ...} on error
     """
     client = _get_client()
 
+    payload: dict[str, Any] = {"instructions": instructions}
+    if library_agents:
+        payload["library_agents"] = library_agents
+
     try:
-        response = await client.post(
-            "/api/generate-agent", json={"instructions": instructions}
-        )
+        response = await client.post("/api/generate-agent", json=payload)
         response.raise_for_status()
         data = response.json()
 
@@ -229,8 +237,7 @@ async def generate_agent_external(
             error_msg = data.get("error", "Unknown error from Agent Generator")
             error_type = data.get("error_type", "unknown")
             logger.error(
-                f"Agent Generator generation failed: {error_msg} "
-                f"(type: {error_type})"
+                f"Agent Generator generation failed: {error_msg} (type: {error_type})"
             )
             return _create_error_response(error_msg, error_type)
 
@@ -251,27 +258,31 @@ async def generate_agent_external(
 
 
 async def generate_agent_patch_external(
-    update_request: str, current_agent: dict[str, Any]
+    update_request: str,
+    current_agent: dict[str, Any],
+    library_agents: list[dict[str, Any]] | None = None,
 ) -> 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
+        library_agents: User's library agents available for sub-agent composition
 
     Returns:
         Updated agent JSON, clarifying questions dict, or error dict on error
     """
     client = _get_client()
 
+    payload: dict[str, Any] = {
+        "update_request": update_request,
+        "current_agent_json": current_agent,
+    }
+    if library_agents:
+        payload["library_agents"] = library_agents
+
     try:
-        response = await client.post(
-            "/api/update-agent",
-            json={
-                "update_request": update_request,
-                "current_agent_json": current_agent,
-            },
-        )
+        response = await client.post("/api/update-agent", json=payload)
         response.raise_for_status()
         data = response.json()
 

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

@@ -1,6 +1,7 @@
 """Shared agent search functionality for find_agent and find_library_agent tools."""
 
 import logging
+import re
 from typing import Literal
 
 from backend.api.features.library import db as library_db
@@ -19,6 +20,85 @@ logger = logging.getLogger(__name__)
 
 SearchSource = Literal["marketplace", "library"]
 
+_UUID_PATTERN = 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}$",
+    re.IGNORECASE,
+)
+
+
+def _is_uuid(text: str) -> bool:
+    """Check if text is a valid UUID v4."""
+    return bool(_UUID_PATTERN.match(text.strip()))
+
+
+async def _get_library_agent_by_id(user_id: str, agent_id: str) -> AgentInfo | None:
+    """Fetch a library agent by ID (library agent ID or graph_id).
+
+    Tries multiple lookup strategies:
+    1. First by graph_id (AgentGraph primary key)
+    2. Then by library agent ID (LibraryAgent primary key)
+
+    Args:
+        user_id: The user ID
+        agent_id: The ID to look up (can be graph_id or library agent ID)
+
+    Returns:
+        AgentInfo if found, None otherwise
+    """
+    try:
+        agent = await library_db.get_library_agent_by_graph_id(user_id, agent_id)
+        if agent:
+            logger.debug(f"Found library agent by graph_id: {agent.name}")
+            return AgentInfo(
+                id=agent.id,
+                name=agent.name,
+                description=agent.description or "",
+                source="library",
+                in_library=True,
+                creator=agent.creator_name,
+                status=agent.status.value,
+                can_access_graph=agent.can_access_graph,
+                has_external_trigger=agent.has_external_trigger,
+                new_output=agent.new_output,
+                graph_id=agent.graph_id,
+            )
+    except DatabaseError:
+        raise
+    except Exception as e:
+        logger.warning(
+            f"Could not fetch library agent by graph_id {agent_id}: {e}",
+            exc_info=True,
+        )
+
+    try:
+        agent = await library_db.get_library_agent(agent_id, user_id)
+        if agent:
+            logger.debug(f"Found library agent by library_id: {agent.name}")
+            return AgentInfo(
+                id=agent.id,
+                name=agent.name,
+                description=agent.description or "",
+                source="library",
+                in_library=True,
+                creator=agent.creator_name,
+                status=agent.status.value,
+                can_access_graph=agent.can_access_graph,
+                has_external_trigger=agent.has_external_trigger,
+                new_output=agent.new_output,
+                graph_id=agent.graph_id,
+            )
+    except NotFoundError:
+        logger.debug(f"Library agent not found by library_id: {agent_id}")
+    except DatabaseError:
+        raise
+    except Exception as e:
+        logger.warning(
+            f"Could not fetch library agent by library_id {agent_id}: {e}",
+            exc_info=True,
+        )
+
+    return None
+
 
 async def search_agents(
     query: str,
@@ -69,29 +149,37 @@ async def search_agents(
                         is_featured=False,
                     )
                 )
-        else:  # library
-            logger.info(f"Searching user library for: {query}")
-            results = await library_db.list_library_agents(
-                user_id=user_id,  # type: ignore[arg-type]
-                search_term=query,
-                page_size=10,
-            )
-            for agent in results.agents:
-                agents.append(
-                    AgentInfo(
-                        id=agent.id,
-                        name=agent.name,
-                        description=agent.description or "",
-                        source="library",
-                        in_library=True,
-                        creator=agent.creator_name,
-                        status=agent.status.value,
-                        can_access_graph=agent.can_access_graph,
-                        has_external_trigger=agent.has_external_trigger,
-                        new_output=agent.new_output,
-                        graph_id=agent.graph_id,
-                    )
+        else:
+            if _is_uuid(query):
+                logger.info(f"Query looks like UUID, trying direct lookup: {query}")
+                agent = await _get_library_agent_by_id(user_id, query)  # type: ignore[arg-type]
+                if agent:
+                    agents.append(agent)
+                    logger.info(f"Found agent by direct ID lookup: {agent.name}")
+
+            if not agents:
+                logger.info(f"Searching user library for: {query}")
+                results = await library_db.list_library_agents(
+                    user_id=user_id,  # type: ignore[arg-type]
+                    search_term=query,
+                    page_size=10,
                 )
+                for agent in results.agents:
+                    agents.append(
+                        AgentInfo(
+                            id=agent.id,
+                            name=agent.name,
+                            description=agent.description or "",
+                            source="library",
+                            in_library=True,
+                            creator=agent.creator_name,
+                            status=agent.status.value,
+                            can_access_graph=agent.can_access_graph,
+                            has_external_trigger=agent.has_external_trigger,
+                            new_output=agent.new_output,
+                            graph_id=agent.graph_id,
+                        )
+                    )
         logger.info(f"Found {len(agents)} agents in {source}")
     except NotFoundError:
         pass

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

@@ -8,7 +8,9 @@ from backend.api.features.chat.model import ChatSession
 from .agent_generator import (
     AgentGeneratorNotConfiguredError,
     decompose_goal,
+    enrich_library_agents_from_steps,
     generate_agent,
+    get_all_relevant_agents_for_generation,
     get_user_message_for_error,
     save_agent_to_library,
 )
@@ -103,9 +105,24 @@ class CreateAgentTool(BaseTool):
                 session_id=session_id,
             )
 
-        # Step 1: Decompose goal into steps
+        library_agents = None
+        if user_id:
+            try:
+                library_agents = await get_all_relevant_agents_for_generation(
+                    user_id=user_id,
+                    search_query=description,
+                    include_marketplace=True,
+                )
+                logger.debug(
+                    f"Found {len(library_agents)} relevant agents for sub-agent composition"
+                )
+            except Exception as e:
+                logger.warning(f"Failed to fetch library agents: {e}")
+
         try:
-            decomposition_result = await decompose_goal(description, context)
+            decomposition_result = await decompose_goal(
+                description, context, library_agents
+            )
         except AgentGeneratorNotConfiguredError:
             return ErrorResponse(
                 message=(
@@ -124,7 +141,6 @@ class CreateAgentTool(BaseTool):
                 session_id=session_id,
             )
 
-        # Check if the result is an error from the external service
         if decomposition_result.get("type") == "error":
             error_msg = decomposition_result.get("error", "Unknown error")
             error_type = decomposition_result.get("error_type", "unknown")
@@ -144,7 +160,6 @@ class CreateAgentTool(BaseTool):
                 session_id=session_id,
             )
 
-        # Check if LLM returned clarifying questions
         if decomposition_result.get("type") == "clarifying_questions":
             questions = decomposition_result.get("questions", [])
             return ClarificationNeededResponse(
@@ -163,7 +178,6 @@ class CreateAgentTool(BaseTool):
                 session_id=session_id,
             )
 
-        # Check for unachievable/vague goals
         if decomposition_result.get("type") == "unachievable_goal":
             suggested = decomposition_result.get("suggested_goal", "")
             reason = decomposition_result.get("reason", "")
@@ -190,9 +204,22 @@ class CreateAgentTool(BaseTool):
                 session_id=session_id,
             )
 
-        # Step 2: Generate agent JSON (external service handles fixing and validation)
+        if user_id and library_agents is not None:
+            try:
+                library_agents = await enrich_library_agents_from_steps(
+                    user_id=user_id,
+                    decomposition_result=decomposition_result,
+                    existing_agents=library_agents,
+                    include_marketplace=True,
+                )
+                logger.debug(
+                    f"After enrichment: {len(library_agents)} total agents for sub-agent composition"
+                )
+            except Exception as e:
+                logger.warning(f"Failed to enrich library agents from steps: {e}")
+
         try:
-            agent_json = await generate_agent(decomposition_result)
+            agent_json = await generate_agent(decomposition_result, library_agents)
         except AgentGeneratorNotConfiguredError:
             return ErrorResponse(
                 message=(
@@ -211,7 +238,6 @@ class CreateAgentTool(BaseTool):
                 session_id=session_id,
             )
 
-        # Check if the result is an error from the external service
         if isinstance(agent_json, dict) and agent_json.get("type") == "error":
             error_msg = agent_json.get("error", "Unknown error")
             error_type = agent_json.get("error_type", "unknown")
@@ -219,7 +245,12 @@ class CreateAgentTool(BaseTool):
                 error_type,
                 operation="generate the agent",
                 llm_parse_message="The AI had trouble generating the agent. Please try again or simplify your goal.",
-                validation_message="The generated agent failed validation. Please try rephrasing your goal.",
+                validation_message=(
+                    "I wasn't able to create a valid agent for this request. "
+                    "The generated workflow had some structural issues. "
+                    "Please try simplifying your goal or breaking it into smaller steps."
+                ),
+                error_details=error_msg,
             )
             return ErrorResponse(
                 message=user_message,
@@ -237,7 +268,6 @@ class CreateAgentTool(BaseTool):
         node_count = len(agent_json.get("nodes", []))
         link_count = len(agent_json.get("links", []))
 
-        # Step 3: Preview or save
         if not save:
             return AgentPreviewResponse(
                 message=(
@@ -252,7 +282,6 @@ class CreateAgentTool(BaseTool):
                 session_id=session_id,
             )
 
-        # Save to library
         if not user_id:
             return ErrorResponse(
                 message="You must be logged in to save agents.",
@@ -270,7 +299,7 @@ class CreateAgentTool(BaseTool):
                 agent_id=created_graph.id,
                 agent_name=created_graph.name,
                 library_agent_id=library_agent.id,
-                library_agent_link=f"/library/{library_agent.id}",
+                library_agent_link=f"/library/agents/{library_agent.id}",
                 agent_page_link=f"/build?flowID={created_graph.id}",
                 session_id=session_id,
             )

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

@@ -9,6 +9,7 @@ from .agent_generator import (
     AgentGeneratorNotConfiguredError,
     generate_agent_patch,
     get_agent_as_json,
+    get_all_relevant_agents_for_generation,
     get_user_message_for_error,
     save_agent_to_library,
 )
@@ -117,7 +118,6 @@ class EditAgentTool(BaseTool):
                 session_id=session_id,
             )
 
-        # Step 1: Fetch current agent
         current_agent = await get_agent_as_json(agent_id, user_id)
 
         if current_agent is None:
@@ -127,14 +127,30 @@ class EditAgentTool(BaseTool):
                 session_id=session_id,
             )
 
-        # Build the update request with context
+        library_agents = None
+        if user_id:
+            try:
+                graph_id = current_agent.get("id")
+                library_agents = await get_all_relevant_agents_for_generation(
+                    user_id=user_id,
+                    search_query=changes,
+                    exclude_graph_id=graph_id,
+                    include_marketplace=True,
+                )
+                logger.debug(
+                    f"Found {len(library_agents)} relevant agents for sub-agent composition"
+                )
+            except Exception as e:
+                logger.warning(f"Failed to fetch library agents: {e}")
+
         update_request = changes
         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)
+            result = await generate_agent_patch(
+                update_request, current_agent, library_agents
+            )
         except AgentGeneratorNotConfiguredError:
             return ErrorResponse(
                 message=(
@@ -153,7 +169,6 @@ class EditAgentTool(BaseTool):
                 session_id=session_id,
             )
 
-        # Check if the result is an error from the external service
         if isinstance(result, dict) and result.get("type") == "error":
             error_msg = result.get("error", "Unknown error")
             error_type = result.get("error_type", "unknown")
@@ -162,6 +177,7 @@ class EditAgentTool(BaseTool):
                 operation="generate the changes",
                 llm_parse_message="The AI had trouble generating the changes. Please try again or simplify your request.",
                 validation_message="The generated changes failed validation. Please try rephrasing your request.",
+                error_details=error_msg,
             )
             return ErrorResponse(
                 message=user_message,
@@ -175,7 +191,6 @@ class EditAgentTool(BaseTool):
                 session_id=session_id,
             )
 
-        # Check if LLM returned clarifying questions
         if result.get("type") == "clarifying_questions":
             questions = result.get("questions", [])
             return ClarificationNeededResponse(
@@ -194,7 +209,6 @@ class EditAgentTool(BaseTool):
                 session_id=session_id,
             )
 
-        # Result is the updated agent JSON
         updated_agent = result
 
         agent_name = updated_agent.get("name", "Updated Agent")
@@ -202,7 +216,6 @@ class EditAgentTool(BaseTool):
         node_count = len(updated_agent.get("nodes", []))
         link_count = len(updated_agent.get("links", []))
 
-        # Step 3: Preview or save
         if not save:
             return AgentPreviewResponse(
                 message=(
@@ -218,7 +231,6 @@ class EditAgentTool(BaseTool):
                 session_id=session_id,
             )
 
-        # Save to library (creates a new version)
         if not user_id:
             return ErrorResponse(
                 message="You must be logged in to save agents.",
@@ -236,7 +248,7 @@ class EditAgentTool(BaseTool):
                 agent_id=created_graph.id,
                 agent_name=created_graph.name,
                 library_agent_id=library_agent.id,
-                library_agent_link=f"/library/{library_agent.id}",
+                library_agent_link=f"/library/agents/{library_agent.id}",
                 agent_page_link=f"/build?flowID={created_graph.id}",
                 session_id=session_id,
             )

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 Credentials, CredentialsFieldInfo, CredentialsMetaInput
 from backend.integrations.creds_manager import IntegrationCredentialsManager
 from backend.util.exceptions import NotFoundError
 
@@ -266,13 +266,14 @@ async def match_user_credentials_to_graph(
         credential_requirements,
         _node_fields,
     ) in aggregated_creds.items():
-        # Find first matching credential by provider and type
+        # Find first matching credential by provider, type, and scopes
         matching_cred = next(
             (
                 cred
                 for cred in available_creds
                 if cred.provider in credential_requirements.provider
                 and cred.type in credential_requirements.supported_types
+                and _credential_has_required_scopes(cred, credential_requirements)
             ),
             None,
         )
@@ -296,10 +297,17 @@ async def match_user_credentials_to_graph(
                     f"{credential_field_name} (validation failed: {e})"
                 )
         else:
+            # Build a helpful error message including scope requirements
+            error_parts = [
+                f"provider in {list(credential_requirements.provider)}",
+                f"type in {list(credential_requirements.supported_types)}",
+            ]
+            if credential_requirements.required_scopes:
+                error_parts.append(
+                    f"scopes including {list(credential_requirements.required_scopes)}"
+                )
             missing_creds.append(
-                f"{credential_field_name} "
-                f"(requires provider in {list(credential_requirements.provider)}, "
-                f"type in {list(credential_requirements.supported_types)})"
+                f"{credential_field_name} (requires {', '.join(error_parts)})"
             )
 
     logger.info(
@@ -309,6 +317,28 @@ async def match_user_credentials_to_graph(
     return graph_credentials_inputs, missing_creds
 
 
+def _credential_has_required_scopes(
+    credential: Credentials,
+    requirements: CredentialsFieldInfo,
+) -> bool:
+    """
+    Check if a credential has all the scopes required by the block.
+
+    For OAuth2 credentials, verifies that the credential's scopes are a superset
+    of the required scopes. For other credential types, returns True (no scope check).
+    """
+    # Only OAuth2 credentials have scopes to check
+    if credential.type != "oauth2":
+        return True
+
+    # If no scopes are required, any credential matches
+    if not requirements.required_scopes:
+        return True
+
+    # Check that credential scopes are a superset of required scopes
+    return set(credential.scopes).issuperset(requirements.required_scopes)
+
+
 async def check_user_has_required_credentials(
     user_id: str,
     required_credentials: list[CredentialsMetaInput],

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

@@ -39,6 +39,7 @@ async def list_library_agents(
     sort_by: library_model.LibraryAgentSort = library_model.LibraryAgentSort.UPDATED_AT,
     page: int = 1,
     page_size: int = 50,
+    include_executions: bool = False,
 ) -> library_model.LibraryAgentResponse:
     """
     Retrieves a paginated list of LibraryAgent records for a given user.
@@ -49,6 +50,9 @@ async def list_library_agents(
         sort_by: Sorting field (createdAt, updatedAt, isFavorite, isCreatedByUser).
         page: Current page (1-indexed).
         page_size: Number of items per page.
+        include_executions: Whether to include execution data for status calculation.
+            Defaults to False for performance (UI fetches status separately).
+            Set to True when accurate status/metrics are needed (e.g., agent generator).
 
     Returns:
         A LibraryAgentResponse containing the list of agents and pagination details.
@@ -76,7 +80,6 @@ async def list_library_agents(
         "isArchived": False,
     }
 
-    # Build search filter if applicable
     if search_term:
         where_clause["OR"] = [
             {
@@ -93,7 +96,6 @@ async def list_library_agents(
             },
         ]
 
-    # Determine sorting
     order_by: prisma.types.LibraryAgentOrderByInput | None = None
 
     if sort_by == library_model.LibraryAgentSort.CREATED_AT:
@@ -105,7 +107,7 @@ async def list_library_agents(
         library_agents = await prisma.models.LibraryAgent.prisma().find_many(
             where=where_clause,
             include=library_agent_include(
-                user_id, include_nodes=False, include_executions=False
+                user_id, include_nodes=False, include_executions=include_executions
             ),
             order=order_by,
             skip=(page - 1) * page_size,

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

@@ -9,6 +9,7 @@ import pydantic
 from backend.data.block import BlockInput
 from backend.data.graph import GraphModel, GraphSettings, GraphTriggerInfo
 from backend.data.model import CredentialsMetaInput, is_credentials_field_name
+from backend.util.json import loads as json_loads
 from backend.util.models import Pagination
 
 if TYPE_CHECKING:
@@ -16,10 +17,10 @@ if TYPE_CHECKING:
 
 
 class LibraryAgentStatus(str, Enum):
-    COMPLETED = "COMPLETED"  # All runs completed
-    HEALTHY = "HEALTHY"  # Agent is running (not all runs have completed)
-    WAITING = "WAITING"  # Agent is queued or waiting to start
-    ERROR = "ERROR"  # Agent is in an error state
+    COMPLETED = "COMPLETED"
+    HEALTHY = "HEALTHY"
+    WAITING = "WAITING"
+    ERROR = "ERROR"
 
 
 class MarketplaceListingCreator(pydantic.BaseModel):
@@ -39,6 +40,30 @@ class MarketplaceListing(pydantic.BaseModel):
     creator: MarketplaceListingCreator
 
 
+class RecentExecution(pydantic.BaseModel):
+    """Summary of a recent execution for quality assessment.
+
+    Used by the LLM to understand the agent's recent performance with specific examples
+    rather than just aggregate statistics.
+    """
+
+    status: str
+    correctness_score: float | None = None
+    activity_summary: str | None = None
+
+
+def _parse_settings(settings: dict | str | None) -> GraphSettings:
+    """Parse settings from database, handling both dict and string formats."""
+    if settings is None:
+        return GraphSettings()
+    try:
+        if isinstance(settings, str):
+            settings = json_loads(settings)
+        return GraphSettings.model_validate(settings)
+    except Exception:
+        return GraphSettings()
+
+
 class LibraryAgent(pydantic.BaseModel):
     """
     Represents an agent in the library, including metadata for display and
@@ -48,7 +73,7 @@ class LibraryAgent(pydantic.BaseModel):
     id: str
     graph_id: str
     graph_version: int
-    owner_user_id: str  # ID of user who owns/created this agent graph
+    owner_user_id: str
 
     image_url: str | None
 
@@ -64,7 +89,7 @@ class LibraryAgent(pydantic.BaseModel):
     description: str
     instructions: str | None = None
 
-    input_schema: dict[str, Any]  # Should be BlockIOObjectSubSchema in frontend
+    input_schema: dict[str, Any]
     output_schema: dict[str, Any]
     credentials_input_schema: dict[str, Any] | None = pydantic.Field(
         description="Input schema for credentials required by the agent",
@@ -81,25 +106,19 @@ class LibraryAgent(pydantic.BaseModel):
     )
     trigger_setup_info: Optional[GraphTriggerInfo] = None
 
-    # Indicates whether there's a new output (based on recent runs)
     new_output: bool
-
-    # Whether the user can access the underlying graph
+    execution_count: int = 0
+    success_rate: float | None = None
+    avg_correctness_score: float | None = None
+    recent_executions: list[RecentExecution] = pydantic.Field(
+        default_factory=list,
+        description="List of recent executions with status, score, and summary",
+    )
     can_access_graph: bool
-
-    # Indicates if this agent is the latest version
     is_latest_version: bool
-
-    # Whether the agent is marked as favorite by the user
     is_favorite: bool
-
-    # Recommended schedule cron (from marketplace agents)
     recommended_schedule_cron: str | None = None
-
-    # User-specific settings for this library agent
     settings: GraphSettings = pydantic.Field(default_factory=GraphSettings)
-
-    # Marketplace listing information if the agent has been published
     marketplace_listing: Optional["MarketplaceListing"] = None
 
     @staticmethod
@@ -123,7 +142,6 @@ class LibraryAgent(pydantic.BaseModel):
         agent_updated_at = agent.AgentGraph.updatedAt
         lib_agent_updated_at = agent.updatedAt
 
-        # Compute updated_at as the latest between library agent and graph
         updated_at = (
             max(agent_updated_at, lib_agent_updated_at)
             if agent_updated_at
@@ -136,7 +154,6 @@ class LibraryAgent(pydantic.BaseModel):
             creator_name = agent.Creator.name or "Unknown"
             creator_image_url = agent.Creator.avatarUrl or ""
 
-        # Logic to calculate status and new_output
         week_ago = datetime.datetime.now(datetime.timezone.utc) - datetime.timedelta(
             days=7
         )
@@ -145,13 +162,55 @@ class LibraryAgent(pydantic.BaseModel):
         status = status_result.status
         new_output = status_result.new_output
 
-        # Check if user can access the graph
-        can_access_graph = agent.AgentGraph.userId == agent.userId
+        execution_count = len(executions)
+        success_rate: float | None = None
+        avg_correctness_score: float | None = None
+        if execution_count > 0:
+            success_count = sum(
+                1
+                for e in executions
+                if e.executionStatus == prisma.enums.AgentExecutionStatus.COMPLETED
+            )
+            success_rate = (success_count / execution_count) * 100
 
-        # Hard-coded to True until a method to check is implemented
+            correctness_scores = []
+            for e in executions:
+                if e.stats and isinstance(e.stats, dict):
+                    score = e.stats.get("correctness_score")
+                    if score is not None and isinstance(score, (int, float)):
+                        correctness_scores.append(float(score))
+            if correctness_scores:
+                avg_correctness_score = sum(correctness_scores) / len(
+                    correctness_scores
+                )
+
+        recent_executions: list[RecentExecution] = []
+        for e in executions:
+            exec_score: float | None = None
+            exec_summary: str | None = None
+            if e.stats and isinstance(e.stats, dict):
+                score = e.stats.get("correctness_score")
+                if score is not None and isinstance(score, (int, float)):
+                    exec_score = float(score)
+                summary = e.stats.get("activity_status")
+                if summary is not None and isinstance(summary, str):
+                    exec_summary = summary
+            exec_status = (
+                e.executionStatus.value
+                if hasattr(e.executionStatus, "value")
+                else str(e.executionStatus)
+            )
+            recent_executions.append(
+                RecentExecution(
+                    status=exec_status,
+                    correctness_score=exec_score,
+                    activity_summary=exec_summary,
+                )
+            )
+
+        can_access_graph = agent.AgentGraph.userId == agent.userId
         is_latest_version = True
 
-        # Build marketplace_listing if available
         marketplace_listing_data = None
         if store_listing and store_listing.ActiveVersion and profile:
             creator_data = MarketplaceListingCreator(
@@ -190,11 +249,15 @@ class LibraryAgent(pydantic.BaseModel):
             has_sensitive_action=graph.has_sensitive_action,
             trigger_setup_info=graph.trigger_setup_info,
             new_output=new_output,
+            execution_count=execution_count,
+            success_rate=success_rate,
+            avg_correctness_score=avg_correctness_score,
+            recent_executions=recent_executions,
             can_access_graph=can_access_graph,
             is_latest_version=is_latest_version,
             is_favorite=agent.isFavorite,
             recommended_schedule_cron=agent.AgentGraph.recommendedScheduleCron,
-            settings=GraphSettings.model_validate(agent.settings),
+            settings=_parse_settings(agent.settings),
             marketplace_listing=marketplace_listing_data,
         )
 
@@ -220,18 +283,15 @@ def _calculate_agent_status(
     if not executions:
         return AgentStatusResult(status=LibraryAgentStatus.COMPLETED, new_output=False)
 
-    # Track how many times each execution status appears
     status_counts = {status: 0 for status in prisma.enums.AgentExecutionStatus}
     new_output = False
 
     for execution in executions:
-        # Check if there's a completed run more recent than `recent_threshold`
         if execution.createdAt >= recent_threshold:
             if execution.executionStatus == prisma.enums.AgentExecutionStatus.COMPLETED:
                 new_output = True
         status_counts[execution.executionStatus] += 1
 
-    # Determine the final status based on counts
     if status_counts[prisma.enums.AgentExecutionStatus.FAILED] > 0:
         return AgentStatusResult(status=LibraryAgentStatus.ERROR, new_output=new_output)
     elif status_counts[prisma.enums.AgentExecutionStatus.QUEUED] > 0:

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

@@ -112,6 +112,7 @@ async def get_store_agents(
                             description=agent["description"],
                             runs=agent["runs"],
                             rating=agent["rating"],
+                            agent_graph_id=agent.get("agentGraphId", ""),
                         )
                         store_agents.append(store_agent)
                     except Exception as e:
@@ -170,6 +171,7 @@ async def get_store_agents(
                         description=agent.description,
                         runs=agent.runs,
                         rating=agent.rating,
+                        agent_graph_id=agent.agentGraphId,
                     )
                     # Add to the list only if creation was successful
                     store_agents.append(store_agent)

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

@@ -600,6 +600,7 @@ async def hybrid_search(
                 sa.featured,
                 sa.is_available,
                 sa.updated_at,
+                sa."agentGraphId",
                 -- Searchable text for BM25 reranking
                 COALESCE(sa.agent_name, '') || ' ' || COALESCE(sa.sub_heading, '') || ' ' || COALESCE(sa.description, '') as searchable_text,
                 -- Semantic score
@@ -659,6 +660,7 @@ async def hybrid_search(
                 featured,
                 is_available,
                 updated_at,
+                "agentGraphId",
                 searchable_text,
                 semantic_score,
                 lexical_score,

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

@@ -38,6 +38,7 @@ class StoreAgent(pydantic.BaseModel):
     description: str
     runs: int
     rating: float
+    agent_graph_id: str
 
 
 class StoreAgentsResponse(pydantic.BaseModel):

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

@@ -26,11 +26,13 @@ def test_store_agent():
         description="Test description",
         runs=50,
         rating=4.5,
+        agent_graph_id="test-graph-id",
     )
     assert agent.slug == "test-agent"
     assert agent.agent_name == "Test Agent"
     assert agent.runs == 50
     assert agent.rating == 4.5
+    assert agent.agent_graph_id == "test-graph-id"
 
 
 def test_store_agents_response():
@@ -46,6 +48,7 @@ def test_store_agents_response():
                 description="Test description",
                 runs=50,
                 rating=4.5,
+                agent_graph_id="test-graph-id",
             )
         ],
         pagination=store_model.Pagination(

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

@@ -82,6 +82,7 @@ def test_get_agents_featured(
                 description="Featured agent description",
                 runs=100,
                 rating=4.5,
+                agent_graph_id="test-graph-1",
             )
         ],
         pagination=store_model.Pagination(
@@ -127,6 +128,7 @@ def test_get_agents_by_creator(
                 description="Creator agent description",
                 runs=50,
                 rating=4.0,
+                agent_graph_id="test-graph-2",
             )
         ],
         pagination=store_model.Pagination(
@@ -172,6 +174,7 @@ def test_get_agents_sorted(
                 description="Top agent description",
                 runs=1000,
                 rating=5.0,
+                agent_graph_id="test-graph-3",
             )
         ],
         pagination=store_model.Pagination(
@@ -217,6 +220,7 @@ def test_get_agents_search(
                 description="Specific search term description",
                 runs=75,
                 rating=4.2,
+                agent_graph_id="test-graph-search",
             )
         ],
         pagination=store_model.Pagination(
@@ -262,6 +266,7 @@ def test_get_agents_category(
                 description="Category agent description",
                 runs=60,
                 rating=4.1,
+                agent_graph_id="test-graph-category",
             )
         ],
         pagination=store_model.Pagination(
@@ -306,6 +311,7 @@ def test_get_agents_pagination(
                 description=f"Agent {i} description",
                 runs=i * 10,
                 rating=4.0,
+                agent_graph_id="test-graph-2",
             )
             for i in range(5)
         ],

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

@@ -33,6 +33,7 @@ class TestCacheDeletion:
                     description="Test description",
                     runs=100,
                     rating=4.5,
+                    agent_graph_id="test-graph-id",
                 )
             ],
             pagination=Pagination(

autogpt_platform/backend/backend/api/ws_api.py

@@ -66,18 +66,24 @@ async def event_broadcaster(manager: ConnectionManager):
     execution_bus = AsyncRedisExecutionEventBus()
     notification_bus = AsyncRedisNotificationEventBus()
 
-    async def execution_worker():
-        async for event in execution_bus.listen("*"):
-            await manager.send_execution_update(event)
+    try:
 
-    async def notification_worker():
-        async for notification in notification_bus.listen("*"):
-            await manager.send_notification(
-                user_id=notification.user_id,
-                payload=notification.payload,
-            )
+        async def execution_worker():
+            async for event in execution_bus.listen("*"):
+                await manager.send_execution_update(event)
 
-    await asyncio.gather(execution_worker(), notification_worker())
+        async def notification_worker():
+            async for notification in notification_bus.listen("*"):
+                await manager.send_notification(
+                    user_id=notification.user_id,
+                    payload=notification.payload,
+                )
+
+        await asyncio.gather(execution_worker(), notification_worker())
+    finally:
+        # Ensure PubSub connections are closed on any exit to prevent leaks
+        await execution_bus.close()
+        await notification_bus.close()
 
 
 async def authenticate_websocket(websocket: WebSocket) -> str:

autogpt_platform/backend/backend/blocks/elevenlabs/_auth.py

@@ -1,28 +0,0 @@
-"""ElevenLabs integration blocks - test credentials and shared utilities."""
-
-from typing import Literal
-
-from pydantic import SecretStr
-
-from backend.data.model import APIKeyCredentials, CredentialsMetaInput
-from backend.integrations.providers import ProviderName
-
-TEST_CREDENTIALS = APIKeyCredentials(
-    id="01234567-89ab-cdef-0123-456789abcdef",
-    provider="elevenlabs",
-    api_key=SecretStr("mock-elevenlabs-api-key"),
-    title="Mock ElevenLabs API key",
-    expires_at=None,
-)
-
-TEST_CREDENTIALS_INPUT = {
-    "provider": TEST_CREDENTIALS.provider,
-    "id": TEST_CREDENTIALS.id,
-    "type": TEST_CREDENTIALS.type,
-    "title": TEST_CREDENTIALS.title,
-}
-
-ElevenLabsCredentials = APIKeyCredentials
-ElevenLabsCredentialsInput = CredentialsMetaInput[
-    Literal[ProviderName.ELEVENLABS], Literal["api_key"]
-]

autogpt_platform/backend/backend/blocks/llm.py

@@ -32,7 +32,7 @@ from backend.data.model import (
 from backend.integrations.providers import ProviderName
 from backend.util import json
 from backend.util.logging import TruncatedLogger
-from backend.util.prompt import compress_prompt, estimate_token_count
+from backend.util.prompt import compress_context, estimate_token_count
 from backend.util.text import TextFormatter
 
 logger = TruncatedLogger(logging.getLogger(__name__), "[LLM-Block]")
@@ -115,7 +115,6 @@ class LlmModel(str, Enum, metaclass=LlmModelMeta):
     CLAUDE_4_5_OPUS = "claude-opus-4-5-20251101"
     CLAUDE_4_5_SONNET = "claude-sonnet-4-5-20250929"
     CLAUDE_4_5_HAIKU = "claude-haiku-4-5-20251001"
-    CLAUDE_3_7_SONNET = "claude-3-7-sonnet-20250219"
     CLAUDE_3_HAIKU = "claude-3-haiku-20240307"
     # AI/ML API models
     AIML_API_QWEN2_5_72B = "Qwen/Qwen2.5-72B-Instruct-Turbo"
@@ -280,9 +279,6 @@ MODEL_METADATA = {
     LlmModel.CLAUDE_4_5_HAIKU: ModelMetadata(
         "anthropic", 200000, 64000, "Claude Haiku 4.5", "Anthropic", "Anthropic", 2
     ),  # claude-haiku-4-5-20251001
-    LlmModel.CLAUDE_3_7_SONNET: ModelMetadata(
-        "anthropic", 200000, 64000, "Claude 3.7 Sonnet", "Anthropic", "Anthropic", 2
-    ),  # claude-3-7-sonnet-20250219
     LlmModel.CLAUDE_3_HAIKU: ModelMetadata(
         "anthropic", 200000, 4096, "Claude 3 Haiku", "Anthropic", "Anthropic", 1
     ),  # claude-3-haiku-20240307
@@ -638,11 +634,18 @@ async def llm_call(
     context_window = llm_model.context_window
 
     if compress_prompt_to_fit:
-        prompt = compress_prompt(
+        result = await compress_context(
             messages=prompt,
             target_tokens=llm_model.context_window // 2,
-            lossy_ok=True,
+            client=None,  # Truncation-only, no LLM summarization
+            reserve=0,  # Caller handles response token budget separately
         )
+        if result.error:
+            logger.warning(
+                f"Prompt compression did not meet target: {result.error}. "
+                f"Proceeding with {result.token_count} tokens."
+            )
+        prompt = result.messages
 
     # Calculate available tokens based on context window and input length
     estimated_input_tokens = estimate_token_count(prompt)

autogpt_platform/backend/backend/blocks/media.py

@@ -0,0 +1,246 @@
+import os
+import tempfile
+from typing import Optional
+
+from moviepy.audio.io.AudioFileClip import AudioFileClip
+from moviepy.video.fx.Loop import Loop
+from moviepy.video.io.VideoFileClip import VideoFileClip
+
+from backend.data.block import (
+    Block,
+    BlockCategory,
+    BlockOutput,
+    BlockSchemaInput,
+    BlockSchemaOutput,
+)
+from backend.data.execution import ExecutionContext
+from backend.data.model import SchemaField
+from backend.util.file import MediaFileType, get_exec_file_path, store_media_file
+
+
+class MediaDurationBlock(Block):
+
+    class Input(BlockSchemaInput):
+        media_in: MediaFileType = SchemaField(
+            description="Media input (URL, data URI, or local path)."
+        )
+        is_video: bool = SchemaField(
+            description="Whether the media is a video (True) or audio (False).",
+            default=True,
+        )
+
+    class Output(BlockSchemaOutput):
+        duration: float = SchemaField(
+            description="Duration of the media file (in seconds)."
+        )
+
+    def __init__(self):
+        super().__init__(
+            id="d8b91fd4-da26-42d4-8ecb-8b196c6d84b6",
+            description="Block to get the duration of a media file.",
+            categories={BlockCategory.MULTIMEDIA},
+            input_schema=MediaDurationBlock.Input,
+            output_schema=MediaDurationBlock.Output,
+        )
+
+    async def run(
+        self,
+        input_data: Input,
+        *,
+        execution_context: ExecutionContext,
+        **kwargs,
+    ) -> BlockOutput:
+        # 1) Store the input media locally
+        local_media_path = await store_media_file(
+            file=input_data.media_in,
+            execution_context=execution_context,
+            return_format="for_local_processing",
+        )
+        assert execution_context.graph_exec_id is not None
+        media_abspath = get_exec_file_path(
+            execution_context.graph_exec_id, local_media_path
+        )
+
+        # 2) Load the clip
+        if input_data.is_video:
+            clip = VideoFileClip(media_abspath)
+        else:
+            clip = AudioFileClip(media_abspath)
+
+        yield "duration", clip.duration
+
+
+class LoopVideoBlock(Block):
+    """
+    Block for looping (repeating) a video clip until a given duration or number of loops.
+    """
+
+    class Input(BlockSchemaInput):
+        video_in: MediaFileType = SchemaField(
+            description="The input video (can be a URL, data URI, or local path)."
+        )
+        # Provide EITHER a `duration` or `n_loops` or both. We'll demonstrate `duration`.
+        duration: Optional[float] = SchemaField(
+            description="Target duration (in seconds) to loop the video to. If omitted, defaults to no looping.",
+            default=None,
+            ge=0.0,
+        )
+        n_loops: Optional[int] = SchemaField(
+            description="Number of times to repeat the video. If omitted, defaults to 1 (no repeat).",
+            default=None,
+            ge=1,
+        )
+
+    class Output(BlockSchemaOutput):
+        video_out: str = SchemaField(
+            description="Looped video returned either as a relative path or a data URI."
+        )
+
+    def __init__(self):
+        super().__init__(
+            id="8bf9eef6-5451-4213-b265-25306446e94b",
+            description="Block to loop a video to a given duration or number of repeats.",
+            categories={BlockCategory.MULTIMEDIA},
+            input_schema=LoopVideoBlock.Input,
+            output_schema=LoopVideoBlock.Output,
+        )
+
+    async def run(
+        self,
+        input_data: Input,
+        *,
+        execution_context: ExecutionContext,
+        **kwargs,
+    ) -> BlockOutput:
+        assert execution_context.graph_exec_id is not None
+        assert execution_context.node_exec_id is not None
+        graph_exec_id = execution_context.graph_exec_id
+        node_exec_id = execution_context.node_exec_id
+
+        # 1) Store the input video locally
+        local_video_path = await store_media_file(
+            file=input_data.video_in,
+            execution_context=execution_context,
+            return_format="for_local_processing",
+        )
+        input_abspath = get_exec_file_path(graph_exec_id, local_video_path)
+
+        # 2) Load the clip
+        clip = VideoFileClip(input_abspath)
+
+        # 3) Apply the loop effect
+        looped_clip = clip
+        if input_data.duration:
+            # Loop until we reach the specified duration
+            looped_clip = looped_clip.with_effects([Loop(duration=input_data.duration)])
+        elif input_data.n_loops:
+            looped_clip = looped_clip.with_effects([Loop(n=input_data.n_loops)])
+        else:
+            raise ValueError("Either 'duration' or 'n_loops' must be provided.")
+
+        assert isinstance(looped_clip, VideoFileClip)
+
+        # 4) Save the looped output
+        output_filename = MediaFileType(
+            f"{node_exec_id}_looped_{os.path.basename(local_video_path)}"
+        )
+        output_abspath = get_exec_file_path(graph_exec_id, output_filename)
+
+        looped_clip = looped_clip.with_audio(clip.audio)
+        looped_clip.write_videofile(output_abspath, codec="libx264", audio_codec="aac")
+
+        # Return output - for_block_output returns workspace:// if available, else data URI
+        video_out = await store_media_file(
+            file=output_filename,
+            execution_context=execution_context,
+            return_format="for_block_output",
+        )
+
+        yield "video_out", video_out
+
+
+class AddAudioToVideoBlock(Block):
+    """
+    Block that adds (attaches) an audio track to an existing video.
+    Optionally scale the volume of the new track.
+    """
+
+    class Input(BlockSchemaInput):
+        video_in: MediaFileType = SchemaField(
+            description="Video input (URL, data URI, or local path)."
+        )
+        audio_in: MediaFileType = SchemaField(
+            description="Audio input (URL, data URI, or local path)."
+        )
+        volume: float = SchemaField(
+            description="Volume scale for the newly attached audio track (1.0 = original).",
+            default=1.0,
+        )
+
+    class Output(BlockSchemaOutput):
+        video_out: MediaFileType = SchemaField(
+            description="Final video (with attached audio), as a path or data URI."
+        )
+
+    def __init__(self):
+        super().__init__(
+            id="3503748d-62b6-4425-91d6-725b064af509",
+            description="Block to attach an audio file to a video file using moviepy.",
+            categories={BlockCategory.MULTIMEDIA},
+            input_schema=AddAudioToVideoBlock.Input,
+            output_schema=AddAudioToVideoBlock.Output,
+        )
+
+    async def run(
+        self,
+        input_data: Input,
+        *,
+        execution_context: ExecutionContext,
+        **kwargs,
+    ) -> BlockOutput:
+        assert execution_context.graph_exec_id is not None
+        assert execution_context.node_exec_id is not None
+        graph_exec_id = execution_context.graph_exec_id
+        node_exec_id = execution_context.node_exec_id
+
+        # 1) Store the inputs locally
+        local_video_path = await store_media_file(
+            file=input_data.video_in,
+            execution_context=execution_context,
+            return_format="for_local_processing",
+        )
+        local_audio_path = await store_media_file(
+            file=input_data.audio_in,
+            execution_context=execution_context,
+            return_format="for_local_processing",
+        )
+
+        abs_temp_dir = os.path.join(tempfile.gettempdir(), "exec_file", graph_exec_id)
+        video_abspath = os.path.join(abs_temp_dir, local_video_path)
+        audio_abspath = os.path.join(abs_temp_dir, local_audio_path)
+
+        # 2) Load video + audio with moviepy
+        video_clip = VideoFileClip(video_abspath)
+        audio_clip = AudioFileClip(audio_abspath)
+        # Optionally scale volume
+        if input_data.volume != 1.0:
+            audio_clip = audio_clip.with_volume_scaled(input_data.volume)
+
+        # 3) Attach the new audio track
+        final_clip = video_clip.with_audio(audio_clip)
+
+        # 4) Write to output file
+        output_filename = MediaFileType(
+            f"{node_exec_id}_audio_attached_{os.path.basename(local_video_path)}"
+        )
+        output_abspath = os.path.join(abs_temp_dir, output_filename)
+        final_clip.write_videofile(output_abspath, codec="libx264", audio_codec="aac")
+
+        # 5) Return output - for_block_output returns workspace:// if available, else data URI
+        video_out = await store_media_file(
+            file=output_filename,
+            execution_context=execution_context,
+            return_format="for_block_output",
+        )
+
+        yield "video_out", video_out

autogpt_platform/backend/backend/blocks/stagehand/blocks.py

@@ -83,7 +83,7 @@ class StagehandRecommendedLlmModel(str, Enum):
     GPT41_MINI = "gpt-4.1-mini-2025-04-14"
 
     # Anthropic
-    CLAUDE_3_7_SONNET = "claude-3-7-sonnet-20250219"
+    CLAUDE_4_5_SONNET = "claude-sonnet-4-5-20250929"
 
     @property
     def provider_name(self) -> str:
@@ -137,7 +137,7 @@ class StagehandObserveBlock(Block):
         model: StagehandRecommendedLlmModel = SchemaField(
             title="LLM Model",
             description="LLM to use for Stagehand (provider is inferred)",
-            default=StagehandRecommendedLlmModel.CLAUDE_3_7_SONNET,
+            default=StagehandRecommendedLlmModel.CLAUDE_4_5_SONNET,
             advanced=False,
         )
         model_credentials: AICredentials = AICredentialsField()
@@ -230,7 +230,7 @@ class StagehandActBlock(Block):
         model: StagehandRecommendedLlmModel = SchemaField(
             title="LLM Model",
             description="LLM to use for Stagehand (provider is inferred)",
-            default=StagehandRecommendedLlmModel.CLAUDE_3_7_SONNET,
+            default=StagehandRecommendedLlmModel.CLAUDE_4_5_SONNET,
             advanced=False,
         )
         model_credentials: AICredentials = AICredentialsField()
@@ -330,7 +330,7 @@ class StagehandExtractBlock(Block):
         model: StagehandRecommendedLlmModel = SchemaField(
             title="LLM Model",
             description="LLM to use for Stagehand (provider is inferred)",
-            default=StagehandRecommendedLlmModel.CLAUDE_3_7_SONNET,
+            default=StagehandRecommendedLlmModel.CLAUDE_4_5_SONNET,
             advanced=False,
         )
         model_credentials: AICredentials = AICredentialsField()

autogpt_platform/backend/backend/blocks/video/__init__.py

@@ -1,37 +0,0 @@
-"""Video editing blocks for AutoGPT Platform.
-
-This module provides blocks for:
-- Downloading videos from URLs (YouTube, Vimeo, news sites, direct links)
-- Clipping/trimming video segments
-- Concatenating multiple videos
-- Adding text overlays
-- Adding AI-generated narration
-- Getting media duration
-- Looping videos
-- Adding audio to videos
-
-Dependencies:
-- yt-dlp: For video downloading
-- moviepy: For video editing operations
-- elevenlabs: For AI narration (optional)
-"""
-
-from backend.blocks.video.add_audio import AddAudioToVideoBlock
-from backend.blocks.video.clip import VideoClipBlock
-from backend.blocks.video.concat import VideoConcatBlock
-from backend.blocks.video.download import VideoDownloadBlock
-from backend.blocks.video.duration import MediaDurationBlock
-from backend.blocks.video.loop import LoopVideoBlock
-from backend.blocks.video.narration import VideoNarrationBlock
-from backend.blocks.video.text_overlay import VideoTextOverlayBlock
-
-__all__ = [
-    "AddAudioToVideoBlock",
-    "LoopVideoBlock",
-    "MediaDurationBlock",
-    "VideoClipBlock",
-    "VideoConcatBlock",
-    "VideoDownloadBlock",
-    "VideoNarrationBlock",
-    "VideoTextOverlayBlock",
-]

autogpt_platform/backend/backend/blocks/video/_utils.py

@@ -1,34 +0,0 @@
-"""Shared utilities for video blocks."""
-
-import os
-
-
-def get_video_codecs(output_path: str) -> tuple[str, str]:
-    """Get appropriate video and audio codecs based on output file extension.
-
-    Args:
-        output_path: Path to the output file (used to determine extension)
-
-    Returns:
-        Tuple of (video_codec, audio_codec)
-
-    Codec mappings:
-        - .mp4: H.264 + AAC (universal compatibility)
-        - .webm: VP8 + Vorbis (web streaming)
-        - .mkv: H.264 + AAC (container supports many codecs)
-        - .mov: H.264 + AAC (Apple QuickTime, widely compatible)
-        - .m4v: H.264 + AAC (Apple iTunes/devices)
-        - .avi: MPEG-4 + MP3 (legacy Windows)
-    """
-    ext = os.path.splitext(output_path)[1].lower()
-
-    codec_map: dict[str, tuple[str, str]] = {
-        ".mp4": ("libx264", "aac"),
-        ".webm": ("libvpx", "libvorbis"),
-        ".mkv": ("libx264", "aac"),
-        ".mov": ("libx264", "aac"),
-        ".m4v": ("libx264", "aac"),
-        ".avi": ("mpeg4", "libmp3lame"),
-    }
-
-    return codec_map.get(ext, ("libx264", "aac"))

autogpt_platform/backend/backend/blocks/video/add_audio.py

@@ -1,102 +0,0 @@
-"""AddAudioToVideoBlock - Attach an audio track to a video file."""
-
-import os
-import tempfile
-
-from moviepy.audio.io.AudioFileClip import AudioFileClip
-from moviepy.video.io.VideoFileClip import VideoFileClip
-
-from backend.data.block import (
-    Block,
-    BlockCategory,
-    BlockOutput,
-    BlockSchemaInput,
-    BlockSchemaOutput,
-)
-from backend.data.execution import ExecutionContext
-from backend.data.model import SchemaField
-from backend.util.file import MediaFileType, store_media_file
-
-
-class AddAudioToVideoBlock(Block):
-    """Add (attach) an audio track to an existing video."""
-
-    class Input(BlockSchemaInput):
-        video_in: MediaFileType = SchemaField(
-            description="Video input (URL, data URI, or local path)."
-        )
-        audio_in: MediaFileType = SchemaField(
-            description="Audio input (URL, data URI, or local path)."
-        )
-        volume: float = SchemaField(
-            description="Volume scale for the newly attached audio track (1.0 = original).",
-            default=1.0,
-        )
-
-    class Output(BlockSchemaOutput):
-        video_out: MediaFileType = SchemaField(
-            description="Final video (with attached audio), as a path or data URI."
-        )
-
-    def __init__(self):
-        super().__init__(
-            id="3503748d-62b6-4425-91d6-725b064af509",
-            description="Block to attach an audio file to a video file using moviepy.",
-            categories={BlockCategory.MULTIMEDIA},
-            input_schema=AddAudioToVideoBlock.Input,
-            output_schema=AddAudioToVideoBlock.Output,
-        )
-
-    async def run(
-        self,
-        input_data: Input,
-        *,
-        execution_context: ExecutionContext,
-        **kwargs,
-    ) -> BlockOutput:
-        assert execution_context.graph_exec_id is not None
-        assert execution_context.node_exec_id is not None
-        graph_exec_id = execution_context.graph_exec_id
-        node_exec_id = execution_context.node_exec_id
-
-        # 1) Store the inputs locally
-        local_video_path = await store_media_file(
-            file=input_data.video_in,
-            execution_context=execution_context,
-            return_format="for_local_processing",
-        )
-        local_audio_path = await store_media_file(
-            file=input_data.audio_in,
-            execution_context=execution_context,
-            return_format="for_local_processing",
-        )
-
-        abs_temp_dir = os.path.join(tempfile.gettempdir(), "exec_file", graph_exec_id)
-        video_abspath = os.path.join(abs_temp_dir, local_video_path)
-        audio_abspath = os.path.join(abs_temp_dir, local_audio_path)
-
-        # 2) Load video + audio with moviepy
-        video_clip = VideoFileClip(video_abspath)
-        audio_clip = AudioFileClip(audio_abspath)
-        # Optionally scale volume
-        if input_data.volume != 1.0:
-            audio_clip = audio_clip.with_volume_scaled(input_data.volume)
-
-        # 3) Attach the new audio track
-        final_clip = video_clip.with_audio(audio_clip)
-
-        # 4) Write to output file
-        output_filename = MediaFileType(
-            f"{node_exec_id}_audio_attached_{os.path.basename(local_video_path)}"
-        )
-        output_abspath = os.path.join(abs_temp_dir, output_filename)
-        final_clip.write_videofile(output_abspath, codec="libx264", audio_codec="aac")
-
-        # 5) Return output - for_block_output returns workspace:// if available, else data URI
-        video_out = await store_media_file(
-            file=output_filename,
-            execution_context=execution_context,
-            return_format="for_block_output",
-        )
-
-        yield "video_out", video_out

autogpt_platform/backend/backend/blocks/video/clip.py

@@ -1,165 +0,0 @@
-"""VideoClipBlock - Extract a segment from a video file."""
-
-import os
-from typing import Literal
-
-from moviepy.video.io.VideoFileClip import VideoFileClip
-
-from backend.blocks.video._utils import get_video_codecs
-from backend.data.block import (
-    Block,
-    BlockCategory,
-    BlockOutput,
-    BlockSchemaInput,
-    BlockSchemaOutput,
-)
-from backend.data.execution import ExecutionContext
-from backend.data.model import SchemaField
-from backend.util.exceptions import BlockExecutionError
-from backend.util.file import MediaFileType, get_exec_file_path, store_media_file
-
-
-class VideoClipBlock(Block):
-    """Extract a time segment from a video."""
-
-    class Input(BlockSchemaInput):
-        video_in: MediaFileType = SchemaField(
-            description="Input video (URL, data URI, or local path)"
-        )
-        start_time: float = SchemaField(description="Start time in seconds", ge=0.0)
-        end_time: float = SchemaField(description="End time in seconds", ge=0.0)
-        output_format: Literal["mp4", "webm", "mkv", "mov"] = SchemaField(
-            description="Output format", default="mp4", advanced=True
-        )
-
-    class Output(BlockSchemaOutput):
-        video_out: MediaFileType = SchemaField(
-            description="Clipped video file (path or data URI)"
-        )
-        duration: float = SchemaField(description="Clip duration in seconds")
-
-    def __init__(self):
-        super().__init__(
-            id="8f539119-e580-4d86-ad41-86fbcb22abb1",
-            description="Extract a time segment from a video",
-            categories={BlockCategory.MULTIMEDIA},
-            input_schema=self.Input,
-            output_schema=self.Output,
-            test_input={
-                "video_in": "/tmp/test.mp4",
-                "start_time": 0.0,
-                "end_time": 10.0,
-            },
-            test_output=[("video_out", str), ("duration", float)],
-            test_mock={
-                "_clip_video": lambda *args: 10.0,
-                "_store_input_video": lambda *args, **kwargs: "test.mp4",
-                "_store_output_video": lambda *args, **kwargs: "clip_test.mp4",
-            },
-        )
-
-    async def _store_input_video(
-        self, execution_context: ExecutionContext, file: MediaFileType
-    ) -> MediaFileType:
-        """Store input video. Extracted for testability."""
-        return await store_media_file(
-            file=file,
-            execution_context=execution_context,
-            return_format="for_local_processing",
-        )
-
-    async def _store_output_video(
-        self, execution_context: ExecutionContext, file: MediaFileType
-    ) -> MediaFileType:
-        """Store output video. Extracted for testability."""
-        return await store_media_file(
-            file=file,
-            execution_context=execution_context,
-            return_format="for_block_output",
-        )
-
-    def _clip_video(
-        self,
-        video_abspath: str,
-        output_abspath: str,
-        start_time: float,
-        end_time: float,
-    ) -> float:
-        """Extract a clip from a video. Extracted for testability."""
-        clip = None
-        subclip = None
-        try:
-            clip = VideoFileClip(video_abspath)
-            subclip = clip.subclipped(start_time, end_time)
-            video_codec, audio_codec = get_video_codecs(output_abspath)
-            subclip.write_videofile(
-                output_abspath, codec=video_codec, audio_codec=audio_codec
-            )
-            return subclip.duration
-        finally:
-            if subclip:
-                subclip.close()
-            if clip:
-                clip.close()
-
-    async def run(
-        self,
-        input_data: Input,
-        *,
-        execution_context: ExecutionContext,
-        node_exec_id: str,
-        **kwargs,
-    ) -> BlockOutput:
-        # Validate time range
-        if input_data.end_time <= input_data.start_time:
-            raise BlockExecutionError(
-                message=f"end_time ({input_data.end_time}) must be greater than start_time ({input_data.start_time})",
-                block_name=self.name,
-                block_id=str(self.id),
-            )
-
-        try:
-            assert execution_context.graph_exec_id is not None
-
-            # Store the input video locally
-            local_video_path = await self._store_input_video(
-                execution_context, input_data.video_in
-            )
-            video_abspath = get_exec_file_path(
-                execution_context.graph_exec_id, local_video_path
-            )
-
-            # Build output path
-            output_filename = MediaFileType(
-                f"{node_exec_id}_clip_{os.path.basename(local_video_path)}"
-            )
-            # Ensure correct extension
-            base, _ = os.path.splitext(output_filename)
-            output_filename = MediaFileType(f"{base}.{input_data.output_format}")
-            output_abspath = get_exec_file_path(
-                execution_context.graph_exec_id, output_filename
-            )
-
-            duration = self._clip_video(
-                video_abspath,
-                output_abspath,
-                input_data.start_time,
-                input_data.end_time,
-            )
-
-            # Return as workspace path or data URI based on context
-            video_out = await self._store_output_video(
-                execution_context, output_filename
-            )
-
-            yield "video_out", video_out
-            yield "duration", duration
-
-        except BlockExecutionError:
-            raise
-        except Exception as e:
-            raise BlockExecutionError(
-                message=f"Failed to clip video: {e}",
-                block_name=self.name,
-                block_id=str(self.id),
-            ) from e

autogpt_platform/backend/backend/blocks/video/concat.py

@@ -1,197 +0,0 @@
-"""VideoConcatBlock - Concatenate multiple video clips into one."""
-
-from typing import Literal
-
-from moviepy import concatenate_videoclips
-from moviepy.video.fx import CrossFadeIn, CrossFadeOut, FadeIn, FadeOut
-from moviepy.video.io.VideoFileClip import VideoFileClip
-
-from backend.blocks.video._utils import get_video_codecs
-from backend.data.block import (
-    Block,
-    BlockCategory,
-    BlockOutput,
-    BlockSchemaInput,
-    BlockSchemaOutput,
-)
-from backend.data.execution import ExecutionContext
-from backend.data.model import SchemaField
-from backend.util.exceptions import BlockExecutionError
-from backend.util.file import MediaFileType, get_exec_file_path, store_media_file
-
-
-class VideoConcatBlock(Block):
-    """Merge multiple video clips into one continuous video."""
-
-    class Input(BlockSchemaInput):
-        videos: list[MediaFileType] = SchemaField(
-            description="List of video files to concatenate (in order)"
-        )
-        transition: Literal["none", "crossfade", "fade_black"] = SchemaField(
-            description="Transition between clips", default="none"
-        )
-        transition_duration: int = SchemaField(
-            description="Transition duration in seconds",
-            default=1,
-            ge=0,
-            advanced=True,
-        )
-        output_format: Literal["mp4", "webm", "mkv", "mov"] = SchemaField(
-            description="Output format", default="mp4", advanced=True
-        )
-
-    class Output(BlockSchemaOutput):
-        video_out: MediaFileType = SchemaField(
-            description="Concatenated video file (path or data URI)"
-        )
-        total_duration: float = SchemaField(description="Total duration in seconds")
-
-    def __init__(self):
-        super().__init__(
-            id="9b0f531a-1118-487f-aeec-3fa63ea8900a",
-            description="Merge multiple video clips into one continuous video",
-            categories={BlockCategory.MULTIMEDIA},
-            input_schema=self.Input,
-            output_schema=self.Output,
-            test_input={"videos": ["/tmp/a.mp4", "/tmp/b.mp4"]},
-            test_output=[("video_out", str), ("total_duration", float)],
-            test_mock={
-                "_concat_videos": lambda *args: 20.0,
-                "_store_input_video": lambda *args, **kwargs: "test.mp4",
-                "_store_output_video": lambda *args, **kwargs: "concat_test.mp4",
-            },
-        )
-
-    async def _store_input_video(
-        self, execution_context: ExecutionContext, file: MediaFileType
-    ) -> MediaFileType:
-        """Store input video. Extracted for testability."""
-        return await store_media_file(
-            file=file,
-            execution_context=execution_context,
-            return_format="for_local_processing",
-        )
-
-    async def _store_output_video(
-        self, execution_context: ExecutionContext, file: MediaFileType
-    ) -> MediaFileType:
-        """Store output video. Extracted for testability."""
-        return await store_media_file(
-            file=file,
-            execution_context=execution_context,
-            return_format="for_block_output",
-        )
-
-    def _concat_videos(
-        self,
-        video_abspaths: list[str],
-        output_abspath: str,
-        transition: str,
-        transition_duration: int,
-    ) -> float:
-        """Concatenate videos. Extracted for testability."""
-        clips = []
-        faded_clips = []
-        final = None
-        try:
-            # Load clips
-            for v in video_abspaths:
-                clips.append(VideoFileClip(v))
-
-            if transition == "crossfade":
-                for i, clip in enumerate(clips):
-                    effects = []
-                    if i > 0:
-                        effects.append(CrossFadeIn(transition_duration))
-                    if i < len(clips) - 1:
-                        effects.append(CrossFadeOut(transition_duration))
-                    if effects:
-                        clip = clip.with_effects(effects)
-                    faded_clips.append(clip)
-                final = concatenate_videoclips(
-                    faded_clips,
-                    method="compose",
-                    padding=-transition_duration,
-                )
-            elif transition == "fade_black":
-                for clip in clips:
-                    faded = clip.with_effects(
-                        [FadeIn(transition_duration), FadeOut(transition_duration)]
-                    )
-                    faded_clips.append(faded)
-                final = concatenate_videoclips(faded_clips)
-            else:
-                final = concatenate_videoclips(clips)
-
-            video_codec, audio_codec = get_video_codecs(output_abspath)
-            final.write_videofile(
-                output_abspath, codec=video_codec, audio_codec=audio_codec
-            )
-
-            return final.duration
-        finally:
-            if final:
-                final.close()
-            for clip in faded_clips:
-                clip.close()
-            for clip in clips:
-                clip.close()
-
-    async def run(
-        self,
-        input_data: Input,
-        *,
-        execution_context: ExecutionContext,
-        node_exec_id: str,
-        **kwargs,
-    ) -> BlockOutput:
-        # Validate minimum clips
-        if len(input_data.videos) < 2:
-            raise BlockExecutionError(
-                message="At least 2 videos are required for concatenation",
-                block_name=self.name,
-                block_id=str(self.id),
-            )
-
-        try:
-            assert execution_context.graph_exec_id is not None
-
-            # Store all input videos locally
-            video_abspaths = []
-            for video in input_data.videos:
-                local_path = await self._store_input_video(execution_context, video)
-                video_abspaths.append(
-                    get_exec_file_path(execution_context.graph_exec_id, local_path)
-                )
-
-            # Build output path
-            output_filename = MediaFileType(
-                f"{node_exec_id}_concat.{input_data.output_format}"
-            )
-            output_abspath = get_exec_file_path(
-                execution_context.graph_exec_id, output_filename
-            )
-
-            total_duration = self._concat_videos(
-                video_abspaths,
-                output_abspath,
-                input_data.transition,
-                input_data.transition_duration,
-            )
-
-            # Return as workspace path or data URI based on context
-            video_out = await self._store_output_video(
-                execution_context, output_filename
-            )
-
-            yield "video_out", video_out
-            yield "total_duration", total_duration
-
-        except BlockExecutionError:
-            raise
-        except Exception as e:
-            raise BlockExecutionError(
-                message=f"Failed to concatenate videos: {e}",
-                block_name=self.name,
-                block_id=str(self.id),
-            ) from e

autogpt_platform/backend/backend/blocks/video/download.py

@@ -1,167 +0,0 @@
-"""VideoDownloadBlock - Download video from URL (YouTube, Vimeo, news sites, direct links)."""
-
-import os
-import typing
-from typing import Literal
-
-import yt_dlp
-
-if typing.TYPE_CHECKING:
-    from yt_dlp import _Params
-
-from backend.data.block import (
-    Block,
-    BlockCategory,
-    BlockOutput,
-    BlockSchemaInput,
-    BlockSchemaOutput,
-)
-from backend.data.execution import ExecutionContext
-from backend.data.model import SchemaField
-from backend.util.exceptions import BlockExecutionError
-from backend.util.file import MediaFileType, get_exec_file_path, store_media_file
-
-
-class VideoDownloadBlock(Block):
-    """Download video from URL using yt-dlp."""
-
-    class Input(BlockSchemaInput):
-        url: str = SchemaField(
-            description="URL of the video to download (YouTube, Vimeo, direct link, etc.)",
-            placeholder="https://www.youtube.com/watch?v=...",
-        )
-        quality: Literal["best", "1080p", "720p", "480p", "audio_only"] = SchemaField(
-            description="Video quality preference", default="720p"
-        )
-        output_format: Literal["mp4", "webm", "mkv"] = SchemaField(
-            description="Output video format", default="mp4", advanced=True
-        )
-
-    class Output(BlockSchemaOutput):
-        video_file: MediaFileType = SchemaField(
-            description="Downloaded video (path or data URI)"
-        )
-        duration: float = SchemaField(description="Video duration in seconds")
-        title: str = SchemaField(description="Video title from source")
-        source_url: str = SchemaField(description="Original source URL")
-
-    def __init__(self):
-        super().__init__(
-            id="c35daabb-cd60-493b-b9ad-51f1fe4b50c4",
-            description="Download video from URL (YouTube, Vimeo, news sites, direct links)",
-            categories={BlockCategory.MULTIMEDIA},
-            input_schema=self.Input,
-            output_schema=self.Output,
-            test_input={
-                "url": "https://www.youtube.com/watch?v=dQw4w9WgXcQ",
-                "quality": "480p",
-            },
-            test_output=[
-                ("video_file", str),
-                ("duration", float),
-                ("title", str),
-                ("source_url", str),
-            ],
-            test_mock={
-                "_download_video": lambda *args: ("video.mp4", 212.0, "Test Video"),
-                "_store_output_video": lambda *args, **kwargs: "video.mp4",
-            },
-        )
-
-    async def _store_output_video(
-        self, execution_context: ExecutionContext, file: MediaFileType
-    ) -> MediaFileType:
-        """Store output video. Extracted for testability."""
-        return await store_media_file(
-            file=file,
-            execution_context=execution_context,
-            return_format="for_block_output",
-        )
-
-    def _get_format_string(self, quality: str) -> str:
-        formats = {
-            "best": "bestvideo+bestaudio/best",
-            "1080p": "bestvideo[height<=1080]+bestaudio/best[height<=1080]",
-            "720p": "bestvideo[height<=720]+bestaudio/best[height<=720]",
-            "480p": "bestvideo[height<=480]+bestaudio/best[height<=480]",
-            "audio_only": "bestaudio/best",
-        }
-        return formats.get(quality, formats["720p"])
-
-    def _download_video(
-        self,
-        url: str,
-        quality: str,
-        output_format: str,
-        output_dir: str,
-        node_exec_id: str,
-    ) -> tuple[str, float, str]:
-        """Download video. Extracted for testability."""
-        output_template = os.path.join(
-            output_dir, f"{node_exec_id}_%(title).50s.%(ext)s"
-        )
-
-        ydl_opts: "_Params" = {
-            "format": self._get_format_string(quality),
-            "outtmpl": output_template,
-            "merge_output_format": output_format,
-            "quiet": True,
-            "no_warnings": True,
-        }
-
-        with yt_dlp.YoutubeDL(ydl_opts) as ydl:
-            info = ydl.extract_info(url, download=True)
-            video_path = ydl.prepare_filename(info)
-
-            # Handle format conversion in filename
-            if not video_path.endswith(f".{output_format}"):
-                video_path = video_path.rsplit(".", 1)[0] + f".{output_format}"
-
-            # Return just the filename, not the full path
-            filename = os.path.basename(video_path)
-
-            return (
-                filename,
-                info.get("duration") or 0.0,
-                info.get("title") or "Unknown",
-            )
-
-    async def run(
-        self,
-        input_data: Input,
-        *,
-        execution_context: ExecutionContext,
-        node_exec_id: str,
-        **kwargs,
-    ) -> BlockOutput:
-        try:
-            assert execution_context.graph_exec_id is not None
-
-            # Get the exec file directory
-            output_dir = get_exec_file_path(execution_context.graph_exec_id, "")
-            os.makedirs(output_dir, exist_ok=True)
-
-            filename, duration, title = self._download_video(
-                input_data.url,
-                input_data.quality,
-                input_data.output_format,
-                output_dir,
-                node_exec_id,
-            )
-
-            # Return as workspace path or data URI based on context
-            video_out = await self._store_output_video(
-                execution_context, MediaFileType(filename)
-            )
-
-            yield "video_file", video_out
-            yield "duration", duration
-            yield "title", title
-            yield "source_url", input_data.url
-
-        except Exception as e:
-            raise BlockExecutionError(
-                message=f"Failed to download video: {e}",
-                block_name=self.name,
-                block_id=str(self.id),
-            ) from e

autogpt_platform/backend/backend/blocks/video/duration.py

@@ -1,68 +0,0 @@
-"""MediaDurationBlock - Get the duration of a media file."""
-
-from moviepy.audio.io.AudioFileClip import AudioFileClip
-from moviepy.video.io.VideoFileClip import VideoFileClip
-
-from backend.data.block import (
-    Block,
-    BlockCategory,
-    BlockOutput,
-    BlockSchemaInput,
-    BlockSchemaOutput,
-)
-from backend.data.execution import ExecutionContext
-from backend.data.model import SchemaField
-from backend.util.file import MediaFileType, get_exec_file_path, store_media_file
-
-
-class MediaDurationBlock(Block):
-    """Get the duration of a media file (video or audio)."""
-
-    class Input(BlockSchemaInput):
-        media_in: MediaFileType = SchemaField(
-            description="Media input (URL, data URI, or local path)."
-        )
-        is_video: bool = SchemaField(
-            description="Whether the media is a video (True) or audio (False).",
-            default=True,
-        )
-
-    class Output(BlockSchemaOutput):
-        duration: float = SchemaField(
-            description="Duration of the media file (in seconds)."
-        )
-
-    def __init__(self):
-        super().__init__(
-            id="d8b91fd4-da26-42d4-8ecb-8b196c6d84b6",
-            description="Block to get the duration of a media file.",
-            categories={BlockCategory.MULTIMEDIA},
-            input_schema=MediaDurationBlock.Input,
-            output_schema=MediaDurationBlock.Output,
-        )
-
-    async def run(
-        self,
-        input_data: Input,
-        *,
-        execution_context: ExecutionContext,
-        **kwargs,
-    ) -> BlockOutput:
-        # 1) Store the input media locally
-        local_media_path = await store_media_file(
-            file=input_data.media_in,
-            execution_context=execution_context,
-            return_format="for_local_processing",
-        )
-        assert execution_context.graph_exec_id is not None
-        media_abspath = get_exec_file_path(
-            execution_context.graph_exec_id, local_media_path
-        )
-
-        # 2) Load the clip
-        if input_data.is_video:
-            clip = VideoFileClip(media_abspath)
-        else:
-            clip = AudioFileClip(media_abspath)
-
-        yield "duration", clip.duration

autogpt_platform/backend/backend/blocks/video/loop.py

@@ -1,104 +0,0 @@
-"""LoopVideoBlock - Loop a video to a given duration or number of repeats."""
-
-import os
-from typing import Optional
-
-from moviepy.video.fx.Loop import Loop
-from moviepy.video.io.VideoFileClip import VideoFileClip
-
-from backend.data.block import (
-    Block,
-    BlockCategory,
-    BlockOutput,
-    BlockSchemaInput,
-    BlockSchemaOutput,
-)
-from backend.data.execution import ExecutionContext
-from backend.data.model import SchemaField
-from backend.util.file import MediaFileType, get_exec_file_path, store_media_file
-
-
-class LoopVideoBlock(Block):
-    """Loop (repeat) a video clip until a given duration or number of loops."""
-
-    class Input(BlockSchemaInput):
-        video_in: MediaFileType = SchemaField(
-            description="The input video (can be a URL, data URI, or local path)."
-        )
-        duration: Optional[float] = SchemaField(
-            description="Target duration (in seconds) to loop the video to. If omitted, defaults to no looping.",
-            default=None,
-            ge=0.0,
-        )
-        n_loops: Optional[int] = SchemaField(
-            description="Number of times to repeat the video. If omitted, defaults to 1 (no repeat).",
-            default=None,
-            ge=1,
-        )
-
-    class Output(BlockSchemaOutput):
-        video_out: str = SchemaField(
-            description="Looped video returned either as a relative path or a data URI."
-        )
-
-    def __init__(self):
-        super().__init__(
-            id="8bf9eef6-5451-4213-b265-25306446e94b",
-            description="Block to loop a video to a given duration or number of repeats.",
-            categories={BlockCategory.MULTIMEDIA},
-            input_schema=LoopVideoBlock.Input,
-            output_schema=LoopVideoBlock.Output,
-        )
-
-    async def run(
-        self,
-        input_data: Input,
-        *,
-        execution_context: ExecutionContext,
-        **kwargs,
-    ) -> BlockOutput:
-        assert execution_context.graph_exec_id is not None
-        assert execution_context.node_exec_id is not None
-        graph_exec_id = execution_context.graph_exec_id
-        node_exec_id = execution_context.node_exec_id
-
-        # 1) Store the input video locally
-        local_video_path = await store_media_file(
-            file=input_data.video_in,
-            execution_context=execution_context,
-            return_format="for_local_processing",
-        )
-        input_abspath = get_exec_file_path(graph_exec_id, local_video_path)
-
-        # 2) Load the clip
-        clip = VideoFileClip(input_abspath)
-
-        # 3) Apply the loop effect
-        looped_clip = clip
-        if input_data.duration:
-            # Loop until we reach the specified duration
-            looped_clip = looped_clip.with_effects([Loop(duration=input_data.duration)])
-        elif input_data.n_loops:
-            looped_clip = looped_clip.with_effects([Loop(n=input_data.n_loops)])
-        else:
-            raise ValueError("Either 'duration' or 'n_loops' must be provided.")
-
-        assert isinstance(looped_clip, VideoFileClip)
-
-        # 4) Save the looped output
-        output_filename = MediaFileType(
-            f"{node_exec_id}_looped_{os.path.basename(local_video_path)}"
-        )
-        output_abspath = get_exec_file_path(graph_exec_id, output_filename)
-
-        looped_clip = looped_clip.with_audio(clip.audio)
-        looped_clip.write_videofile(output_abspath, codec="libx264", audio_codec="aac")
-
-        # Return output - for_block_output returns workspace:// if available, else data URI
-        video_out = await store_media_file(
-            file=output_filename,
-            execution_context=execution_context,
-            return_format="for_block_output",
-        )
-
-        yield "video_out", video_out

autogpt_platform/backend/backend/blocks/video/narration.py

@@ -1,263 +0,0 @@
-"""VideoNarrationBlock - Generate AI voice narration and add to video."""
-
-import os
-from typing import Literal
-
-from elevenlabs import ElevenLabs
-from moviepy import CompositeAudioClip
-from moviepy.audio.io.AudioFileClip import AudioFileClip
-from moviepy.video.io.VideoFileClip import VideoFileClip
-
-from backend.blocks.elevenlabs._auth import (
-    TEST_CREDENTIALS,
-    TEST_CREDENTIALS_INPUT,
-    ElevenLabsCredentials,
-    ElevenLabsCredentialsInput,
-)
-from backend.blocks.video._utils import get_video_codecs
-from backend.data.block import (
-    Block,
-    BlockCategory,
-    BlockOutput,
-    BlockSchemaInput,
-    BlockSchemaOutput,
-)
-from backend.data.execution import ExecutionContext
-from backend.data.model import CredentialsField, SchemaField
-from backend.util.exceptions import BlockExecutionError
-from backend.util.file import MediaFileType, get_exec_file_path, store_media_file
-
-
-class VideoNarrationBlock(Block):
-    """Generate AI narration and add to video."""
-
-    class Input(BlockSchemaInput):
-        credentials: ElevenLabsCredentialsInput = CredentialsField(
-            description="ElevenLabs API key for voice synthesis"
-        )
-        video_in: MediaFileType = SchemaField(
-            description="Input video (URL, data URI, or local path)"
-        )
-        script: str = SchemaField(description="Narration script text")
-        voice_id: str = SchemaField(
-            description="ElevenLabs voice ID", default="21m00Tcm4TlvDq8ikWAM"  # Rachel
-        )
-        model_id: Literal[
-            "eleven_multilingual_v2",
-            "eleven_flash_v2_5",
-            "eleven_turbo_v2_5",
-            "eleven_turbo_v2",
-        ] = SchemaField(
-            description="ElevenLabs TTS model",
-            default="eleven_multilingual_v2",
-        )
-        mix_mode: Literal["replace", "mix", "ducking"] = SchemaField(
-            description="How to combine with original audio. 'ducking' applies stronger attenuation than 'mix'.",
-            default="ducking",
-        )
-        narration_volume: float = SchemaField(
-            description="Narration volume (0.0 to 2.0)",
-            default=1.0,
-            ge=0.0,
-            le=2.0,
-            advanced=True,
-        )
-        original_volume: float = SchemaField(
-            description="Original audio volume when mixing (0.0 to 1.0)",
-            default=0.3,
-            ge=0.0,
-            le=1.0,
-            advanced=True,
-        )
-
-    class Output(BlockSchemaOutput):
-        video_out: MediaFileType = SchemaField(
-            description="Video with narration (path or data URI)"
-        )
-        audio_file: MediaFileType = SchemaField(
-            description="Generated audio file (path or data URI)"
-        )
-
-    def __init__(self):
-        super().__init__(
-            id="3d036b53-859c-4b17-9826-ca340f736e0e",
-            description="Generate AI narration and add to video",
-            categories={BlockCategory.MULTIMEDIA, BlockCategory.AI},
-            input_schema=self.Input,
-            output_schema=self.Output,
-            test_input={
-                "video_in": "/tmp/test.mp4",
-                "script": "Hello world",
-                "credentials": TEST_CREDENTIALS_INPUT,
-            },
-            test_credentials=TEST_CREDENTIALS,
-            test_output=[("video_out", str), ("audio_file", str)],
-            test_mock={
-                "_generate_narration_audio": lambda *args: b"mock audio content",
-                "_add_narration_to_video": lambda *args: None,
-                "_store_input_video": lambda *args, **kwargs: "test.mp4",
-                "_store_output_video": lambda *args, **kwargs: "narrated_test.mp4",
-            },
-        )
-
-    async def _store_input_video(
-        self, execution_context: ExecutionContext, file: MediaFileType
-    ) -> MediaFileType:
-        """Store input video. Extracted for testability."""
-        return await store_media_file(
-            file=file,
-            execution_context=execution_context,
-            return_format="for_local_processing",
-        )
-
-    async def _store_output_video(
-        self, execution_context: ExecutionContext, file: MediaFileType
-    ) -> MediaFileType:
-        """Store output video. Extracted for testability."""
-        return await store_media_file(
-            file=file,
-            execution_context=execution_context,
-            return_format="for_block_output",
-        )
-
-    def _generate_narration_audio(
-        self, api_key: str, script: str, voice_id: str, model_id: str
-    ) -> bytes:
-        """Generate narration audio via ElevenLabs API."""
-        client = ElevenLabs(api_key=api_key)
-        audio_generator = client.text_to_speech.convert(
-            voice_id=voice_id,
-            text=script,
-            model_id=model_id,
-        )
-        # The SDK returns a generator, collect all chunks
-        return b"".join(audio_generator)
-
-    def _add_narration_to_video(
-        self,
-        video_abspath: str,
-        audio_abspath: str,
-        output_abspath: str,
-        mix_mode: str,
-        narration_volume: float,
-        original_volume: float,
-    ) -> None:
-        """Add narration audio to video. Extracted for testability."""
-        video = None
-        final = None
-        narration_original = None
-        narration_scaled = None
-        original = None
-
-        try:
-            video = VideoFileClip(video_abspath)
-            narration_original = AudioFileClip(audio_abspath)
-            narration_scaled = narration_original.with_volume_scaled(narration_volume)
-            narration = narration_scaled
-
-            if mix_mode == "replace":
-                final_audio = narration
-            elif mix_mode == "mix":
-                if video.audio:
-                    original = video.audio.with_volume_scaled(original_volume)
-                    final_audio = CompositeAudioClip([original, narration])
-                else:
-                    final_audio = narration
-            else:  # ducking - apply stronger attenuation
-                if video.audio:
-                    # Ducking uses a much lower volume for original audio
-                    ducking_volume = original_volume * 0.3
-                    original = video.audio.with_volume_scaled(ducking_volume)
-                    final_audio = CompositeAudioClip([original, narration])
-                else:
-                    final_audio = narration
-
-            final = video.with_audio(final_audio)
-            video_codec, audio_codec = get_video_codecs(output_abspath)
-            final.write_videofile(
-                output_abspath, codec=video_codec, audio_codec=audio_codec
-            )
-
-        finally:
-            if original:
-                original.close()
-            if narration_scaled:
-                narration_scaled.close()
-            if narration_original:
-                narration_original.close()
-            if final:
-                final.close()
-            if video:
-                video.close()
-
-    async def run(
-        self,
-        input_data: Input,
-        *,
-        credentials: ElevenLabsCredentials,
-        execution_context: ExecutionContext,
-        node_exec_id: str,
-        **kwargs,
-    ) -> BlockOutput:
-        try:
-            assert execution_context.graph_exec_id is not None
-
-            # Store the input video locally
-            local_video_path = await self._store_input_video(
-                execution_context, input_data.video_in
-            )
-            video_abspath = get_exec_file_path(
-                execution_context.graph_exec_id, local_video_path
-            )
-
-            # Generate narration audio via ElevenLabs
-            audio_content = self._generate_narration_audio(
-                credentials.api_key.get_secret_value(),
-                input_data.script,
-                input_data.voice_id,
-                input_data.model_id,
-            )
-
-            # Save audio to exec file path
-            audio_filename = MediaFileType(f"{node_exec_id}_narration.mp3")
-            audio_abspath = get_exec_file_path(
-                execution_context.graph_exec_id, audio_filename
-            )
-            os.makedirs(os.path.dirname(audio_abspath), exist_ok=True)
-            with open(audio_abspath, "wb") as f:
-                f.write(audio_content)
-
-            # Add narration to video
-            output_filename = MediaFileType(
-                f"{node_exec_id}_narrated_{os.path.basename(local_video_path)}"
-            )
-            output_abspath = get_exec_file_path(
-                execution_context.graph_exec_id, output_filename
-            )
-
-            self._add_narration_to_video(
-                video_abspath,
-                audio_abspath,
-                output_abspath,
-                input_data.mix_mode,
-                input_data.narration_volume,
-                input_data.original_volume,
-            )
-
-            # Return as workspace path or data URI based on context
-            video_out = await self._store_output_video(
-                execution_context, output_filename
-            )
-            audio_out = await self._store_output_video(
-                execution_context, audio_filename
-            )
-
-            yield "video_out", video_out
-            yield "audio_file", audio_out
-
-        except Exception as e:
-            raise BlockExecutionError(
-                message=f"Failed to add narration: {e}",
-                block_name=self.name,
-                block_id=str(self.id),
-            ) from e

autogpt_platform/backend/backend/blocks/video/text_overlay.py

@@ -1,227 +0,0 @@
-"""VideoTextOverlayBlock - Add text overlay to video."""
-
-import os
-from typing import Literal
-
-from moviepy import CompositeVideoClip, TextClip
-from moviepy.video.io.VideoFileClip import VideoFileClip
-
-from backend.blocks.video._utils import get_video_codecs
-from backend.data.block import (
-    Block,
-    BlockCategory,
-    BlockOutput,
-    BlockSchemaInput,
-    BlockSchemaOutput,
-)
-from backend.data.execution import ExecutionContext
-from backend.data.model import SchemaField
-from backend.util.exceptions import BlockExecutionError
-from backend.util.file import MediaFileType, get_exec_file_path, store_media_file
-
-
-class VideoTextOverlayBlock(Block):
-    """Add text overlay/caption to video."""
-
-    class Input(BlockSchemaInput):
-        video_in: MediaFileType = SchemaField(
-            description="Input video (URL, data URI, or local path)"
-        )
-        text: str = SchemaField(description="Text to overlay on video")
-        position: Literal[
-            "top",
-            "center",
-            "bottom",
-            "top-left",
-            "top-right",
-            "bottom-left",
-            "bottom-right",
-        ] = SchemaField(description="Position of text on screen", default="bottom")
-        start_time: float | None = SchemaField(
-            description="When to show text (seconds). None = entire video",
-            default=None,
-            advanced=True,
-        )
-        end_time: float | None = SchemaField(
-            description="When to hide text (seconds). None = until end",
-            default=None,
-            advanced=True,
-        )
-        font_size: int = SchemaField(
-            description="Font size", default=48, ge=12, le=200, advanced=True
-        )
-        font_color: str = SchemaField(
-            description="Font color (hex or name)", default="white", advanced=True
-        )
-        bg_color: str | None = SchemaField(
-            description="Background color behind text (None for transparent)",
-            default=None,
-            advanced=True,
-        )
-
-    class Output(BlockSchemaOutput):
-        video_out: MediaFileType = SchemaField(
-            description="Video with text overlay (path or data URI)"
-        )
-
-    def __init__(self):
-        super().__init__(
-            id="8ef14de6-cc90-430a-8cfa-3a003be92454",
-            description="Add text overlay/caption to video",
-            categories={BlockCategory.MULTIMEDIA},
-            input_schema=self.Input,
-            output_schema=self.Output,
-            test_input={"video_in": "/tmp/test.mp4", "text": "Hello World"},
-            test_output=[("video_out", str)],
-            test_mock={
-                "_add_text_overlay": lambda *args: None,
-                "_store_input_video": lambda *args, **kwargs: "test.mp4",
-                "_store_output_video": lambda *args, **kwargs: "overlay_test.mp4",
-            },
-        )
-
-    async def _store_input_video(
-        self, execution_context: ExecutionContext, file: MediaFileType
-    ) -> MediaFileType:
-        """Store input video. Extracted for testability."""
-        return await store_media_file(
-            file=file,
-            execution_context=execution_context,
-            return_format="for_local_processing",
-        )
-
-    async def _store_output_video(
-        self, execution_context: ExecutionContext, file: MediaFileType
-    ) -> MediaFileType:
-        """Store output video. Extracted for testability."""
-        return await store_media_file(
-            file=file,
-            execution_context=execution_context,
-            return_format="for_block_output",
-        )
-
-    def _add_text_overlay(
-        self,
-        video_abspath: str,
-        output_abspath: str,
-        text: str,
-        position: str,
-        start_time: float | None,
-        end_time: float | None,
-        font_size: int,
-        font_color: str,
-        bg_color: str | None,
-    ) -> None:
-        """Add text overlay to video. Extracted for testability."""
-        video = None
-        final = None
-        txt_clip = None
-        try:
-            video = VideoFileClip(video_abspath)
-
-            txt_clip = TextClip(
-                text=text,
-                font_size=font_size,
-                color=font_color,
-                bg_color=bg_color,
-            )
-
-            # Position mapping
-            pos_map = {
-                "top": ("center", "top"),
-                "center": ("center", "center"),
-                "bottom": ("center", "bottom"),
-                "top-left": ("left", "top"),
-                "top-right": ("right", "top"),
-                "bottom-left": ("left", "bottom"),
-                "bottom-right": ("right", "bottom"),
-            }
-
-            txt_clip = txt_clip.with_position(pos_map[position])
-
-            # Set timing
-            start = start_time or 0
-            end = end_time or video.duration
-            duration = max(0, end - start)
-            txt_clip = txt_clip.with_start(start).with_end(end).with_duration(duration)
-
-            final = CompositeVideoClip([video, txt_clip])
-            video_codec, audio_codec = get_video_codecs(output_abspath)
-            final.write_videofile(
-                output_abspath, codec=video_codec, audio_codec=audio_codec
-            )
-
-        finally:
-            if txt_clip:
-                txt_clip.close()
-            if final:
-                final.close()
-            if video:
-                video.close()
-
-    async def run(
-        self,
-        input_data: Input,
-        *,
-        execution_context: ExecutionContext,
-        node_exec_id: str,
-        **kwargs,
-    ) -> BlockOutput:
-        # Validate time range if both are provided
-        if (
-            input_data.start_time is not None
-            and input_data.end_time is not None
-            and input_data.end_time <= input_data.start_time
-        ):
-            raise BlockExecutionError(
-                message=f"end_time ({input_data.end_time}) must be greater than start_time ({input_data.start_time})",
-                block_name=self.name,
-                block_id=str(self.id),
-            )
-
-        try:
-            assert execution_context.graph_exec_id is not None
-
-            # Store the input video locally
-            local_video_path = await self._store_input_video(
-                execution_context, input_data.video_in
-            )
-            video_abspath = get_exec_file_path(
-                execution_context.graph_exec_id, local_video_path
-            )
-
-            # Build output path
-            output_filename = MediaFileType(
-                f"{node_exec_id}_overlay_{os.path.basename(local_video_path)}"
-            )
-            output_abspath = get_exec_file_path(
-                execution_context.graph_exec_id, output_filename
-            )
-
-            self._add_text_overlay(
-                video_abspath,
-                output_abspath,
-                input_data.text,
-                input_data.position,
-                input_data.start_time,
-                input_data.end_time,
-                input_data.font_size,
-                input_data.font_color,
-                input_data.bg_color,
-            )
-
-            # Return as workspace path or data URI based on context
-            video_out = await self._store_output_video(
-                execution_context, output_filename
-            )
-
-            yield "video_out", video_out
-
-        except BlockExecutionError:
-            raise
-        except Exception as e:
-            raise BlockExecutionError(
-                message=f"Failed to add text overlay: {e}",
-                block_name=self.name,
-                block_id=str(self.id),
-            ) from e

autogpt_platform/backend/backend/data/block_cost_config.py

@@ -36,14 +36,12 @@ from backend.blocks.replicate.replicate_block import ReplicateModelBlock
 from backend.blocks.smart_decision_maker import SmartDecisionMakerBlock
 from backend.blocks.talking_head import CreateTalkingAvatarVideoBlock
 from backend.blocks.text_to_speech_block import UnrealTextToSpeechBlock
-from backend.blocks.video.narration import VideoNarrationBlock
 from backend.data.block import Block, BlockCost, BlockCostType
 from backend.integrations.credentials_store import (
     aiml_api_credentials,
     anthropic_credentials,
     apollo_credentials,
     did_credentials,
-    elevenlabs_credentials,
     enrichlayer_credentials,
     groq_credentials,
     ideogram_credentials,
@@ -83,7 +81,6 @@ MODEL_COST: dict[LlmModel, int] = {
     LlmModel.CLAUDE_4_5_HAIKU: 4,
     LlmModel.CLAUDE_4_5_OPUS: 14,
     LlmModel.CLAUDE_4_5_SONNET: 9,
-    LlmModel.CLAUDE_3_7_SONNET: 5,
     LlmModel.CLAUDE_3_HAIKU: 1,
     LlmModel.AIML_API_QWEN2_5_72B: 1,
     LlmModel.AIML_API_LLAMA3_1_70B: 1,
@@ -642,16 +639,4 @@ BLOCK_COSTS: dict[Type[Block], list[BlockCost]] = {
             },
         ),
     ],
-    VideoNarrationBlock: [
-        BlockCost(
-            cost_amount=5,  # ElevenLabs TTS cost
-            cost_filter={
-                "credentials": {
-                    "id": elevenlabs_credentials.id,
-                    "provider": elevenlabs_credentials.provider,
-                    "type": elevenlabs_credentials.type,
-                }
-            },
-        )
-    ],
 }

autogpt_platform/backend/backend/data/event_bus.py

@@ -133,10 +133,23 @@ class RedisEventBus(BaseRedisEventBus[M], ABC):
 
 
 class AsyncRedisEventBus(BaseRedisEventBus[M], ABC):
+    def __init__(self):
+        self._pubsub: AsyncPubSub | None = None
+
     @property
     async def connection(self) -> redis.AsyncRedis:
         return await redis.get_redis_async()
 
+    async def close(self) -> None:
+        """Close the PubSub connection if it exists."""
+        if self._pubsub is not None:
+            try:
+                await self._pubsub.close()
+            except Exception:
+                logger.warning("Failed to close PubSub connection", exc_info=True)
+            finally:
+                self._pubsub = None
+
     async def publish_event(self, event: M, channel_key: str):
         """
         Publish an event to Redis. Gracefully handles connection failures
@@ -157,6 +170,7 @@ class AsyncRedisEventBus(BaseRedisEventBus[M], ABC):
             await self.connection, channel_key
         )
         assert isinstance(pubsub, AsyncPubSub)
+        self._pubsub = pubsub
 
         if "*" in channel_key:
             await pubsub.psubscribe(full_channel_name)

autogpt_platform/backend/backend/data/graph.py

@@ -1028,6 +1028,39 @@ async def get_graph(
     return GraphModel.from_db(graph, for_export)
 
 
+async def get_store_listed_graphs(*graph_ids: str) -> dict[str, GraphModel]:
+    """Batch-fetch multiple store-listed graphs by their IDs.
+
+    Only returns graphs that have approved store listings (publicly available).
+    Does not require permission checks since store-listed graphs are public.
+
+    Args:
+        *graph_ids: Variable number of graph IDs to fetch
+
+    Returns:
+        Dict mapping graph_id to GraphModel for graphs with approved store listings
+    """
+    if not graph_ids:
+        return {}
+
+    store_listings = await StoreListingVersion.prisma().find_many(
+        where={
+            "agentGraphId": {"in": list(graph_ids)},
+            "submissionStatus": SubmissionStatus.APPROVED,
+            "isDeleted": False,
+        },
+        include={"AgentGraph": {"include": AGENT_GRAPH_INCLUDE}},
+        distinct=["agentGraphId"],
+        order={"agentGraphVersion": "desc"},
+    )
+
+    return {
+        listing.agentGraphId: GraphModel.from_db(listing.AgentGraph)
+        for listing in store_listings
+        if listing.AgentGraph
+    }
+
+
 async def get_graph_as_admin(
     graph_id: str,
     version: int | None = None,

autogpt_platform/backend/backend/data/model.py

@@ -666,10 +666,16 @@ class CredentialsFieldInfo(BaseModel, Generic[CP, CT]):
         if not (self.discriminator and self.discriminator_mapping):
             return self
 
+        try:
+            provider = self.discriminator_mapping[discriminator_value]
+        except KeyError:
+            raise ValueError(
+                f"Model '{discriminator_value}' is not supported. "
+                "It may have been deprecated. Please update your agent configuration."
+            )
+
         return CredentialsFieldInfo(
-            credentials_provider=frozenset(
-                [self.discriminator_mapping[discriminator_value]]
-            ),
+            credentials_provider=frozenset([provider]),
             credentials_types=self.supported_types,
             credentials_scopes=self.required_scopes,
             discriminator=self.discriminator,

autogpt_platform/backend/backend/executor/database.py

@@ -17,6 +17,7 @@ from backend.data.analytics import (
     get_accuracy_trends_and_alerts,
     get_marketplace_graphs_for_monitoring,
 )
+from backend.data.auth.oauth import cleanup_expired_oauth_tokens
 from backend.data.credit import UsageTransactionMetadata, get_user_credit_model
 from backend.data.execution import (
     create_graph_execution,
@@ -219,6 +220,9 @@ class DatabaseManager(AppService):
     # Onboarding
     increment_onboarding_runs = _(increment_onboarding_runs)
 
+    # OAuth
+    cleanup_expired_oauth_tokens = _(cleanup_expired_oauth_tokens)
+
     # Store
     get_store_agents = _(get_store_agents)
     get_store_agent_details = _(get_store_agent_details)
@@ -349,6 +353,9 @@ class DatabaseManagerAsyncClient(AppServiceClient):
     # Onboarding
     increment_onboarding_runs = d.increment_onboarding_runs
 
+    # OAuth
+    cleanup_expired_oauth_tokens = d.cleanup_expired_oauth_tokens
+
     # Store
     get_store_agents = d.get_store_agents
     get_store_agent_details = d.get_store_agent_details

autogpt_platform/backend/backend/executor/scheduler.py

@@ -24,11 +24,9 @@ from dotenv import load_dotenv
 from pydantic import BaseModel, Field, ValidationError
 from sqlalchemy import MetaData, create_engine
 
-from backend.data.auth.oauth import cleanup_expired_oauth_tokens
 from backend.data.block import BlockInput
 from backend.data.execution import GraphExecutionWithNodes
 from backend.data.model import CredentialsMetaInput
-from backend.data.onboarding import increment_onboarding_runs
 from backend.executor import utils as execution_utils
 from backend.monitoring import (
     NotificationJobArgs,
@@ -38,7 +36,11 @@ from backend.monitoring import (
     report_execution_accuracy_alerts,
     report_late_executions,
 )
-from backend.util.clients import get_database_manager_client, get_scheduler_client
+from backend.util.clients import (
+    get_database_manager_async_client,
+    get_database_manager_client,
+    get_scheduler_client,
+)
 from backend.util.cloud_storage import cleanup_expired_files_async
 from backend.util.exceptions import (
     GraphNotFoundError,
@@ -148,6 +150,7 @@ def execute_graph(**kwargs):
 async def _execute_graph(**kwargs):
     args = GraphExecutionJobArgs(**kwargs)
     start_time = asyncio.get_event_loop().time()
+    db = get_database_manager_async_client()
     try:
         logger.info(f"Executing recurring job for graph #{args.graph_id}")
         graph_exec: GraphExecutionWithNodes = await execution_utils.add_graph_execution(
@@ -157,7 +160,7 @@ async def _execute_graph(**kwargs):
             inputs=args.input_data,
             graph_credentials_inputs=args.input_credentials,
         )
-        await increment_onboarding_runs(args.user_id)
+        await db.increment_onboarding_runs(args.user_id)
         elapsed = asyncio.get_event_loop().time() - start_time
         logger.info(
             f"Graph execution started with ID {graph_exec.id} for graph {args.graph_id} "
@@ -246,8 +249,13 @@ def cleanup_expired_files():
 
 def cleanup_oauth_tokens():
     """Clean up expired OAuth tokens from the database."""
+
     # Wait for completion
-    run_async(cleanup_expired_oauth_tokens())
+    async def _cleanup():
+        db = get_database_manager_async_client()
+        return await db.cleanup_expired_oauth_tokens()
+
+    run_async(_cleanup())
 
 
 def execution_accuracy_alerts():

autogpt_platform/backend/backend/integrations/credentials_store.py

@@ -224,14 +224,6 @@ openweathermap_credentials = APIKeyCredentials(
     expires_at=None,
 )
 
-elevenlabs_credentials = APIKeyCredentials(
-    id="f4a8b6c2-3d1e-4f5a-9b8c-7d6e5f4a3b2c",
-    provider="elevenlabs",
-    api_key=SecretStr(settings.secrets.elevenlabs_api_key),
-    title="Use Credits for ElevenLabs",
-    expires_at=None,
-)
-
 DEFAULT_CREDENTIALS = [
     ollama_credentials,
     revid_credentials,
@@ -260,7 +252,6 @@ DEFAULT_CREDENTIALS = [
     v0_credentials,
     webshare_proxy_credentials,
     openweathermap_credentials,
-    elevenlabs_credentials,
 ]
 
 SYSTEM_CREDENTIAL_IDS = {cred.id for cred in DEFAULT_CREDENTIALS}
@@ -375,8 +366,6 @@ class IntegrationCredentialsStore:
             all_credentials.append(webshare_proxy_credentials)
         if settings.secrets.openweathermap_api_key:
             all_credentials.append(openweathermap_credentials)
-        if settings.secrets.elevenlabs_api_key:
-            all_credentials.append(elevenlabs_credentials)
         return all_credentials
 
     async def get_creds_by_id(

autogpt_platform/backend/backend/integrations/providers.py

@@ -18,7 +18,6 @@ class ProviderName(str, Enum):
     DISCORD = "discord"
     D_ID = "d_id"
     E2B = "e2b"
-    ELEVENLABS = "elevenlabs"
     FAL = "fal"
     GITHUB = "github"
     GOOGLE = "google"

autogpt_platform/backend/backend/integrations/webhooks/utils_test.py

@@ -0,0 +1,39 @@
+from urllib.parse import urlparse
+
+import fastapi
+from fastapi.routing import APIRoute
+
+from backend.api.features.integrations.router import router as integrations_router
+from backend.integrations.providers import ProviderName
+from backend.integrations.webhooks import utils as webhooks_utils
+
+
+def test_webhook_ingress_url_matches_route(monkeypatch) -> None:
+    app = fastapi.FastAPI()
+    app.include_router(integrations_router, prefix="/api/integrations")
+
+    provider = ProviderName.GITHUB
+    webhook_id = "webhook_123"
+    base_url = "https://example.com"
+
+    monkeypatch.setattr(webhooks_utils.app_config, "platform_base_url", base_url)
+
+    route = next(
+        route
+        for route in integrations_router.routes
+        if isinstance(route, APIRoute)
+        and route.path == "/{provider}/webhooks/{webhook_id}/ingress"
+        and "POST" in route.methods
+    )
+    expected_path = f"/api/integrations{route.path}".format(
+        provider=provider.value,
+        webhook_id=webhook_id,
+    )
+    actual_url = urlparse(webhooks_utils.webhook_ingress_url(provider, webhook_id))
+    expected_base = urlparse(base_url)
+
+    assert (actual_url.scheme, actual_url.netloc) == (
+        expected_base.scheme,
+        expected_base.netloc,
+    )
+    assert actual_url.path == expected_path

autogpt_platform/backend/backend/util/prompt.py

@@ -1,10 +1,19 @@
+from __future__ import annotations
+
+import logging
 from copy import deepcopy
-from typing import Any
+from dataclasses import dataclass
+from typing import TYPE_CHECKING, Any
 
 from tiktoken import encoding_for_model
 
 from backend.util import json
 
+if TYPE_CHECKING:
+    from openai import AsyncOpenAI
+
+logger = logging.getLogger(__name__)
+
 # ---------------------------------------------------------------------------#
 #  CONSTANTS                                                                 #
 # ---------------------------------------------------------------------------#
@@ -100,9 +109,17 @@ def _is_objective_message(msg: dict) -> bool:
 def _truncate_tool_message_content(msg: dict, enc, max_tokens: int) -> None:
     """
     Carefully truncate tool message content while preserving tool structure.
-    Only truncates tool_result content, leaves tool_use intact.
+    Handles both Anthropic-style (list content) and OpenAI-style (string content) tool messages.
     """
     content = msg.get("content")
+
+    # OpenAI-style tool message: role="tool" with string content
+    if msg.get("role") == "tool" and isinstance(content, str):
+        if _tok_len(content, enc) > max_tokens:
+            msg["content"] = _truncate_middle_tokens(content, enc, max_tokens)
+        return
+
+    # Anthropic-style: list content with tool_result items
     if not isinstance(content, list):
         return
 
@@ -140,141 +157,6 @@ def _truncate_middle_tokens(text: str, enc, max_tok: int) -> str:
 # ---------------------------------------------------------------------------#
 
 
-def compress_prompt(
-    messages: list[dict],
-    target_tokens: int,
-    *,
-    model: str = "gpt-4o",
-    reserve: int = 2_048,
-    start_cap: int = 8_192,
-    floor_cap: int = 128,
-    lossy_ok: bool = True,
-) -> list[dict]:
-    """
-    Shrink *messages* so that::
-
-        token_count(prompt) + reserve  ≤  target_tokens
-
-    Strategy
-    --------
-    1. **Token-aware truncation** – progressively halve a per-message cap
-       (`start_cap`, `start_cap/2`, … `floor_cap`) and apply it to the
-       *content* of every message except the first and last.  Tool shells
-       are included: we keep the envelope but shorten huge payloads.
-    2. **Middle-out deletion** – if still over the limit, delete whole
-       messages working outward from the centre, **skipping** any message
-       that contains ``tool_calls`` or has ``role == "tool"``.
-    3. **Last-chance trim** – if still too big, truncate the *first* and
-       *last* message bodies down to `floor_cap` tokens.
-    4. If the prompt is *still* too large:
-         • raise ``ValueError``      when ``lossy_ok == False`` (default)
-         • return the partially-trimmed prompt when ``lossy_ok == True``
-
-    Parameters
-    ----------
-    messages        Complete chat history (will be deep-copied).
-    model           Model name; passed to tiktoken to pick the right
-                    tokenizer (gpt-4o → 'o200k_base', others fallback).
-    target_tokens   Hard ceiling for prompt size **excluding** the model's
-                    forthcoming answer.
-    reserve         How many tokens you want to leave available for that
-                    answer (`max_tokens` in your subsequent completion call).
-    start_cap       Initial per-message truncation ceiling (tokens).
-    floor_cap       Lowest cap we'll accept before moving to deletions.
-    lossy_ok        If *True* return best-effort prompt instead of raising
-                    after all trim passes have been exhausted.
-
-    Returns
-    -------
-    list[dict]  – A *new* messages list that abides by the rules above.
-    """
-    enc = encoding_for_model(model)  # best-match tokenizer
-    msgs = deepcopy(messages)  # never mutate caller
-
-    def total_tokens() -> int:
-        """Current size of *msgs* in tokens."""
-        return sum(_msg_tokens(m, enc) for m in msgs)
-
-    original_token_count = total_tokens()
-
-    if original_token_count + reserve <= target_tokens:
-        return msgs
-
-    # ---- STEP 0 : normalise content --------------------------------------
-    # Convert non-string payloads to strings so token counting is coherent.
-    for i, m in enumerate(msgs):
-        if not isinstance(m.get("content"), str) and m.get("content") is not None:
-            if _is_tool_message(m):
-                continue
-
-            # Keep first and last messages intact (unless they're tool messages)
-            if i == 0 or i == len(msgs) - 1:
-                continue
-
-            # Reasonable 20k-char ceiling prevents pathological blobs
-            content_str = json.dumps(m["content"], separators=(",", ":"))
-            if len(content_str) > 20_000:
-                content_str = _truncate_middle_tokens(content_str, enc, 20_000)
-            m["content"] = content_str
-
-    # ---- STEP 1 : token-aware truncation ---------------------------------
-    cap = start_cap
-    while total_tokens() + reserve > target_tokens and cap >= floor_cap:
-        for m in msgs[1:-1]:  # keep first & last intact
-            if _is_tool_message(m):
-                # For tool messages, only truncate tool result content, preserve structure
-                _truncate_tool_message_content(m, enc, cap)
-                continue
-
-            if _is_objective_message(m):
-                # Never truncate objective messages - they contain the core task
-                continue
-
-            content = m.get("content") or ""
-            if _tok_len(content, enc) > cap:
-                m["content"] = _truncate_middle_tokens(content, enc, cap)
-        cap //= 2  # tighten the screw
-
-    # ---- STEP 2 : middle-out deletion -----------------------------------
-    while total_tokens() + reserve > target_tokens and len(msgs) > 2:
-        # Identify all deletable messages (not first/last, not tool messages, not objective messages)
-        deletable_indices = []
-        for i in range(1, len(msgs) - 1):  # Skip first and last
-            if not _is_tool_message(msgs[i]) and not _is_objective_message(msgs[i]):
-                deletable_indices.append(i)
-
-        if not deletable_indices:
-            break  # nothing more we can drop
-
-        # Delete from center outward - find the index closest to center
-        centre = len(msgs) // 2
-        to_delete = min(deletable_indices, key=lambda i: abs(i - centre))
-        del msgs[to_delete]
-
-    # ---- STEP 3 : final safety-net trim on first & last ------------------
-    cap = start_cap
-    while total_tokens() + reserve > target_tokens and cap >= floor_cap:
-        for idx in (0, -1):  # first and last
-            if _is_tool_message(msgs[idx]):
-                # For tool messages at first/last position, truncate tool result content only
-                _truncate_tool_message_content(msgs[idx], enc, cap)
-                continue
-
-            text = msgs[idx].get("content") or ""
-            if _tok_len(text, enc) > cap:
-                msgs[idx]["content"] = _truncate_middle_tokens(text, enc, cap)
-        cap //= 2  # tighten the screw
-
-    # ---- STEP 4 : success or fail-gracefully -----------------------------
-    if total_tokens() + reserve > target_tokens and not lossy_ok:
-        raise ValueError(
-            "compress_prompt: prompt still exceeds budget "
-            f"({total_tokens() + reserve} > {target_tokens})."
-        )
-
-    return msgs
-
-
 def estimate_token_count(
     messages: list[dict],
     *,
@@ -293,7 +175,8 @@ def estimate_token_count(
     -------
     int  – Token count.
     """
-    enc = encoding_for_model(model)  # best-match tokenizer
+    token_model = _normalize_model_for_tokenizer(model)
+    enc = encoding_for_model(token_model)
     return sum(_msg_tokens(m, enc) for m in messages)
 
 
@@ -315,6 +198,543 @@ def estimate_token_count_str(
     -------
     int  – Token count.
     """
-    enc = encoding_for_model(model)  # best-match tokenizer
+    token_model = _normalize_model_for_tokenizer(model)
+    enc = encoding_for_model(token_model)
     text = json.dumps(text) if not isinstance(text, str) else text
     return _tok_len(text, enc)
+
+
+# ---------------------------------------------------------------------------#
+#  UNIFIED CONTEXT COMPRESSION                                               #
+# ---------------------------------------------------------------------------#
+
+# Default thresholds
+DEFAULT_TOKEN_THRESHOLD = 120_000
+DEFAULT_KEEP_RECENT = 15
+
+
+@dataclass
+class CompressResult:
+    """Result of context compression."""
+
+    messages: list[dict]
+    token_count: int
+    was_compacted: bool
+    error: str | None = None
+    original_token_count: int = 0
+    messages_summarized: int = 0
+    messages_dropped: int = 0
+
+
+def _normalize_model_for_tokenizer(model: str) -> str:
+    """Normalize model name for tiktoken tokenizer selection."""
+    if "/" in model:
+        model = model.split("/")[-1]
+    if "claude" in model.lower() or not any(
+        known in model.lower() for known in ["gpt", "o1", "chatgpt", "text-"]
+    ):
+        return "gpt-4o"
+    return model
+
+
+def _extract_tool_call_ids_from_message(msg: dict) -> set[str]:
+    """
+    Extract tool_call IDs from an assistant message.
+
+    Supports both formats:
+    - OpenAI: {"role": "assistant", "tool_calls": [{"id": "..."}]}
+    - Anthropic: {"role": "assistant", "content": [{"type": "tool_use", "id": "..."}]}
+
+    Returns:
+        Set of tool_call IDs found in the message.
+    """
+    ids: set[str] = set()
+    if msg.get("role") != "assistant":
+        return ids
+
+    # OpenAI format: tool_calls array
+    if msg.get("tool_calls"):
+        for tc in msg["tool_calls"]:
+            tc_id = tc.get("id")
+            if tc_id:
+                ids.add(tc_id)
+
+    # Anthropic format: content list with tool_use blocks
+    content = msg.get("content")
+    if isinstance(content, list):
+        for block in content:
+            if isinstance(block, dict) and block.get("type") == "tool_use":
+                tc_id = block.get("id")
+                if tc_id:
+                    ids.add(tc_id)
+
+    return ids
+
+
+def _extract_tool_response_ids_from_message(msg: dict) -> set[str]:
+    """
+    Extract tool_call IDs that this message is responding to.
+
+    Supports both formats:
+    - OpenAI: {"role": "tool", "tool_call_id": "..."}
+    - Anthropic: {"role": "user", "content": [{"type": "tool_result", "tool_use_id": "..."}]}
+
+    Returns:
+        Set of tool_call IDs this message responds to.
+    """
+    ids: set[str] = set()
+
+    # OpenAI format: role=tool with tool_call_id
+    if msg.get("role") == "tool":
+        tc_id = msg.get("tool_call_id")
+        if tc_id:
+            ids.add(tc_id)
+
+    # Anthropic format: content list with tool_result blocks
+    content = msg.get("content")
+    if isinstance(content, list):
+        for block in content:
+            if isinstance(block, dict) and block.get("type") == "tool_result":
+                tc_id = block.get("tool_use_id")
+                if tc_id:
+                    ids.add(tc_id)
+
+    return ids
+
+
+def _is_tool_response_message(msg: dict) -> bool:
+    """Check if message is a tool response (OpenAI or Anthropic format)."""
+    # OpenAI format
+    if msg.get("role") == "tool":
+        return True
+    # Anthropic format
+    content = msg.get("content")
+    if isinstance(content, list):
+        for block in content:
+            if isinstance(block, dict) and block.get("type") == "tool_result":
+                return True
+    return False
+
+
+def _remove_orphan_tool_responses(
+    messages: list[dict], orphan_ids: set[str]
+) -> list[dict]:
+    """
+    Remove tool response messages/blocks that reference orphan tool_call IDs.
+
+    Supports both OpenAI and Anthropic formats.
+    For Anthropic messages with mixed valid/orphan tool_result blocks,
+    filters out only the orphan blocks instead of dropping the entire message.
+    """
+    result = []
+    for msg in messages:
+        # OpenAI format: role=tool - drop entire message if orphan
+        if msg.get("role") == "tool":
+            tc_id = msg.get("tool_call_id")
+            if tc_id and tc_id in orphan_ids:
+                continue
+            result.append(msg)
+            continue
+
+        # Anthropic format: content list may have mixed tool_result blocks
+        content = msg.get("content")
+        if isinstance(content, list):
+            has_tool_results = any(
+                isinstance(b, dict) and b.get("type") == "tool_result" for b in content
+            )
+            if has_tool_results:
+                # Filter out orphan tool_result blocks, keep valid ones
+                filtered_content = [
+                    block
+                    for block in content
+                    if not (
+                        isinstance(block, dict)
+                        and block.get("type") == "tool_result"
+                        and block.get("tool_use_id") in orphan_ids
+                    )
+                ]
+                # Only keep message if it has remaining content
+                if filtered_content:
+                    msg = msg.copy()
+                    msg["content"] = filtered_content
+                    result.append(msg)
+                continue
+
+        result.append(msg)
+    return result
+
+
+def _ensure_tool_pairs_intact(
+    recent_messages: list[dict],
+    all_messages: list[dict],
+    start_index: int,
+) -> list[dict]:
+    """
+    Ensure tool_call/tool_response pairs stay together after slicing.
+
+    When slicing messages for context compaction, a naive slice can separate
+    an assistant message containing tool_calls from its corresponding tool
+    response messages. This causes API validation errors (e.g., Anthropic's
+    "unexpected tool_use_id found in tool_result blocks").
+
+    This function checks for orphan tool responses in the slice and extends
+    backwards to include their corresponding assistant messages.
+
+    Supports both formats:
+    - OpenAI: tool_calls array + role="tool" responses
+    - Anthropic: tool_use blocks + tool_result blocks
+
+    Args:
+        recent_messages: The sliced messages to validate
+        all_messages: The complete message list (for looking up missing assistants)
+        start_index: The index in all_messages where recent_messages begins
+
+    Returns:
+        A potentially extended list of messages with tool pairs intact
+    """
+    if not recent_messages:
+        return recent_messages
+
+    # Collect all tool_call_ids from assistant messages in the slice
+    available_tool_call_ids: set[str] = set()
+    for msg in recent_messages:
+        available_tool_call_ids |= _extract_tool_call_ids_from_message(msg)
+
+    # Find orphan tool responses (responses whose tool_call_id is missing)
+    orphan_tool_call_ids: set[str] = set()
+    for msg in recent_messages:
+        response_ids = _extract_tool_response_ids_from_message(msg)
+        for tc_id in response_ids:
+            if tc_id not in available_tool_call_ids:
+                orphan_tool_call_ids.add(tc_id)
+
+    if not orphan_tool_call_ids:
+        # No orphans, slice is valid
+        return recent_messages
+
+    # Find the assistant messages that contain the orphan tool_call_ids
+    # Search backwards from start_index in all_messages
+    messages_to_prepend: list[dict] = []
+    for i in range(start_index - 1, -1, -1):
+        msg = all_messages[i]
+        msg_tool_ids = _extract_tool_call_ids_from_message(msg)
+        if msg_tool_ids & orphan_tool_call_ids:
+            # This assistant message has tool_calls we need
+            # Also collect its contiguous tool responses that follow it
+            assistant_and_responses: list[dict] = [msg]
+
+            # Scan forward from this assistant to collect tool responses
+            for j in range(i + 1, start_index):
+                following_msg = all_messages[j]
+                following_response_ids = _extract_tool_response_ids_from_message(
+                    following_msg
+                )
+                if following_response_ids and following_response_ids & msg_tool_ids:
+                    assistant_and_responses.append(following_msg)
+                elif not _is_tool_response_message(following_msg):
+                    # Stop at first non-tool-response message
+                    break
+
+            # Prepend the assistant and its tool responses (maintain order)
+            messages_to_prepend = assistant_and_responses + messages_to_prepend
+            # Mark these as found
+            orphan_tool_call_ids -= msg_tool_ids
+            # Also add this assistant's tool_call_ids to available set
+            available_tool_call_ids |= msg_tool_ids
+
+        if not orphan_tool_call_ids:
+            # Found all missing assistants
+            break
+
+    if orphan_tool_call_ids:
+        # Some tool_call_ids couldn't be resolved - remove those tool responses
+        # This shouldn't happen in normal operation but handles edge cases
+        logger.warning(
+            f"Could not find assistant messages for tool_call_ids: {orphan_tool_call_ids}. "
+            "Removing orphan tool responses."
+        )
+        recent_messages = _remove_orphan_tool_responses(
+            recent_messages, orphan_tool_call_ids
+        )
+
+    if messages_to_prepend:
+        logger.info(
+            f"Extended recent messages by {len(messages_to_prepend)} to preserve "
+            f"tool_call/tool_response pairs"
+        )
+        return messages_to_prepend + recent_messages
+
+    return recent_messages
+
+
+async def _summarize_messages_llm(
+    messages: list[dict],
+    client: AsyncOpenAI,
+    model: str,
+    timeout: float = 30.0,
+) -> str:
+    """Summarize messages using an LLM."""
+    conversation = []
+    for msg in messages:
+        role = msg.get("role", "")
+        content = msg.get("content", "")
+        if content and role in ("user", "assistant", "tool"):
+            conversation.append(f"{role.upper()}: {content}")
+
+    conversation_text = "\n\n".join(conversation)
+
+    if not conversation_text:
+        return "No conversation history available."
+
+    # Limit to ~100k chars for safety
+    MAX_CHARS = 100_000
+    if len(conversation_text) > MAX_CHARS:
+        conversation_text = conversation_text[:MAX_CHARS] + "\n\n[truncated]"
+
+    response = await client.with_options(timeout=timeout).chat.completions.create(
+        model=model,
+        messages=[
+            {
+                "role": "system",
+                "content": (
+                    "Create a detailed summary of the conversation so far. "
+                    "This summary will be used as context when continuing the conversation.\n\n"
+                    "Before writing the summary, analyze each message chronologically to identify:\n"
+                    "- User requests and their explicit goals\n"
+                    "- Your approach and key decisions made\n"
+                    "- Technical specifics (file names, tool outputs, function signatures)\n"
+                    "- Errors encountered and resolutions applied\n\n"
+                    "You MUST include ALL of the following sections:\n\n"
+                    "## 1. Primary Request and Intent\n"
+                    "The user's explicit goals and what they are trying to accomplish.\n\n"
+                    "## 2. Key Technical Concepts\n"
+                    "Technologies, frameworks, tools, and patterns being used or discussed.\n\n"
+                    "## 3. Files and Resources Involved\n"
+                    "Specific files examined or modified, with relevant snippets and identifiers.\n\n"
+                    "## 4. Errors and Fixes\n"
+                    "Problems encountered, error messages, and their resolutions. "
+                    "Include any user feedback on fixes.\n\n"
+                    "## 5. Problem Solving\n"
+                    "Issues that have been resolved and how they were addressed.\n\n"
+                    "## 6. All User Messages\n"
+                    "A complete list of all user inputs (excluding tool outputs) to preserve their exact requests.\n\n"
+                    "## 7. Pending Tasks\n"
+                    "Work items the user explicitly requested that have not yet been completed.\n\n"
+                    "## 8. Current Work\n"
+                    "Precise description of what was being worked on most recently, including relevant context.\n\n"
+                    "## 9. Next Steps\n"
+                    "What should happen next, aligned with the user's most recent requests. "
+                    "Include verbatim quotes of recent instructions if relevant."
+                ),
+            },
+            {"role": "user", "content": f"Summarize:\n\n{conversation_text}"},
+        ],
+        max_tokens=1500,
+        temperature=0.3,
+    )
+
+    return response.choices[0].message.content or "No summary available."
+
+
+async def compress_context(
+    messages: list[dict],
+    target_tokens: int = DEFAULT_TOKEN_THRESHOLD,
+    *,
+    model: str = "gpt-4o",
+    client: AsyncOpenAI | None = None,
+    keep_recent: int = DEFAULT_KEEP_RECENT,
+    reserve: int = 2_048,
+    start_cap: int = 8_192,
+    floor_cap: int = 128,
+) -> CompressResult:
+    """
+    Unified context compression that combines summarization and truncation strategies.
+
+    Strategy (in order):
+    1. **LLM summarization** – If client provided, summarize old messages into a
+       single context message while keeping recent messages intact. This is the
+       primary strategy for chat service.
+    2. **Content truncation** – Progressively halve a per-message cap and truncate
+       bloated message content (tool outputs, large pastes). Preserves all messages
+       but shortens their content. Primary strategy when client=None (LLM blocks).
+    3. **Middle-out deletion** – Delete whole messages one at a time from the center
+       outward, skipping tool messages and objective messages.
+    4. **First/last trim** – Truncate first and last message content as last resort.
+
+    Parameters
+    ----------
+    messages        Complete chat history (will be deep-copied).
+    target_tokens   Hard ceiling for prompt size.
+    model           Model name for tokenization and summarization.
+    client          AsyncOpenAI client. If provided, enables LLM summarization
+                    as the first strategy. If None, skips to truncation strategies.
+    keep_recent     Number of recent messages to preserve during summarization.
+    reserve         Tokens to reserve for model response.
+    start_cap       Initial per-message truncation ceiling (tokens).
+    floor_cap       Lowest cap before moving to deletions.
+
+    Returns
+    -------
+    CompressResult with compressed messages and metadata.
+    """
+    # Guard clause for empty messages
+    if not messages:
+        return CompressResult(
+            messages=[],
+            token_count=0,
+            was_compacted=False,
+            original_token_count=0,
+        )
+
+    token_model = _normalize_model_for_tokenizer(model)
+    enc = encoding_for_model(token_model)
+    msgs = deepcopy(messages)
+
+    def total_tokens() -> int:
+        return sum(_msg_tokens(m, enc) for m in msgs)
+
+    original_count = total_tokens()
+
+    # Already under limit
+    if original_count + reserve <= target_tokens:
+        return CompressResult(
+            messages=msgs,
+            token_count=original_count,
+            was_compacted=False,
+            original_token_count=original_count,
+        )
+
+    messages_summarized = 0
+    messages_dropped = 0
+
+    # ---- STEP 1: LLM summarization (if client provided) -------------------
+    # This is the primary compression strategy for chat service.
+    # Summarize old messages while keeping recent ones intact.
+    if client is not None:
+        has_system = len(msgs) > 0 and msgs[0].get("role") == "system"
+        system_msg = msgs[0] if has_system else None
+
+        # Calculate old vs recent messages
+        if has_system:
+            if len(msgs) > keep_recent + 1:
+                old_msgs = msgs[1:-keep_recent]
+                recent_msgs = msgs[-keep_recent:]
+            else:
+                old_msgs = []
+                recent_msgs = msgs[1:] if len(msgs) > 1 else []
+        else:
+            if len(msgs) > keep_recent:
+                old_msgs = msgs[:-keep_recent]
+                recent_msgs = msgs[-keep_recent:]
+            else:
+                old_msgs = []
+                recent_msgs = msgs
+
+        # Ensure tool pairs stay intact
+        slice_start = max(0, len(msgs) - keep_recent)
+        recent_msgs = _ensure_tool_pairs_intact(recent_msgs, msgs, slice_start)
+
+        if old_msgs:
+            try:
+                summary_text = await _summarize_messages_llm(old_msgs, client, model)
+                summary_msg = {
+                    "role": "assistant",
+                    "content": f"[Previous conversation summary — for context only]: {summary_text}",
+                }
+                messages_summarized = len(old_msgs)
+
+                if has_system:
+                    msgs = [system_msg, summary_msg] + recent_msgs
+                else:
+                    msgs = [summary_msg] + recent_msgs
+
+                logger.info(
+                    f"Context summarized: {original_count} -> {total_tokens()} tokens, "
+                    f"summarized {messages_summarized} messages"
+                )
+            except Exception as e:
+                logger.warning(f"Summarization failed, continuing with truncation: {e}")
+                # Fall through to content truncation
+
+    # ---- STEP 2: Normalize content ----------------------------------------
+    # Convert non-string payloads to strings so token counting is coherent.
+    # Always run this before truncation to ensure consistent token counting.
+    for i, m in enumerate(msgs):
+        if not isinstance(m.get("content"), str) and m.get("content") is not None:
+            if _is_tool_message(m):
+                continue
+            if i == 0 or i == len(msgs) - 1:
+                continue
+            content_str = json.dumps(m["content"], separators=(",", ":"))
+            if len(content_str) > 20_000:
+                content_str = _truncate_middle_tokens(content_str, enc, 20_000)
+            m["content"] = content_str
+
+    # ---- STEP 3: Token-aware content truncation ---------------------------
+    # Progressively halve per-message cap and truncate bloated content.
+    # This preserves all messages but shortens their content.
+    cap = start_cap
+    while total_tokens() + reserve > target_tokens and cap >= floor_cap:
+        for m in msgs[1:-1]:
+            if _is_tool_message(m):
+                _truncate_tool_message_content(m, enc, cap)
+                continue
+            if _is_objective_message(m):
+                continue
+            content = m.get("content") or ""
+            if _tok_len(content, enc) > cap:
+                m["content"] = _truncate_middle_tokens(content, enc, cap)
+        cap //= 2
+
+    # ---- STEP 4: Middle-out deletion --------------------------------------
+    # Delete messages one at a time from the center outward.
+    # This is more granular than dropping all old messages at once.
+    while total_tokens() + reserve > target_tokens and len(msgs) > 2:
+        deletable: list[int] = []
+        for i in range(1, len(msgs) - 1):
+            msg = msgs[i]
+            if (
+                msg is not None
+                and not _is_tool_message(msg)
+                and not _is_objective_message(msg)
+            ):
+                deletable.append(i)
+        if not deletable:
+            break
+        centre = len(msgs) // 2
+        to_delete = min(deletable, key=lambda i: abs(i - centre))
+        del msgs[to_delete]
+        messages_dropped += 1
+
+    # ---- STEP 5: Final trim on first/last ---------------------------------
+    cap = start_cap
+    while total_tokens() + reserve > target_tokens and cap >= floor_cap:
+        for idx in (0, -1):
+            msg = msgs[idx]
+            if msg is None:
+                continue
+            if _is_tool_message(msg):
+                _truncate_tool_message_content(msg, enc, cap)
+                continue
+            text = msg.get("content") or ""
+            if _tok_len(text, enc) > cap:
+                msg["content"] = _truncate_middle_tokens(text, enc, cap)
+        cap //= 2
+
+    # Filter out any None values that may have been introduced
+    final_msgs: list[dict] = [m for m in msgs if m is not None]
+    final_count = sum(_msg_tokens(m, enc) for m in final_msgs)
+    error = None
+    if final_count + reserve > target_tokens:
+        error = f"Could not compress below target ({final_count + reserve} > {target_tokens})"
+        logger.warning(error)
+
+    return CompressResult(
+        messages=final_msgs,
+        token_count=final_count,
+        was_compacted=True,
+        error=error,
+        original_token_count=original_count,
+        messages_summarized=messages_summarized,
+        messages_dropped=messages_dropped,
+    )

autogpt_platform/backend/backend/util/prompt_test.py

@@ -1,10 +1,21 @@
 """Tests for prompt utility functions, especially tool call token counting."""
 
+from unittest.mock import AsyncMock, MagicMock
+
 import pytest
 from tiktoken import encoding_for_model
 
 from backend.util import json
-from backend.util.prompt import _msg_tokens, estimate_token_count
+from backend.util.prompt import (
+    CompressResult,
+    _ensure_tool_pairs_intact,
+    _msg_tokens,
+    _normalize_model_for_tokenizer,
+    _truncate_middle_tokens,
+    _truncate_tool_message_content,
+    compress_context,
+    estimate_token_count,
+)
 
 
 class TestMsgTokens:
@@ -276,3 +287,690 @@ class TestEstimateTokenCount:
 
         assert total_tokens == expected_total
         assert total_tokens > 20  # Should be substantial
+
+
+class TestNormalizeModelForTokenizer:
+    """Test model name normalization for tiktoken."""
+
+    def test_openai_models_unchanged(self):
+        """Test that OpenAI models are returned as-is."""
+        assert _normalize_model_for_tokenizer("gpt-4o") == "gpt-4o"
+        assert _normalize_model_for_tokenizer("gpt-4") == "gpt-4"
+        assert _normalize_model_for_tokenizer("gpt-3.5-turbo") == "gpt-3.5-turbo"
+
+    def test_claude_models_normalized(self):
+        """Test that Claude models are normalized to gpt-4o."""
+        assert _normalize_model_for_tokenizer("claude-3-opus") == "gpt-4o"
+        assert _normalize_model_for_tokenizer("claude-3-sonnet") == "gpt-4o"
+        assert _normalize_model_for_tokenizer("anthropic/claude-3-haiku") == "gpt-4o"
+
+    def test_openrouter_paths_extracted(self):
+        """Test that OpenRouter model paths are handled."""
+        assert _normalize_model_for_tokenizer("openai/gpt-4o") == "gpt-4o"
+        assert _normalize_model_for_tokenizer("anthropic/claude-3-opus") == "gpt-4o"
+
+    def test_unknown_models_default_to_gpt4o(self):
+        """Test that unknown models default to gpt-4o."""
+        assert _normalize_model_for_tokenizer("some-random-model") == "gpt-4o"
+        assert _normalize_model_for_tokenizer("llama-3-70b") == "gpt-4o"
+
+
+class TestTruncateToolMessageContent:
+    """Test tool message content truncation."""
+
+    @pytest.fixture
+    def enc(self):
+        return encoding_for_model("gpt-4o")
+
+    def test_truncate_openai_tool_message(self, enc):
+        """Test truncation of OpenAI-style tool message with string content."""
+        long_content = "x" * 10000
+        msg = {"role": "tool", "tool_call_id": "call_123", "content": long_content}
+
+        _truncate_tool_message_content(msg, enc, max_tokens=100)
+
+        # Content should be truncated
+        assert len(msg["content"]) < len(long_content)
+        assert "…" in msg["content"]  # Has ellipsis marker
+
+    def test_truncate_anthropic_tool_result(self, enc):
+        """Test truncation of Anthropic-style tool_result."""
+        long_content = "y" * 10000
+        msg = {
+            "role": "user",
+            "content": [
+                {
+                    "type": "tool_result",
+                    "tool_use_id": "toolu_123",
+                    "content": long_content,
+                }
+            ],
+        }
+
+        _truncate_tool_message_content(msg, enc, max_tokens=100)
+
+        # Content should be truncated
+        result_content = msg["content"][0]["content"]
+        assert len(result_content) < len(long_content)
+        assert "…" in result_content
+
+    def test_preserve_tool_use_blocks(self, enc):
+        """Test that tool_use blocks are not truncated."""
+        msg = {
+            "role": "assistant",
+            "content": [
+                {
+                    "type": "tool_use",
+                    "id": "toolu_123",
+                    "name": "some_function",
+                    "input": {"key": "value" * 1000},  # Large input
+                }
+            ],
+        }
+
+        original = json.dumps(msg["content"][0]["input"])
+        _truncate_tool_message_content(msg, enc, max_tokens=10)
+
+        # tool_use should be unchanged
+        assert json.dumps(msg["content"][0]["input"]) == original
+
+    def test_no_truncation_when_under_limit(self, enc):
+        """Test that short content is not modified."""
+        msg = {"role": "tool", "tool_call_id": "call_123", "content": "Short content"}
+
+        original = msg["content"]
+        _truncate_tool_message_content(msg, enc, max_tokens=1000)
+
+        assert msg["content"] == original
+
+
+class TestTruncateMiddleTokens:
+    """Test middle truncation of text."""
+
+    @pytest.fixture
+    def enc(self):
+        return encoding_for_model("gpt-4o")
+
+    def test_truncates_long_text(self, enc):
+        """Test that long text is truncated with ellipsis in middle."""
+        long_text = "word " * 1000
+        result = _truncate_middle_tokens(long_text, enc, max_tok=50)
+
+        assert len(enc.encode(result)) <= 52  # Allow some slack for ellipsis
+        assert "…" in result
+        assert result.startswith("word")  # Head preserved
+        assert result.endswith("word ")  # Tail preserved
+
+    def test_preserves_short_text(self, enc):
+        """Test that short text is not modified."""
+        short_text = "Hello world"
+        result = _truncate_middle_tokens(short_text, enc, max_tok=100)
+
+        assert result == short_text
+
+
+class TestEnsureToolPairsIntact:
+    """Test tool call/response pair preservation for both OpenAI and Anthropic formats."""
+
+    # ---- OpenAI Format Tests ----
+
+    def test_openai_adds_missing_tool_call(self):
+        """Test that orphaned OpenAI tool_response gets its tool_call prepended."""
+        all_msgs = [
+            {"role": "system", "content": "You are helpful."},
+            {
+                "role": "assistant",
+                "tool_calls": [
+                    {"id": "call_1", "type": "function", "function": {"name": "f1"}}
+                ],
+            },
+            {"role": "tool", "tool_call_id": "call_1", "content": "result"},
+            {"role": "user", "content": "Thanks!"},
+        ]
+        # Recent messages start at index 2 (the tool response)
+        recent = [all_msgs[2], all_msgs[3]]
+        start_index = 2
+
+        result = _ensure_tool_pairs_intact(recent, all_msgs, start_index)
+
+        # Should prepend the tool_call message
+        assert len(result) == 3
+        assert result[0]["role"] == "assistant"
+        assert "tool_calls" in result[0]
+
+    def test_openai_keeps_complete_pairs(self):
+        """Test that complete OpenAI pairs are unchanged."""
+        all_msgs = [
+            {"role": "system", "content": "System"},
+            {
+                "role": "assistant",
+                "tool_calls": [
+                    {"id": "call_1", "type": "function", "function": {"name": "f1"}}
+                ],
+            },
+            {"role": "tool", "tool_call_id": "call_1", "content": "result"},
+        ]
+        recent = all_msgs[1:]  # Include both tool_call and response
+        start_index = 1
+
+        result = _ensure_tool_pairs_intact(recent, all_msgs, start_index)
+
+        assert len(result) == 2  # No messages added
+
+    def test_openai_multiple_tool_calls(self):
+        """Test multiple OpenAI tool calls in one assistant message."""
+        all_msgs = [
+            {"role": "system", "content": "System"},
+            {
+                "role": "assistant",
+                "tool_calls": [
+                    {"id": "call_1", "type": "function", "function": {"name": "f1"}},
+                    {"id": "call_2", "type": "function", "function": {"name": "f2"}},
+                ],
+            },
+            {"role": "tool", "tool_call_id": "call_1", "content": "result1"},
+            {"role": "tool", "tool_call_id": "call_2", "content": "result2"},
+            {"role": "user", "content": "Thanks!"},
+        ]
+        # Recent messages start at index 2 (first tool response)
+        recent = [all_msgs[2], all_msgs[3], all_msgs[4]]
+        start_index = 2
+
+        result = _ensure_tool_pairs_intact(recent, all_msgs, start_index)
+
+        # Should prepend the assistant message with both tool_calls
+        assert len(result) == 4
+        assert result[0]["role"] == "assistant"
+        assert len(result[0]["tool_calls"]) == 2
+
+    # ---- Anthropic Format Tests ----
+
+    def test_anthropic_adds_missing_tool_use(self):
+        """Test that orphaned Anthropic tool_result gets its tool_use prepended."""
+        all_msgs = [
+            {"role": "system", "content": "You are helpful."},
+            {
+                "role": "assistant",
+                "content": [
+                    {
+                        "type": "tool_use",
+                        "id": "toolu_123",
+                        "name": "get_weather",
+                        "input": {"location": "SF"},
+                    }
+                ],
+            },
+            {
+                "role": "user",
+                "content": [
+                    {
+                        "type": "tool_result",
+                        "tool_use_id": "toolu_123",
+                        "content": "22°C and sunny",
+                    }
+                ],
+            },
+            {"role": "user", "content": "Thanks!"},
+        ]
+        # Recent messages start at index 2 (the tool_result)
+        recent = [all_msgs[2], all_msgs[3]]
+        start_index = 2
+
+        result = _ensure_tool_pairs_intact(recent, all_msgs, start_index)
+
+        # Should prepend the tool_use message
+        assert len(result) == 3
+        assert result[0]["role"] == "assistant"
+        assert result[0]["content"][0]["type"] == "tool_use"
+
+    def test_anthropic_keeps_complete_pairs(self):
+        """Test that complete Anthropic pairs are unchanged."""
+        all_msgs = [
+            {"role": "system", "content": "System"},
+            {
+                "role": "assistant",
+                "content": [
+                    {
+                        "type": "tool_use",
+                        "id": "toolu_456",
+                        "name": "calculator",
+                        "input": {"expr": "2+2"},
+                    }
+                ],
+            },
+            {
+                "role": "user",
+                "content": [
+                    {
+                        "type": "tool_result",
+                        "tool_use_id": "toolu_456",
+                        "content": "4",
+                    }
+                ],
+            },
+        ]
+        recent = all_msgs[1:]  # Include both tool_use and result
+        start_index = 1
+
+        result = _ensure_tool_pairs_intact(recent, all_msgs, start_index)
+
+        assert len(result) == 2  # No messages added
+
+    def test_anthropic_multiple_tool_uses(self):
+        """Test multiple Anthropic tool_use blocks in one message."""
+        all_msgs = [
+            {"role": "system", "content": "System"},
+            {
+                "role": "assistant",
+                "content": [
+                    {"type": "text", "text": "Let me check both..."},
+                    {
+                        "type": "tool_use",
+                        "id": "toolu_1",
+                        "name": "get_weather",
+                        "input": {"city": "NYC"},
+                    },
+                    {
+                        "type": "tool_use",
+                        "id": "toolu_2",
+                        "name": "get_weather",
+                        "input": {"city": "LA"},
+                    },
+                ],
+            },
+            {
+                "role": "user",
+                "content": [
+                    {
+                        "type": "tool_result",
+                        "tool_use_id": "toolu_1",
+                        "content": "Cold",
+                    },
+                    {
+                        "type": "tool_result",
+                        "tool_use_id": "toolu_2",
+                        "content": "Warm",
+                    },
+                ],
+            },
+            {"role": "user", "content": "Thanks!"},
+        ]
+        # Recent messages start at index 2 (tool_result)
+        recent = [all_msgs[2], all_msgs[3]]
+        start_index = 2
+
+        result = _ensure_tool_pairs_intact(recent, all_msgs, start_index)
+
+        # Should prepend the assistant message with both tool_uses
+        assert len(result) == 3
+        assert result[0]["role"] == "assistant"
+        tool_use_count = sum(
+            1 for b in result[0]["content"] if b.get("type") == "tool_use"
+        )
+        assert tool_use_count == 2
+
+    # ---- Mixed/Edge Case Tests ----
+
+    def test_anthropic_with_type_message_field(self):
+        """Test Anthropic format with 'type': 'message' field (smart_decision_maker style)."""
+        all_msgs = [
+            {"role": "system", "content": "You are helpful."},
+            {
+                "role": "assistant",
+                "content": [
+                    {
+                        "type": "tool_use",
+                        "id": "toolu_abc",
+                        "name": "search",
+                        "input": {"q": "test"},
+                    }
+                ],
+            },
+            {
+                "role": "user",
+                "type": "message",  # Extra field from smart_decision_maker
+                "content": [
+                    {
+                        "type": "tool_result",
+                        "tool_use_id": "toolu_abc",
+                        "content": "Found results",
+                    }
+                ],
+            },
+            {"role": "user", "content": "Thanks!"},
+        ]
+        # Recent messages start at index 2 (the tool_result with 'type': 'message')
+        recent = [all_msgs[2], all_msgs[3]]
+        start_index = 2
+
+        result = _ensure_tool_pairs_intact(recent, all_msgs, start_index)
+
+        # Should prepend the tool_use message
+        assert len(result) == 3
+        assert result[0]["role"] == "assistant"
+        assert result[0]["content"][0]["type"] == "tool_use"
+
+    def test_handles_no_tool_messages(self):
+        """Test messages without tool calls."""
+        all_msgs = [
+            {"role": "user", "content": "Hello"},
+            {"role": "assistant", "content": "Hi there!"},
+        ]
+        recent = all_msgs
+        start_index = 0
+
+        result = _ensure_tool_pairs_intact(recent, all_msgs, start_index)
+
+        assert result == all_msgs
+
+    def test_handles_empty_messages(self):
+        """Test empty message list."""
+        result = _ensure_tool_pairs_intact([], [], 0)
+        assert result == []
+
+    def test_mixed_text_and_tool_content(self):
+        """Test Anthropic message with mixed text and tool_use content."""
+        all_msgs = [
+            {
+                "role": "assistant",
+                "content": [
+                    {"type": "text", "text": "I'll help you with that."},
+                    {
+                        "type": "tool_use",
+                        "id": "toolu_mixed",
+                        "name": "search",
+                        "input": {"q": "test"},
+                    },
+                ],
+            },
+            {
+                "role": "user",
+                "content": [
+                    {
+                        "type": "tool_result",
+                        "tool_use_id": "toolu_mixed",
+                        "content": "Found results",
+                    }
+                ],
+            },
+            {"role": "assistant", "content": "Here are the results..."},
+        ]
+        # Start from tool_result
+        recent = [all_msgs[1], all_msgs[2]]
+        start_index = 1
+
+        result = _ensure_tool_pairs_intact(recent, all_msgs, start_index)
+
+        # Should prepend the assistant message with tool_use
+        assert len(result) == 3
+        assert result[0]["content"][0]["type"] == "text"
+        assert result[0]["content"][1]["type"] == "tool_use"
+
+
+class TestCompressContext:
+    """Test the async compress_context function."""
+
+    @pytest.mark.asyncio
+    async def test_no_compression_needed(self):
+        """Test messages under limit return without compression."""
+        messages = [
+            {"role": "system", "content": "You are helpful."},
+            {"role": "user", "content": "Hello!"},
+        ]
+
+        result = await compress_context(messages, target_tokens=100000)
+
+        assert isinstance(result, CompressResult)
+        assert result.was_compacted is False
+        assert len(result.messages) == 2
+        assert result.error is None
+
+    @pytest.mark.asyncio
+    async def test_truncation_without_client(self):
+        """Test that truncation works without LLM client."""
+        long_content = "x" * 50000
+        messages = [
+            {"role": "system", "content": "System"},
+            {"role": "user", "content": long_content},
+            {"role": "assistant", "content": "Response"},
+        ]
+
+        result = await compress_context(
+            messages, target_tokens=1000, client=None, reserve=100
+        )
+
+        assert result.was_compacted is True
+        # Should have truncated without summarization
+        assert result.messages_summarized == 0
+
+    @pytest.mark.asyncio
+    async def test_with_mocked_llm_client(self):
+        """Test summarization with mocked LLM client."""
+        # Create many messages to trigger summarization
+        messages = [{"role": "system", "content": "System prompt"}]
+        for i in range(30):
+            messages.append({"role": "user", "content": f"User message {i} " * 100})
+            messages.append(
+                {"role": "assistant", "content": f"Assistant response {i} " * 100}
+            )
+
+        # Mock the AsyncOpenAI client
+        mock_client = AsyncMock()
+        mock_response = MagicMock()
+        mock_response.choices = [MagicMock()]
+        mock_response.choices[0].message.content = "Summary of conversation"
+        mock_client.with_options.return_value.chat.completions.create = AsyncMock(
+            return_value=mock_response
+        )
+
+        result = await compress_context(
+            messages,
+            target_tokens=5000,
+            client=mock_client,
+            keep_recent=5,
+            reserve=500,
+        )
+
+        assert result.was_compacted is True
+        # Should have attempted summarization
+        assert mock_client.with_options.called or result.messages_summarized > 0
+
+    @pytest.mark.asyncio
+    async def test_preserves_tool_pairs(self):
+        """Test that tool call/response pairs stay together."""
+        messages = [
+            {"role": "system", "content": "System"},
+            {"role": "user", "content": "Do something"},
+            {
+                "role": "assistant",
+                "tool_calls": [
+                    {"id": "call_1", "type": "function", "function": {"name": "func"}}
+                ],
+            },
+            {"role": "tool", "tool_call_id": "call_1", "content": "Result " * 1000},
+            {"role": "assistant", "content": "Done!"},
+        ]
+
+        result = await compress_context(
+            messages, target_tokens=500, client=None, reserve=50
+        )
+
+        # Check that if tool response exists, its call exists too
+        tool_call_ids = set()
+        tool_response_ids = set()
+        for msg in result.messages:
+            if "tool_calls" in msg:
+                for tc in msg["tool_calls"]:
+                    tool_call_ids.add(tc["id"])
+            if msg.get("role") == "tool":
+                tool_response_ids.add(msg.get("tool_call_id"))
+
+        # All tool responses should have their calls
+        assert tool_response_ids <= tool_call_ids
+
+    @pytest.mark.asyncio
+    async def test_returns_error_when_cannot_compress(self):
+        """Test that error is returned when compression fails."""
+        # Single huge message that can't be compressed enough
+        messages = [
+            {"role": "user", "content": "x" * 100000},
+        ]
+
+        result = await compress_context(
+            messages, target_tokens=100, client=None, reserve=50
+        )
+
+        # Should have an error since we can't get below 100 tokens
+        assert result.error is not None
+        assert result.was_compacted is True
+
+    @pytest.mark.asyncio
+    async def test_empty_messages(self):
+        """Test that empty messages list returns early without error."""
+        result = await compress_context([], target_tokens=1000)
+
+        assert result.messages == []
+        assert result.token_count == 0
+        assert result.was_compacted is False
+        assert result.error is None
+
+
+class TestRemoveOrphanToolResponses:
+    """Test _remove_orphan_tool_responses helper function."""
+
+    def test_removes_openai_orphan(self):
+        """Test removal of orphan OpenAI tool response."""
+        from backend.util.prompt import _remove_orphan_tool_responses
+
+        messages = [
+            {"role": "tool", "tool_call_id": "call_orphan", "content": "result"},
+            {"role": "user", "content": "Hello"},
+        ]
+        orphan_ids = {"call_orphan"}
+
+        result = _remove_orphan_tool_responses(messages, orphan_ids)
+
+        assert len(result) == 1
+        assert result[0]["role"] == "user"
+
+    def test_keeps_valid_openai_tool(self):
+        """Test that valid OpenAI tool responses are kept."""
+        from backend.util.prompt import _remove_orphan_tool_responses
+
+        messages = [
+            {"role": "tool", "tool_call_id": "call_valid", "content": "result"},
+        ]
+        orphan_ids = {"call_other"}
+
+        result = _remove_orphan_tool_responses(messages, orphan_ids)
+
+        assert len(result) == 1
+        assert result[0]["tool_call_id"] == "call_valid"
+
+    def test_filters_anthropic_mixed_blocks(self):
+        """Test filtering individual orphan blocks from Anthropic message with mixed valid/orphan."""
+        from backend.util.prompt import _remove_orphan_tool_responses
+
+        messages = [
+            {
+                "role": "user",
+                "content": [
+                    {
+                        "type": "tool_result",
+                        "tool_use_id": "toolu_valid",
+                        "content": "valid result",
+                    },
+                    {
+                        "type": "tool_result",
+                        "tool_use_id": "toolu_orphan",
+                        "content": "orphan result",
+                    },
+                ],
+            },
+        ]
+        orphan_ids = {"toolu_orphan"}
+
+        result = _remove_orphan_tool_responses(messages, orphan_ids)
+
+        assert len(result) == 1
+        # Should only have the valid tool_result, orphan filtered out
+        assert len(result[0]["content"]) == 1
+        assert result[0]["content"][0]["tool_use_id"] == "toolu_valid"
+
+    def test_removes_anthropic_all_orphan(self):
+        """Test removal of Anthropic message when all tool_results are orphans."""
+        from backend.util.prompt import _remove_orphan_tool_responses
+
+        messages = [
+            {
+                "role": "user",
+                "content": [
+                    {
+                        "type": "tool_result",
+                        "tool_use_id": "toolu_orphan1",
+                        "content": "result1",
+                    },
+                    {
+                        "type": "tool_result",
+                        "tool_use_id": "toolu_orphan2",
+                        "content": "result2",
+                    },
+                ],
+            },
+        ]
+        orphan_ids = {"toolu_orphan1", "toolu_orphan2"}
+
+        result = _remove_orphan_tool_responses(messages, orphan_ids)
+
+        # Message should be completely removed since no content left
+        assert len(result) == 0
+
+    def test_preserves_non_tool_messages(self):
+        """Test that non-tool messages are preserved."""
+        from backend.util.prompt import _remove_orphan_tool_responses
+
+        messages = [
+            {"role": "user", "content": "Hello"},
+            {"role": "assistant", "content": "Hi there!"},
+        ]
+        orphan_ids = {"some_id"}
+
+        result = _remove_orphan_tool_responses(messages, orphan_ids)
+
+        assert result == messages
+
+
+class TestCompressResultDataclass:
+    """Test CompressResult dataclass."""
+
+    def test_default_values(self):
+        """Test default values are set correctly."""
+        result = CompressResult(
+            messages=[{"role": "user", "content": "test"}],
+            token_count=10,
+            was_compacted=False,
+        )
+
+        assert result.error is None
+        assert result.original_token_count == 0  # Defaults to 0, not None
+        assert result.messages_summarized == 0
+        assert result.messages_dropped == 0
+
+    def test_all_fields(self):
+        """Test all fields can be set."""
+        result = CompressResult(
+            messages=[{"role": "user", "content": "test"}],
+            token_count=100,
+            was_compacted=True,
+            error="Some error",
+            original_token_count=500,
+            messages_summarized=10,
+            messages_dropped=5,
+        )
+
+        assert result.token_count == 100
+        assert result.was_compacted is True
+        assert result.error == "Some error"
+        assert result.original_token_count == 500
+        assert result.messages_summarized == 10
+        assert result.messages_dropped == 5

autogpt_platform/backend/backend/util/settings.py

@@ -656,7 +656,6 @@ class Secrets(UpdateTrackingModel["Secrets"], BaseSettings):
     e2b_api_key: str = Field(default="", description="E2B API key")
     nvidia_api_key: str = Field(default="", description="Nvidia API key")
     mem0_api_key: str = Field(default="", description="Mem0 API key")
-    elevenlabs_api_key: str = Field(default="", description="ElevenLabs API key")
 
     linear_client_id: str = Field(default="", description="Linear client ID")
     linear_client_secret: str = Field(default="", description="Linear client secret")

autogpt_platform/backend/migrations/20260126120000_migrate_claude_3_7_to_4_5_sonnet/migration.sql

@@ -0,0 +1,22 @@
+-- Migrate Claude 3.7 Sonnet to Claude 4.5 Sonnet
+-- This updates all AgentNode blocks that use the deprecated Claude 3.7 Sonnet model
+-- Anthropic is retiring claude-3-7-sonnet-20250219 on February 19, 2026
+
+-- Update AgentNode constant inputs
+UPDATE "AgentNode"
+SET    "constantInput" = JSONB_SET(
+         "constantInput"::jsonb,
+         '{model}',
+         '"claude-sonnet-4-5-20250929"'::jsonb
+       )
+WHERE  "constantInput"::jsonb->>'model' = 'claude-3-7-sonnet-20250219';
+
+-- Update AgentPreset input overrides (stored in AgentNodeExecutionInputOutput)
+UPDATE "AgentNodeExecutionInputOutput"
+SET    "data" = JSONB_SET(
+         "data"::jsonb,
+         '{model}',
+         '"claude-sonnet-4-5-20250929"'::jsonb
+       )
+WHERE  "agentPresetId" IS NOT NULL
+  AND  "data"::jsonb->>'model' = 'claude-3-7-sonnet-20250219';

autogpt_platform/backend/poetry.lock

@@ -1169,29 +1169,6 @@ attrs = ">=21.3.0"
 e2b = ">=1.5.4,<2.0.0"
 httpx = ">=0.20.0,<1.0.0"
 
-[[package]]
-name = "elevenlabs"
-version = "1.59.0"
-description = ""
-optional = false
-python-versions = "<4.0,>=3.8"
-groups = ["main"]
-files = [
-    {file = "elevenlabs-1.59.0-py3-none-any.whl", hash = "sha256:468145db81a0bc867708b4a8619699f75583e9481b395ec1339d0b443da771ed"},
-    {file = "elevenlabs-1.59.0.tar.gz", hash = "sha256:16e735bd594e86d415dd445d249c8cc28b09996cfd627fbc10102c0a84698859"},
-]
-
-[package.dependencies]
-httpx = ">=0.21.2"
-pydantic = ">=1.9.2"
-pydantic-core = ">=2.18.2,<3.0.0"
-requests = ">=2.20"
-typing_extensions = ">=4.0.0"
-websockets = ">=11.0"
-
-[package.extras]
-pyaudio = ["pyaudio (>=0.2.14)"]
-
 [[package]]
 name = "email-validator"
 version = "2.2.0"
@@ -7384,28 +7361,6 @@ files = [
 defusedxml = ">=0.7.1,<0.8.0"
 requests = "*"
 
-[[package]]
-name = "yt-dlp"
-version = "2025.12.8"
-description = "A feature-rich command-line audio/video downloader"
-optional = false
-python-versions = ">=3.10"
-groups = ["main"]
-files = [
-    {file = "yt_dlp-2025.12.8-py3-none-any.whl", hash = "sha256:36e2584342e409cfbfa0b5e61448a1c5189e345cf4564294456ee509e7d3e065"},
-    {file = "yt_dlp-2025.12.8.tar.gz", hash = "sha256:b773c81bb6b71cb2c111cfb859f453c7a71cf2ef44eff234ff155877184c3e4f"},
-]
-
-[package.extras]
-build = ["build", "hatchling (>=1.27.0)", "pip", "setuptools (>=71.0.2)", "wheel"]
-curl-cffi = ["curl-cffi (>=0.5.10,<0.6.dev0 || >=0.10.dev0,<0.14) ; implementation_name == \"cpython\""]
-default = ["brotli ; implementation_name == \"cpython\"", "brotlicffi ; implementation_name != \"cpython\"", "certifi", "mutagen", "pycryptodomex", "requests (>=2.32.2,<3)", "urllib3 (>=2.0.2,<3)", "websockets (>=13.0)", "yt-dlp-ejs (==0.3.2)"]
-dev = ["autopep8 (>=2.0,<3.0)", "pre-commit", "pytest (>=8.1,<9.0)", "pytest-rerunfailures (>=14.0,<15.0)", "ruff (>=0.14.0,<0.15.0)"]
-pyinstaller = ["pyinstaller (>=6.17.0)"]
-secretstorage = ["cffi", "secretstorage"]
-static-analysis = ["autopep8 (>=2.0,<3.0)", "ruff (>=0.14.0,<0.15.0)"]
-test = ["pytest (>=8.1,<9.0)", "pytest-rerunfailures (>=14.0,<15.0)"]
-
 [[package]]
 name = "zerobouncesdk"
 version = "1.1.2"
@@ -7557,4 +7512,4 @@ cffi = ["cffi (>=1.11)"]
 [metadata]
 lock-version = "2.1"
 python-versions = ">=3.10,<3.14"
-content-hash = "8239323f9ae6713224dffd1fe8ba8b449fe88b6c3c7a90940294a74f43a0387a"
+content-hash = "ee5742dc1a9df50dfc06d4b26a1682cbb2b25cab6b79ce5625ec272f93e4f4bf"

autogpt_platform/backend/pyproject.toml

@@ -20,7 +20,6 @@ click = "^8.2.0"
 cryptography = "^45.0"
 discord-py = "^2.5.2"
 e2b-code-interpreter = "^1.5.2"
-elevenlabs = "^1.50.0"
 fastapi = "^0.116.1"
 feedparser = "^6.0.11"
 flake8 = "^7.3.0"
@@ -72,7 +71,6 @@ tweepy = "^4.16.0"
 uvicorn = { extras = ["standard"], version = "^0.35.0" }
 websockets = "^15.0"
 youtube-transcript-api = "^1.2.1"
-yt-dlp = "2025.12.08"
 zerobouncesdk = "^1.1.2"
 # NOTE: please insert new dependencies in their alphabetical location
 pytest-snapshot = "^0.9.0"

autogpt_platform/backend/snapshots/agts_by_creator

@@ -9,7 +9,8 @@
       "sub_heading": "Creator agent subheading",
       "description": "Creator agent description",
       "runs": 50,
-      "rating": 4.0
+      "rating": 4.0,
+      "agent_graph_id": "test-graph-2"
     }
   ],
   "pagination": {

autogpt_platform/backend/snapshots/agts_category

@@ -9,7 +9,8 @@
       "sub_heading": "Category agent subheading",
       "description": "Category agent description",
       "runs": 60,
-      "rating": 4.1
+      "rating": 4.1,
+      "agent_graph_id": "test-graph-category"
     }
   ],
   "pagination": {

autogpt_platform/backend/snapshots/agts_pagination

@@ -9,7 +9,8 @@
       "sub_heading": "Agent 0 subheading",
       "description": "Agent 0 description",
       "runs": 0,
-      "rating": 4.0
+      "rating": 4.0,
+      "agent_graph_id": "test-graph-2"
     },
     {
       "slug": "agent-1",
@@ -20,7 +21,8 @@
       "sub_heading": "Agent 1 subheading",
       "description": "Agent 1 description",
       "runs": 10,
-      "rating": 4.0
+      "rating": 4.0,
+      "agent_graph_id": "test-graph-2"
     },
     {
       "slug": "agent-2",
@@ -31,7 +33,8 @@
       "sub_heading": "Agent 2 subheading",
       "description": "Agent 2 description",
       "runs": 20,
-      "rating": 4.0
+      "rating": 4.0,
+      "agent_graph_id": "test-graph-2"
     },
     {
       "slug": "agent-3",
@@ -42,7 +45,8 @@
       "sub_heading": "Agent 3 subheading",
       "description": "Agent 3 description",
       "runs": 30,
-      "rating": 4.0
+      "rating": 4.0,
+      "agent_graph_id": "test-graph-2"
     },
     {
       "slug": "agent-4",
@@ -53,7 +57,8 @@
       "sub_heading": "Agent 4 subheading",
       "description": "Agent 4 description",
       "runs": 40,
-      "rating": 4.0
+      "rating": 4.0,
+      "agent_graph_id": "test-graph-2"
     }
   ],
   "pagination": {

autogpt_platform/backend/snapshots/agts_search

@@ -9,7 +9,8 @@
       "sub_heading": "Search agent subheading",
       "description": "Specific search term description",
       "runs": 75,
-      "rating": 4.2
+      "rating": 4.2,
+      "agent_graph_id": "test-graph-search"
     }
   ],
   "pagination": {

autogpt_platform/backend/snapshots/agts_sorted

@@ -9,7 +9,8 @@
       "sub_heading": "Top agent subheading",
       "description": "Top agent description",
       "runs": 1000,
-      "rating": 5.0
+      "rating": 5.0,
+      "agent_graph_id": "test-graph-3"
     }
   ],
   "pagination": {

autogpt_platform/backend/snapshots/feat_agts

@@ -9,7 +9,8 @@
       "sub_heading": "Featured agent subheading",
       "description": "Featured agent description",
       "runs": 100,
-      "rating": 4.5
+      "rating": 4.5,
+      "agent_graph_id": "test-graph-1"
     }
   ],
   "pagination": {

autogpt_platform/backend/snapshots/lib_agts_search

@@ -31,6 +31,10 @@
       "has_sensitive_action": false,
       "trigger_setup_info": null,
       "new_output": false,
+      "execution_count": 0,
+      "success_rate": null,
+      "avg_correctness_score": null,
+      "recent_executions": [],
       "can_access_graph": true,
       "is_latest_version": true,
       "is_favorite": false,
@@ -72,6 +76,10 @@
       "has_sensitive_action": false,
       "trigger_setup_info": null,
       "new_output": false,
+      "execution_count": 0,
+      "success_rate": null,
+      "avg_correctness_score": null,
+      "recent_executions": [],
       "can_access_graph": false,
       "is_latest_version": true,
       "is_favorite": false,

autogpt_platform/backend/test/agent_generator/test_core_integration.py

@@ -57,7 +57,8 @@ class TestDecomposeGoal:
 
             result = await core.decompose_goal("Build a chatbot")
 
-            mock_external.assert_called_once_with("Build a chatbot", "")
+            # library_agents defaults to None
+            mock_external.assert_called_once_with("Build a chatbot", "", None)
             assert result == expected_result
 
     @pytest.mark.asyncio
@@ -74,7 +75,8 @@ class TestDecomposeGoal:
 
             await core.decompose_goal("Build a chatbot", "Use Python")
 
-            mock_external.assert_called_once_with("Build a chatbot", "Use Python")
+            # library_agents defaults to None
+            mock_external.assert_called_once_with("Build a chatbot", "Use Python", None)
 
     @pytest.mark.asyncio
     async def test_returns_none_on_service_failure(self):
@@ -109,7 +111,8 @@ class TestGenerateAgent:
             instructions = {"type": "instructions", "steps": ["Step 1"]}
             result = await core.generate_agent(instructions)
 
-            mock_external.assert_called_once_with(instructions)
+            # library_agents defaults to None
+            mock_external.assert_called_once_with(instructions, None)
             # Result should have id, version, is_active added if not present
             assert result is not None
             assert result["name"] == "Test Agent"
@@ -174,7 +177,8 @@ class TestGenerateAgentPatch:
             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)
+            # library_agents defaults to None
+            mock_external.assert_called_once_with("Add a node", current_agent, None)
             assert result == expected_result
 
     @pytest.mark.asyncio

autogpt_platform/backend/test/agent_generator/test_library_agents.py

@@ -0,0 +1,857 @@
+"""
+Tests for library agent fetching functionality in agent generator.
+
+This test suite verifies the search-based library agent fetching,
+including the combination of library and marketplace agents.
+"""
+
+from unittest.mock import AsyncMock, MagicMock, patch
+
+import pytest
+
+from backend.api.features.chat.tools.agent_generator import core
+
+
+class TestGetLibraryAgentsForGeneration:
+    """Test get_library_agents_for_generation function."""
+
+    @pytest.mark.asyncio
+    async def test_fetches_agents_with_search_term(self):
+        """Test that search_term is passed to the library db."""
+        # Create a mock agent with proper attribute values
+        mock_agent = MagicMock()
+        mock_agent.graph_id = "agent-123"
+        mock_agent.graph_version = 1
+        mock_agent.name = "Email Agent"
+        mock_agent.description = "Sends emails"
+        mock_agent.input_schema = {"properties": {}}
+        mock_agent.output_schema = {"properties": {}}
+        mock_agent.recent_executions = []
+
+        mock_response = MagicMock()
+        mock_response.agents = [mock_agent]
+
+        with patch.object(
+            core.library_db,
+            "list_library_agents",
+            new_callable=AsyncMock,
+            return_value=mock_response,
+        ) as mock_list:
+            result = await core.get_library_agents_for_generation(
+                user_id="user-123",
+                search_query="send email",
+            )
+
+            mock_list.assert_called_once_with(
+                user_id="user-123",
+                search_term="send email",
+                page=1,
+                page_size=15,
+                include_executions=True,
+            )
+
+        # Verify result format
+        assert len(result) == 1
+        assert result[0]["graph_id"] == "agent-123"
+        assert result[0]["name"] == "Email Agent"
+
+    @pytest.mark.asyncio
+    async def test_excludes_specified_graph_id(self):
+        """Test that agents with excluded graph_id are filtered out."""
+        mock_response = MagicMock()
+        mock_response.agents = [
+            MagicMock(
+                graph_id="agent-123",
+                graph_version=1,
+                name="Agent 1",
+                description="First agent",
+                input_schema={},
+                output_schema={},
+                recent_executions=[],
+            ),
+            MagicMock(
+                graph_id="agent-456",
+                graph_version=1,
+                name="Agent 2",
+                description="Second agent",
+                input_schema={},
+                output_schema={},
+                recent_executions=[],
+            ),
+        ]
+
+        with patch.object(
+            core.library_db,
+            "list_library_agents",
+            new_callable=AsyncMock,
+            return_value=mock_response,
+        ):
+            result = await core.get_library_agents_for_generation(
+                user_id="user-123",
+                exclude_graph_id="agent-123",
+            )
+
+        # Verify the excluded agent is not in results
+        assert len(result) == 1
+        assert result[0]["graph_id"] == "agent-456"
+
+    @pytest.mark.asyncio
+    async def test_respects_max_results(self):
+        """Test that max_results parameter limits the page_size."""
+        mock_response = MagicMock()
+        mock_response.agents = []
+
+        with patch.object(
+            core.library_db,
+            "list_library_agents",
+            new_callable=AsyncMock,
+            return_value=mock_response,
+        ) as mock_list:
+            await core.get_library_agents_for_generation(
+                user_id="user-123",
+                max_results=5,
+            )
+
+            mock_list.assert_called_once_with(
+                user_id="user-123",
+                search_term=None,
+                page=1,
+                page_size=5,
+                include_executions=True,
+            )
+
+
+class TestSearchMarketplaceAgentsForGeneration:
+    """Test search_marketplace_agents_for_generation function."""
+
+    @pytest.mark.asyncio
+    async def test_searches_marketplace_with_query(self):
+        """Test that marketplace is searched with the query."""
+        mock_response = MagicMock()
+        mock_response.agents = [
+            MagicMock(
+                agent_name="Public Agent",
+                description="A public agent",
+                sub_heading="Does something useful",
+                creator="creator-1",
+                agent_graph_id="graph-123",
+            )
+        ]
+
+        mock_graph = MagicMock()
+        mock_graph.id = "graph-123"
+        mock_graph.version = 1
+        mock_graph.input_schema = {"type": "object"}
+        mock_graph.output_schema = {"type": "object"}
+
+        with (
+            patch(
+                "backend.api.features.store.db.get_store_agents",
+                new_callable=AsyncMock,
+                return_value=mock_response,
+            ) as mock_search,
+            patch(
+                "backend.api.features.chat.tools.agent_generator.core.get_store_listed_graphs",
+                new_callable=AsyncMock,
+                return_value={"graph-123": mock_graph},
+            ),
+        ):
+            result = await core.search_marketplace_agents_for_generation(
+                search_query="automation",
+                max_results=10,
+            )
+
+            mock_search.assert_called_once_with(
+                search_query="automation",
+                page=1,
+                page_size=10,
+            )
+
+        assert len(result) == 1
+        assert result[0]["name"] == "Public Agent"
+        assert result[0]["graph_id"] == "graph-123"
+
+    @pytest.mark.asyncio
+    async def test_handles_marketplace_error_gracefully(self):
+        """Test that marketplace errors don't crash the function."""
+        with patch(
+            "backend.api.features.store.db.get_store_agents",
+            new_callable=AsyncMock,
+            side_effect=Exception("Marketplace unavailable"),
+        ):
+            result = await core.search_marketplace_agents_for_generation(
+                search_query="test"
+            )
+
+        # Should return empty list, not raise exception
+        assert result == []
+
+
+class TestGetAllRelevantAgentsForGeneration:
+    """Test get_all_relevant_agents_for_generation function."""
+
+    @pytest.mark.asyncio
+    async def test_combines_library_and_marketplace_agents(self):
+        """Test that agents from both sources are combined."""
+        library_agents = [
+            {
+                "graph_id": "lib-123",
+                "graph_version": 1,
+                "name": "Library Agent",
+                "description": "From library",
+                "input_schema": {},
+                "output_schema": {},
+            }
+        ]
+
+        marketplace_agents = [
+            {
+                "graph_id": "market-456",
+                "graph_version": 1,
+                "name": "Market Agent",
+                "description": "From marketplace",
+                "input_schema": {},
+                "output_schema": {},
+            }
+        ]
+
+        with patch.object(
+            core,
+            "get_library_agents_for_generation",
+            new_callable=AsyncMock,
+            return_value=library_agents,
+        ):
+            with patch.object(
+                core,
+                "search_marketplace_agents_for_generation",
+                new_callable=AsyncMock,
+                return_value=marketplace_agents,
+            ):
+                result = await core.get_all_relevant_agents_for_generation(
+                    user_id="user-123",
+                    search_query="test query",
+                    include_marketplace=True,
+                )
+
+        # Library agents should come first
+        assert len(result) == 2
+        assert result[0]["name"] == "Library Agent"
+        assert result[1]["name"] == "Market Agent"
+
+    @pytest.mark.asyncio
+    async def test_deduplicates_by_graph_id(self):
+        """Test that marketplace agents with same graph_id as library are excluded."""
+        library_agents = [
+            {
+                "graph_id": "shared-123",
+                "graph_version": 1,
+                "name": "Shared Agent",
+                "description": "From library",
+                "input_schema": {},
+                "output_schema": {},
+            }
+        ]
+
+        marketplace_agents = [
+            {
+                "graph_id": "shared-123",  # Same graph_id, should be deduplicated
+                "graph_version": 1,
+                "name": "Shared Agent",
+                "description": "From marketplace",
+                "input_schema": {},
+                "output_schema": {},
+            },
+            {
+                "graph_id": "unique-456",
+                "graph_version": 1,
+                "name": "Unique Agent",
+                "description": "Only in marketplace",
+                "input_schema": {},
+                "output_schema": {},
+            },
+        ]
+
+        with patch.object(
+            core,
+            "get_library_agents_for_generation",
+            new_callable=AsyncMock,
+            return_value=library_agents,
+        ):
+            with patch.object(
+                core,
+                "search_marketplace_agents_for_generation",
+                new_callable=AsyncMock,
+                return_value=marketplace_agents,
+            ):
+                result = await core.get_all_relevant_agents_for_generation(
+                    user_id="user-123",
+                    search_query="test",
+                    include_marketplace=True,
+                )
+
+        # Shared Agent from marketplace should be excluded by graph_id
+        assert len(result) == 2
+        names = [a["name"] for a in result]
+        assert "Shared Agent" in names
+        assert "Unique Agent" in names
+
+    @pytest.mark.asyncio
+    async def test_skips_marketplace_when_disabled(self):
+        """Test that marketplace is not searched when include_marketplace=False."""
+        library_agents = [
+            {
+                "graph_id": "lib-123",
+                "graph_version": 1,
+                "name": "Library Agent",
+                "description": "From library",
+                "input_schema": {},
+                "output_schema": {},
+            }
+        ]
+
+        with patch.object(
+            core,
+            "get_library_agents_for_generation",
+            new_callable=AsyncMock,
+            return_value=library_agents,
+        ):
+            with patch.object(
+                core,
+                "search_marketplace_agents_for_generation",
+                new_callable=AsyncMock,
+            ) as mock_marketplace:
+                result = await core.get_all_relevant_agents_for_generation(
+                    user_id="user-123",
+                    search_query="test",
+                    include_marketplace=False,
+                )
+
+        # Marketplace should not be called
+        mock_marketplace.assert_not_called()
+        assert len(result) == 1
+
+    @pytest.mark.asyncio
+    async def test_skips_marketplace_when_no_search_query(self):
+        """Test that marketplace is not searched without a search query."""
+        library_agents = [
+            {
+                "graph_id": "lib-123",
+                "graph_version": 1,
+                "name": "Library Agent",
+                "description": "From library",
+                "input_schema": {},
+                "output_schema": {},
+            }
+        ]
+
+        with patch.object(
+            core,
+            "get_library_agents_for_generation",
+            new_callable=AsyncMock,
+            return_value=library_agents,
+        ):
+            with patch.object(
+                core,
+                "search_marketplace_agents_for_generation",
+                new_callable=AsyncMock,
+            ) as mock_marketplace:
+                result = await core.get_all_relevant_agents_for_generation(
+                    user_id="user-123",
+                    search_query=None,  # No search query
+                    include_marketplace=True,
+                )
+
+        # Marketplace should not be called without search query
+        mock_marketplace.assert_not_called()
+        assert len(result) == 1
+
+
+class TestExtractSearchTermsFromSteps:
+    """Test extract_search_terms_from_steps function."""
+
+    def test_extracts_terms_from_instructions_type(self):
+        """Test extraction from valid instructions decomposition result."""
+        decomposition_result = {
+            "type": "instructions",
+            "steps": [
+                {
+                    "description": "Send an email notification",
+                    "block_name": "GmailSendBlock",
+                },
+                {"description": "Fetch weather data", "action": "Get weather API"},
+            ],
+        }
+
+        result = core.extract_search_terms_from_steps(decomposition_result)
+
+        assert "Send an email notification" in result
+        assert "GmailSendBlock" in result
+        assert "Fetch weather data" in result
+        assert "Get weather API" in result
+
+    def test_returns_empty_for_non_instructions_type(self):
+        """Test that non-instructions types return empty list."""
+        decomposition_result = {
+            "type": "clarifying_questions",
+            "questions": [{"question": "What email?"}],
+        }
+
+        result = core.extract_search_terms_from_steps(decomposition_result)
+
+        assert result == []
+
+    def test_deduplicates_terms_case_insensitively(self):
+        """Test that duplicate terms are removed (case-insensitive)."""
+        decomposition_result = {
+            "type": "instructions",
+            "steps": [
+                {"description": "Send Email", "name": "send email"},
+                {"description": "Other task"},
+            ],
+        }
+
+        result = core.extract_search_terms_from_steps(decomposition_result)
+
+        # Should only have one "send email" variant
+        email_terms = [t for t in result if "email" in t.lower()]
+        assert len(email_terms) == 1
+
+    def test_filters_short_terms(self):
+        """Test that terms with 3 or fewer characters are filtered out."""
+        decomposition_result = {
+            "type": "instructions",
+            "steps": [
+                {"description": "ab", "action": "xyz"},  # Both too short
+                {"description": "Valid term here"},
+            ],
+        }
+
+        result = core.extract_search_terms_from_steps(decomposition_result)
+
+        assert "ab" not in result
+        assert "xyz" not in result
+        assert "Valid term here" in result
+
+    def test_handles_empty_steps(self):
+        """Test handling of empty steps list."""
+        decomposition_result = {
+            "type": "instructions",
+            "steps": [],
+        }
+
+        result = core.extract_search_terms_from_steps(decomposition_result)
+
+        assert result == []
+
+
+class TestEnrichLibraryAgentsFromSteps:
+    """Test enrich_library_agents_from_steps function."""
+
+    @pytest.mark.asyncio
+    async def test_enriches_with_additional_agents(self):
+        """Test that additional agents are found based on steps."""
+        existing_agents = [
+            {
+                "graph_id": "existing-123",
+                "graph_version": 1,
+                "name": "Existing Agent",
+                "description": "Already fetched",
+                "input_schema": {},
+                "output_schema": {},
+            }
+        ]
+
+        additional_agents = [
+            {
+                "graph_id": "new-456",
+                "graph_version": 1,
+                "name": "Email Agent",
+                "description": "For sending emails",
+                "input_schema": {},
+                "output_schema": {},
+            }
+        ]
+
+        decomposition_result = {
+            "type": "instructions",
+            "steps": [
+                {"description": "Send email notification"},
+            ],
+        }
+
+        with patch.object(
+            core,
+            "get_all_relevant_agents_for_generation",
+            new_callable=AsyncMock,
+            return_value=additional_agents,
+        ):
+            result = await core.enrich_library_agents_from_steps(
+                user_id="user-123",
+                decomposition_result=decomposition_result,
+                existing_agents=existing_agents,
+            )
+
+        # Should have both existing and new agents
+        assert len(result) == 2
+        names = [a["name"] for a in result]
+        assert "Existing Agent" in names
+        assert "Email Agent" in names
+
+    @pytest.mark.asyncio
+    async def test_deduplicates_by_graph_id(self):
+        """Test that agents with same graph_id are not duplicated."""
+        existing_agents = [
+            {
+                "graph_id": "agent-123",
+                "graph_version": 1,
+                "name": "Existing Agent",
+                "description": "Already fetched",
+                "input_schema": {},
+                "output_schema": {},
+            }
+        ]
+
+        # Additional search returns same agent
+        additional_agents = [
+            {
+                "graph_id": "agent-123",  # Same ID
+                "graph_version": 1,
+                "name": "Existing Agent Copy",
+                "description": "Same agent different name",
+                "input_schema": {},
+                "output_schema": {},
+            }
+        ]
+
+        decomposition_result = {
+            "type": "instructions",
+            "steps": [{"description": "Some action"}],
+        }
+
+        with patch.object(
+            core,
+            "get_all_relevant_agents_for_generation",
+            new_callable=AsyncMock,
+            return_value=additional_agents,
+        ):
+            result = await core.enrich_library_agents_from_steps(
+                user_id="user-123",
+                decomposition_result=decomposition_result,
+                existing_agents=existing_agents,
+            )
+
+        # Should not duplicate
+        assert len(result) == 1
+
+    @pytest.mark.asyncio
+    async def test_deduplicates_by_name(self):
+        """Test that agents with same name are not duplicated."""
+        existing_agents = [
+            {
+                "graph_id": "agent-123",
+                "graph_version": 1,
+                "name": "Email Agent",
+                "description": "Already fetched",
+                "input_schema": {},
+                "output_schema": {},
+            }
+        ]
+
+        # Additional search returns agent with same name but different ID
+        additional_agents = [
+            {
+                "graph_id": "agent-456",  # Different ID
+                "graph_version": 1,
+                "name": "Email Agent",  # Same name
+                "description": "Different agent same name",
+                "input_schema": {},
+                "output_schema": {},
+            }
+        ]
+
+        decomposition_result = {
+            "type": "instructions",
+            "steps": [{"description": "Send email"}],
+        }
+
+        with patch.object(
+            core,
+            "get_all_relevant_agents_for_generation",
+            new_callable=AsyncMock,
+            return_value=additional_agents,
+        ):
+            result = await core.enrich_library_agents_from_steps(
+                user_id="user-123",
+                decomposition_result=decomposition_result,
+                existing_agents=existing_agents,
+            )
+
+        # Should not duplicate by name
+        assert len(result) == 1
+        assert result[0].get("graph_id") == "agent-123"  # Original kept
+
+    @pytest.mark.asyncio
+    async def test_returns_existing_when_no_steps(self):
+        """Test that existing agents are returned when no search terms extracted."""
+        existing_agents = [
+            {
+                "graph_id": "existing-123",
+                "graph_version": 1,
+                "name": "Existing Agent",
+                "description": "Already fetched",
+                "input_schema": {},
+                "output_schema": {},
+            }
+        ]
+
+        decomposition_result = {
+            "type": "clarifying_questions",  # Not instructions type
+            "questions": [],
+        }
+
+        result = await core.enrich_library_agents_from_steps(
+            user_id="user-123",
+            decomposition_result=decomposition_result,
+            existing_agents=existing_agents,
+        )
+
+        # Should return existing unchanged
+        assert result == existing_agents
+
+    @pytest.mark.asyncio
+    async def test_limits_search_terms_to_three(self):
+        """Test that only first 3 search terms are used."""
+        existing_agents = []
+
+        decomposition_result = {
+            "type": "instructions",
+            "steps": [
+                {"description": "First action"},
+                {"description": "Second action"},
+                {"description": "Third action"},
+                {"description": "Fourth action"},
+                {"description": "Fifth action"},
+            ],
+        }
+
+        call_count = 0
+
+        async def mock_get_agents(*args, **kwargs):
+            nonlocal call_count
+            call_count += 1
+            return []
+
+        with patch.object(
+            core,
+            "get_all_relevant_agents_for_generation",
+            side_effect=mock_get_agents,
+        ):
+            await core.enrich_library_agents_from_steps(
+                user_id="user-123",
+                decomposition_result=decomposition_result,
+                existing_agents=existing_agents,
+            )
+
+        # Should only make 3 calls (limited to first 3 terms)
+        assert call_count == 3
+
+
+class TestExtractUuidsFromText:
+    """Test extract_uuids_from_text function."""
+
+    def test_extracts_single_uuid(self):
+        """Test extraction of a single UUID from text."""
+        text = "Use my agent 46631191-e8a8-486f-ad90-84f89738321d for this task"
+        result = core.extract_uuids_from_text(text)
+        assert len(result) == 1
+        assert "46631191-e8a8-486f-ad90-84f89738321d" in result
+
+    def test_extracts_multiple_uuids(self):
+        """Test extraction of multiple UUIDs from text."""
+        text = (
+            "Combine agents 11111111-1111-4111-8111-111111111111 "
+            "and 22222222-2222-4222-9222-222222222222"
+        )
+        result = core.extract_uuids_from_text(text)
+        assert len(result) == 2
+        assert "11111111-1111-4111-8111-111111111111" in result
+        assert "22222222-2222-4222-9222-222222222222" in result
+
+    def test_deduplicates_uuids(self):
+        """Test that duplicate UUIDs are deduplicated."""
+        text = (
+            "Use 46631191-e8a8-486f-ad90-84f89738321d twice: "
+            "46631191-e8a8-486f-ad90-84f89738321d"
+        )
+        result = core.extract_uuids_from_text(text)
+        assert len(result) == 1
+
+    def test_normalizes_to_lowercase(self):
+        """Test that UUIDs are normalized to lowercase."""
+        text = "Use 46631191-E8A8-486F-AD90-84F89738321D"
+        result = core.extract_uuids_from_text(text)
+        assert result[0] == "46631191-e8a8-486f-ad90-84f89738321d"
+
+    def test_returns_empty_for_no_uuids(self):
+        """Test that empty list is returned when no UUIDs found."""
+        text = "Create an email agent that sends notifications"
+        result = core.extract_uuids_from_text(text)
+        assert result == []
+
+    def test_ignores_invalid_uuids(self):
+        """Test that invalid UUID-like strings are ignored."""
+        text = "Not a valid UUID: 12345678-1234-1234-1234-123456789abc"
+        result = core.extract_uuids_from_text(text)
+        # UUID v4 requires specific patterns (4 in third group, 8/9/a/b in fourth)
+        assert len(result) == 0
+
+
+class TestGetLibraryAgentById:
+    """Test get_library_agent_by_id function (and its alias get_library_agent_by_graph_id)."""
+
+    @pytest.mark.asyncio
+    async def test_returns_agent_when_found_by_graph_id(self):
+        """Test that agent is returned when found by graph_id."""
+        mock_agent = MagicMock()
+        mock_agent.graph_id = "agent-123"
+        mock_agent.graph_version = 1
+        mock_agent.name = "Test Agent"
+        mock_agent.description = "Test description"
+        mock_agent.input_schema = {"properties": {}}
+        mock_agent.output_schema = {"properties": {}}
+
+        with patch.object(
+            core.library_db,
+            "get_library_agent_by_graph_id",
+            new_callable=AsyncMock,
+            return_value=mock_agent,
+        ):
+            result = await core.get_library_agent_by_id("user-123", "agent-123")
+
+        assert result is not None
+        assert result["graph_id"] == "agent-123"
+        assert result["name"] == "Test Agent"
+
+    @pytest.mark.asyncio
+    async def test_falls_back_to_library_agent_id(self):
+        """Test that lookup falls back to library agent ID when graph_id not found."""
+        mock_agent = MagicMock()
+        mock_agent.graph_id = "graph-456"  # Different from the lookup ID
+        mock_agent.graph_version = 1
+        mock_agent.name = "Library Agent"
+        mock_agent.description = "Found by library ID"
+        mock_agent.input_schema = {"properties": {}}
+        mock_agent.output_schema = {"properties": {}}
+
+        with (
+            patch.object(
+                core.library_db,
+                "get_library_agent_by_graph_id",
+                new_callable=AsyncMock,
+                return_value=None,  # Not found by graph_id
+            ),
+            patch.object(
+                core.library_db,
+                "get_library_agent",
+                new_callable=AsyncMock,
+                return_value=mock_agent,  # Found by library ID
+            ),
+        ):
+            result = await core.get_library_agent_by_id("user-123", "library-id-123")
+
+        assert result is not None
+        assert result["graph_id"] == "graph-456"
+        assert result["name"] == "Library Agent"
+
+    @pytest.mark.asyncio
+    async def test_returns_none_when_not_found_by_either_method(self):
+        """Test that None is returned when agent not found by either method."""
+        with (
+            patch.object(
+                core.library_db,
+                "get_library_agent_by_graph_id",
+                new_callable=AsyncMock,
+                return_value=None,
+            ),
+            patch.object(
+                core.library_db,
+                "get_library_agent",
+                new_callable=AsyncMock,
+                side_effect=core.NotFoundError("Not found"),
+            ),
+        ):
+            result = await core.get_library_agent_by_id("user-123", "nonexistent")
+
+        assert result is None
+
+    @pytest.mark.asyncio
+    async def test_returns_none_on_exception(self):
+        """Test that None is returned when exception occurs in both lookups."""
+        with (
+            patch.object(
+                core.library_db,
+                "get_library_agent_by_graph_id",
+                new_callable=AsyncMock,
+                side_effect=Exception("Database error"),
+            ),
+            patch.object(
+                core.library_db,
+                "get_library_agent",
+                new_callable=AsyncMock,
+                side_effect=Exception("Database error"),
+            ),
+        ):
+            result = await core.get_library_agent_by_id("user-123", "agent-123")
+
+        assert result is None
+
+    @pytest.mark.asyncio
+    async def test_alias_works(self):
+        """Test that get_library_agent_by_graph_id is an alias for get_library_agent_by_id."""
+        assert core.get_library_agent_by_graph_id is core.get_library_agent_by_id
+
+
+class TestGetAllRelevantAgentsWithUuids:
+    """Test UUID extraction in get_all_relevant_agents_for_generation."""
+
+    @pytest.mark.asyncio
+    async def test_fetches_explicitly_mentioned_agents(self):
+        """Test that agents mentioned by UUID are fetched directly."""
+        mock_agent = MagicMock()
+        mock_agent.graph_id = "46631191-e8a8-486f-ad90-84f89738321d"
+        mock_agent.graph_version = 1
+        mock_agent.name = "Mentioned Agent"
+        mock_agent.description = "Explicitly mentioned"
+        mock_agent.input_schema = {}
+        mock_agent.output_schema = {}
+
+        mock_response = MagicMock()
+        mock_response.agents = []
+
+        with (
+            patch.object(
+                core.library_db,
+                "get_library_agent_by_graph_id",
+                new_callable=AsyncMock,
+                return_value=mock_agent,
+            ),
+            patch.object(
+                core.library_db,
+                "list_library_agents",
+                new_callable=AsyncMock,
+                return_value=mock_response,
+            ),
+        ):
+            result = await core.get_all_relevant_agents_for_generation(
+                user_id="user-123",
+                search_query="Use agent 46631191-e8a8-486f-ad90-84f89738321d",
+                include_marketplace=False,
+            )
+
+        assert len(result) == 1
+        assert result[0].get("graph_id") == "46631191-e8a8-486f-ad90-84f89738321d"
+        assert result[0].get("name") == "Mentioned Agent"
+
+
+if __name__ == "__main__":
+    pytest.main([__file__, "-v"])

autogpt_platform/backend/test/agent_generator/test_service.py

@@ -102,7 +102,7 @@ class TestDecomposeGoalExternal:
 
     @pytest.mark.asyncio
     async def test_decompose_goal_with_context(self):
-        """Test decomposition with additional context."""
+        """Test decomposition with additional context enriched into description."""
         mock_response = MagicMock()
         mock_response.json.return_value = {
             "success": True,
@@ -119,9 +119,12 @@ class TestDecomposeGoalExternal:
                 "Build a chatbot", context="Use Python"
             )
 
+        expected_description = (
+            "Build a chatbot\n\nAdditional context from user:\nUse Python"
+        )
         mock_client.post.assert_called_once_with(
             "/api/decompose-description",
-            json={"description": "Build a chatbot", "user_instruction": "Use Python"},
+            json={"description": expected_description},
         )
 
     @pytest.mark.asyncio
@@ -433,5 +436,139 @@ class TestGetBlocksExternal:
         assert result is None
 
 
+class TestLibraryAgentsPassthrough:
+    """Test that library_agents are passed correctly in all requests."""
+
+    def setup_method(self):
+        """Reset client singleton before each test."""
+        service._settings = None
+        service._client = None
+
+    @pytest.mark.asyncio
+    async def test_decompose_goal_passes_library_agents(self):
+        """Test that library_agents are included in decompose goal payload."""
+        library_agents = [
+            {
+                "graph_id": "agent-123",
+                "graph_version": 1,
+                "name": "Email Sender",
+                "description": "Sends emails",
+                "input_schema": {"properties": {"to": {"type": "string"}}},
+                "output_schema": {"properties": {"sent": {"type": "boolean"}}},
+            },
+        ]
+
+        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(
+                "Send an email",
+                library_agents=library_agents,
+            )
+
+        # Verify library_agents was passed in the payload
+        call_args = mock_client.post.call_args
+        assert call_args[1]["json"]["library_agents"] == library_agents
+
+    @pytest.mark.asyncio
+    async def test_generate_agent_passes_library_agents(self):
+        """Test that library_agents are included in generate agent payload."""
+        library_agents = [
+            {
+                "graph_id": "agent-456",
+                "graph_version": 2,
+                "name": "Data Fetcher",
+                "description": "Fetches data from API",
+                "input_schema": {"properties": {"url": {"type": "string"}}},
+                "output_schema": {"properties": {"data": {"type": "object"}}},
+            },
+        ]
+
+        mock_response = MagicMock()
+        mock_response.json.return_value = {
+            "success": True,
+            "agent_json": {"name": "Test Agent", "nodes": []},
+        }
+        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.generate_agent_external(
+                {"steps": ["Step 1"]},
+                library_agents=library_agents,
+            )
+
+        # Verify library_agents was passed in the payload
+        call_args = mock_client.post.call_args
+        assert call_args[1]["json"]["library_agents"] == library_agents
+
+    @pytest.mark.asyncio
+    async def test_generate_agent_patch_passes_library_agents(self):
+        """Test that library_agents are included in patch generation payload."""
+        library_agents = [
+            {
+                "graph_id": "agent-789",
+                "graph_version": 1,
+                "name": "Slack Notifier",
+                "description": "Sends Slack messages",
+                "input_schema": {"properties": {"message": {"type": "string"}}},
+                "output_schema": {"properties": {"success": {"type": "boolean"}}},
+            },
+        ]
+
+        mock_response = MagicMock()
+        mock_response.json.return_value = {
+            "success": True,
+            "agent_json": {"name": "Updated Agent", "nodes": []},
+        }
+        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.generate_agent_patch_external(
+                "Add error handling",
+                {"name": "Original Agent", "nodes": []},
+                library_agents=library_agents,
+            )
+
+        # Verify library_agents was passed in the payload
+        call_args = mock_client.post.call_args
+        assert call_args[1]["json"]["library_agents"] == library_agents
+
+    @pytest.mark.asyncio
+    async def test_decompose_goal_without_library_agents(self):
+        """Test that decompose goal works without library_agents."""
+        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 workflow")
+
+        # Verify library_agents was NOT passed when not provided
+        call_args = mock_client.post.call_args
+        assert "library_agents" not in call_args[1]["json"]
+
+
 if __name__ == "__main__":
     pytest.main([__file__, "-v"])

autogpt_platform/backend/test/e2e_test_data.py

@@ -43,19 +43,24 @@ faker = Faker()
 # Constants for data generation limits (reduced for E2E tests)
 NUM_USERS = 15
 NUM_AGENT_BLOCKS = 30
-MIN_GRAPHS_PER_USER = 15
-MAX_GRAPHS_PER_USER = 15
+MIN_GRAPHS_PER_USER = 25
+MAX_GRAPHS_PER_USER = 25
 MIN_NODES_PER_GRAPH = 3
 MAX_NODES_PER_GRAPH = 6
 MIN_PRESETS_PER_USER = 2
 MAX_PRESETS_PER_USER = 3
-MIN_AGENTS_PER_USER = 15
-MAX_AGENTS_PER_USER = 15
+MIN_AGENTS_PER_USER = 25
+MAX_AGENTS_PER_USER = 25
 MIN_EXECUTIONS_PER_GRAPH = 2
 MAX_EXECUTIONS_PER_GRAPH = 8
 MIN_REVIEWS_PER_VERSION = 2
 MAX_REVIEWS_PER_VERSION = 5
 
+# Guaranteed minimums for marketplace tests (deterministic)
+GUARANTEED_FEATURED_AGENTS = 8
+GUARANTEED_FEATURED_CREATORS = 5
+GUARANTEED_TOP_AGENTS = 10
+
 
 def get_image():
     """Generate a consistent image URL using picsum.photos service."""
@@ -385,7 +390,7 @@ class TestDataCreator:
 
         library_agents = []
         for user in self.users:
-            num_agents = 10  # Create exactly 10 agents per user
+            num_agents = random.randint(MIN_AGENTS_PER_USER, MAX_AGENTS_PER_USER)
 
             # Get available graphs for this user
             user_graphs = [
@@ -507,14 +512,17 @@ class TestDataCreator:
             existing_profiles, min(num_creators, len(existing_profiles))
         )
 
-        # Mark about 50% of creators as featured (more for testing)
-        num_featured = max(2, int(num_creators * 0.5))
+        # Guarantee at least GUARANTEED_FEATURED_CREATORS featured creators
+        num_featured = max(GUARANTEED_FEATURED_CREATORS, int(num_creators * 0.5))
         num_featured = min(
             num_featured, len(selected_profiles)
         )  # Don't exceed available profiles
         featured_profile_ids = set(
             random.sample([p.id for p in selected_profiles], num_featured)
         )
+        print(
+            f"🎯 Creating {num_featured} featured creators (min: {GUARANTEED_FEATURED_CREATORS})"
+        )
 
         for profile in selected_profiles:
             try:
@@ -545,21 +553,25 @@ class TestDataCreator:
         return profiles
 
     async def create_test_store_submissions(self) -> List[Dict[str, Any]]:
-        """Create test store submissions using the API function."""
+        """Create test store submissions using the API function.
+
+        DETERMINISTIC: Guarantees minimum featured agents for E2E tests.
+        """
         print("Creating test store submissions...")
 
         submissions = []
         approved_submissions = []
+        featured_count = 0
+        submission_counter = 0
 
-        # Create a special test submission for test123@gmail.com
+        # Create a special test submission for test123@gmail.com (ALWAYS approved + featured)
         test_user = next(
             (user for user in self.users if user["email"] == "test123@gmail.com"), None
         )
-        if test_user:
-            # Special test data for consistent testing
+        if test_user and self.agent_graphs:
             test_submission_data = {
                 "user_id": test_user["id"],
-                "agent_id": self.agent_graphs[0]["id"],  # Use first available graph
+                "agent_id": self.agent_graphs[0]["id"],
                 "agent_version": 1,
                 "slug": "test-agent-submission",
                 "name": "Test Agent Submission",
@@ -580,37 +592,24 @@ class TestDataCreator:
                 submissions.append(test_submission.model_dump())
                 print("✅ Created special test store submission for test123@gmail.com")
 
-                # Randomly approve, reject, or leave pending the test submission
+                # ALWAYS approve and feature the test submission
                 if test_submission.store_listing_version_id:
-                    random_value = random.random()
-                    if random_value < 0.4:  # 40% chance to approve
-                        approved_submission = await review_store_submission(
-                            store_listing_version_id=test_submission.store_listing_version_id,
-                            is_approved=True,
-                            external_comments="Test submission approved",
-                            internal_comments="Auto-approved test submission",
-                            reviewer_id=test_user["id"],
-                        )
-                        approved_submissions.append(approved_submission.model_dump())
-                        print("✅ Approved test store submission")
+                    approved_submission = await review_store_submission(
+                        store_listing_version_id=test_submission.store_listing_version_id,
+                        is_approved=True,
+                        external_comments="Test submission approved",
+                        internal_comments="Auto-approved test submission",
+                        reviewer_id=test_user["id"],
+                    )
+                    approved_submissions.append(approved_submission.model_dump())
+                    print("✅ Approved test store submission")
 
-                        # Mark approved submission as featured
-                        await prisma.storelistingversion.update(
-                            where={"id": test_submission.store_listing_version_id},
-                            data={"isFeatured": True},
-                        )
-                        print("🌟 Marked test agent as FEATURED")
-                    elif random_value < 0.7:  # 30% chance to reject (40% to 70%)
-                        await review_store_submission(
-                            store_listing_version_id=test_submission.store_listing_version_id,
-                            is_approved=False,
-                            external_comments="Test submission rejected - needs improvements",
-                            internal_comments="Auto-rejected test submission for E2E testing",
-                            reviewer_id=test_user["id"],
-                        )
-                        print("❌ Rejected test store submission")
-                    else:  # 30% chance to leave pending (70% to 100%)
-                        print("⏳ Left test submission pending for review")
+                    await prisma.storelistingversion.update(
+                        where={"id": test_submission.store_listing_version_id},
+                        data={"isFeatured": True},
+                    )
+                    featured_count += 1
+                    print("🌟 Marked test agent as FEATURED")
 
             except Exception as e:
                 print(f"Error creating test store submission: {e}")
@@ -620,7 +619,6 @@ class TestDataCreator:
 
         # Create regular submissions for all users
         for user in self.users:
-            # Get available graphs for this specific user
             user_graphs = [
                 g for g in self.agent_graphs if g.get("userId") == user["id"]
             ]
@@ -631,18 +629,17 @@ class TestDataCreator:
                 )
                 continue
 
-            # Create exactly 4 store submissions per user
             for submission_index in range(4):
                 graph = random.choice(user_graphs)
+                submission_counter += 1
 
                 try:
                     print(
-                        f"Creating store submission for user {user['id']} with graph {graph['id']} (owner: {graph.get('userId')})"
+                        f"Creating store submission for user {user['id']} with graph {graph['id']}"
                     )
 
-                    # Use the API function to create store submission with correct parameters
                     submission = await create_store_submission(
-                        user_id=user["id"],  # Must match graph's userId
+                        user_id=user["id"],
                         agent_id=graph["id"],
                         agent_version=graph.get("version", 1),
                         slug=faker.slug(),
@@ -651,22 +648,24 @@ class TestDataCreator:
                         video_url=get_video_url() if random.random() < 0.3 else None,
                         image_urls=[get_image() for _ in range(3)],
                         description=faker.text(),
-                        categories=[
-                            get_category()
-                        ],  # Single category from predefined list
+                        categories=[get_category()],
                         changes_summary="Initial E2E test submission",
                     )
                     submissions.append(submission.model_dump())
                     print(f"✅ Created store submission: {submission.name}")
 
-                    # Randomly approve, reject, or leave pending the submission
                     if submission.store_listing_version_id:
-                        random_value = random.random()
-                        if random_value < 0.4:  # 40% chance to approve
-                            try:
-                                # Pick a random user as the reviewer (admin)
-                                reviewer_id = random.choice(self.users)["id"]
+                        # DETERMINISTIC: First N submissions are always approved
+                        # First GUARANTEED_FEATURED_AGENTS of those are always featured
+                        should_approve = (
+                            submission_counter <= GUARANTEED_TOP_AGENTS
+                            or random.random() < 0.4
+                        )
+                        should_feature = featured_count < GUARANTEED_FEATURED_AGENTS
 
+                        if should_approve:
+                            try:
+                                reviewer_id = random.choice(self.users)["id"]
                                 approved_submission = await review_store_submission(
                                     store_listing_version_id=submission.store_listing_version_id,
                                     is_approved=True,
@@ -681,16 +680,7 @@ class TestDataCreator:
                                     f"✅ Approved store submission: {submission.name}"
                                 )
 
-                                # Mark some agents as featured during creation (30% chance)
-                                # More likely for creators and first submissions
-                                is_creator = user["id"] in [
-                                    p.get("userId") for p in self.profiles
-                                ]
-                                feature_chance = (
-                                    0.5 if is_creator else 0.2
-                                )  # 50% for creators, 20% for others
-
-                                if random.random() < feature_chance:
+                                if should_feature:
                                     try:
                                         await prisma.storelistingversion.update(
                                             where={
@@ -698,8 +688,25 @@ class TestDataCreator:
                                             },
                                             data={"isFeatured": True},
                                         )
+                                        featured_count += 1
                                         print(
-                                            f"🌟 Marked agent as FEATURED: {submission.name}"
+                                            f"🌟 Marked agent as FEATURED ({featured_count}/{GUARANTEED_FEATURED_AGENTS}): {submission.name}"
+                                        )
+                                    except Exception as e:
+                                        print(
+                                            f"Warning: Could not mark submission as featured: {e}"
+                                        )
+                                elif random.random() < 0.2:
+                                    try:
+                                        await prisma.storelistingversion.update(
+                                            where={
+                                                "id": submission.store_listing_version_id
+                                            },
+                                            data={"isFeatured": True},
+                                        )
+                                        featured_count += 1
+                                        print(
+                                            f"🌟 Marked agent as FEATURED (bonus): {submission.name}"
                                         )
                                     except Exception as e:
                                         print(
@@ -710,11 +717,9 @@ class TestDataCreator:
                                 print(
                                     f"Warning: Could not approve submission {submission.name}: {e}"
                                 )
-                        elif random_value < 0.7:  # 30% chance to reject (40% to 70%)
+                        elif random.random() < 0.5:
                             try:
-                                # Pick a random user as the reviewer (admin)
                                 reviewer_id = random.choice(self.users)["id"]
-
                                 await review_store_submission(
                                     store_listing_version_id=submission.store_listing_version_id,
                                     is_approved=False,
@@ -729,7 +734,7 @@ class TestDataCreator:
                                 print(
                                     f"Warning: Could not reject submission {submission.name}: {e}"
                                 )
-                        else:  # 30% chance to leave pending (70% to 100%)
+                        else:
                             print(
                                 f"⏳ Left submission pending for review: {submission.name}"
                             )
@@ -743,9 +748,13 @@ class TestDataCreator:
                     traceback.print_exc()
                     continue
 
+        print("\n📊 Store Submissions Summary:")
+        print(f"   Created: {len(submissions)}")
+        print(f"   Approved: {len(approved_submissions)}")
         print(
-            f"Created {len(submissions)} store submissions, approved {len(approved_submissions)}"
+            f"   Featured: {featured_count} (guaranteed min: {GUARANTEED_FEATURED_AGENTS})"
         )
+
         self.store_submissions = submissions
         return submissions
 
@@ -825,12 +834,15 @@ class TestDataCreator:
         print(f"✅ Agent blocks available: {len(self.agent_blocks)}")
         print(f"✅ Agent graphs created: {len(self.agent_graphs)}")
         print(f"✅ Library agents created: {len(self.library_agents)}")
-        print(f"✅ Creator profiles updated: {len(self.profiles)} (some featured)")
-        print(
-            f"✅ Store submissions created: {len(self.store_submissions)} (some marked as featured during creation)"
-        )
+        print(f"✅ Creator profiles updated: {len(self.profiles)}")
+        print(f"✅ Store submissions created: {len(self.store_submissions)}")
         print(f"✅ API keys created: {len(self.api_keys)}")
         print(f"✅ Presets created: {len(self.presets)}")
+        print("\n🎯 Deterministic Guarantees:")
+        print(f"   • Featured agents: >= {GUARANTEED_FEATURED_AGENTS}")
+        print(f"   • Featured creators: >= {GUARANTEED_FEATURED_CREATORS}")
+        print(f"   • Top agents (approved): >= {GUARANTEED_TOP_AGENTS}")
+        print(f"   • Library agents per user: >= {MIN_AGENTS_PER_USER}")
         print("\n🚀 Your E2E test database is ready to use!")
 
 

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

@@ -857,7 +857,7 @@ export const CustomNode = React.memo(
     })();
 
     const hasAdvancedFields =
-      data.inputSchema &&
+      data.inputSchema?.properties &&
       Object.entries(data.inputSchema.properties).some(([key, value]) => {
         return (
           value.advanced === true && !data.inputSchema.required?.includes(key)

autogpt_platform/frontend/src/app/api/openapi.json

@@ -7981,6 +7981,25 @@
             ]
           },
           "new_output": { "type": "boolean", "title": "New Output" },
+          "execution_count": {
+            "type": "integer",
+            "title": "Execution Count",
+            "default": 0
+          },
+          "success_rate": {
+            "anyOf": [{ "type": "number" }, { "type": "null" }],
+            "title": "Success Rate"
+          },
+          "avg_correctness_score": {
+            "anyOf": [{ "type": "number" }, { "type": "null" }],
+            "title": "Avg Correctness Score"
+          },
+          "recent_executions": {
+            "items": { "$ref": "#/components/schemas/RecentExecution" },
+            "type": "array",
+            "title": "Recent Executions",
+            "description": "List of recent executions with status, score, and summary"
+          },
           "can_access_graph": {
             "type": "boolean",
             "title": "Can Access Graph"
@@ -9374,6 +9393,23 @@
         "required": ["providers", "pagination"],
         "title": "ProviderResponse"
       },
+      "RecentExecution": {
+        "properties": {
+          "status": { "type": "string", "title": "Status" },
+          "correctness_score": {
+            "anyOf": [{ "type": "number" }, { "type": "null" }],
+            "title": "Correctness Score"
+          },
+          "activity_summary": {
+            "anyOf": [{ "type": "string" }, { "type": "null" }],
+            "title": "Activity Summary"
+          }
+        },
+        "type": "object",
+        "required": ["status"],
+        "title": "RecentExecution",
+        "description": "Summary of a recent execution for quality assessment.\n\nUsed by the LLM to understand the agent's recent performance with specific examples\nrather than just aggregate statistics."
+      },
       "RefundRequest": {
         "properties": {
           "id": { "type": "string", "title": "Id" },
@@ -9797,7 +9833,8 @@
           "sub_heading": { "type": "string", "title": "Sub Heading" },
           "description": { "type": "string", "title": "Description" },
           "runs": { "type": "integer", "title": "Runs" },
-          "rating": { "type": "number", "title": "Rating" }
+          "rating": { "type": "number", "title": "Rating" },
+          "agent_graph_id": { "type": "string", "title": "Agent Graph Id" }
         },
         "type": "object",
         "required": [
@@ -9809,7 +9846,8 @@
           "sub_heading",
           "description",
           "runs",
-          "rating"
+          "rating",
+          "agent_graph_id"
         ],
         "title": "StoreAgent"
       },

autogpt_platform/frontend/src/components/atoms/FileInput/FileInput.tsx

@@ -104,7 +104,28 @@ export function FileInput(props: Props) {
     return false;
   }
 
-  const getFileLabelFromValue = (val: string) => {
+  const getFileLabelFromValue = (val: unknown): string => {
+    // Handle object format from external API: { name, type, size, data }
+    if (val && typeof val === "object") {
+      const obj = val as Record<string, unknown>;
+      if (typeof obj.name === "string") {
+        return getFileLabel(obj.name, (obj.type as string) || "");
+      }
+      if (typeof obj.type === "string") {
+        const mimeParts = obj.type.split("/");
+        if (mimeParts.length > 1) {
+          return `${mimeParts[1].toUpperCase()} file`;
+        }
+        return `${obj.type} file`;
+      }
+      return "File";
+    }
+
+    // Handle string values (data URIs or file paths)
+    if (typeof val !== "string") {
+      return "File";
+    }
+
     if (val.startsWith("data:")) {
       const matches = val.match(/^data:([^;]+);/);
       if (matches?.[1]) {

autogpt_platform/frontend/src/components/contextual/Chat/components/ChatInput/ChatInput.tsx

@@ -57,6 +57,7 @@ export function ChatInput({
     isStreaming,
     value,
     baseHandleKeyDown,
+    inputId,
   });
 
   return (

autogpt_platform/frontend/src/components/contextual/Chat/components/ChatInput/useVoiceRecording.ts

@@ -15,6 +15,7 @@ interface Args {
   isStreaming?: boolean;
   value: string;
   baseHandleKeyDown: (event: KeyboardEvent<HTMLTextAreaElement>) => void;
+  inputId?: string;
 }
 
 export function useVoiceRecording({
@@ -23,6 +24,7 @@ export function useVoiceRecording({
   isStreaming = false,
   value,
   baseHandleKeyDown,
+  inputId,
 }: Args) {
   const [isRecording, setIsRecording] = useState(false);
   const [isTranscribing, setIsTranscribing] = useState(false);
@@ -103,7 +105,7 @@ export function useVoiceRecording({
         setIsTranscribing(false);
       }
     },
-    [handleTranscription],
+    [handleTranscription, inputId],
   );
 
   const stopRecording = useCallback(() => {
@@ -201,6 +203,15 @@ export function useVoiceRecording({
     }
   }, [error, toast]);
 
+  useEffect(() => {
+    if (!isTranscribing && inputId) {
+      const inputElement = document.getElementById(inputId);
+      if (inputElement) {
+        inputElement.focus();
+      }
+    }
+  }, [isTranscribing, inputId]);
+
   const handleKeyDown = useCallback(
     (event: KeyboardEvent<HTMLTextAreaElement>) => {
       if (event.key === " " && !value.trim() && !isTranscribing) {

autogpt_platform/frontend/src/components/contextual/Chat/components/ChatMessage/ChatMessage.tsx

@@ -156,11 +156,19 @@ export function ChatMessage({
   }
 
   if (isClarificationNeeded && message.type === "clarification_needed") {
+    const hasUserReplyAfter =
+      index >= 0 &&
+      messages
+        .slice(index + 1)
+        .some((m) => m.type === "message" && m.role === "user");
+
     return (
       <ClarificationQuestionsWidget
         questions={message.questions}
         message={message.message}
+        sessionId={message.sessionId}
         onSubmitAnswers={handleClarificationAnswers}
+        isAnswered={hasUserReplyAfter}
         className={className}
       />
     );

autogpt_platform/frontend/src/components/contextual/Chat/components/ClarificationQuestionsWidget/ClarificationQuestionsWidget.tsx

@@ -6,7 +6,7 @@ import { Input } from "@/components/atoms/Input/Input";
 import { Text } from "@/components/atoms/Text/Text";
 import { cn } from "@/lib/utils";
 import { CheckCircleIcon, QuestionIcon } from "@phosphor-icons/react";
-import { useState } from "react";
+import { useState, useEffect, useRef } from "react";
 
 export interface ClarifyingQuestion {
   question: string;
@@ -17,39 +17,96 @@ export interface ClarifyingQuestion {
 interface Props {
   questions: ClarifyingQuestion[];
   message: string;
+  sessionId?: string;
   onSubmitAnswers: (answers: Record<string, string>) => void;
   onCancel?: () => void;
+  isAnswered?: boolean;
   className?: string;
 }
 
+function getStorageKey(sessionId?: string): string | null {
+  if (!sessionId) return null;
+  return `clarification_answers_${sessionId}`;
+}
+
 export function ClarificationQuestionsWidget({
   questions,
   message,
+  sessionId,
   onSubmitAnswers,
   onCancel,
+  isAnswered = false,
   className,
 }: Props) {
   const [answers, setAnswers] = useState<Record<string, string>>({});
   const [isSubmitted, setIsSubmitted] = useState(false);
+  const lastSessionIdRef = useRef<string | undefined>(undefined);
+
+  useEffect(() => {
+    const storageKey = getStorageKey(sessionId);
+    if (!storageKey) {
+      setAnswers({});
+      setIsSubmitted(false);
+      lastSessionIdRef.current = sessionId;
+      return;
+    }
+
+    try {
+      const saved = localStorage.getItem(storageKey);
+      if (saved) {
+        const parsed = JSON.parse(saved) as Record<string, string>;
+        setAnswers(parsed);
+      } else {
+        setAnswers({});
+      }
+      setIsSubmitted(false);
+    } catch {
+      setAnswers({});
+      setIsSubmitted(false);
+    }
+    lastSessionIdRef.current = sessionId;
+  }, [sessionId]);
+
+  useEffect(() => {
+    if (lastSessionIdRef.current !== sessionId) {
+      return;
+    }
+    const storageKey = getStorageKey(sessionId);
+    if (!storageKey) return;
+
+    const hasAnswers = Object.values(answers).some((v) => v.trim());
+    try {
+      if (hasAnswers) {
+        localStorage.setItem(storageKey, JSON.stringify(answers));
+      } else {
+        localStorage.removeItem(storageKey);
+      }
+    } catch {}
+  }, [answers, sessionId]);
 
   function handleAnswerChange(keyword: string, value: string) {
     setAnswers((prev) => ({ ...prev, [keyword]: value }));
   }
 
   function handleSubmit() {
-    // Check if all questions are answered
     const allAnswered = questions.every((q) => answers[q.keyword]?.trim());
     if (!allAnswered) {
       return;
     }
     setIsSubmitted(true);
     onSubmitAnswers(answers);
+
+    const storageKey = getStorageKey(sessionId);
+    try {
+      if (storageKey) {
+        localStorage.removeItem(storageKey);
+      }
+    } catch {}
   }
 
   const allAnswered = questions.every((q) => answers[q.keyword]?.trim());
 
-  // Show submitted state after answers are submitted
-  if (isSubmitted) {
+  if (isAnswered || isSubmitted) {
     return (
       <div
         className={cn(

autogpt_platform/frontend/src/components/contextual/Chat/components/ToolResponseMessage/helpers.ts

@@ -30,9 +30,9 @@ export function getErrorMessage(result: unknown): string {
   }
   if (typeof result === "object" && result !== null) {
     const response = result as Record<string, unknown>;
-    if (response.error) return stripInternalReasoning(String(response.error));
     if (response.message)
       return stripInternalReasoning(String(response.message));
+    if (response.error) return stripInternalReasoning(String(response.error));
   }
   return "An error occurred";
 }
@@ -363,8 +363,8 @@ export function formatToolResponse(result: unknown, toolName: string): string {
 
     case "error":
       const errorMsg =
-        (response.error as string) || response.message || "An error occurred";
-      return `Error: ${errorMsg}`;
+        (response.message as string) || response.error || "An error occurred";
+      return stripInternalReasoning(String(errorMsg));
 
     case "no_results":
       const suggestions = (response.suggestions as string[]) || [];

autogpt_platform/frontend/src/components/contextual/CredentialsInput/helpers.ts

@@ -26,7 +26,6 @@ export const providerIcons: Partial<
   nvidia: fallbackIcon,
   discord: FaDiscord,
   d_id: fallbackIcon,
-  elevenlabs: fallbackIcon,
   google_maps: FaGoogle,
   jina: fallbackIcon,
   ideogram: fallbackIcon,

autogpt_platform/frontend/src/tests/library.spec.ts

@@ -59,12 +59,13 @@ test.describe("Library", () => {
   });
 
   test("pagination works correctly", async ({ page }, testInfo) => {
-    test.setTimeout(testInfo.timeout * 3); // Increase timeout for pagination operations
+    test.setTimeout(testInfo.timeout * 3);
     await page.goto("/library");
 
+    const PAGE_SIZE = 20;
     const paginationResult = await libraryPage.testPagination();
 
-    if (paginationResult.initialCount >= 10) {
+    if (paginationResult.initialCount >= PAGE_SIZE) {
       expect(paginationResult.finalCount).toBeGreaterThanOrEqual(
         paginationResult.initialCount,
       );
@@ -133,7 +134,10 @@ test.describe("Library", () => {
     test.expect(clearedSearchValue).toBe("");
   });
 
-  test("pagination while searching works correctly", async ({ page }) => {
+  test("pagination while searching works correctly", async ({
+    page,
+  }, testInfo) => {
+    test.setTimeout(testInfo.timeout * 3);
     await page.goto("/library");
 
     const allAgents = await libraryPage.getAgents();
@@ -152,9 +156,10 @@ test.describe("Library", () => {
     );
     expect(matchingResults.length).toEqual(initialSearchResults.length);
 
+    const PAGE_SIZE = 20;
     const searchPaginationResult = await libraryPage.testPagination();
 
-    if (searchPaginationResult.initialCount >= 10) {
+    if (searchPaginationResult.initialCount >= PAGE_SIZE) {
       expect(searchPaginationResult.finalCount).toBeGreaterThanOrEqual(
         searchPaginationResult.initialCount,
       );

autogpt_platform/frontend/src/tests/marketplace-creator.spec.ts

@@ -69,9 +69,12 @@ test.describe("Marketplace Creator Page – Basic Functionality", () => {
       await marketplacePage.getFirstCreatorProfile(page);
     await firstCreatorProfile.click();
     await page.waitForURL("**/marketplace/creator/**");
+    await page.waitForLoadState("networkidle").catch(() => {});
+
     const firstAgent = page
       .locator('[data-testid="store-card"]:visible')
       .first();
+    await firstAgent.waitFor({ state: "visible", timeout: 30000 });
 
     await firstAgent.click();
     await page.waitForURL("**/marketplace/agent/**");

autogpt_platform/frontend/src/tests/marketplace.spec.ts

@@ -77,7 +77,6 @@ test.describe("Marketplace – Basic Functionality", () => {
 
     const firstFeaturedAgent =
       await marketplacePage.getFirstFeaturedAgent(page);
-    await firstFeaturedAgent.waitFor({ state: "visible" });
     await firstFeaturedAgent.click();
     await page.waitForURL("**/marketplace/agent/**");
     await matchesUrl(page, /\/marketplace\/agent\/.+/);
@@ -116,7 +115,15 @@ test.describe("Marketplace – Basic Functionality", () => {
     const searchTerm = page.getByText("DummyInput").first();
     await isVisible(searchTerm);
 
-    await page.waitForTimeout(10000);
+    await page.waitForLoadState("networkidle").catch(() => {});
+
+    await page
+      .waitForFunction(
+        () =>
+          document.querySelectorAll('[data-testid="store-card"]').length > 0,
+        { timeout: 15000 },
+      )
+      .catch(() => console.log("No search results appeared within timeout"));
 
     const results = await marketplacePage.getSearchResultsCount(page);
     expect(results).toBeGreaterThan(0);

autogpt_platform/frontend/src/tests/pages/library.page.ts

@@ -300,21 +300,27 @@ export class LibraryPage extends BasePage {
   async scrollToLoadMore(): Promise<void> {
     console.log(`scrolling to load more agents`);
 
-    // Get initial agent count
-    const initialCount = await this.getAgentCount();
-    console.log(`Initial agent count: ${initialCount}`);
+    const initialCount = await this.getAgentCountByListLength();
+    console.log(`Initial agent count (DOM cards): ${initialCount}`);
 
-    // Scroll down to trigger pagination
     await this.scrollToBottom();
 
-    // Wait for potential new agents to load
-    await this.page.waitForTimeout(2000);
+    await this.page
+      .waitForLoadState("networkidle", { timeout: 10000 })
+      .catch(() => console.log("Network idle timeout, continuing..."));
 
-    // Check if more agents loaded
-    const newCount = await this.getAgentCount();
-    console.log(`New agent count after scroll: ${newCount}`);
+    await this.page
+      .waitForFunction(
+        (prevCount) =>
+          document.querySelectorAll('[data-testid="library-agent-card"]')
+            .length > prevCount,
+        initialCount,
+        { timeout: 5000 },
+      )
+      .catch(() => {});
 
-    return;
+    const newCount = await this.getAgentCountByListLength();
+    console.log(`New agent count after scroll (DOM cards): ${newCount}`);
   }
 
   async testPagination(): Promise<{

autogpt_platform/frontend/src/tests/pages/marketplace.page.ts

@@ -9,6 +9,7 @@ export class MarketplacePage extends BasePage {
 
   async goto(page: Page) {
     await page.goto("/marketplace");
+    await page.waitForLoadState("networkidle").catch(() => {});
   }
 
   async getMarketplaceTitle(page: Page) {
@@ -109,16 +110,24 @@ export class MarketplacePage extends BasePage {
 
   async getFirstFeaturedAgent(page: Page) {
     const { getId } = getSelectors(page);
-    return getId("featured-store-card").first();
+    const card = getId("featured-store-card").first();
+    await card.waitFor({ state: "visible", timeout: 30000 });
+    return card;
   }
 
   async getFirstTopAgent() {
-    return this.page.locator('[data-testid="store-card"]:visible').first();
+    const card = this.page
+      .locator('[data-testid="store-card"]:visible')
+      .first();
+    await card.waitFor({ state: "visible", timeout: 30000 });
+    return card;
   }
 
   async getFirstCreatorProfile(page: Page) {
     const { getId } = getSelectors(page);
-    return getId("creator-card").first();
+    const card = getId("creator-card").first();
+    await card.waitFor({ state: "visible", timeout: 30000 });
+    return card;
   }
 
   async getSearchResultsCount(page: Page) {

docs/integrations/README.md

@@ -233,7 +233,6 @@ Below is a comprehensive list of all available blocks, categorized by their prim
 | [Stagehand Extract](block-integrations/stagehand/blocks.md#stagehand-extract) | Extract structured data from a webpage |
 | [Stagehand Observe](block-integrations/stagehand/blocks.md#stagehand-observe) | Find suggested actions for your workflows |
 | [Unreal Text To Speech](block-integrations/llm.md#unreal-text-to-speech) | Converts text to speech using the Unreal Speech API |
-| [Video Narration](block-integrations/video/narration.md#video-narration) | Generate AI narration and add to video |
 
 ## Search and Information Retrieval
 
@@ -473,13 +472,9 @@ Below is a comprehensive list of all available blocks, categorized by their prim
 
 | Block Name | Description |
 |------------|-------------|
-| [Add Audio To Video](block-integrations/video/add_audio.md#add-audio-to-video) | Block to attach an audio file to a video file using moviepy |
-| [Loop Video](block-integrations/video/loop.md#loop-video) | Block to loop a video to a given duration or number of repeats |
-| [Media Duration](block-integrations/video/duration.md#media-duration) | Block to get the duration of a media file |
-| [Video Clip](block-integrations/video/clip.md#video-clip) | Extract a time segment from a video |
-| [Video Concat](block-integrations/video/concat.md#video-concat) | Merge multiple video clips into one continuous video |
-| [Video Download](block-integrations/video/download.md#video-download) | Download video from URL (YouTube, Vimeo, news sites, direct links) |
-| [Video Text Overlay](block-integrations/video/text_overlay.md#video-text-overlay) | Add text overlay/caption to video |
+| [Add Audio To Video](block-integrations/multimedia.md#add-audio-to-video) | Block to attach an audio file to a video file using moviepy |
+| [Loop Video](block-integrations/multimedia.md#loop-video) | Block to loop a video to a given duration or number of repeats |
+| [Media Duration](block-integrations/multimedia.md#media-duration) | Block to get the duration of a media file |
 
 ## Productivity
 

docs/integrations/SUMMARY.md

@@ -85,6 +85,7 @@
 * [LLM](block-integrations/llm.md)
 * [Logic](block-integrations/logic.md)
 * [Misc](block-integrations/misc.md)
+* [Multimedia](block-integrations/multimedia.md)
 * [Notion Create Page](block-integrations/notion/create_page.md)
 * [Notion Read Database](block-integrations/notion/read_database.md)
 * [Notion Read Page](block-integrations/notion/read_page.md)
@@ -128,13 +129,5 @@
 * [Twitter Timeline](block-integrations/twitter/timeline.md)
 * [Twitter Tweet Lookup](block-integrations/twitter/tweet_lookup.md)
 * [Twitter User Lookup](block-integrations/twitter/user_lookup.md)
-* [Video Add Audio](block-integrations/video/add_audio.md)
-* [Video Clip](block-integrations/video/clip.md)
-* [Video Concat](block-integrations/video/concat.md)
-* [Video Download](block-integrations/video/download.md)
-* [Video Duration](block-integrations/video/duration.md)
-* [Video Loop](block-integrations/video/loop.md)
-* [Video Narration](block-integrations/video/narration.md)
-* [Video Text Overlay](block-integrations/video/text_overlay.md)
 * [Wolfram LLM API](block-integrations/wolfram/llm_api.md)
 * [Zerobounce Validate Emails](block-integrations/zerobounce/validate_emails.md)

docs/integrations/block-integrations/llm.md

@@ -65,7 +65,7 @@ The result routes data to yes_output or no_output, enabling intelligent branchin
 | condition | A plaintext English description of the condition to evaluate | str | Yes |
 | yes_value | (Optional) Value to output if the condition is true. If not provided, input_value will be used. | Yes Value | No |
 | no_value | (Optional) Value to output if the condition is false. If not provided, input_value will be used. | No Value | No |
-| model | The language model to use for evaluating the condition. | "o3-mini" \| "o3-2025-04-16" \| "o1" \| "o1-mini" \| "gpt-5.2-2025-12-11" \| "gpt-5.1-2025-11-13" \| "gpt-5-2025-08-07" \| "gpt-5-mini-2025-08-07" \| "gpt-5-nano-2025-08-07" \| "gpt-5-chat-latest" \| "gpt-4.1-2025-04-14" \| "gpt-4.1-mini-2025-04-14" \| "gpt-4o-mini" \| "gpt-4o" \| "gpt-4-turbo" \| "gpt-3.5-turbo" \| "claude-opus-4-1-20250805" \| "claude-opus-4-20250514" \| "claude-sonnet-4-20250514" \| "claude-opus-4-5-20251101" \| "claude-sonnet-4-5-20250929" \| "claude-haiku-4-5-20251001" \| "claude-3-7-sonnet-20250219" \| "claude-3-haiku-20240307" \| "Qwen/Qwen2.5-72B-Instruct-Turbo" \| "nvidia/llama-3.1-nemotron-70b-instruct" \| "meta-llama/Llama-3.3-70B-Instruct-Turbo" \| "meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo" \| "meta-llama/Llama-3.2-3B-Instruct-Turbo" \| "llama-3.3-70b-versatile" \| "llama-3.1-8b-instant" \| "llama3.3" \| "llama3.2" \| "llama3" \| "llama3.1:405b" \| "dolphin-mistral:latest" \| "openai/gpt-oss-120b" \| "openai/gpt-oss-20b" \| "google/gemini-2.5-pro-preview-03-25" \| "google/gemini-3-pro-preview" \| "google/gemini-2.5-flash" \| "google/gemini-2.0-flash-001" \| "google/gemini-2.5-flash-lite-preview-06-17" \| "google/gemini-2.0-flash-lite-001" \| "mistralai/mistral-nemo" \| "cohere/command-r-08-2024" \| "cohere/command-r-plus-08-2024" \| "deepseek/deepseek-chat" \| "deepseek/deepseek-r1-0528" \| "perplexity/sonar" \| "perplexity/sonar-pro" \| "perplexity/sonar-deep-research" \| "nousresearch/hermes-3-llama-3.1-405b" \| "nousresearch/hermes-3-llama-3.1-70b" \| "amazon/nova-lite-v1" \| "amazon/nova-micro-v1" \| "amazon/nova-pro-v1" \| "microsoft/wizardlm-2-8x22b" \| "gryphe/mythomax-l2-13b" \| "meta-llama/llama-4-scout" \| "meta-llama/llama-4-maverick" \| "x-ai/grok-4" \| "x-ai/grok-4-fast" \| "x-ai/grok-4.1-fast" \| "x-ai/grok-code-fast-1" \| "moonshotai/kimi-k2" \| "qwen/qwen3-235b-a22b-thinking-2507" \| "qwen/qwen3-coder" \| "Llama-4-Scout-17B-16E-Instruct-FP8" \| "Llama-4-Maverick-17B-128E-Instruct-FP8" \| "Llama-3.3-8B-Instruct" \| "Llama-3.3-70B-Instruct" \| "v0-1.5-md" \| "v0-1.5-lg" \| "v0-1.0-md" | No |
+| model | The language model to use for evaluating the condition. | "o3-mini" \| "o3-2025-04-16" \| "o1" \| "o1-mini" \| "gpt-5.2-2025-12-11" \| "gpt-5.1-2025-11-13" \| "gpt-5-2025-08-07" \| "gpt-5-mini-2025-08-07" \| "gpt-5-nano-2025-08-07" \| "gpt-5-chat-latest" \| "gpt-4.1-2025-04-14" \| "gpt-4.1-mini-2025-04-14" \| "gpt-4o-mini" \| "gpt-4o" \| "gpt-4-turbo" \| "gpt-3.5-turbo" \| "claude-opus-4-1-20250805" \| "claude-opus-4-20250514" \| "claude-sonnet-4-20250514" \| "claude-opus-4-5-20251101" \| "claude-sonnet-4-5-20250929" \| "claude-haiku-4-5-20251001" \| "claude-3-haiku-20240307" \| "Qwen/Qwen2.5-72B-Instruct-Turbo" \| "nvidia/llama-3.1-nemotron-70b-instruct" \| "meta-llama/Llama-3.3-70B-Instruct-Turbo" \| "meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo" \| "meta-llama/Llama-3.2-3B-Instruct-Turbo" \| "llama-3.3-70b-versatile" \| "llama-3.1-8b-instant" \| "llama3.3" \| "llama3.2" \| "llama3" \| "llama3.1:405b" \| "dolphin-mistral:latest" \| "openai/gpt-oss-120b" \| "openai/gpt-oss-20b" \| "google/gemini-2.5-pro-preview-03-25" \| "google/gemini-3-pro-preview" \| "google/gemini-2.5-flash" \| "google/gemini-2.0-flash-001" \| "google/gemini-2.5-flash-lite-preview-06-17" \| "google/gemini-2.0-flash-lite-001" \| "mistralai/mistral-nemo" \| "cohere/command-r-08-2024" \| "cohere/command-r-plus-08-2024" \| "deepseek/deepseek-chat" \| "deepseek/deepseek-r1-0528" \| "perplexity/sonar" \| "perplexity/sonar-pro" \| "perplexity/sonar-deep-research" \| "nousresearch/hermes-3-llama-3.1-405b" \| "nousresearch/hermes-3-llama-3.1-70b" \| "amazon/nova-lite-v1" \| "amazon/nova-micro-v1" \| "amazon/nova-pro-v1" \| "microsoft/wizardlm-2-8x22b" \| "gryphe/mythomax-l2-13b" \| "meta-llama/llama-4-scout" \| "meta-llama/llama-4-maverick" \| "x-ai/grok-4" \| "x-ai/grok-4-fast" \| "x-ai/grok-4.1-fast" \| "x-ai/grok-code-fast-1" \| "moonshotai/kimi-k2" \| "qwen/qwen3-235b-a22b-thinking-2507" \| "qwen/qwen3-coder" \| "Llama-4-Scout-17B-16E-Instruct-FP8" \| "Llama-4-Maverick-17B-128E-Instruct-FP8" \| "Llama-3.3-8B-Instruct" \| "Llama-3.3-70B-Instruct" \| "v0-1.5-md" \| "v0-1.5-lg" \| "v0-1.0-md" | No |
 
 ### Outputs
 
@@ -103,7 +103,7 @@ The block sends the entire conversation history to the chosen LLM, including sys
 |-------|-------------|------|----------|
 | prompt | The prompt to send to the language model. | str | No |
 | messages | List of messages in the conversation. | List[Any] | Yes |
-| model | The language model to use for the conversation. | "o3-mini" \| "o3-2025-04-16" \| "o1" \| "o1-mini" \| "gpt-5.2-2025-12-11" \| "gpt-5.1-2025-11-13" \| "gpt-5-2025-08-07" \| "gpt-5-mini-2025-08-07" \| "gpt-5-nano-2025-08-07" \| "gpt-5-chat-latest" \| "gpt-4.1-2025-04-14" \| "gpt-4.1-mini-2025-04-14" \| "gpt-4o-mini" \| "gpt-4o" \| "gpt-4-turbo" \| "gpt-3.5-turbo" \| "claude-opus-4-1-20250805" \| "claude-opus-4-20250514" \| "claude-sonnet-4-20250514" \| "claude-opus-4-5-20251101" \| "claude-sonnet-4-5-20250929" \| "claude-haiku-4-5-20251001" \| "claude-3-7-sonnet-20250219" \| "claude-3-haiku-20240307" \| "Qwen/Qwen2.5-72B-Instruct-Turbo" \| "nvidia/llama-3.1-nemotron-70b-instruct" \| "meta-llama/Llama-3.3-70B-Instruct-Turbo" \| "meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo" \| "meta-llama/Llama-3.2-3B-Instruct-Turbo" \| "llama-3.3-70b-versatile" \| "llama-3.1-8b-instant" \| "llama3.3" \| "llama3.2" \| "llama3" \| "llama3.1:405b" \| "dolphin-mistral:latest" \| "openai/gpt-oss-120b" \| "openai/gpt-oss-20b" \| "google/gemini-2.5-pro-preview-03-25" \| "google/gemini-3-pro-preview" \| "google/gemini-2.5-flash" \| "google/gemini-2.0-flash-001" \| "google/gemini-2.5-flash-lite-preview-06-17" \| "google/gemini-2.0-flash-lite-001" \| "mistralai/mistral-nemo" \| "cohere/command-r-08-2024" \| "cohere/command-r-plus-08-2024" \| "deepseek/deepseek-chat" \| "deepseek/deepseek-r1-0528" \| "perplexity/sonar" \| "perplexity/sonar-pro" \| "perplexity/sonar-deep-research" \| "nousresearch/hermes-3-llama-3.1-405b" \| "nousresearch/hermes-3-llama-3.1-70b" \| "amazon/nova-lite-v1" \| "amazon/nova-micro-v1" \| "amazon/nova-pro-v1" \| "microsoft/wizardlm-2-8x22b" \| "gryphe/mythomax-l2-13b" \| "meta-llama/llama-4-scout" \| "meta-llama/llama-4-maverick" \| "x-ai/grok-4" \| "x-ai/grok-4-fast" \| "x-ai/grok-4.1-fast" \| "x-ai/grok-code-fast-1" \| "moonshotai/kimi-k2" \| "qwen/qwen3-235b-a22b-thinking-2507" \| "qwen/qwen3-coder" \| "Llama-4-Scout-17B-16E-Instruct-FP8" \| "Llama-4-Maverick-17B-128E-Instruct-FP8" \| "Llama-3.3-8B-Instruct" \| "Llama-3.3-70B-Instruct" \| "v0-1.5-md" \| "v0-1.5-lg" \| "v0-1.0-md" | No |
+| model | The language model to use for the conversation. | "o3-mini" \| "o3-2025-04-16" \| "o1" \| "o1-mini" \| "gpt-5.2-2025-12-11" \| "gpt-5.1-2025-11-13" \| "gpt-5-2025-08-07" \| "gpt-5-mini-2025-08-07" \| "gpt-5-nano-2025-08-07" \| "gpt-5-chat-latest" \| "gpt-4.1-2025-04-14" \| "gpt-4.1-mini-2025-04-14" \| "gpt-4o-mini" \| "gpt-4o" \| "gpt-4-turbo" \| "gpt-3.5-turbo" \| "claude-opus-4-1-20250805" \| "claude-opus-4-20250514" \| "claude-sonnet-4-20250514" \| "claude-opus-4-5-20251101" \| "claude-sonnet-4-5-20250929" \| "claude-haiku-4-5-20251001" \| "claude-3-haiku-20240307" \| "Qwen/Qwen2.5-72B-Instruct-Turbo" \| "nvidia/llama-3.1-nemotron-70b-instruct" \| "meta-llama/Llama-3.3-70B-Instruct-Turbo" \| "meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo" \| "meta-llama/Llama-3.2-3B-Instruct-Turbo" \| "llama-3.3-70b-versatile" \| "llama-3.1-8b-instant" \| "llama3.3" \| "llama3.2" \| "llama3" \| "llama3.1:405b" \| "dolphin-mistral:latest" \| "openai/gpt-oss-120b" \| "openai/gpt-oss-20b" \| "google/gemini-2.5-pro-preview-03-25" \| "google/gemini-3-pro-preview" \| "google/gemini-2.5-flash" \| "google/gemini-2.0-flash-001" \| "google/gemini-2.5-flash-lite-preview-06-17" \| "google/gemini-2.0-flash-lite-001" \| "mistralai/mistral-nemo" \| "cohere/command-r-08-2024" \| "cohere/command-r-plus-08-2024" \| "deepseek/deepseek-chat" \| "deepseek/deepseek-r1-0528" \| "perplexity/sonar" \| "perplexity/sonar-pro" \| "perplexity/sonar-deep-research" \| "nousresearch/hermes-3-llama-3.1-405b" \| "nousresearch/hermes-3-llama-3.1-70b" \| "amazon/nova-lite-v1" \| "amazon/nova-micro-v1" \| "amazon/nova-pro-v1" \| "microsoft/wizardlm-2-8x22b" \| "gryphe/mythomax-l2-13b" \| "meta-llama/llama-4-scout" \| "meta-llama/llama-4-maverick" \| "x-ai/grok-4" \| "x-ai/grok-4-fast" \| "x-ai/grok-4.1-fast" \| "x-ai/grok-code-fast-1" \| "moonshotai/kimi-k2" \| "qwen/qwen3-235b-a22b-thinking-2507" \| "qwen/qwen3-coder" \| "Llama-4-Scout-17B-16E-Instruct-FP8" \| "Llama-4-Maverick-17B-128E-Instruct-FP8" \| "Llama-3.3-8B-Instruct" \| "Llama-3.3-70B-Instruct" \| "v0-1.5-md" \| "v0-1.5-lg" \| "v0-1.0-md" | No |
 | max_tokens | The maximum number of tokens to generate in the chat completion. | int | No |
 | ollama_host | Ollama host for local  models | str | No |
 
@@ -257,7 +257,7 @@ The block formulates a prompt based on the given focus or source data, sends it
 |-------|-------------|------|----------|
 | focus | The focus of the list to generate. | str | No |
 | source_data | The data to generate the list from. | str | No |
-| model | The language model to use for generating the list. | "o3-mini" \| "o3-2025-04-16" \| "o1" \| "o1-mini" \| "gpt-5.2-2025-12-11" \| "gpt-5.1-2025-11-13" \| "gpt-5-2025-08-07" \| "gpt-5-mini-2025-08-07" \| "gpt-5-nano-2025-08-07" \| "gpt-5-chat-latest" \| "gpt-4.1-2025-04-14" \| "gpt-4.1-mini-2025-04-14" \| "gpt-4o-mini" \| "gpt-4o" \| "gpt-4-turbo" \| "gpt-3.5-turbo" \| "claude-opus-4-1-20250805" \| "claude-opus-4-20250514" \| "claude-sonnet-4-20250514" \| "claude-opus-4-5-20251101" \| "claude-sonnet-4-5-20250929" \| "claude-haiku-4-5-20251001" \| "claude-3-7-sonnet-20250219" \| "claude-3-haiku-20240307" \| "Qwen/Qwen2.5-72B-Instruct-Turbo" \| "nvidia/llama-3.1-nemotron-70b-instruct" \| "meta-llama/Llama-3.3-70B-Instruct-Turbo" \| "meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo" \| "meta-llama/Llama-3.2-3B-Instruct-Turbo" \| "llama-3.3-70b-versatile" \| "llama-3.1-8b-instant" \| "llama3.3" \| "llama3.2" \| "llama3" \| "llama3.1:405b" \| "dolphin-mistral:latest" \| "openai/gpt-oss-120b" \| "openai/gpt-oss-20b" \| "google/gemini-2.5-pro-preview-03-25" \| "google/gemini-3-pro-preview" \| "google/gemini-2.5-flash" \| "google/gemini-2.0-flash-001" \| "google/gemini-2.5-flash-lite-preview-06-17" \| "google/gemini-2.0-flash-lite-001" \| "mistralai/mistral-nemo" \| "cohere/command-r-08-2024" \| "cohere/command-r-plus-08-2024" \| "deepseek/deepseek-chat" \| "deepseek/deepseek-r1-0528" \| "perplexity/sonar" \| "perplexity/sonar-pro" \| "perplexity/sonar-deep-research" \| "nousresearch/hermes-3-llama-3.1-405b" \| "nousresearch/hermes-3-llama-3.1-70b" \| "amazon/nova-lite-v1" \| "amazon/nova-micro-v1" \| "amazon/nova-pro-v1" \| "microsoft/wizardlm-2-8x22b" \| "gryphe/mythomax-l2-13b" \| "meta-llama/llama-4-scout" \| "meta-llama/llama-4-maverick" \| "x-ai/grok-4" \| "x-ai/grok-4-fast" \| "x-ai/grok-4.1-fast" \| "x-ai/grok-code-fast-1" \| "moonshotai/kimi-k2" \| "qwen/qwen3-235b-a22b-thinking-2507" \| "qwen/qwen3-coder" \| "Llama-4-Scout-17B-16E-Instruct-FP8" \| "Llama-4-Maverick-17B-128E-Instruct-FP8" \| "Llama-3.3-8B-Instruct" \| "Llama-3.3-70B-Instruct" \| "v0-1.5-md" \| "v0-1.5-lg" \| "v0-1.0-md" | No |
+| model | The language model to use for generating the list. | "o3-mini" \| "o3-2025-04-16" \| "o1" \| "o1-mini" \| "gpt-5.2-2025-12-11" \| "gpt-5.1-2025-11-13" \| "gpt-5-2025-08-07" \| "gpt-5-mini-2025-08-07" \| "gpt-5-nano-2025-08-07" \| "gpt-5-chat-latest" \| "gpt-4.1-2025-04-14" \| "gpt-4.1-mini-2025-04-14" \| "gpt-4o-mini" \| "gpt-4o" \| "gpt-4-turbo" \| "gpt-3.5-turbo" \| "claude-opus-4-1-20250805" \| "claude-opus-4-20250514" \| "claude-sonnet-4-20250514" \| "claude-opus-4-5-20251101" \| "claude-sonnet-4-5-20250929" \| "claude-haiku-4-5-20251001" \| "claude-3-haiku-20240307" \| "Qwen/Qwen2.5-72B-Instruct-Turbo" \| "nvidia/llama-3.1-nemotron-70b-instruct" \| "meta-llama/Llama-3.3-70B-Instruct-Turbo" \| "meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo" \| "meta-llama/Llama-3.2-3B-Instruct-Turbo" \| "llama-3.3-70b-versatile" \| "llama-3.1-8b-instant" \| "llama3.3" \| "llama3.2" \| "llama3" \| "llama3.1:405b" \| "dolphin-mistral:latest" \| "openai/gpt-oss-120b" \| "openai/gpt-oss-20b" \| "google/gemini-2.5-pro-preview-03-25" \| "google/gemini-3-pro-preview" \| "google/gemini-2.5-flash" \| "google/gemini-2.0-flash-001" \| "google/gemini-2.5-flash-lite-preview-06-17" \| "google/gemini-2.0-flash-lite-001" \| "mistralai/mistral-nemo" \| "cohere/command-r-08-2024" \| "cohere/command-r-plus-08-2024" \| "deepseek/deepseek-chat" \| "deepseek/deepseek-r1-0528" \| "perplexity/sonar" \| "perplexity/sonar-pro" \| "perplexity/sonar-deep-research" \| "nousresearch/hermes-3-llama-3.1-405b" \| "nousresearch/hermes-3-llama-3.1-70b" \| "amazon/nova-lite-v1" \| "amazon/nova-micro-v1" \| "amazon/nova-pro-v1" \| "microsoft/wizardlm-2-8x22b" \| "gryphe/mythomax-l2-13b" \| "meta-llama/llama-4-scout" \| "meta-llama/llama-4-maverick" \| "x-ai/grok-4" \| "x-ai/grok-4-fast" \| "x-ai/grok-4.1-fast" \| "x-ai/grok-code-fast-1" \| "moonshotai/kimi-k2" \| "qwen/qwen3-235b-a22b-thinking-2507" \| "qwen/qwen3-coder" \| "Llama-4-Scout-17B-16E-Instruct-FP8" \| "Llama-4-Maverick-17B-128E-Instruct-FP8" \| "Llama-3.3-8B-Instruct" \| "Llama-3.3-70B-Instruct" \| "v0-1.5-md" \| "v0-1.5-lg" \| "v0-1.0-md" | No |
 | max_retries | Maximum number of retries for generating a valid list. | int | No |
 | force_json_output | Whether to force the LLM to produce a JSON-only response. This can increase the block's reliability, but may also reduce the quality of the response because it prohibits the LLM from reasoning before providing its JSON response. | bool | No |
 | max_tokens | The maximum number of tokens to generate in the chat completion. | int | No |
@@ -424,7 +424,7 @@ The block sends the input prompt to a chosen LLM, along with any system prompts
 | prompt | The prompt to send to the language model. | str | Yes |
 | expected_format | Expected format of the response. If provided, the response will be validated against this format. The keys should be the expected fields in the response, and the values should be the description of the field. | Dict[str, str] | Yes |
 | list_result | Whether the response should be a list of objects in the expected format. | bool | No |
-| model | The language model to use for answering the prompt. | "o3-mini" \| "o3-2025-04-16" \| "o1" \| "o1-mini" \| "gpt-5.2-2025-12-11" \| "gpt-5.1-2025-11-13" \| "gpt-5-2025-08-07" \| "gpt-5-mini-2025-08-07" \| "gpt-5-nano-2025-08-07" \| "gpt-5-chat-latest" \| "gpt-4.1-2025-04-14" \| "gpt-4.1-mini-2025-04-14" \| "gpt-4o-mini" \| "gpt-4o" \| "gpt-4-turbo" \| "gpt-3.5-turbo" \| "claude-opus-4-1-20250805" \| "claude-opus-4-20250514" \| "claude-sonnet-4-20250514" \| "claude-opus-4-5-20251101" \| "claude-sonnet-4-5-20250929" \| "claude-haiku-4-5-20251001" \| "claude-3-7-sonnet-20250219" \| "claude-3-haiku-20240307" \| "Qwen/Qwen2.5-72B-Instruct-Turbo" \| "nvidia/llama-3.1-nemotron-70b-instruct" \| "meta-llama/Llama-3.3-70B-Instruct-Turbo" \| "meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo" \| "meta-llama/Llama-3.2-3B-Instruct-Turbo" \| "llama-3.3-70b-versatile" \| "llama-3.1-8b-instant" \| "llama3.3" \| "llama3.2" \| "llama3" \| "llama3.1:405b" \| "dolphin-mistral:latest" \| "openai/gpt-oss-120b" \| "openai/gpt-oss-20b" \| "google/gemini-2.5-pro-preview-03-25" \| "google/gemini-3-pro-preview" \| "google/gemini-2.5-flash" \| "google/gemini-2.0-flash-001" \| "google/gemini-2.5-flash-lite-preview-06-17" \| "google/gemini-2.0-flash-lite-001" \| "mistralai/mistral-nemo" \| "cohere/command-r-08-2024" \| "cohere/command-r-plus-08-2024" \| "deepseek/deepseek-chat" \| "deepseek/deepseek-r1-0528" \| "perplexity/sonar" \| "perplexity/sonar-pro" \| "perplexity/sonar-deep-research" \| "nousresearch/hermes-3-llama-3.1-405b" \| "nousresearch/hermes-3-llama-3.1-70b" \| "amazon/nova-lite-v1" \| "amazon/nova-micro-v1" \| "amazon/nova-pro-v1" \| "microsoft/wizardlm-2-8x22b" \| "gryphe/mythomax-l2-13b" \| "meta-llama/llama-4-scout" \| "meta-llama/llama-4-maverick" \| "x-ai/grok-4" \| "x-ai/grok-4-fast" \| "x-ai/grok-4.1-fast" \| "x-ai/grok-code-fast-1" \| "moonshotai/kimi-k2" \| "qwen/qwen3-235b-a22b-thinking-2507" \| "qwen/qwen3-coder" \| "Llama-4-Scout-17B-16E-Instruct-FP8" \| "Llama-4-Maverick-17B-128E-Instruct-FP8" \| "Llama-3.3-8B-Instruct" \| "Llama-3.3-70B-Instruct" \| "v0-1.5-md" \| "v0-1.5-lg" \| "v0-1.0-md" | No |
+| model | The language model to use for answering the prompt. | "o3-mini" \| "o3-2025-04-16" \| "o1" \| "o1-mini" \| "gpt-5.2-2025-12-11" \| "gpt-5.1-2025-11-13" \| "gpt-5-2025-08-07" \| "gpt-5-mini-2025-08-07" \| "gpt-5-nano-2025-08-07" \| "gpt-5-chat-latest" \| "gpt-4.1-2025-04-14" \| "gpt-4.1-mini-2025-04-14" \| "gpt-4o-mini" \| "gpt-4o" \| "gpt-4-turbo" \| "gpt-3.5-turbo" \| "claude-opus-4-1-20250805" \| "claude-opus-4-20250514" \| "claude-sonnet-4-20250514" \| "claude-opus-4-5-20251101" \| "claude-sonnet-4-5-20250929" \| "claude-haiku-4-5-20251001" \| "claude-3-haiku-20240307" \| "Qwen/Qwen2.5-72B-Instruct-Turbo" \| "nvidia/llama-3.1-nemotron-70b-instruct" \| "meta-llama/Llama-3.3-70B-Instruct-Turbo" \| "meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo" \| "meta-llama/Llama-3.2-3B-Instruct-Turbo" \| "llama-3.3-70b-versatile" \| "llama-3.1-8b-instant" \| "llama3.3" \| "llama3.2" \| "llama3" \| "llama3.1:405b" \| "dolphin-mistral:latest" \| "openai/gpt-oss-120b" \| "openai/gpt-oss-20b" \| "google/gemini-2.5-pro-preview-03-25" \| "google/gemini-3-pro-preview" \| "google/gemini-2.5-flash" \| "google/gemini-2.0-flash-001" \| "google/gemini-2.5-flash-lite-preview-06-17" \| "google/gemini-2.0-flash-lite-001" \| "mistralai/mistral-nemo" \| "cohere/command-r-08-2024" \| "cohere/command-r-plus-08-2024" \| "deepseek/deepseek-chat" \| "deepseek/deepseek-r1-0528" \| "perplexity/sonar" \| "perplexity/sonar-pro" \| "perplexity/sonar-deep-research" \| "nousresearch/hermes-3-llama-3.1-405b" \| "nousresearch/hermes-3-llama-3.1-70b" \| "amazon/nova-lite-v1" \| "amazon/nova-micro-v1" \| "amazon/nova-pro-v1" \| "microsoft/wizardlm-2-8x22b" \| "gryphe/mythomax-l2-13b" \| "meta-llama/llama-4-scout" \| "meta-llama/llama-4-maverick" \| "x-ai/grok-4" \| "x-ai/grok-4-fast" \| "x-ai/grok-4.1-fast" \| "x-ai/grok-code-fast-1" \| "moonshotai/kimi-k2" \| "qwen/qwen3-235b-a22b-thinking-2507" \| "qwen/qwen3-coder" \| "Llama-4-Scout-17B-16E-Instruct-FP8" \| "Llama-4-Maverick-17B-128E-Instruct-FP8" \| "Llama-3.3-8B-Instruct" \| "Llama-3.3-70B-Instruct" \| "v0-1.5-md" \| "v0-1.5-lg" \| "v0-1.0-md" | No |
 | force_json_output | Whether to force the LLM to produce a JSON-only response. This can increase the block's reliability, but may also reduce the quality of the response because it prohibits the LLM from reasoning before providing its JSON response. | bool | No |
 | sys_prompt | The system prompt to provide additional context to the model. | str | No |
 | conversation_history | The conversation history to provide context for the prompt. | List[Dict[str, Any]] | No |
@@ -464,7 +464,7 @@ The block sends the input prompt to a chosen LLM, processes the response, and re
 | Input | Description | Type | Required |
 |-------|-------------|------|----------|
 | prompt | The prompt to send to the language model. You can use any of the {keys} from Prompt Values to fill in the prompt with values from the prompt values dictionary by putting them in curly braces. | str | Yes |
-| model | The language model to use for answering the prompt. | "o3-mini" \| "o3-2025-04-16" \| "o1" \| "o1-mini" \| "gpt-5.2-2025-12-11" \| "gpt-5.1-2025-11-13" \| "gpt-5-2025-08-07" \| "gpt-5-mini-2025-08-07" \| "gpt-5-nano-2025-08-07" \| "gpt-5-chat-latest" \| "gpt-4.1-2025-04-14" \| "gpt-4.1-mini-2025-04-14" \| "gpt-4o-mini" \| "gpt-4o" \| "gpt-4-turbo" \| "gpt-3.5-turbo" \| "claude-opus-4-1-20250805" \| "claude-opus-4-20250514" \| "claude-sonnet-4-20250514" \| "claude-opus-4-5-20251101" \| "claude-sonnet-4-5-20250929" \| "claude-haiku-4-5-20251001" \| "claude-3-7-sonnet-20250219" \| "claude-3-haiku-20240307" \| "Qwen/Qwen2.5-72B-Instruct-Turbo" \| "nvidia/llama-3.1-nemotron-70b-instruct" \| "meta-llama/Llama-3.3-70B-Instruct-Turbo" \| "meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo" \| "meta-llama/Llama-3.2-3B-Instruct-Turbo" \| "llama-3.3-70b-versatile" \| "llama-3.1-8b-instant" \| "llama3.3" \| "llama3.2" \| "llama3" \| "llama3.1:405b" \| "dolphin-mistral:latest" \| "openai/gpt-oss-120b" \| "openai/gpt-oss-20b" \| "google/gemini-2.5-pro-preview-03-25" \| "google/gemini-3-pro-preview" \| "google/gemini-2.5-flash" \| "google/gemini-2.0-flash-001" \| "google/gemini-2.5-flash-lite-preview-06-17" \| "google/gemini-2.0-flash-lite-001" \| "mistralai/mistral-nemo" \| "cohere/command-r-08-2024" \| "cohere/command-r-plus-08-2024" \| "deepseek/deepseek-chat" \| "deepseek/deepseek-r1-0528" \| "perplexity/sonar" \| "perplexity/sonar-pro" \| "perplexity/sonar-deep-research" \| "nousresearch/hermes-3-llama-3.1-405b" \| "nousresearch/hermes-3-llama-3.1-70b" \| "amazon/nova-lite-v1" \| "amazon/nova-micro-v1" \| "amazon/nova-pro-v1" \| "microsoft/wizardlm-2-8x22b" \| "gryphe/mythomax-l2-13b" \| "meta-llama/llama-4-scout" \| "meta-llama/llama-4-maverick" \| "x-ai/grok-4" \| "x-ai/grok-4-fast" \| "x-ai/grok-4.1-fast" \| "x-ai/grok-code-fast-1" \| "moonshotai/kimi-k2" \| "qwen/qwen3-235b-a22b-thinking-2507" \| "qwen/qwen3-coder" \| "Llama-4-Scout-17B-16E-Instruct-FP8" \| "Llama-4-Maverick-17B-128E-Instruct-FP8" \| "Llama-3.3-8B-Instruct" \| "Llama-3.3-70B-Instruct" \| "v0-1.5-md" \| "v0-1.5-lg" \| "v0-1.0-md" | No |
+| model | The language model to use for answering the prompt. | "o3-mini" \| "o3-2025-04-16" \| "o1" \| "o1-mini" \| "gpt-5.2-2025-12-11" \| "gpt-5.1-2025-11-13" \| "gpt-5-2025-08-07" \| "gpt-5-mini-2025-08-07" \| "gpt-5-nano-2025-08-07" \| "gpt-5-chat-latest" \| "gpt-4.1-2025-04-14" \| "gpt-4.1-mini-2025-04-14" \| "gpt-4o-mini" \| "gpt-4o" \| "gpt-4-turbo" \| "gpt-3.5-turbo" \| "claude-opus-4-1-20250805" \| "claude-opus-4-20250514" \| "claude-sonnet-4-20250514" \| "claude-opus-4-5-20251101" \| "claude-sonnet-4-5-20250929" \| "claude-haiku-4-5-20251001" \| "claude-3-haiku-20240307" \| "Qwen/Qwen2.5-72B-Instruct-Turbo" \| "nvidia/llama-3.1-nemotron-70b-instruct" \| "meta-llama/Llama-3.3-70B-Instruct-Turbo" \| "meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo" \| "meta-llama/Llama-3.2-3B-Instruct-Turbo" \| "llama-3.3-70b-versatile" \| "llama-3.1-8b-instant" \| "llama3.3" \| "llama3.2" \| "llama3" \| "llama3.1:405b" \| "dolphin-mistral:latest" \| "openai/gpt-oss-120b" \| "openai/gpt-oss-20b" \| "google/gemini-2.5-pro-preview-03-25" \| "google/gemini-3-pro-preview" \| "google/gemini-2.5-flash" \| "google/gemini-2.0-flash-001" \| "google/gemini-2.5-flash-lite-preview-06-17" \| "google/gemini-2.0-flash-lite-001" \| "mistralai/mistral-nemo" \| "cohere/command-r-08-2024" \| "cohere/command-r-plus-08-2024" \| "deepseek/deepseek-chat" \| "deepseek/deepseek-r1-0528" \| "perplexity/sonar" \| "perplexity/sonar-pro" \| "perplexity/sonar-deep-research" \| "nousresearch/hermes-3-llama-3.1-405b" \| "nousresearch/hermes-3-llama-3.1-70b" \| "amazon/nova-lite-v1" \| "amazon/nova-micro-v1" \| "amazon/nova-pro-v1" \| "microsoft/wizardlm-2-8x22b" \| "gryphe/mythomax-l2-13b" \| "meta-llama/llama-4-scout" \| "meta-llama/llama-4-maverick" \| "x-ai/grok-4" \| "x-ai/grok-4-fast" \| "x-ai/grok-4.1-fast" \| "x-ai/grok-code-fast-1" \| "moonshotai/kimi-k2" \| "qwen/qwen3-235b-a22b-thinking-2507" \| "qwen/qwen3-coder" \| "Llama-4-Scout-17B-16E-Instruct-FP8" \| "Llama-4-Maverick-17B-128E-Instruct-FP8" \| "Llama-3.3-8B-Instruct" \| "Llama-3.3-70B-Instruct" \| "v0-1.5-md" \| "v0-1.5-lg" \| "v0-1.0-md" | No |
 | sys_prompt | The system prompt to provide additional context to the model. | str | No |
 | retry | Number of times to retry the LLM call if the response does not match the expected format. | int | No |
 | prompt_values | Values used to fill in the prompt. The values can be used in the prompt by putting them in a double curly braces, e.g. {{variable_name}}. | Dict[str, str] | No |
@@ -501,7 +501,7 @@ The block splits the input text into smaller chunks, sends each chunk to an LLM
 | Input | Description | Type | Required |
 |-------|-------------|------|----------|
 | text | The text to summarize. | str | Yes |
-| model | The language model to use for summarizing the text. | "o3-mini" \| "o3-2025-04-16" \| "o1" \| "o1-mini" \| "gpt-5.2-2025-12-11" \| "gpt-5.1-2025-11-13" \| "gpt-5-2025-08-07" \| "gpt-5-mini-2025-08-07" \| "gpt-5-nano-2025-08-07" \| "gpt-5-chat-latest" \| "gpt-4.1-2025-04-14" \| "gpt-4.1-mini-2025-04-14" \| "gpt-4o-mini" \| "gpt-4o" \| "gpt-4-turbo" \| "gpt-3.5-turbo" \| "claude-opus-4-1-20250805" \| "claude-opus-4-20250514" \| "claude-sonnet-4-20250514" \| "claude-opus-4-5-20251101" \| "claude-sonnet-4-5-20250929" \| "claude-haiku-4-5-20251001" \| "claude-3-7-sonnet-20250219" \| "claude-3-haiku-20240307" \| "Qwen/Qwen2.5-72B-Instruct-Turbo" \| "nvidia/llama-3.1-nemotron-70b-instruct" \| "meta-llama/Llama-3.3-70B-Instruct-Turbo" \| "meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo" \| "meta-llama/Llama-3.2-3B-Instruct-Turbo" \| "llama-3.3-70b-versatile" \| "llama-3.1-8b-instant" \| "llama3.3" \| "llama3.2" \| "llama3" \| "llama3.1:405b" \| "dolphin-mistral:latest" \| "openai/gpt-oss-120b" \| "openai/gpt-oss-20b" \| "google/gemini-2.5-pro-preview-03-25" \| "google/gemini-3-pro-preview" \| "google/gemini-2.5-flash" \| "google/gemini-2.0-flash-001" \| "google/gemini-2.5-flash-lite-preview-06-17" \| "google/gemini-2.0-flash-lite-001" \| "mistralai/mistral-nemo" \| "cohere/command-r-08-2024" \| "cohere/command-r-plus-08-2024" \| "deepseek/deepseek-chat" \| "deepseek/deepseek-r1-0528" \| "perplexity/sonar" \| "perplexity/sonar-pro" \| "perplexity/sonar-deep-research" \| "nousresearch/hermes-3-llama-3.1-405b" \| "nousresearch/hermes-3-llama-3.1-70b" \| "amazon/nova-lite-v1" \| "amazon/nova-micro-v1" \| "amazon/nova-pro-v1" \| "microsoft/wizardlm-2-8x22b" \| "gryphe/mythomax-l2-13b" \| "meta-llama/llama-4-scout" \| "meta-llama/llama-4-maverick" \| "x-ai/grok-4" \| "x-ai/grok-4-fast" \| "x-ai/grok-4.1-fast" \| "x-ai/grok-code-fast-1" \| "moonshotai/kimi-k2" \| "qwen/qwen3-235b-a22b-thinking-2507" \| "qwen/qwen3-coder" \| "Llama-4-Scout-17B-16E-Instruct-FP8" \| "Llama-4-Maverick-17B-128E-Instruct-FP8" \| "Llama-3.3-8B-Instruct" \| "Llama-3.3-70B-Instruct" \| "v0-1.5-md" \| "v0-1.5-lg" \| "v0-1.0-md" | No |
+| model | The language model to use for summarizing the text. | "o3-mini" \| "o3-2025-04-16" \| "o1" \| "o1-mini" \| "gpt-5.2-2025-12-11" \| "gpt-5.1-2025-11-13" \| "gpt-5-2025-08-07" \| "gpt-5-mini-2025-08-07" \| "gpt-5-nano-2025-08-07" \| "gpt-5-chat-latest" \| "gpt-4.1-2025-04-14" \| "gpt-4.1-mini-2025-04-14" \| "gpt-4o-mini" \| "gpt-4o" \| "gpt-4-turbo" \| "gpt-3.5-turbo" \| "claude-opus-4-1-20250805" \| "claude-opus-4-20250514" \| "claude-sonnet-4-20250514" \| "claude-opus-4-5-20251101" \| "claude-sonnet-4-5-20250929" \| "claude-haiku-4-5-20251001" \| "claude-3-haiku-20240307" \| "Qwen/Qwen2.5-72B-Instruct-Turbo" \| "nvidia/llama-3.1-nemotron-70b-instruct" \| "meta-llama/Llama-3.3-70B-Instruct-Turbo" \| "meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo" \| "meta-llama/Llama-3.2-3B-Instruct-Turbo" \| "llama-3.3-70b-versatile" \| "llama-3.1-8b-instant" \| "llama3.3" \| "llama3.2" \| "llama3" \| "llama3.1:405b" \| "dolphin-mistral:latest" \| "openai/gpt-oss-120b" \| "openai/gpt-oss-20b" \| "google/gemini-2.5-pro-preview-03-25" \| "google/gemini-3-pro-preview" \| "google/gemini-2.5-flash" \| "google/gemini-2.0-flash-001" \| "google/gemini-2.5-flash-lite-preview-06-17" \| "google/gemini-2.0-flash-lite-001" \| "mistralai/mistral-nemo" \| "cohere/command-r-08-2024" \| "cohere/command-r-plus-08-2024" \| "deepseek/deepseek-chat" \| "deepseek/deepseek-r1-0528" \| "perplexity/sonar" \| "perplexity/sonar-pro" \| "perplexity/sonar-deep-research" \| "nousresearch/hermes-3-llama-3.1-405b" \| "nousresearch/hermes-3-llama-3.1-70b" \| "amazon/nova-lite-v1" \| "amazon/nova-micro-v1" \| "amazon/nova-pro-v1" \| "microsoft/wizardlm-2-8x22b" \| "gryphe/mythomax-l2-13b" \| "meta-llama/llama-4-scout" \| "meta-llama/llama-4-maverick" \| "x-ai/grok-4" \| "x-ai/grok-4-fast" \| "x-ai/grok-4.1-fast" \| "x-ai/grok-code-fast-1" \| "moonshotai/kimi-k2" \| "qwen/qwen3-235b-a22b-thinking-2507" \| "qwen/qwen3-coder" \| "Llama-4-Scout-17B-16E-Instruct-FP8" \| "Llama-4-Maverick-17B-128E-Instruct-FP8" \| "Llama-3.3-8B-Instruct" \| "Llama-3.3-70B-Instruct" \| "v0-1.5-md" \| "v0-1.5-lg" \| "v0-1.0-md" | No |
 | focus | The topic to focus on in the summary | str | No |
 | style | The style of the summary to generate. | "concise" \| "detailed" \| "bullet points" \| "numbered list" | No |
 | max_tokens | The maximum number of tokens to generate in the chat completion. | int | No |
@@ -763,7 +763,7 @@ Configure agent_mode_max_iterations to control loop behavior: 0 for single decis
 | Input | Description | Type | Required |
 |-------|-------------|------|----------|
 | prompt | The prompt to send to the language model. | str | Yes |
-| model | The language model to use for answering the prompt. | "o3-mini" \| "o3-2025-04-16" \| "o1" \| "o1-mini" \| "gpt-5.2-2025-12-11" \| "gpt-5.1-2025-11-13" \| "gpt-5-2025-08-07" \| "gpt-5-mini-2025-08-07" \| "gpt-5-nano-2025-08-07" \| "gpt-5-chat-latest" \| "gpt-4.1-2025-04-14" \| "gpt-4.1-mini-2025-04-14" \| "gpt-4o-mini" \| "gpt-4o" \| "gpt-4-turbo" \| "gpt-3.5-turbo" \| "claude-opus-4-1-20250805" \| "claude-opus-4-20250514" \| "claude-sonnet-4-20250514" \| "claude-opus-4-5-20251101" \| "claude-sonnet-4-5-20250929" \| "claude-haiku-4-5-20251001" \| "claude-3-7-sonnet-20250219" \| "claude-3-haiku-20240307" \| "Qwen/Qwen2.5-72B-Instruct-Turbo" \| "nvidia/llama-3.1-nemotron-70b-instruct" \| "meta-llama/Llama-3.3-70B-Instruct-Turbo" \| "meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo" \| "meta-llama/Llama-3.2-3B-Instruct-Turbo" \| "llama-3.3-70b-versatile" \| "llama-3.1-8b-instant" \| "llama3.3" \| "llama3.2" \| "llama3" \| "llama3.1:405b" \| "dolphin-mistral:latest" \| "openai/gpt-oss-120b" \| "openai/gpt-oss-20b" \| "google/gemini-2.5-pro-preview-03-25" \| "google/gemini-3-pro-preview" \| "google/gemini-2.5-flash" \| "google/gemini-2.0-flash-001" \| "google/gemini-2.5-flash-lite-preview-06-17" \| "google/gemini-2.0-flash-lite-001" \| "mistralai/mistral-nemo" \| "cohere/command-r-08-2024" \| "cohere/command-r-plus-08-2024" \| "deepseek/deepseek-chat" \| "deepseek/deepseek-r1-0528" \| "perplexity/sonar" \| "perplexity/sonar-pro" \| "perplexity/sonar-deep-research" \| "nousresearch/hermes-3-llama-3.1-405b" \| "nousresearch/hermes-3-llama-3.1-70b" \| "amazon/nova-lite-v1" \| "amazon/nova-micro-v1" \| "amazon/nova-pro-v1" \| "microsoft/wizardlm-2-8x22b" \| "gryphe/mythomax-l2-13b" \| "meta-llama/llama-4-scout" \| "meta-llama/llama-4-maverick" \| "x-ai/grok-4" \| "x-ai/grok-4-fast" \| "x-ai/grok-4.1-fast" \| "x-ai/grok-code-fast-1" \| "moonshotai/kimi-k2" \| "qwen/qwen3-235b-a22b-thinking-2507" \| "qwen/qwen3-coder" \| "Llama-4-Scout-17B-16E-Instruct-FP8" \| "Llama-4-Maverick-17B-128E-Instruct-FP8" \| "Llama-3.3-8B-Instruct" \| "Llama-3.3-70B-Instruct" \| "v0-1.5-md" \| "v0-1.5-lg" \| "v0-1.0-md" | No |
+| model | The language model to use for answering the prompt. | "o3-mini" \| "o3-2025-04-16" \| "o1" \| "o1-mini" \| "gpt-5.2-2025-12-11" \| "gpt-5.1-2025-11-13" \| "gpt-5-2025-08-07" \| "gpt-5-mini-2025-08-07" \| "gpt-5-nano-2025-08-07" \| "gpt-5-chat-latest" \| "gpt-4.1-2025-04-14" \| "gpt-4.1-mini-2025-04-14" \| "gpt-4o-mini" \| "gpt-4o" \| "gpt-4-turbo" \| "gpt-3.5-turbo" \| "claude-opus-4-1-20250805" \| "claude-opus-4-20250514" \| "claude-sonnet-4-20250514" \| "claude-opus-4-5-20251101" \| "claude-sonnet-4-5-20250929" \| "claude-haiku-4-5-20251001" \| "claude-3-haiku-20240307" \| "Qwen/Qwen2.5-72B-Instruct-Turbo" \| "nvidia/llama-3.1-nemotron-70b-instruct" \| "meta-llama/Llama-3.3-70B-Instruct-Turbo" \| "meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo" \| "meta-llama/Llama-3.2-3B-Instruct-Turbo" \| "llama-3.3-70b-versatile" \| "llama-3.1-8b-instant" \| "llama3.3" \| "llama3.2" \| "llama3" \| "llama3.1:405b" \| "dolphin-mistral:latest" \| "openai/gpt-oss-120b" \| "openai/gpt-oss-20b" \| "google/gemini-2.5-pro-preview-03-25" \| "google/gemini-3-pro-preview" \| "google/gemini-2.5-flash" \| "google/gemini-2.0-flash-001" \| "google/gemini-2.5-flash-lite-preview-06-17" \| "google/gemini-2.0-flash-lite-001" \| "mistralai/mistral-nemo" \| "cohere/command-r-08-2024" \| "cohere/command-r-plus-08-2024" \| "deepseek/deepseek-chat" \| "deepseek/deepseek-r1-0528" \| "perplexity/sonar" \| "perplexity/sonar-pro" \| "perplexity/sonar-deep-research" \| "nousresearch/hermes-3-llama-3.1-405b" \| "nousresearch/hermes-3-llama-3.1-70b" \| "amazon/nova-lite-v1" \| "amazon/nova-micro-v1" \| "amazon/nova-pro-v1" \| "microsoft/wizardlm-2-8x22b" \| "gryphe/mythomax-l2-13b" \| "meta-llama/llama-4-scout" \| "meta-llama/llama-4-maverick" \| "x-ai/grok-4" \| "x-ai/grok-4-fast" \| "x-ai/grok-4.1-fast" \| "x-ai/grok-code-fast-1" \| "moonshotai/kimi-k2" \| "qwen/qwen3-235b-a22b-thinking-2507" \| "qwen/qwen3-coder" \| "Llama-4-Scout-17B-16E-Instruct-FP8" \| "Llama-4-Maverick-17B-128E-Instruct-FP8" \| "Llama-3.3-8B-Instruct" \| "Llama-3.3-70B-Instruct" \| "v0-1.5-md" \| "v0-1.5-lg" \| "v0-1.0-md" | No |
 | multiple_tool_calls | Whether to allow multiple tool calls in a single response. | bool | No |
 | sys_prompt | The system prompt to provide additional context to the model. | str | No |
 | conversation_history | The conversation history to provide context for the prompt. | List[Dict[str, Any]] | No |

docs/integrations/block-integrations/stagehand/blocks.md

@@ -20,7 +20,7 @@ Configure timeouts for DOM settlement and page loading. Variables can be passed
 | Input | Description | Type | Required |
 |-------|-------------|------|----------|
 | browserbase_project_id | Browserbase project ID (required if using Browserbase) | str | Yes |
-| model | LLM to use for Stagehand (provider is inferred) | "gpt-4.1-2025-04-14" \| "gpt-4.1-mini-2025-04-14" \| "claude-3-7-sonnet-20250219" | No |
+| model | LLM to use for Stagehand (provider is inferred) | "gpt-4.1-2025-04-14" \| "gpt-4.1-mini-2025-04-14" \| "claude-sonnet-4-5-20250929" | No |
 | url | URL to navigate to. | str | Yes |
 | action | Action to perform. Suggested actions are: click, fill, type, press, scroll, select from dropdown. For multi-step actions, add an entry for each step. | List[str] | Yes |
 | variables | Variables to use in the action. Variables contains data you want the action to use. | Dict[str, str] | No |
@@ -65,7 +65,7 @@ Supports searching within iframes and configurable timeouts for dynamic content
 | Input | Description | Type | Required |
 |-------|-------------|------|----------|
 | browserbase_project_id | Browserbase project ID (required if using Browserbase) | str | Yes |
-| model | LLM to use for Stagehand (provider is inferred) | "gpt-4.1-2025-04-14" \| "gpt-4.1-mini-2025-04-14" \| "claude-3-7-sonnet-20250219" | No |
+| model | LLM to use for Stagehand (provider is inferred) | "gpt-4.1-2025-04-14" \| "gpt-4.1-mini-2025-04-14" \| "claude-sonnet-4-5-20250929" | No |
 | url | URL to navigate to. | str | Yes |
 | instruction | Natural language description of elements or actions to discover. | str | Yes |
 | iframes | Whether to search within iframes. If True, Stagehand will search for actions within iframes. | bool | No |
@@ -106,7 +106,7 @@ Use this to explore a page's interactive elements before building automated work
 | Input | Description | Type | Required |
 |-------|-------------|------|----------|
 | browserbase_project_id | Browserbase project ID (required if using Browserbase) | str | Yes |
-| model | LLM to use for Stagehand (provider is inferred) | "gpt-4.1-2025-04-14" \| "gpt-4.1-mini-2025-04-14" \| "claude-3-7-sonnet-20250219" | No |
+| model | LLM to use for Stagehand (provider is inferred) | "gpt-4.1-2025-04-14" \| "gpt-4.1-mini-2025-04-14" \| "claude-sonnet-4-5-20250929" | No |
 | url | URL to navigate to. | str | Yes |
 | instruction | Natural language description of elements or actions to discover. | str | Yes |
 | iframes | Whether to search within iframes. If True, Stagehand will search for actions within iframes. | bool | No |

docs/integrations/block-integrations/video/add_audio.md

@@ -1,39 +0,0 @@
-# Video Add Audio
-<!-- MANUAL: file_description -->
-This block allows you to attach a separate audio track to a video file, replacing or combining with the original audio.
-<!-- END MANUAL -->
-
-## Add Audio To Video
-
-### What it is
-Block to attach an audio file to a video file using moviepy.
-
-### How it works
-<!-- MANUAL: how_it_works -->
-The block uses MoviePy to combine video and audio files. It loads the video and audio inputs (which can be URLs, data URIs, or local paths), optionally scales the audio volume, then writes the combined result to a new video file using H.264 video codec and AAC audio codec.
-<!-- END MANUAL -->
-
-### Inputs
-
-| Input | Description | Type | Required |
-|-------|-------------|------|----------|
-| video_in | Video input (URL, data URI, or local path). | str (file) | Yes |
-| audio_in | Audio input (URL, data URI, or local path). | str (file) | Yes |
-| volume | Volume scale for the newly attached audio track (1.0 = original). | float | No |
-
-### Outputs
-
-| Output | Description | Type |
-|--------|-------------|------|
-| error | Error message if the operation failed | str |
-| video_out | Final video (with attached audio), as a path or data URI. | str (file) |
-
-### Possible use case
-<!-- MANUAL: use_case -->
-- Adding background music to a silent screen recording
-- Replacing original audio with a voiceover or translated audio track
-- Combining AI-generated speech with stock footage
-- Adding sound effects to video content
-<!-- END MANUAL -->
-
----

docs/integrations/block-integrations/video/clip.md

@@ -1,41 +0,0 @@
-# Video Clip
-<!-- MANUAL: file_description -->
-This block extracts a specific time segment from a video file, allowing you to trim videos to precise start and end times.
-<!-- END MANUAL -->
-
-## Video Clip
-
-### What it is
-Extract a time segment from a video
-
-### How it works
-<!-- MANUAL: how_it_works -->
-The block uses MoviePy's `subclipped` function to extract a portion of the video between specified start and end times. It validates that end time is greater than start time, then creates a new video file containing only the selected segment. The output is encoded with H.264 video codec and AAC audio codec, preserving both video and audio from the original clip.
-<!-- END MANUAL -->
-
-### Inputs
-
-| Input | Description | Type | Required |
-|-------|-------------|------|----------|
-| video_in | Input video (URL, data URI, or local path) | str (file) | Yes |
-| start_time | Start time in seconds | float | Yes |
-| end_time | End time in seconds | float | Yes |
-| output_format | Output format | "mp4" \| "webm" \| "mkv" \| "mov" | No |
-
-### Outputs
-
-| Output | Description | Type |
-|--------|-------------|------|
-| error | Error message if the operation failed | str |
-| video_out | Clipped video file (path or data URI) | str (file) |
-| duration | Clip duration in seconds | float |
-
-### Possible use case
-<!-- MANUAL: use_case -->
-- Extracting highlights from a longer video
-- Trimming intro/outro from recorded content
-- Creating short clips for social media from longer videos
-- Isolating specific segments for further processing in a workflow
-<!-- END MANUAL -->
-
----

docs/integrations/block-integrations/video/concat.md

@@ -1,41 +0,0 @@
-# Video Concat
-<!-- MANUAL: file_description -->
-This block merges multiple video clips into a single continuous video, with optional transitions between clips.
-<!-- END MANUAL -->
-
-## Video Concat
-
-### What it is
-Merge multiple video clips into one continuous video
-
-### How it works
-<!-- MANUAL: how_it_works -->
-The block uses MoviePy's `concatenate_videoclips` function to join multiple videos in sequence. It supports three transition modes: **none** (direct concatenation), **crossfade** (smooth blending where clips overlap), and **fade_black** (each clip fades out to black and the next fades in). At least 2 videos are required. The output is encoded with H.264 video codec and AAC audio codec.
-<!-- END MANUAL -->
-
-### Inputs
-
-| Input | Description | Type | Required |
-|-------|-------------|------|----------|
-| videos | List of video files to concatenate (in order) | List[str (file)] | Yes |
-| transition | Transition between clips | "none" \| "crossfade" \| "fade_black" | No |
-| transition_duration | Transition duration in seconds | int | No |
-| output_format | Output format | "mp4" \| "webm" \| "mkv" \| "mov" | No |
-
-### Outputs
-
-| Output | Description | Type |
-|--------|-------------|------|
-| error | Error message if the operation failed | str |
-| video_out | Concatenated video file (path or data URI) | str (file) |
-| total_duration | Total duration in seconds | float |
-
-### Possible use case
-<!-- MANUAL: use_case -->
-- Combining multiple clips into a compilation video
-- Assembling intro, main content, and outro segments
-- Creating montages from multiple source videos
-- Building video playlists or slideshows with transitions
-<!-- END MANUAL -->
-
----

docs/integrations/block-integrations/video/download.md

@@ -1,42 +0,0 @@
-# Video Download
-<!-- MANUAL: file_description -->
-This block downloads videos from URLs, supporting a wide range of video platforms and direct links.
-<!-- END MANUAL -->
-
-## Video Download
-
-### What it is
-Download video from URL (YouTube, Vimeo, news sites, direct links)
-
-### How it works
-<!-- MANUAL: how_it_works -->
-The block uses yt-dlp, a powerful video downloading library that supports over 1000 websites. It accepts a URL, quality preference, and output format, then downloads the video while merging the best available video and audio streams for the selected quality. Quality options: **best** (highest available), **1080p/720p/480p** (maximum resolution at that height), **audio_only** (extracts just the audio track).
-<!-- END MANUAL -->
-
-### Inputs
-
-| Input | Description | Type | Required |
-|-------|-------------|------|----------|
-| url | URL of the video to download (YouTube, Vimeo, direct link, etc.) | str | Yes |
-| quality | Video quality preference | "best" \| "1080p" \| "720p" \| "480p" \| "audio_only" | No |
-| output_format | Output video format | "mp4" \| "webm" \| "mkv" | No |
-
-### Outputs
-
-| Output | Description | Type |
-|--------|-------------|------|
-| error | Error message if the operation failed | str |
-| video_file | Downloaded video (path or data URI) | str (file) |
-| duration | Video duration in seconds | float |
-| title | Video title from source | str |
-| source_url | Original source URL | str |
-
-### Possible use case
-<!-- MANUAL: use_case -->
-- Downloading source videos for editing or remixing
-- Archiving video content for offline processing
-- Extracting audio from videos for transcription or podcast creation
-- Gathering video content for automated content pipelines
-<!-- END MANUAL -->
-
----

docs/integrations/block-integrations/video/duration.md

@@ -1,38 +0,0 @@
-# Video Duration
-<!-- MANUAL: file_description -->
-This block retrieves the duration of video or audio files, useful for planning and conditional logic in media workflows.
-<!-- END MANUAL -->
-
-## Media Duration
-
-### What it is
-Block to get the duration of a media file.
-
-### How it works
-<!-- MANUAL: how_it_works -->
-The block uses MoviePy to load the media file and extract its duration property. It supports both video files (using VideoFileClip) and audio files (using AudioFileClip), determined by the `is_video` flag. The media can be provided as a URL, data URI, or local file path. The duration is returned in seconds as a floating-point number.
-<!-- END MANUAL -->
-
-### Inputs
-
-| Input | Description | Type | Required |
-|-------|-------------|------|----------|
-| media_in | Media input (URL, data URI, or local path). | str (file) | Yes |
-| is_video | Whether the media is a video (True) or audio (False). | bool | No |
-
-### Outputs
-
-| Output | Description | Type |
-|--------|-------------|------|
-| error | Error message if the operation failed | str |
-| duration | Duration of the media file (in seconds). | float |
-
-### Possible use case
-<!-- MANUAL: use_case -->
-- Checking video length before processing to avoid timeout issues
-- Calculating how many times to loop a video to reach a target duration
-- Validating that uploaded content meets length requirements
-- Building conditional workflows based on media duration
-<!-- END MANUAL -->
-
----

docs/integrations/block-integrations/video/loop.md

@@ -1,39 +0,0 @@
-# Video Loop
-<!-- MANUAL: file_description -->
-This block repeats a video to extend its duration, either to a specific length or a set number of repetitions.
-<!-- END MANUAL -->
-
-## Loop Video
-
-### What it is
-Block to loop a video to a given duration or number of repeats.
-
-### How it works
-<!-- MANUAL: how_it_works -->
-The block uses MoviePy's Loop effect to repeat a video clip. You can specify either a target duration (the video will repeat until reaching that length) or a number of loops (the video will repeat that many times). The Loop effect handles both video and audio looping automatically, maintaining sync. Either `duration` or `n_loops` must be provided. The output is encoded with H.264 video codec and AAC audio codec.
-<!-- END MANUAL -->
-
-### Inputs
-
-| Input | Description | Type | Required |
-|-------|-------------|------|----------|
-| video_in | The input video (can be a URL, data URI, or local path). | str (file) | Yes |
-| duration | Target duration (in seconds) to loop the video to. If omitted, defaults to no looping. | float | No |
-| n_loops | Number of times to repeat the video. If omitted, defaults to 1 (no repeat). | int | No |
-
-### Outputs
-
-| Output | Description | Type |
-|--------|-------------|------|
-| error | Error message if the operation failed | str |
-| video_out | Looped video returned either as a relative path or a data URI. | str |
-
-### Possible use case
-<!-- MANUAL: use_case -->
-- Extending a short background video to match the length of narration audio
-- Creating seamless looping content for digital signage
-- Repeating a product demo video multiple times for emphasis
-- Extending short clips to meet minimum duration requirements for platforms
-<!-- END MANUAL -->
-
----

docs/integrations/block-integrations/video/narration.md

@@ -1,44 +0,0 @@
-# Video Narration
-<!-- MANUAL: file_description -->
-This block generates AI voiceover narration using ElevenLabs and adds it to a video, with flexible audio mixing options.
-<!-- END MANUAL -->
-
-## Video Narration
-
-### What it is
-Generate AI narration and add to video
-
-### How it works
-<!-- MANUAL: how_it_works -->
-The block uses ElevenLabs text-to-speech API to generate natural-sounding narration from your script. It then combines the narration with the video using MoviePy. Three audio mixing modes are available: **replace** (completely replaces original audio), **mix** (blends narration with original audio at configurable volumes), and **ducking** (similar to mix but applies stronger attenuation to original audio, making narration more prominent). The block outputs both the final video and the generated audio file separately.
-<!-- END MANUAL -->
-
-### Inputs
-
-| Input | Description | Type | Required |
-|-------|-------------|------|----------|
-| video_in | Input video (URL, data URI, or local path) | str (file) | Yes |
-| script | Narration script text | str | Yes |
-| voice_id | ElevenLabs voice ID | str | No |
-| model_id | ElevenLabs TTS model | "eleven_multilingual_v2" \| "eleven_flash_v2_5" \| "eleven_turbo_v2_5" \| "eleven_turbo_v2" | No |
-| mix_mode | How to combine with original audio. 'ducking' applies stronger attenuation than 'mix'. | "replace" \| "mix" \| "ducking" | No |
-| narration_volume | Narration volume (0.0 to 2.0) | float | No |
-| original_volume | Original audio volume when mixing (0.0 to 1.0) | float | No |
-
-### Outputs
-
-| Output | Description | Type |
-|--------|-------------|------|
-| error | Error message if the operation failed | str |
-| video_out | Video with narration (path or data URI) | str (file) |
-| audio_file | Generated audio file (path or data URI) | str (file) |
-
-### Possible use case
-<!-- MANUAL: use_case -->
-- Adding professional voiceover to product demos or tutorials
-- Creating narrated explainer videos from screen recordings
-- Generating multi-language versions of video content
-- Adding commentary to gameplay or walkthrough videos
-<!-- END MANUAL -->
-
----

docs/integrations/block-integrations/video/text_overlay.md

@@ -1,44 +0,0 @@
-# Video Text Overlay
-<!-- MANUAL: file_description -->
-This block adds customizable text captions or titles to videos, with control over positioning, timing, and styling.
-<!-- END MANUAL -->
-
-## Video Text Overlay
-
-### What it is
-Add text overlay/caption to video
-
-### How it works
-<!-- MANUAL: how_it_works -->
-The block uses MoviePy's TextClip and CompositeVideoClip to render text onto video frames. The text is created as a separate clip with configurable font size, color, and optional background color, then composited over the video at the specified position. Timing can be controlled to show text only during specific portions of the video. Position options include center alignments (top, center, bottom) and corner positions (top-left, top-right, bottom-left, bottom-right). The output is encoded with H.264 video codec and AAC audio codec.
-<!-- END MANUAL -->
-
-### Inputs
-
-| Input | Description | Type | Required |
-|-------|-------------|------|----------|
-| video_in | Input video (URL, data URI, or local path) | str (file) | Yes |
-| text | Text to overlay on video | str | Yes |
-| position | Position of text on screen | "top" \| "center" \| "bottom" \| "top-left" \| "top-right" \| "bottom-left" \| "bottom-right" | No |
-| start_time | When to show text (seconds). None = entire video | float | No |
-| end_time | When to hide text (seconds). None = until end | float | No |
-| font_size | Font size | int | No |
-| font_color | Font color (hex or name) | str | No |
-| bg_color | Background color behind text (None for transparent) | str | No |
-
-### Outputs
-
-| Output | Description | Type |
-|--------|-------------|------|
-| error | Error message if the operation failed | str |
-| video_out | Video with text overlay (path or data URI) | str (file) |
-
-### Possible use case
-<!-- MANUAL: use_case -->
-- Adding titles or chapter headings to video content
-- Creating lower-thirds with speaker names or captions
-- Watermarking videos with branding text
-- Adding call-to-action text at specific moments in a video
-<!-- END MANUAL -->
-
----

docs/platform/create-basic-agent.md

@@ -4,6 +4,28 @@
 
 This guide walks through creating a simple question-answer AI agent using AutoGPT's visual builder. This is a basic example that can be expanded into more complex agents.
 
+## **Prerequisites**
+
+### **Cloud-Hosted AutoGPT**
+If you're using the cloud-hosted version at [agpt.co](https://agpt.co), you're ready to go! AI blocks come with **built-in credits** — no API keys required to get started. If you'd prefer to use your own API keys, you can add them via **Profile → Integrations**.
+
+### **Self-Hosted (Docker)**
+If you're running AutoGPT locally with Docker, you'll need to add your own API keys to `autogpt_platform/backend/.env`:
+
+```bash
+# Create or edit backend/.env
+OPENAI_API_KEY=sk-your-key-here
+ANTHROPIC_API_KEY=sk-ant-your-key-here
+# Add other provider keys as needed
+```
+
+After adding keys, restart the services:
+```bash
+docker compose down && docker compose up -d
+```
+
+**Note:** The Calculator example below doesn't require any API credentials — it's a good way to test your setup before adding AI blocks.
+
 ## **Example Agent: Q&A (with AI)**
 
 A step-by-step guide to creating a simple Q&A agent using input and output blocks.