feat(copilot): Add detailed API error logging for debugging

Adds comprehensive error logging for OpenRouter/OpenAI API errors to help
diagnose issues like provider routing failures, context length exceeded,
rate limits, etc.

Changes:
- Add _extract_api_error_details() to extract rich info from API errors
  including status code, response body, OpenRouter headers, etc.
- Add _log_api_error() helper to log errors with context (session ID,
  message count, model, retry count)
- Update error handling in _stream_chat_chunks() to use new logging
- Update error handling in _generate_llm_continuation() to use new logging
- Extract provider's error message from response body for better user feedback

This helps debug issues like SECRT-1859 where OpenRouter returns
'provider returned error' with provider_name='unknown' without
capturing the actual error details.

Refs: SECRT-1859

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,
@@ -1034,314 +1235,38 @@ async def _stream_chat_chunks(
         messages = [system_message] + messages
 
     # Apply context window management
-    token_count = 0  # Initialize for exception handler
-    try:
-        from backend.util.prompt import estimate_token_count
+    context_result = await _manage_context_window(
+        messages=messages,
+        model=model,
+        api_key=config.api_key,
+        base_url=config.base_url,
+    )
 
-        # Convert to dict for token counting
-        # OpenAI message types are TypedDicts, so they're already dict-like
-        messages_dict = []
-        for msg in messages:
-            # TypedDict objects are already dicts, just filter None values
-            if isinstance(msg, dict):
-                msg_dict = {k: v for k, v in msg.items() if v is not None}
-            else:
-                # Fallback for unexpected types
-                msg_dict = dict(msg)
-            messages_dict.append(msg_dict)
-
-        # Estimate tokens using appropriate tokenizer
-        # Normalize model name for token counting (tiktoken only supports OpenAI models)
-        token_count_model = model
-        if "/" in model:
-            # Strip provider prefix (e.g., "anthropic/claude-opus-4.5" -> "claude-opus-4.5")
-            token_count_model = model.split("/")[-1]
-
-        # For Claude and other non-OpenAI models, approximate with gpt-4o tokenizer
-        # Most modern LLMs have similar tokenization (~1 token per 4 chars)
-        if "claude" in token_count_model.lower() or not any(
-            known in token_count_model.lower()
-            for known in ["gpt", "o1", "chatgpt", "text-"]
-        ):
-            token_count_model = "gpt-4o"
-
-        # Attempt token counting with error handling
-        try:
-            token_count = estimate_token_count(messages_dict, model=token_count_model)
-        except Exception as token_error:
-            # If token counting fails, use gpt-4o as fallback approximation
-            logger.warning(
-                f"Token counting failed for model {token_count_model}: {token_error}. "
-                "Using gpt-4o approximation."
-            )
-            token_count = estimate_token_count(messages_dict, model="gpt-4o")
-
-        # If over threshold, summarize old messages
-        if token_count > 120_000:
-            KEEP_RECENT = 15
-
-            # Check if we have a system prompt at the start
-            has_system_prompt = (
-                len(messages) > 0 and messages[0].get("role") == "system"
-            )
-
-            # Always attempt mitigation when over limit, even with few messages
-            if messages:
-                # Split messages based on whether system prompt exists
-                # Calculate start index for the slice
-                slice_start = max(0, len(messages_dict) - KEEP_RECENT)
-                recent_messages = messages_dict[-KEEP_RECENT:]
-
-                # Ensure tool_call/tool_response pairs stay together
-                # This prevents API errors from orphan tool responses
-                recent_messages = _ensure_tool_pairs_intact(
-                    recent_messages, messages_dict, slice_start
-                )
-
-                if has_system_prompt:
-                    # Keep system prompt separate, summarize everything between system and recent
-                    system_msg = messages[0]
-                    old_messages_dict = messages_dict[1:-KEEP_RECENT]
-                else:
-                    # No system prompt, summarize everything except recent
-                    system_msg = None
-                    old_messages_dict = messages_dict[:-KEEP_RECENT]
-
-                # Summarize any non-empty old messages (no minimum threshold)
-                # If we're over the token limit, we need to compress whatever we can
-                if old_messages_dict:
-                    # Summarize old messages using the same model as chat
-                    summary_text = await _summarize_messages(
-                        old_messages_dict,
-                        model=model,
-                        api_key=config.api_key,
-                        base_url=config.base_url,
-                    )
-
-                    # Build new message list
-                    # Use assistant role (not system) to prevent privilege escalation
-                    # of user-influenced content to instruction-level authority
-                    from openai.types.chat import ChatCompletionAssistantMessageParam
-
-                    summary_msg = ChatCompletionAssistantMessageParam(
-                        role="assistant",
-                        content=(
-                            "[Previous conversation summary — for context only]: "
-                            f"{summary_text}"
-                        ),
-                    )
-
-                    # Rebuild messages based on whether we have a system prompt
-                    if has_system_prompt:
-                        # system_prompt + summary + recent_messages
-                        messages = [system_msg, summary_msg] + recent_messages
-                    else:
-                        # summary + recent_messages (no original system prompt)
-                        messages = [summary_msg] + recent_messages
-
-                    logger.info(
-                        f"Context summarized: {token_count} tokens, "
-                        f"summarized {len(old_messages_dict)} old messages, "
-                        f"kept last {KEEP_RECENT} messages"
-                    )
-
-                    # Fallback: If still over limit after summarization, progressively drop recent messages
-                    # This handles edge cases where recent messages are extremely large
-                    new_messages_dict = []
-                    for msg in messages:
-                        if isinstance(msg, dict):
-                            msg_dict = {k: v for k, v in msg.items() if v is not None}
-                        else:
-                            msg_dict = dict(msg)
-                        new_messages_dict.append(msg_dict)
-
-                    new_token_count = estimate_token_count(
-                        new_messages_dict, model=token_count_model
-                    )
-
-                    if new_token_count > 120_000:
-                        # Still over limit - progressively reduce KEEP_RECENT
-                        logger.warning(
-                            f"Still over limit after summarization: {new_token_count} tokens. "
-                            "Reducing number of recent messages kept."
-                        )
-
-                        for keep_count in [12, 10, 8, 5, 3, 2, 1, 0]:
-                            if keep_count == 0:
-                                # Try with just system prompt + summary (no recent messages)
-                                if has_system_prompt:
-                                    messages = [system_msg, summary_msg]
-                                else:
-                                    messages = [summary_msg]
-                                logger.info(
-                                    "Trying with 0 recent messages (system + summary only)"
-                                )
-                            else:
-                                # Slice from ORIGINAL recent_messages to avoid duplicating summary
-                                reduced_recent = (
-                                    recent_messages[-keep_count:]
-                                    if len(recent_messages) >= keep_count
-                                    else recent_messages
-                                )
-                                # Ensure tool pairs stay intact in the reduced slice
-                                reduced_slice_start = max(
-                                    0, len(recent_messages) - keep_count
-                                )
-                                reduced_recent = _ensure_tool_pairs_intact(
-                                    reduced_recent, recent_messages, reduced_slice_start
-                                )
-                                if has_system_prompt:
-                                    messages = [
-                                        system_msg,
-                                        summary_msg,
-                                    ] + reduced_recent
-                                else:
-                                    messages = [summary_msg] + reduced_recent
-
-                            new_messages_dict = []
-                            for msg in messages:
-                                if isinstance(msg, dict):
-                                    msg_dict = {
-                                        k: v for k, v in msg.items() if v is not None
-                                    }
-                                else:
-                                    msg_dict = dict(msg)
-                                new_messages_dict.append(msg_dict)
-
-                            new_token_count = estimate_token_count(
-                                new_messages_dict, model=token_count_model
-                            )
-
-                            if new_token_count <= 120_000:
-                                logger.info(
-                                    f"Reduced to {keep_count} recent messages, "
-                                    f"now {new_token_count} tokens"
-                                )
-                                break
-                        else:
-                            logger.error(
-                                f"Unable to reduce token count below threshold even with 0 messages. "
-                                f"Final count: {new_token_count} tokens"
-                            )
-                            # ABSOLUTE LAST RESORT: Drop system prompt
-                            # This should only happen if summary itself is massive
-                            if has_system_prompt and len(messages) > 1:
-                                messages = messages[1:]  # Drop system prompt
-                                logger.critical(
-                                    "CRITICAL: Dropped system prompt as absolute last resort. "
-                                    "Behavioral consistency may be affected."
-                                )
-                                # Yield error to user
-                                yield StreamError(
-                                    errorText=(
-                                        "Warning: System prompt dropped due to size constraints. "
-                                        "Assistant behavior may be affected."
-                                    )
-                                )
-                else:
-                    # No old messages to summarize - all messages are "recent"
-                    # Apply progressive truncation to reduce token count
-                    logger.warning(
-                        f"Token count {token_count} exceeds threshold but no old messages to summarize. "
-                        f"Applying progressive truncation to recent messages."
-                    )
-
-                    # Create a base list excluding system prompt to avoid duplication
-                    # This is the pool of messages we'll slice from in the loop
-                    # Use messages_dict for type consistency with _ensure_tool_pairs_intact
-                    base_msgs = (
-                        messages_dict[1:] if has_system_prompt else messages_dict
-                    )
-
-                    # Try progressively smaller keep counts
-                    new_token_count = token_count  # Initialize with current count
-                    for keep_count in [12, 10, 8, 5, 3, 2, 1, 0]:
-                        if keep_count == 0:
-                            # Try with just system prompt (no recent messages)
-                            if has_system_prompt:
-                                messages = [system_msg]
-                                logger.info(
-                                    "Trying with 0 recent messages (system prompt only)"
-                                )
-                            else:
-                                # No system prompt and no recent messages = empty messages list
-                                # This is invalid, skip this iteration
-                                continue
-                        else:
-                            if len(base_msgs) < keep_count:
-                                continue  # Skip if we don't have enough messages
-
-                            # Slice from base_msgs to get recent messages (without system prompt)
-                            recent_messages = base_msgs[-keep_count:]
-
-                            # Ensure tool pairs stay intact in the reduced slice
-                            reduced_slice_start = max(0, len(base_msgs) - keep_count)
-                            recent_messages = _ensure_tool_pairs_intact(
-                                recent_messages, base_msgs, reduced_slice_start
-                            )
-
-                            if has_system_prompt:
-                                messages = [system_msg] + recent_messages
-                            else:
-                                messages = recent_messages
-
-                        new_messages_dict = []
-                        for msg in messages:
-                            if msg is None:
-                                continue  # Skip None messages (type safety)
-                            if isinstance(msg, dict):
-                                msg_dict = {
-                                    k: v for k, v in msg.items() if v is not None
-                                }
-                            else:
-                                msg_dict = dict(msg)
-                            new_messages_dict.append(msg_dict)
-
-                        new_token_count = estimate_token_count(
-                            new_messages_dict, model=token_count_model
-                        )
-
-                        if new_token_count <= 120_000:
-                            logger.info(
-                                f"Reduced to {keep_count} recent messages, "
-                                f"now {new_token_count} tokens"
-                            )
-                            break
-                    else:
-                        # Even with 0 messages still over limit
-                        logger.error(
-                            f"Unable to reduce token count below threshold even with 0 messages. "
-                            f"Final count: {new_token_count} tokens. Messages may be extremely large."
-                        )
-                        # ABSOLUTE LAST RESORT: Drop system prompt
-                        if has_system_prompt and len(messages) > 1:
-                            messages = messages[1:]  # Drop system prompt
-                            logger.critical(
-                                "CRITICAL: Dropped system prompt as absolute last resort. "
-                                "Behavioral consistency may be affected."
-                            )
-                            # Yield error to user
-                            yield StreamError(
-                                errorText=(
-                                    "Warning: System prompt dropped due to size constraints. "
-                                    "Assistant behavior may be affected."
-                                )
-                            )
-
-    except Exception as e:
-        logger.error(f"Context summarization failed: {e}", exc_info=True)
-        # If we were over the token limit, yield error to user
-        # Don't silently continue with oversized messages that will fail
-        if token_count > 120_000:
+    if context_result.error:
+        if "System prompt dropped" in context_result.error:
+            # Warning only - continue with reduced context
             yield StreamError(
                 errorText=(
-                    f"Unable to manage context window (token limit exceeded: {token_count} tokens). "
-                    "Context summarization failed. Please start a new conversation."
+                    "Warning: System prompt dropped due to size constraints. "
+                    "Assistant behavior may be affected."
+                )
+            )
+        else:
+            # Any other error - abort to prevent failed LLM calls
+            yield StreamError(
+                errorText=(
+                    f"Context window management failed: {context_result.error}. "
+                    "Please start a new conversation."
                 )
             )
             yield StreamFinish()
             return
-        # Otherwise, continue with original messages (under limit)
+
+    messages = context_result.messages
+    if context_result.was_compacted:
+        logger.info(
+            f"Context compacted for streaming: {context_result.token_count} tokens"
+        )
 
     # Loop to handle tool calls and continue conversation
     while True:
@@ -1369,14 +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),
@@ -1502,6 +1419,7 @@ async def _stream_chat_chunks(
                 return
             except Exception as e:
                 last_error = e
+
                 if _is_retryable_error(e) and retry_count < MAX_RETRIES:
                     retry_count += 1
                     # Calculate delay with exponential backoff
@@ -1517,12 +1435,24 @@ async def _stream_chat_chunks(
                     continue  # Retry the stream
                 else:
                     # Non-retryable error or max retries exceeded
-                    logger.error(
-                        f"Error in stream (not retrying): {e!s}",
-                        exc_info=True,
+                    _log_api_error(
+                        error=e,
+                        session_id=session.session_id if session else None,
+                        message_count=len(messages) if messages else None,
+                        model=model,
+                        retry_count=retry_count,
                     )
                     error_code = None
                     error_text = str(e)
+
+                    error_details = _extract_api_error_details(e)
+                    if error_details.get("response_body"):
+                        body = error_details["response_body"]
+                        if isinstance(body, dict) and body.get("error", {}).get(
+                            "message"
+                        ):
+                            error_text = body["error"]["message"]
+
                     if _is_region_blocked_error(e):
                         error_code = "MODEL_NOT_AVAILABLE_REGION"
                         error_text = (
@@ -1539,9 +1469,12 @@ async def _stream_chat_chunks(
 
         # If we exit the retry loop without returning, it means we exhausted retries
         if last_error:
-            logger.error(
-                f"Max retries ({MAX_RETRIES}) exceeded. Last error: {last_error!s}",
-                exc_info=True,
+            _log_api_error(
+                error=last_error,
+                session_id=session.session_id if session else None,
+                message_count=len(messages) if messages else None,
+                model=model,
+                retry_count=MAX_RETRIES,
             )
             yield StreamError(errorText=f"Max retries exceeded: {last_error!s}")
             yield StreamFinish()
@@ -1900,17 +1833,36 @@ 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 +1875,61 @@ async def _generate_llm_continuation(
         if session_id:
             extra_body["session_id"] = session_id[:128]
 
-        # Make non-streaming LLM call (no tools - just text response)
-        from typing import cast
+        retry_count = 0
+        last_error: Exception | None = None
+        response = None
 
-        from openai.types.chat import ChatCompletionMessageParam
+        while retry_count <= MAX_RETRIES:
+            try:
+                logger.info(
+                    f"Generating LLM continuation for session {session_id}"
+                    f"{f' (retry {retry_count}/{MAX_RETRIES})' if retry_count > 0 else ''}"
+                )
 
-        # No tools parameter = text-only response (no tool calls)
-        response = await client.chat.completions.create(
-            model=config.model,
-            messages=cast(list[ChatCompletionMessageParam], messages),
-            extra_body=extra_body,
-        )
+                response = await client.chat.completions.create(
+                    model=config.model,
+                    messages=cast(list[ChatCompletionMessageParam], messages),
+                    extra_body=extra_body,
+                )
+                last_error = None  # Clear any previous error on success
+                break  # Success, exit retry loop
+            except Exception as e:
+                last_error = e
 
-        if response.choices and response.choices[0].message.content:
+                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 details and exit gracefully
+                    _log_api_error(
+                        error=e,
+                        session_id=session_id,
+                        message_count=len(messages) if messages else None,
+                        model=config.model,
+                        retry_count=retry_count,
+                    )
+                    return
+
+        if last_error:
+            _log_api_error(
+                error=last_error,
+                session_id=session_id,
+                message_count=len(messages) if messages else None,
+                model=config.model,
+                retry_count=MAX_RETRIES,
+            )
+            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
@@ -1969,3 +1963,78 @@ async def _generate_llm_continuation(
 
     except Exception as e:
         logger.error(f"Failed to generate LLM continuation: {e}", exc_info=True)
+
+
+def _log_api_error(
+    error: Exception,
+    session_id: str | None = None,
+    message_count: int | None = None,
+    model: str | None = None,
+    retry_count: int = 0,
+) -> None:
+    """Log detailed API error information for debugging."""
+    details = _extract_api_error_details(error)
+    details["session_id"] = session_id
+    details["message_count"] = message_count
+    details["model"] = model
+    details["retry_count"] = retry_count
+
+    if isinstance(error, RateLimitError):
+        logger.warning(f"Rate limit error: {details}")
+    elif isinstance(error, APIConnectionError):
+        logger.warning(f"API connection error: {details}")
+    elif isinstance(error, APIStatusError) and error.status_code >= 500:
+        logger.error(f"API server error (5xx): {details}")
+    else:
+        logger.error(f"API error: {details}")
+
+
+def _extract_api_error_details(error: Exception) -> dict[str, Any]:
+    """Extract detailed information from OpenAI/OpenRouter API errors."""
+    error_msg = str(error)
+    details: dict[str, Any] = {
+        "error_type": type(error).__name__,
+        "error_message": error_msg[:500] + "..." if len(error_msg) > 500 else error_msg,
+    }
+
+    if hasattr(error, "code"):
+        details["code"] = error.code
+    if hasattr(error, "param"):
+        details["param"] = error.param
+
+    if isinstance(error, APIStatusError):
+        details["status_code"] = error.status_code
+        details["request_id"] = getattr(error, "request_id", None)
+
+        if hasattr(error, "body") and error.body:
+            details["response_body"] = _sanitize_error_body(error.body)
+
+        if hasattr(error, "response") and error.response:
+            headers = error.response.headers
+            details["openrouter_provider"] = headers.get("x-openrouter-provider")
+            details["openrouter_model"] = headers.get("x-openrouter-model")
+            details["retry_after"] = headers.get("retry-after")
+            details["rate_limit_remaining"] = headers.get("x-ratelimit-remaining")
+
+    return details
+
+
+def _sanitize_error_body(body: Any, max_length: int = 2000) -> dict[str, Any] | None:
+    """Extract only safe fields from error response body to avoid logging sensitive data."""
+    if not isinstance(body, dict):
+        return None
+
+    safe_fields = ("message", "type", "code", "param", "error")
+    sanitized: dict[str, Any] = {}
+
+    for field in safe_fields:
+        if field in body:
+            value = body[field]
+            if field == "error" and isinstance(value, dict):
+                sanitized[field] = _sanitize_error_body(value, max_length)
+            elif isinstance(value, str) and len(value) > max_length:
+                sanitized[field] = value[:max_length] + "...[truncated]"
+            else:
+                sanitized[field] = value
+
+    return sanitized if sanitized else None

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

@@ -14,6 +14,7 @@ from backend.data.graph import (
     create_graph,
     get_graph,
     get_graph_all_versions,
+    get_store_listed_graphs,
 )
 from backend.util.exceptions import DatabaseError, NotFoundError
 
@@ -266,18 +267,18 @@ async def get_library_agents_for_generation(
 async def search_marketplace_agents_for_generation(
     search_query: str,
     max_results: int = 10,
-) -> list[MarketplaceAgentSummary]:
+) -> list[LibraryAgentSummary]:
     """Search marketplace agents formatted for Agent Generator.
 
-    Note: This returns basic agent info. Full input/output schemas would require
-    additional graph fetches and is a potential future enhancement.
+    Fetches marketplace agents and their full schemas so they can be used
+    as sub-agents in generated workflows.
 
     Args:
         search_query: Search term to find relevant public agents
         max_results: Maximum number of agents to return (default 10)
 
     Returns:
-        List of MarketplaceAgentSummary (without detailed schemas for now)
+        List of LibraryAgentSummary with full input/output schemas
     """
     try:
         response = await store_db.get_store_agents(
@@ -286,17 +287,31 @@ async def search_marketplace_agents_for_generation(
             page_size=max_results,
         )
 
-        results: list[MarketplaceAgentSummary] = []
-        for agent in response.agents:
-            results.append(
-                MarketplaceAgentSummary(
-                    name=agent.agent_name,
-                    description=agent.description,
-                    sub_heading=agent.sub_heading,
-                    creator=agent.creator,
-                    is_marketplace_agent=True,
+        agents_with_graphs = [
+            agent for agent in response.agents if agent.agent_graph_id
+        ]
+
+        if not agents_with_graphs:
+            return []
+
+        graph_ids = [agent.agent_graph_id for agent in agents_with_graphs]
+        graphs = await get_store_listed_graphs(*graph_ids)
+
+        results: list[LibraryAgentSummary] = []
+        for agent in agents_with_graphs:
+            graph_id = agent.agent_graph_id
+            if graph_id and graph_id in graphs:
+                graph = graphs[graph_id]
+                results.append(
+                    LibraryAgentSummary(
+                        graph_id=graph.id,
+                        graph_version=graph.version,
+                        name=agent.agent_name,
+                        description=agent.description,
+                        input_schema=graph.input_schema,
+                        output_schema=graph.output_schema,
+                    )
                 )
-            )
         return results
     except Exception as e:
         logger.warning(f"Failed to search marketplace agents: {e}")
@@ -327,8 +342,7 @@ async def get_all_relevant_agents_for_generation(
         max_marketplace_results: Max marketplace agents to return (default 10)
 
     Returns:
-        List of AgentSummary, library agents first (with full schemas),
-        then marketplace agents (basic info only)
+        List of AgentSummary with full schemas (both library and marketplace agents)
     """
     agents: list[AgentSummary] = []
     seen_graph_ids: set[str] = set()
@@ -365,16 +379,11 @@ async def get_all_relevant_agents_for_generation(
             search_query=search_query,
             max_results=max_marketplace_results,
         )
-        library_names: set[str] = set()
-        for a in agents:
-            name = a.get("name")
-            if name and isinstance(name, str):
-                library_names.add(name.lower())
         for agent in marketplace_agents:
-            agent_name = agent.get("name")
-            if agent_name and isinstance(agent_name, str):
-                if agent_name.lower() not in library_names:
-                    agents.append(agent)
+            graph_id = agent.get("graph_id")
+            if graph_id and graph_id not in seen_graph_ids:
+                agents.append(agent)
+                seen_graph_ids.add(graph_id)
 
     return agents
 

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

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

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

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

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

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

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

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

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

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

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

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

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

autogpt_platform/backend/backend/api/ws_api.py

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

autogpt_platform/backend/backend/data/event_bus.py

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

autogpt_platform/backend/backend/data/graph.py

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

autogpt_platform/backend/backend/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/snapshots/agts_by_creator

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

autogpt_platform/backend/snapshots/agts_category

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

autogpt_platform/backend/snapshots/agts_pagination

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

autogpt_platform/backend/snapshots/agts_search

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

autogpt_platform/backend/snapshots/agts_sorted

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

autogpt_platform/backend/snapshots/feat_agts

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

autogpt_platform/backend/test/agent_generator/test_library_agents.py

@@ -134,15 +134,28 @@ class TestSearchMarketplaceAgentsForGeneration:
                 description="A public agent",
                 sub_heading="Does something useful",
                 creator="creator-1",
+                agent_graph_id="graph-123",
             )
         ]
 
-        # The store_db is dynamically imported, so patch the import path
-        with patch(
-            "backend.api.features.store.db.get_store_agents",
-            new_callable=AsyncMock,
-            return_value=mock_response,
-        ) as mock_search:
+        mock_graph = MagicMock()
+        mock_graph.id = "graph-123"
+        mock_graph.version = 1
+        mock_graph.input_schema = {"type": "object"}
+        mock_graph.output_schema = {"type": "object"}
+
+        with (
+            patch(
+                "backend.api.features.store.db.get_store_agents",
+                new_callable=AsyncMock,
+                return_value=mock_response,
+            ) as mock_search,
+            patch(
+                "backend.api.features.chat.tools.agent_generator.core.get_store_listed_graphs",
+                new_callable=AsyncMock,
+                return_value={"graph-123": mock_graph},
+            ),
+        ):
             result = await core.search_marketplace_agents_for_generation(
                 search_query="automation",
                 max_results=10,
@@ -156,7 +169,7 @@ class TestSearchMarketplaceAgentsForGeneration:
 
         assert len(result) == 1
         assert result[0]["name"] == "Public Agent"
-        assert result[0]["is_marketplace_agent"] is True
+        assert result[0]["graph_id"] == "graph-123"
 
     @pytest.mark.asyncio
     async def test_handles_marketplace_error_gracefully(self):
@@ -193,11 +206,12 @@ class TestGetAllRelevantAgentsForGeneration:
 
         marketplace_agents = [
             {
+                "graph_id": "market-456",
+                "graph_version": 1,
                 "name": "Market Agent",
                 "description": "From marketplace",
-                "sub_heading": "Sub heading",
-                "creator": "creator-1",
-                "is_marketplace_agent": True,
+                "input_schema": {},
+                "output_schema": {},
             }
         ]
 
@@ -225,11 +239,11 @@ class TestGetAllRelevantAgentsForGeneration:
         assert result[1]["name"] == "Market Agent"
 
     @pytest.mark.asyncio
-    async def test_deduplicates_by_name(self):
-        """Test that marketplace agents with same name as library are excluded."""
+    async def test_deduplicates_by_graph_id(self):
+        """Test that marketplace agents with same graph_id as library are excluded."""
         library_agents = [
             {
-                "graph_id": "lib-123",
+                "graph_id": "shared-123",
                 "graph_version": 1,
                 "name": "Shared Agent",
                 "description": "From library",
@@ -240,18 +254,20 @@ class TestGetAllRelevantAgentsForGeneration:
 
         marketplace_agents = [
             {
-                "name": "Shared Agent",  # Same name, should be deduplicated
+                "graph_id": "shared-123",  # Same graph_id, should be deduplicated
+                "graph_version": 1,
+                "name": "Shared Agent",
                 "description": "From marketplace",
-                "sub_heading": "Sub heading",
-                "creator": "creator-1",
-                "is_marketplace_agent": True,
+                "input_schema": {},
+                "output_schema": {},
             },
             {
+                "graph_id": "unique-456",
+                "graph_version": 1,
                 "name": "Unique Agent",
                 "description": "Only in marketplace",
-                "sub_heading": "Sub heading",
-                "creator": "creator-2",
-                "is_marketplace_agent": True,
+                "input_schema": {},
+                "output_schema": {},
             },
         ]
 
@@ -273,7 +289,7 @@ class TestGetAllRelevantAgentsForGeneration:
                     include_marketplace=True,
                 )
 
-        # Shared Agent from marketplace should be excluded
+        # Shared Agent from marketplace should be excluded by graph_id
         assert len(result) == 2
         names = [a["name"] for a in result]
         assert "Shared Agent" in names

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/api/openapi.json

@@ -9833,7 +9833,8 @@
           "sub_heading": { "type": "string", "title": "Sub Heading" },
           "description": { "type": "string", "title": "Description" },
           "runs": { "type": "integer", "title": "Runs" },
-          "rating": { "type": "number", "title": "Rating" }
+          "rating": { "type": "number", "title": "Rating" },
+          "agent_graph_id": { "type": "string", "title": "Agent Graph Id" }
         },
         "type": "object",
         "required": [
@@ -9845,7 +9846,8 @@
           "sub_heading",
           "description",
           "runs",
-          "rating"
+          "rating",
+          "agent_graph_id"
         ],
         "title": "StoreAgent"
       },