refactor(backend): extract context window management and fix LLM continuation (#11936)

## Summary

Fixes CoPilot becoming unresponsive after long-running tools complete,
and refactors context window management into a reusable function.

## Problem

After `create_agent` completes, `_generate_llm_continuation()` was
sending ALL messages to OpenRouter without any context compaction. When
conversations exceeded ~50 messages, OpenRouter rejected requests with
`provider_name: 'unknown'` (no provider would accept).

**Evidence:** Langfuse session
[44fbb803-092e-4ebd-b288-852959f4faf5](https://cloud.langfuse.com/project/cmk5qhf210003ad079sd8utjt/sessions/44fbb803-092e-4ebd-b288-852959f4faf5)
showed:
- Successful calls: 32-50 messages, known providers
- Failed calls: 52+ messages, `provider: unknown`, `completion: null`

## Changes

### Refactor: Extract reusable `_manage_context_window()`
- Counts tokens and checks against 120k threshold
- Summarizes old messages while keeping recent 15
- Ensures tool_call/tool_response pairs stay intact
- Progressive truncation if still over limit
- Returns `ContextWindowResult` dataclass with messages, token count,
compaction status, and errors
- Helper `_messages_to_dicts()` reduces code duplication

### Fix: Update `_generate_llm_continuation()`
- Now calls `_manage_context_window()` before making LLM calls
- Adds retry logic with exponential backoff (matching
`_stream_chat_chunks` behavior)

### Cleanup: Update `_stream_chat_chunks()`
- Replaced inline context management with call to
`_manage_context_window()`
- Eliminates code duplication between the two functions

## Testing

- Syntax check: ✅
- Ruff lint: ✅
- Import verification: ✅

## Checklist

- [x] My code follows the style guidelines of this project
- [x] I have performed a self-review of my own code
- [x] My changes generate no new warnings
- [x] I have checked that my changes do not break existing functionality

---------

Co-authored-by: Otto <otto@agpt.co>

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

@@ -3,7 +3,8 @@ import logging
 import time
 from asyncio import CancelledError
 from collections.abc import AsyncGenerator
-from typing import Any
+from dataclasses import dataclass
+from typing import Any, cast
 
 import openai
 import orjson
@@ -15,7 +16,14 @@ from openai import (
     PermissionDeniedError,
     RateLimitError,
 )
-from openai.types.chat import ChatCompletionChunk, ChatCompletionToolParam
+from openai.types.chat import (
+    ChatCompletionAssistantMessageParam,
+    ChatCompletionChunk,
+    ChatCompletionMessageParam,
+    ChatCompletionStreamOptionsParam,
+    ChatCompletionSystemMessageParam,
+    ChatCompletionToolParam,
+)
 
 from backend.data.redis_client import get_redis_async
 from backend.data.understanding import (
@@ -23,6 +31,7 @@ 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
@@ -794,6 +803,201 @@ 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,
@@ -1022,11 +1226,8 @@ 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,
@@ -1034,204 +1235,16 @@ async def _stream_chat_chunks(
         messages = [system_message] + messages
 
     # Apply context window management
-    token_count = 0  # Initialize for exception handler
+    context_result = await _manage_context_window(
-    try:
+        messages=messages,
-        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,
     )
 
-                    # Build new message list
+    if context_result.error:
-                    # Use assistant role (not system) to prevent privilege escalation
+        if "System prompt dropped" in context_result.error:
-                    # of user-influenced content to instruction-level authority
+            # Warning only - continue with reduced context
-                    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. "
@@ -1239,109 +1252,21 @@ async def _stream_chat_chunks(
                 )
             )
         else:
-                    # No old messages to summarize - all messages are "recent"
+            # Any other error - abort to prevent failed LLM calls
-                    # 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. "
+                    f"Context window management failed: {context_result.error}. "
-                                    "Assistant behavior may be affected."
+                    "Please start a new conversation."
-                                )
-                            )
-
-    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:
@@ -1369,14 +1294,6 @@ 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),
@@ -1900,17 +1817,36 @@ 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": {
@@ -1923,19 +1859,54 @@ 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)
+        retry_count = 0
-        from typing import cast
+        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,
                 )
+                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/service.py

@@ -139,11 +139,10 @@ 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
+        description = f"{description}\n\nAdditional context from user:\n{context}"
-        payload["user_instruction"] = context
+
+    payload: dict[str, Any] = {"description": description}
     if library_agents:
         payload["library_agents"] = library_agents
 

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

@@ -104,52 +104,10 @@ 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,
+                user_id, include_nodes=False, include_executions=include_executions
-                    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,
@@ -387,20 +345,6 @@ 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,
@@ -684,17 +628,6 @@ 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}
@@ -1188,20 +1121,6 @@ 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, timedelta, timezone
+from datetime import datetime
 
 import prisma.enums
 import prisma.models
@@ -9,7 +9,6 @@ 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
@@ -226,183 +225,3 @@ 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

@@ -442,7 +442,6 @@ 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.LAST_EXECUTED,
+        library_model.LibraryAgentSort.UPDATED_AT,
         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.LAST_EXECUTED,
+        sort_by=library_model.LibraryAgentSort.UPDATED_AT,
         page=1,
         page_size=15,
     )

autogpt_platform/backend/backend/data/includes.py

@@ -119,9 +119,7 @@ def library_agent_include(
         if include_executions:
             agent_graph_include["Executions"] = {
                 "where": {"userId": user_id},
-                "order_by": {
+                "order_by": {"createdAt": "desc"},
-                    "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

@@ -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/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

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

@@ -23,13 +23,10 @@ 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 Executed" />
+          <SelectValue placeholder="Last Modified" />
         </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

@@ -11,14 +11,12 @@ 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 Executed";
+        return "Last Modified";
     }
   };
 

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, useMemo, useState } from "react";
+import { useCallback, useEffect, useMemo, useState } from "react";
 
 const sortParser = parseAsStringEnum(Object.values(LibraryAgentSort));
 
@@ -11,7 +11,14 @@ export function useLibraryListPage() {
   const [uploadedFile, setUploadedFile] = useState<File | null>(null);
   const [librarySortRaw, setLibrarySortRaw] = useQueryState("sort", sortParser);
 
-  const librarySort = librarySortRaw || LibraryAgentSort.lastExecuted;
+  // Ensure sort param is always present in URL (even if default)
+  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": "lastExecuted"
+              "default": "updatedAt"
             },
             "description": "Criteria to sort results by"
           },
@@ -8239,7 +8239,7 @@
       },
       "LibraryAgentSort": {
         "type": "string",
-        "enum": ["createdAt", "updatedAt", "lastExecuted"],
+        "enum": ["createdAt", "updatedAt"],
         "title": "LibraryAgentSort",
         "description": "Possible sort options for sorting library agents."
       },

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

@@ -612,7 +612,6 @@ 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: "Last Executed" | "Creation Date" | "Last Modified",
+    sortOption: "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");
+  await hasUrl(page, "/library?sort=updatedAt");
 });
 
 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");
+  await hasUrl(page, "/library?sort=updatedAt");
 });