fix(tests): revert unnecessary signup test changes

The signup flow still ends at /marketplace via the test helper.
Only the signin tests needed updating since the sort param useEffect was removed.

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

@@ -3,8 +3,7 @@ import logging
 import time
 from asyncio import CancelledError
 from collections.abc import AsyncGenerator
-from dataclasses import dataclass
+from typing import Any
-from typing import Any, cast
 
 import openai
 import orjson
@@ -16,14 +15,7 @@ from openai import (
     PermissionDeniedError,
     RateLimitError,
 )
-from openai.types.chat import (
+from openai.types.chat import ChatCompletionChunk, ChatCompletionToolParam
-    ChatCompletionAssistantMessageParam,
-    ChatCompletionChunk,
-    ChatCompletionMessageParam,
-    ChatCompletionStreamOptionsParam,
-    ChatCompletionSystemMessageParam,
-    ChatCompletionToolParam,
-)
 
 from backend.data.redis_client import get_redis_async
 from backend.data.understanding import (
@@ -31,7 +23,6 @@ from backend.data.understanding import (
     get_business_understanding,
 )
 from backend.util.exceptions import NotFoundError
-from backend.util.prompt import estimate_token_count
 from backend.util.settings import Settings
 
 from . import db as chat_db
@@ -803,201 +794,6 @@ def _is_region_blocked_error(error: Exception) -> bool:
     return "not available in your region" in str(error).lower()
 
 
-# Context window management constants
-TOKEN_THRESHOLD = 120_000
-KEEP_RECENT_MESSAGES = 15
-
-
-@dataclass
-class ContextWindowResult:
-    """Result of context window management."""
-
-    messages: list[dict[str, Any]]
-    token_count: int
-    was_compacted: bool
-    error: str | None = None
-
-
-def _messages_to_dicts(messages: list) -> list[dict[str, Any]]:
-    """Convert message objects to dicts, filtering None values.
-
-    Handles both TypedDict (dict-like) and other message formats.
-    """
-    result = []
-    for msg in messages:
-        if msg is None:
-            continue
-        if isinstance(msg, dict):
-            msg_dict = {k: v for k, v in msg.items() if v is not None}
-        else:
-            msg_dict = dict(msg)
-        result.append(msg_dict)
-    return result
-
-
-async def _manage_context_window(
-    messages: list,
-    model: str,
-    api_key: str | None = None,
-    base_url: str | None = None,
-) -> ContextWindowResult:
-    """
-    Manage context window by summarizing old messages if token count exceeds threshold.
-
-    This function handles context compaction for LLM calls by:
-    1. Counting tokens in the message list
-    2. If over threshold, summarizing old messages while keeping recent ones
-    3. Ensuring tool_call/tool_response pairs stay intact
-    4. Progressively reducing message count if still over limit
-
-    Args:
-        messages: List of messages in OpenAI format (with system prompt if present)
-        model: Model name for token counting
-        api_key: API key for summarization calls
-        base_url: Base URL for summarization calls
-
-    Returns:
-        ContextWindowResult with compacted messages and metadata
-    """
-    if not messages:
-        return ContextWindowResult([], 0, False, "No messages to compact")
-
-    messages_dict = _messages_to_dicts(messages)
-
-    # Normalize model name for token counting (tiktoken only supports OpenAI models)
-    token_count_model = model.split("/")[-1] if "/" in model else model
-    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"
-
-    try:
-        token_count = estimate_token_count(messages_dict, model=token_count_model)
-    except Exception as e:
-        logger.warning(f"Token counting failed: {e}. Using gpt-4o approximation.")
-        token_count_model = "gpt-4o"
-        token_count = estimate_token_count(messages_dict, model=token_count_model)
-
-    if token_count <= TOKEN_THRESHOLD:
-        return ContextWindowResult(messages, token_count, False)
-
-    has_system_prompt = messages[0].get("role") == "system"
-    slice_start = max(0, len(messages_dict) - KEEP_RECENT_MESSAGES)
-    recent_messages = _ensure_tool_pairs_intact(
-        messages_dict[-KEEP_RECENT_MESSAGES:], messages_dict, slice_start
-    )
-
-    # Determine old messages to summarize (explicit bounds to avoid slice edge cases)
-    system_msg = messages[0] if has_system_prompt else None
-    if has_system_prompt:
-        old_messages_dict = (
-            messages_dict[1:-KEEP_RECENT_MESSAGES]
-            if len(messages_dict) > KEEP_RECENT_MESSAGES + 1
-            else []
-        )
-    else:
-        old_messages_dict = (
-            messages_dict[:-KEEP_RECENT_MESSAGES]
-            if len(messages_dict) > KEEP_RECENT_MESSAGES
-            else []
-        )
-
-    # Try to summarize old messages, fall back to truncation on failure
-    summary_msg = None
-    if old_messages_dict:
-        try:
-            summary_text = await _summarize_messages(
-                old_messages_dict, model=model, api_key=api_key, base_url=base_url
-            )
-            summary_msg = ChatCompletionAssistantMessageParam(
-                role="assistant",
-                content=f"[Previous conversation summary — for context only]: {summary_text}",
-            )
-            base = [system_msg, summary_msg] if has_system_prompt else [summary_msg]
-            messages = base + recent_messages
-            logger.info(
-                f"Context summarized: {token_count} tokens, "
-                f"summarized {len(old_messages_dict)} msgs, kept {KEEP_RECENT_MESSAGES}"
-            )
-        except Exception as e:
-            logger.warning(f"Summarization failed, falling back to truncation: {e}")
-            messages = (
-                [system_msg] + recent_messages if has_system_prompt else recent_messages
-            )
-    else:
-        logger.warning(
-            f"Token count {token_count} exceeds threshold but no old messages to summarize"
-        )
-
-    new_token_count = estimate_token_count(
-        _messages_to_dicts(messages), model=token_count_model
-    )
-
-    # Progressive truncation if still over limit
-    if new_token_count > TOKEN_THRESHOLD:
-        logger.warning(
-            f"Still over limit: {new_token_count} tokens. Reducing messages."
-        )
-        base_msgs = (
-            recent_messages
-            if old_messages_dict
-            else (messages_dict[1:] if has_system_prompt else messages_dict)
-        )
-
-        def build_messages(recent: list) -> list:
-            """Build message list with optional system prompt and summary."""
-            prefix = []
-            if has_system_prompt and system_msg:
-                prefix.append(system_msg)
-            if summary_msg:
-                prefix.append(summary_msg)
-            return prefix + recent
-
-        for keep_count in [12, 10, 8, 5, 3, 2, 1, 0]:
-            if keep_count == 0:
-                messages = build_messages([])
-                if not messages:
-                    continue
-            elif len(base_msgs) < keep_count:
-                continue
-            else:
-                reduced = _ensure_tool_pairs_intact(
-                    base_msgs[-keep_count:],
-                    base_msgs,
-                    max(0, len(base_msgs) - keep_count),
-                )
-                messages = build_messages(reduced)
-
-            new_token_count = estimate_token_count(
-                _messages_to_dicts(messages), model=token_count_model
-            )
-            if new_token_count <= TOKEN_THRESHOLD:
-                logger.info(
-                    f"Reduced to {keep_count} messages, {new_token_count} tokens"
-                )
-                break
-        else:
-            logger.error(
-                f"Cannot reduce below threshold. Final: {new_token_count} tokens"
-            )
-            if has_system_prompt and len(messages) > 1:
-                messages = messages[1:]
-                logger.critical("Dropped system prompt as last resort")
-                return ContextWindowResult(
-                    messages, new_token_count, True, "System prompt dropped"
-                )
-            # No system prompt to drop - return error so callers don't proceed with oversized context
-            return ContextWindowResult(
-                messages,
-                new_token_count,
-                True,
-                "Unable to reduce context below token limit",
-            )
-
-    return ContextWindowResult(messages, new_token_count, True)
-
-
 async def _summarize_messages(
     messages: list,
     model: str,
@@ -1226,8 +1022,11 @@ 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,
@@ -1235,16 +1034,204 @@ async def _stream_chat_chunks(
         messages = [system_message] + messages
 
     # Apply context window management
-    context_result = await _manage_context_window(
+    token_count = 0  # Initialize for exception handler
-        messages=messages,
+    try:
+        from backend.util.prompt import estimate_token_count
+
+        # 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,
                     )
 
-    if context_result.error:
+                    # Build new message list
-        if "System prompt dropped" in context_result.error:
+                    # Use assistant role (not system) to prevent privilege escalation
-            # Warning only - continue with reduced context
+                    # 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. "
@@ -1252,21 +1239,109 @@ async def _stream_chat_chunks(
                                     )
                                 )
                 else:
-            # Any other error - abort to prevent failed LLM calls
+                    # 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=(
-                    f"Context window management failed: {context_result.error}. "
+                                    "Warning: System prompt dropped due to size constraints. "
-                    "Please start a new conversation."
+                                    "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:
+            yield StreamError(
+                errorText=(
+                    f"Unable to manage context window (token limit exceeded: {token_count} tokens). "
+                    "Context summarization failed. 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:
@@ -1294,6 +1369,14 @@ 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),
@@ -1817,36 +1900,17 @@ 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": {
@@ -1859,54 +1923,19 @@ async def _generate_llm_continuation(
         if session_id:
             extra_body["session_id"] = session_id[:128]
 
-        retry_count = 0
+        # Make non-streaming LLM call (no tools - just text response)
-        last_error: Exception | None = None
+        from typing import cast
-        response = None
 
-        while retry_count <= MAX_RETRIES:
+        from openai.types.chat import ChatCompletionMessageParam
-            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,
         )
-                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 last_error:
+        if response.choices and response.choices[0].message.content:
-            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/service.py

@@ -139,10 +139,11 @@ async def decompose_goal_external(
     """
     client = _get_client()
 
-    if context:
+    # Build the request payload
-        description = f"{description}\n\nAdditional context from user:\n{context}"
-
     payload: dict[str, Any] = {"description": description}
+    if context:
+        # The external service uses user_instruction for additional context
+        payload["user_instruction"] = context
     if library_agents:
         payload["library_agents"] = library_agents
 

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

@@ -104,10 +104,52 @@ async def list_library_agents(
         order_by = {"updatedAt": "desc"}
 
     try:
+        # For LAST_EXECUTED sorting, we need to fetch execution data and sort in Python
+        # since Prisma doesn't support sorting by nested relations
+        if sort_by == library_model.LibraryAgentSort.LAST_EXECUTED:
+            # TODO: This fetches all agents into memory for sorting, which may cause
+            # performance issues for users with many agents. Prisma doesn't support
+            # sorting by nested relations, so a dedicated lastExecutedAt column or
+            # raw SQL query would be needed for database-level pagination.
             library_agents = await prisma.models.LibraryAgent.prisma().find_many(
                 where=where_clause,
                 include=library_agent_include(
-                user_id, include_nodes=False, include_executions=include_executions
+                    user_id,
+                    include_nodes=False,
+                    include_executions=True,
+                    execution_limit=1,
+                ),
+            )
+
+            def get_sort_key(
+                agent: prisma.models.LibraryAgent,
+            ) -> tuple[int, float]:
+                """
+                Returns a tuple for sorting: (has_no_executions, -timestamp).
+
+                Agents WITH executions come first (sorted by most recent execution),
+                agents WITHOUT executions come last (sorted by creation date).
+                """
+                graph = agent.AgentGraph
+                if graph and graph.Executions and len(graph.Executions) > 0:
+                    execution = graph.Executions[0]
+                    timestamp = execution.updatedAt or execution.createdAt
+                    return (0, -timestamp.timestamp())
+                return (1, -agent.createdAt.timestamp())
+
+            library_agents.sort(key=get_sort_key)
+
+            # Apply pagination after sorting
+            agent_count = len(library_agents)
+            start_idx = (page - 1) * page_size
+            end_idx = start_idx + page_size
+            library_agents = library_agents[start_idx:end_idx]
+        else:
+            # Standard sorting via database
+            library_agents = await prisma.models.LibraryAgent.prisma().find_many(
+                where=where_clause,
+                include=library_agent_include(
+                    user_id, include_nodes=False, include_executions=False
                 ),
                 order=order_by,
                 skip=(page - 1) * page_size,
@@ -345,6 +387,20 @@ async def get_library_agent_by_graph_id(
     graph_id: str,
     graph_version: Optional[int] = None,
 ) -> library_model.LibraryAgent | None:
+    """
+    Retrieves a library agent by its graph ID for a given user.
+
+    Args:
+        user_id: The ID of the user who owns the library agent.
+        graph_id: The ID of the agent graph to look up.
+        graph_version: Optional specific version of the graph to retrieve.
+
+    Returns:
+        The LibraryAgent if found, otherwise None.
+
+    Raises:
+        DatabaseError: If there's an error during retrieval.
+    """
     try:
         filter: prisma.types.LibraryAgentWhereInput = {
             "agentGraphId": graph_id,
@@ -628,6 +684,17 @@ async def update_library_agent(
 async def delete_library_agent(
     library_agent_id: str, user_id: str, soft_delete: bool = True
 ) -> None:
+    """
+    Deletes a library agent and cleans up associated schedules and webhooks.
+
+    Args:
+        library_agent_id: The ID of the library agent to delete.
+        user_id: The ID of the user who owns the library agent.
+        soft_delete: If True, marks the agent as deleted; if False, permanently removes it.
+
+    Raises:
+        NotFoundError: If the library agent is not found or doesn't belong to the user.
+    """
     # First get the agent to find the graph_id for cleanup
     library_agent = await prisma.models.LibraryAgent.prisma().find_unique(
         where={"id": library_agent_id}, include={"AgentGraph": True}
@@ -1121,6 +1188,20 @@ async def update_preset(
 async def set_preset_webhook(
     user_id: str, preset_id: str, webhook_id: str | None
 ) -> library_model.LibraryAgentPreset:
+    """
+    Sets or removes a webhook connection for a preset.
+
+    Args:
+        user_id: The ID of the user who owns the preset.
+        preset_id: The ID of the preset to update.
+        webhook_id: The ID of the webhook to connect, or None to disconnect.
+
+    Returns:
+        The updated LibraryAgentPreset.
+
+    Raises:
+        NotFoundError: If the preset is not found or doesn't belong to the user.
+    """
     current = await prisma.models.AgentPreset.prisma().find_unique(
         where={"id": preset_id},
         include=AGENT_PRESET_INCLUDE,

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

@@ -1,4 +1,4 @@
-from datetime import datetime
+from datetime import datetime, timedelta, timezone
 
 import prisma.enums
 import prisma.models
@@ -9,6 +9,7 @@ from backend.data.db import connect
 from backend.data.includes import library_agent_include
 
 from . import db
+from . import model as library_model
 
 
 @pytest.mark.asyncio
@@ -225,3 +226,183 @@ async def test_add_agent_to_library_not_found(mocker):
     mock_store_listing_version.return_value.find_unique.assert_called_once_with(
         where={"id": "version123"}, include={"AgentGraph": True}
     )
+
+
+@pytest.mark.asyncio
+async def test_list_library_agents_sort_by_last_executed(mocker):
+    """
+    Test LAST_EXECUTED sorting behavior:
+    - Agents WITH executions come first, sorted by most recent execution (updatedAt)
+    - Agents WITHOUT executions come last, sorted by creation date
+    """
+    now = datetime.now(timezone.utc)
+
+    # Agent 1: Has execution that finished 1 hour ago
+    agent1_execution = prisma.models.AgentGraphExecution(
+        id="exec1",
+        agentGraphId="agent1",
+        agentGraphVersion=1,
+        userId="test-user",
+        createdAt=now - timedelta(hours=2),
+        updatedAt=now - timedelta(hours=1),  # Finished 1 hour ago
+        executionStatus=prisma.enums.AgentExecutionStatus.COMPLETED,
+        isDeleted=False,
+        isShared=False,
+    )
+    agent1_graph = prisma.models.AgentGraph(
+        id="agent1",
+        version=1,
+        name="Agent With Recent Execution",
+        description="Has execution finished 1 hour ago",
+        userId="test-user",
+        isActive=True,
+        createdAt=now - timedelta(days=5),
+        Executions=[agent1_execution],
+    )
+    library_agent1 = prisma.models.LibraryAgent(
+        id="lib1",
+        userId="test-user",
+        agentGraphId="agent1",
+        agentGraphVersion=1,
+        settings="{}",  # type: ignore
+        isCreatedByUser=True,
+        isDeleted=False,
+        isArchived=False,
+        createdAt=now - timedelta(days=5),
+        updatedAt=now - timedelta(days=5),
+        isFavorite=False,
+        useGraphIsActiveVersion=True,
+        AgentGraph=agent1_graph,
+    )
+
+    # Agent 2: Has execution that finished 3 hours ago
+    agent2_execution = prisma.models.AgentGraphExecution(
+        id="exec2",
+        agentGraphId="agent2",
+        agentGraphVersion=1,
+        userId="test-user",
+        createdAt=now - timedelta(hours=5),
+        updatedAt=now - timedelta(hours=3),  # Finished 3 hours ago
+        executionStatus=prisma.enums.AgentExecutionStatus.COMPLETED,
+        isDeleted=False,
+        isShared=False,
+    )
+    agent2_graph = prisma.models.AgentGraph(
+        id="agent2",
+        version=1,
+        name="Agent With Older Execution",
+        description="Has execution finished 3 hours ago",
+        userId="test-user",
+        isActive=True,
+        createdAt=now - timedelta(days=3),
+        Executions=[agent2_execution],
+    )
+    library_agent2 = prisma.models.LibraryAgent(
+        id="lib2",
+        userId="test-user",
+        agentGraphId="agent2",
+        agentGraphVersion=1,
+        settings="{}",  # type: ignore
+        isCreatedByUser=True,
+        isDeleted=False,
+        isArchived=False,
+        createdAt=now - timedelta(days=3),
+        updatedAt=now - timedelta(days=3),
+        isFavorite=False,
+        useGraphIsActiveVersion=True,
+        AgentGraph=agent2_graph,
+    )
+
+    # Agent 3: No executions, created 1 day ago (should come after agents with executions)
+    agent3_graph = prisma.models.AgentGraph(
+        id="agent3",
+        version=1,
+        name="Agent Without Executions (Newer)",
+        description="No executions, created 1 day ago",
+        userId="test-user",
+        isActive=True,
+        createdAt=now - timedelta(days=1),
+        Executions=[],
+    )
+    library_agent3 = prisma.models.LibraryAgent(
+        id="lib3",
+        userId="test-user",
+        agentGraphId="agent3",
+        agentGraphVersion=1,
+        settings="{}",  # type: ignore
+        isCreatedByUser=True,
+        isDeleted=False,
+        isArchived=False,
+        createdAt=now - timedelta(days=1),
+        updatedAt=now - timedelta(days=1),
+        isFavorite=False,
+        useGraphIsActiveVersion=True,
+        AgentGraph=agent3_graph,
+    )
+
+    # Agent 4: No executions, created 2 days ago
+    agent4_graph = prisma.models.AgentGraph(
+        id="agent4",
+        version=1,
+        name="Agent Without Executions (Older)",
+        description="No executions, created 2 days ago",
+        userId="test-user",
+        isActive=True,
+        createdAt=now - timedelta(days=2),
+        Executions=[],
+    )
+    library_agent4 = prisma.models.LibraryAgent(
+        id="lib4",
+        userId="test-user",
+        agentGraphId="agent4",
+        agentGraphVersion=1,
+        settings="{}",  # type: ignore
+        isCreatedByUser=True,
+        isDeleted=False,
+        isArchived=False,
+        createdAt=now - timedelta(days=2),
+        updatedAt=now - timedelta(days=2),
+        isFavorite=False,
+        useGraphIsActiveVersion=True,
+        AgentGraph=agent4_graph,
+    )
+
+    # Return agents in random order to verify sorting works
+    mock_library_agents = [
+        library_agent3,
+        library_agent1,
+        library_agent4,
+        library_agent2,
+    ]
+
+    # Mock prisma calls
+    mock_agent_graph = mocker.patch("prisma.models.AgentGraph.prisma")
+    mock_agent_graph.return_value.find_many = mocker.AsyncMock(return_value=[])
+
+    mock_library_agent = mocker.patch("prisma.models.LibraryAgent.prisma")
+    mock_library_agent.return_value.find_many = mocker.AsyncMock(
+        return_value=mock_library_agents
+    )
+
+    # Call function with LAST_EXECUTED sort
+    result = await db.list_library_agents(
+        "test-user",
+        sort_by=library_model.LibraryAgentSort.LAST_EXECUTED,
+    )
+
+    # Verify sorting order:
+    # 1. Agent 1 (execution finished 1 hour ago) - most recent execution
+    # 2. Agent 2 (execution finished 3 hours ago) - older execution
+    # 3. Agent 3 (no executions, created 1 day ago) - newer creation
+    # 4. Agent 4 (no executions, created 2 days ago) - older creation
+    assert len(result.agents) == 4
+    assert (
+        result.agents[0].id == "lib1"
+    ), "Agent with most recent execution should be first"
+    assert result.agents[1].id == "lib2", "Agent with older execution should be second"
+    assert (
+        result.agents[2].id == "lib3"
+    ), "Agent without executions (newer) should be third"
+    assert (
+        result.agents[3].id == "lib4"
+    ), "Agent without executions (older) should be last"

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

@@ -442,6 +442,7 @@ class LibraryAgentSort(str, Enum):
 
     CREATED_AT = "createdAt"
     UPDATED_AT = "updatedAt"
+    LAST_EXECUTED = "lastExecuted"
 
 
 class LibraryAgentUpdateRequest(pydantic.BaseModel):

autogpt_platform/backend/backend/api/features/library/routes/agents.py

@@ -28,7 +28,7 @@ async def list_library_agents(
         None, description="Search term to filter agents"
     ),
     sort_by: library_model.LibraryAgentSort = Query(
-        library_model.LibraryAgentSort.UPDATED_AT,
+        library_model.LibraryAgentSort.LAST_EXECUTED,
         description="Criteria to sort results by",
     ),
     page: int = Query(

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

@@ -112,7 +112,7 @@ async def test_get_library_agents_success(
     mock_db_call.assert_called_once_with(
         user_id=test_user_id,
         search_term="test",
-        sort_by=library_model.LibraryAgentSort.UPDATED_AT,
+        sort_by=library_model.LibraryAgentSort.LAST_EXECUTED,
         page=1,
         page_size=15,
     )

autogpt_platform/backend/backend/data/includes.py

@@ -119,7 +119,9 @@ def library_agent_include(
         if include_executions:
             agent_graph_include["Executions"] = {
                 "where": {"userId": user_id},
-                "order_by": {"createdAt": "desc"},
+                "order_by": {
+                    "updatedAt": "desc"
+                },  # Uses updatedAt because it reflects when the executioncompleted or last progressed
                 "take": execution_limit,
             }
 

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

@@ -1,39 +0,0 @@
-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/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 enriched into description."""
+        """Test decomposition with additional context."""
         mock_response = MagicMock()
         mock_response.json.return_value = {
             "success": True,
@@ -119,12 +119,9 @@ 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": expected_description},
+            json={"description": "Build a chatbot", "user_instruction": "Use Python"},
         )
 
     @pytest.mark.asyncio

autogpt_platform/frontend/src/app/(platform)/library/components/LibrarySortMenu/LibrarySortMenu.tsx

@@ -23,10 +23,13 @@ export function LibrarySortMenu({ setLibrarySort }: Props) {
       <Select onValueChange={handleSortChange}>
         <SelectTrigger className="ml-1 w-fit space-x-1 border-none px-0 text-base underline underline-offset-4 shadow-none">
           <ArrowDownNarrowWideIcon className="h-4 w-4 sm:hidden" />
-          <SelectValue placeholder="Last Modified" />
+          <SelectValue placeholder="Last Executed" />
         </SelectTrigger>
         <SelectContent>
           <SelectGroup>
+            <SelectItem value={LibraryAgentSort.lastExecuted}>
+              Last Executed
+            </SelectItem>
             <SelectItem value={LibraryAgentSort.createdAt}>
               Creation Date
             </SelectItem>

autogpt_platform/frontend/src/app/(platform)/library/components/LibrarySortMenu/useLibrarySortMenu.ts

@@ -11,12 +11,14 @@ export function useLibrarySortMenu({ setLibrarySort }: Props) {
 
   const getSortLabel = (sort: LibraryAgentSort) => {
     switch (sort) {
+      case LibraryAgentSort.lastExecuted:
+        return "Last Executed";
       case LibraryAgentSort.createdAt:
         return "Creation Date";
       case LibraryAgentSort.updatedAt:
         return "Last Modified";
       default:
-        return "Last Modified";
+        return "Last Executed";
     }
   };
 

autogpt_platform/frontend/src/app/(platform)/library/components/useLibraryListPage.ts

@@ -2,7 +2,7 @@
 
 import { LibraryAgentSort } from "@/app/api/__generated__/models/libraryAgentSort";
 import { parseAsStringEnum, useQueryState } from "nuqs";
-import { useCallback, useEffect, useMemo, useState } from "react";
+import { useCallback, useMemo, useState } from "react";
 
 const sortParser = parseAsStringEnum(Object.values(LibraryAgentSort));
 
@@ -11,14 +11,7 @@ export function useLibraryListPage() {
   const [uploadedFile, setUploadedFile] = useState<File | null>(null);
   const [librarySortRaw, setLibrarySortRaw] = useQueryState("sort", sortParser);
 
-  // Ensure sort param is always present in URL (even if default)
+  const librarySort = librarySortRaw || LibraryAgentSort.lastExecuted;
-  useEffect(() => {
-    if (!librarySortRaw) {
-      setLibrarySortRaw(LibraryAgentSort.updatedAt, { shallow: false });
-    }
-  }, [librarySortRaw, setLibrarySortRaw]);
-
-  const librarySort = librarySortRaw || LibraryAgentSort.updatedAt;
 
   const setLibrarySort = useCallback(
     (value: LibraryAgentSort) => {

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

@@ -3361,7 +3361,7 @@
             "schema": {
               "$ref": "#/components/schemas/LibraryAgentSort",
               "description": "Criteria to sort results by",
-              "default": "updatedAt"
+              "default": "lastExecuted"
             },
             "description": "Criteria to sort results by"
           },
@@ -8239,7 +8239,7 @@
       },
       "LibraryAgentSort": {
         "type": "string",
-        "enum": ["createdAt", "updatedAt"],
+        "enum": ["createdAt", "updatedAt", "lastExecuted"],
         "title": "LibraryAgentSort",
         "description": "Possible sort options for sorting library agents."
       },

autogpt_platform/frontend/src/lib/autogpt-server-api/types.ts

@@ -612,6 +612,7 @@ export type LibraryAgentPresetUpdatable = Partial<
 export enum LibraryAgentSortEnum {
   CREATED_AT = "createdAt",
   UPDATED_AT = "updatedAt",
+  LAST_EXECUTED = "lastExecuted",
 }
 
 /* *** CREDENTIALS *** */

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

@@ -85,7 +85,7 @@ export class LibraryPage extends BasePage {
 
   async selectSortOption(
     page: Page,
-    sortOption: "Creation Date" | "Last Modified",
+    sortOption: "Last Executed" | "Creation Date" | "Last Modified",
   ): Promise<void> {
     const { getRole } = getSelectors(page);
     await getRole("combobox").click();

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

@@ -182,7 +182,7 @@ test("logged in user is redirected from /login to /library", async ({
   await hasUrl(page, "/marketplace");
 
   await page.goto("/login");
-  await hasUrl(page, "/library?sort=updatedAt");
+  await hasUrl(page, "/library");
 });
 
 test("logged in user is redirected from /signup to /library", async ({
@@ -195,5 +195,5 @@ test("logged in user is redirected from /signup to /library", async ({
   await hasUrl(page, "/marketplace");
 
   await page.goto("/signup");
-  await hasUrl(page, "/library?sort=updatedAt");
+  await hasUrl(page, "/library");
 });