test(backend): update tests to expect exceptions instead of None/False

- Update generate_embedding tests to expect RuntimeError/Exception instead of None
- Update store_embedding tests to expect Exception instead of False
- Update ensure_embedding tests to use side_effect=Exception instead of return_value=None
- Update hybrid search tests to mock embed_query with side_effect=Exception
- All tests now verify exception-based error handling instead of return value checking

autogpt_platform/backend/.env.default

@@ -178,10 +178,5 @@ AYRSHARE_JWT_KEY=
 SMARTLEAD_API_KEY=
 ZEROBOUNCE_API_KEY=
 
-# PostHog Analytics
-# Get API key from https://posthog.com - Project Settings > Project API Key
-POSTHOG_API_KEY=
-POSTHOG_HOST=https://eu.i.posthog.com
-
 # Other Services
 AUTOMOD_API_KEY=

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

@@ -48,7 +48,6 @@ from .response_model import (
     StreamUsage,
 )
 from .tools import execute_tool, tools
-from .tracking import track_user_message
 
 logger = logging.getLogger(__name__)
 
@@ -104,33 +103,16 @@ async def _build_system_prompt(user_id: str | None) -> tuple[str, Any]:
     return compiled, understanding
 
 
-async def _generate_session_title(
-    message: str,
-    user_id: str | None = None,
-    session_id: str | None = None,
-) -> str | None:
+async def _generate_session_title(message: str) -> str | None:
     """Generate a concise title for a chat session based on the first message.
 
     Args:
         message: The first user message in the session
-        user_id: User ID for OpenRouter tracing (optional)
-        session_id: Session ID for OpenRouter tracing (optional)
 
     Returns:
         A short title (3-6 words) or None if generation fails
     """
     try:
-        # Build extra_body for OpenRouter tracing and PostHog analytics
-        extra_body: dict[str, Any] = {}
-        if user_id:
-            extra_body["user"] = user_id[:128]  # OpenRouter limit
-            extra_body["posthogDistinctId"] = user_id
-        if session_id:
-            extra_body["session_id"] = session_id[:128]  # OpenRouter limit
-        extra_body["posthogProperties"] = {
-            "environment": settings.config.app_env.value,
-        }
-
         response = await client.chat.completions.create(
             model=config.title_model,
             messages=[
@@ -145,7 +127,6 @@ async def _generate_session_title(
                 {"role": "user", "content": message[:500]},  # Limit input length
             ],
             max_tokens=20,
-            extra_body=extra_body,
         )
         title = response.choices[0].message.content
         if title:
@@ -256,14 +237,6 @@ async def stream_chat_completion(
             f"new message_count={len(session.messages)}"
         )
 
-        # Track user message in PostHog
-        if is_user_message:
-            track_user_message(
-                user_id=user_id,
-                session_id=session_id,
-                message_length=len(message),
-            )
-
     logger.info(
         f"Upserting session: {session.session_id} with user id {session.user_id}, "
         f"message_count={len(session.messages)}"
@@ -283,15 +256,10 @@ async def stream_chat_completion(
             # stale data issues when the main flow modifies the session
             captured_session_id = session_id
             captured_message = message
-            captured_user_id = user_id
 
             async def _update_title():
                 try:
-                    title = await _generate_session_title(
-                        captured_message,
-                        user_id=captured_user_id,
-                        session_id=captured_session_id,
-                    )
+                    title = await _generate_session_title(captured_message)
                     if title:
                         # Use dedicated title update function that doesn't
                         # touch messages, avoiding race conditions
@@ -730,20 +698,6 @@ async def _stream_chat_chunks(
                     f"{f' (retry {retry_count}/{MAX_RETRIES})' if retry_count > 0 else ''}"
                 )
 
-                # Build extra_body for OpenRouter tracing and PostHog analytics
-                extra_body: dict[str, Any] = {
-                    "posthogProperties": {
-                        "environment": settings.config.app_env.value,
-                    },
-                }
-                if session.user_id:
-                    extra_body["user"] = session.user_id[:128]  # OpenRouter limit
-                    extra_body["posthogDistinctId"] = session.user_id
-                if session.session_id:
-                    extra_body["session_id"] = session.session_id[
-                        :128
-                    ]  # OpenRouter limit
-
                 # Create the stream with proper types
                 stream = await client.chat.completions.create(
                     model=model,
@@ -752,7 +706,6 @@ async def _stream_chat_chunks(
                     tool_choice="auto",
                     stream=True,
                     stream_options={"include_usage": True},
-                    extra_body=extra_body,
                 )
 
                 # Variables to accumulate tool calls

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

@@ -1,10 +1,8 @@
-import logging
 from typing import TYPE_CHECKING, Any
 
 from openai.types.chat import ChatCompletionToolParam
 
 from backend.api.features.chat.model import ChatSession
-from backend.api.features.chat.tracking import track_tool_called
 
 from .add_understanding import AddUnderstandingTool
 from .agent_output import AgentOutputTool
@@ -22,8 +20,6 @@ from .search_docs import SearchDocsTool
 if TYPE_CHECKING:
     from backend.api.features.chat.response_model import StreamToolOutputAvailable
 
-logger = logging.getLogger(__name__)
-
 # Single source of truth for all tools
 TOOL_REGISTRY: dict[str, BaseTool] = {
     "add_understanding": AddUnderstandingTool(),
@@ -60,17 +56,4 @@ async def execute_tool(
     tool = TOOL_REGISTRY.get(tool_name)
     if not tool:
         raise ValueError(f"Tool {tool_name} not found")
-
-    # Track tool call in PostHog
-    logger.info(
-        f"Tracking tool call: tool={tool_name}, user={user_id}, "
-        f"session={session.session_id}, call_id={tool_call_id}"
-    )
-    track_tool_called(
-        user_id=user_id,
-        session_id=session.session_id,
-        tool_name=tool_name,
-        tool_call_id=tool_call_id,
-    )
-
     return await tool.execute(user_id, session, tool_call_id, **parameters)

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

@@ -8,10 +8,6 @@ from pydantic import BaseModel, Field, field_validator
 
 from backend.api.features.chat.config import ChatConfig
 from backend.api.features.chat.model import ChatSession
-from backend.api.features.chat.tracking import (
-    track_agent_run_success,
-    track_agent_scheduled,
-)
 from backend.api.features.library import db as library_db
 from backend.data.graph import GraphModel
 from backend.data.model import CredentialsMetaInput
@@ -457,16 +453,6 @@ class RunAgentTool(BaseTool):
             session.successful_agent_runs.get(library_agent.graph_id, 0) + 1
         )
 
-        # Track in PostHog
-        track_agent_run_success(
-            user_id=user_id,
-            session_id=session_id,
-            graph_id=library_agent.graph_id,
-            graph_name=library_agent.name,
-            execution_id=execution.id,
-            library_agent_id=library_agent.id,
-        )
-
         library_agent_link = f"/library/agents/{library_agent.id}"
         return ExecutionStartedResponse(
             message=(
@@ -548,18 +534,6 @@ class RunAgentTool(BaseTool):
             session.successful_agent_schedules.get(library_agent.graph_id, 0) + 1
         )
 
-        # Track in PostHog
-        track_agent_scheduled(
-            user_id=user_id,
-            session_id=session_id,
-            graph_id=library_agent.graph_id,
-            graph_name=library_agent.name,
-            schedule_id=result.id,
-            schedule_name=schedule_name,
-            cron=cron,
-            library_agent_id=library_agent.id,
-        )
-
         library_agent_link = f"/library/agents/{library_agent.id}"
         return ExecutionStartedResponse(
             message=(

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

@@ -1,250 +0,0 @@
-"""PostHog analytics tracking for the chat system."""
-
-import atexit
-import logging
-from typing import Any
-
-from posthog import Posthog
-
-from backend.util.settings import Settings
-
-logger = logging.getLogger(__name__)
-settings = Settings()
-
-# PostHog client instance (lazily initialized)
-_posthog_client: Posthog | None = None
-
-
-def _shutdown_posthog() -> None:
-    """Flush and shutdown PostHog client on process exit."""
-    if _posthog_client is not None:
-        _posthog_client.flush()
-        _posthog_client.shutdown()
-
-
-atexit.register(_shutdown_posthog)
-
-
-def _get_posthog_client() -> Posthog | None:
-    """Get or create the PostHog client instance."""
-    global _posthog_client
-    if _posthog_client is not None:
-        return _posthog_client
-
-    if not settings.secrets.posthog_api_key:
-        logger.debug("PostHog API key not configured, analytics disabled")
-        return None
-
-    _posthog_client = Posthog(
-        settings.secrets.posthog_api_key,
-        host=settings.secrets.posthog_host,
-    )
-    logger.info(
-        f"PostHog client initialized with host: {settings.secrets.posthog_host}"
-    )
-    return _posthog_client
-
-
-def _get_base_properties() -> dict[str, Any]:
-    """Get base properties included in all events."""
-    return {
-        "environment": settings.config.app_env.value,
-        "source": "chat_copilot",
-    }
-
-
-def track_user_message(
-    user_id: str | None,
-    session_id: str,
-    message_length: int,
-) -> None:
-    """Track when a user sends a message in chat.
-
-    Args:
-        user_id: The user's ID (or None for anonymous)
-        session_id: The chat session ID
-        message_length: Length of the user's message
-    """
-    client = _get_posthog_client()
-    if not client:
-        return
-
-    try:
-        properties = {
-            **_get_base_properties(),
-            "session_id": session_id,
-            "message_length": message_length,
-        }
-        client.capture(
-            distinct_id=user_id or f"anonymous_{session_id}",
-            event="copilot_message_sent",
-            properties=properties,
-        )
-    except Exception as e:
-        logger.warning(f"Failed to track user message: {e}")
-
-
-def track_tool_called(
-    user_id: str | None,
-    session_id: str,
-    tool_name: str,
-    tool_call_id: str,
-) -> None:
-    """Track when a tool is called in chat.
-
-    Args:
-        user_id: The user's ID (or None for anonymous)
-        session_id: The chat session ID
-        tool_name: Name of the tool being called
-        tool_call_id: Unique ID of the tool call
-    """
-    client = _get_posthog_client()
-    if not client:
-        logger.info("PostHog client not available for tool tracking")
-        return
-
-    try:
-        properties = {
-            **_get_base_properties(),
-            "session_id": session_id,
-            "tool_name": tool_name,
-            "tool_call_id": tool_call_id,
-        }
-        distinct_id = user_id or f"anonymous_{session_id}"
-        logger.info(
-            f"Sending copilot_tool_called event to PostHog: distinct_id={distinct_id}, "
-            f"tool_name={tool_name}"
-        )
-        client.capture(
-            distinct_id=distinct_id,
-            event="copilot_tool_called",
-            properties=properties,
-        )
-    except Exception as e:
-        logger.warning(f"Failed to track tool call: {e}")
-
-
-def track_agent_run_success(
-    user_id: str,
-    session_id: str,
-    graph_id: str,
-    graph_name: str,
-    execution_id: str,
-    library_agent_id: str,
-) -> None:
-    """Track when an agent is successfully run.
-
-    Args:
-        user_id: The user's ID
-        session_id: The chat session ID
-        graph_id: ID of the agent graph
-        graph_name: Name of the agent
-        execution_id: ID of the execution
-        library_agent_id: ID of the library agent
-    """
-    client = _get_posthog_client()
-    if not client:
-        return
-
-    try:
-        properties = {
-            **_get_base_properties(),
-            "session_id": session_id,
-            "graph_id": graph_id,
-            "graph_name": graph_name,
-            "execution_id": execution_id,
-            "library_agent_id": library_agent_id,
-        }
-        client.capture(
-            distinct_id=user_id,
-            event="copilot_agent_run_success",
-            properties=properties,
-        )
-    except Exception as e:
-        logger.warning(f"Failed to track agent run: {e}")
-
-
-def track_agent_scheduled(
-    user_id: str,
-    session_id: str,
-    graph_id: str,
-    graph_name: str,
-    schedule_id: str,
-    schedule_name: str,
-    cron: str,
-    library_agent_id: str,
-) -> None:
-    """Track when an agent is successfully scheduled.
-
-    Args:
-        user_id: The user's ID
-        session_id: The chat session ID
-        graph_id: ID of the agent graph
-        graph_name: Name of the agent
-        schedule_id: ID of the schedule
-        schedule_name: Name of the schedule
-        cron: Cron expression for the schedule
-        library_agent_id: ID of the library agent
-    """
-    client = _get_posthog_client()
-    if not client:
-        return
-
-    try:
-        properties = {
-            **_get_base_properties(),
-            "session_id": session_id,
-            "graph_id": graph_id,
-            "graph_name": graph_name,
-            "schedule_id": schedule_id,
-            "schedule_name": schedule_name,
-            "cron": cron,
-            "library_agent_id": library_agent_id,
-        }
-        client.capture(
-            distinct_id=user_id,
-            event="copilot_agent_scheduled",
-            properties=properties,
-        )
-    except Exception as e:
-        logger.warning(f"Failed to track agent schedule: {e}")
-
-
-def track_trigger_setup(
-    user_id: str,
-    session_id: str,
-    graph_id: str,
-    graph_name: str,
-    trigger_type: str,
-    library_agent_id: str,
-) -> None:
-    """Track when a trigger is set up for an agent.
-
-    Args:
-        user_id: The user's ID
-        session_id: The chat session ID
-        graph_id: ID of the agent graph
-        graph_name: Name of the agent
-        trigger_type: Type of trigger (e.g., 'webhook')
-        library_agent_id: ID of the library agent
-    """
-    client = _get_posthog_client()
-    if not client:
-        return
-
-    try:
-        properties = {
-            **_get_base_properties(),
-            "session_id": session_id,
-            "graph_id": graph_id,
-            "graph_name": graph_name,
-            "trigger_type": trigger_type,
-            "library_agent_id": library_agent_id,
-        }
-        client.capture(
-            distinct_id=user_id,
-            event="copilot_trigger_setup",
-            properties=properties,
-        )
-    except Exception as e:
-        logger.warning(f"Failed to track trigger setup: {e}")

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

@@ -1552,7 +1552,7 @@ async def review_store_submission(
 
                 # Generate embedding for approved listing (blocking - admin operation)
                 # Inside transaction: if embedding fails, entire transaction rolls back
-                embedding_success = await ensure_embedding(
+                await ensure_embedding(
                     version_id=store_listing_version_id,
                     name=store_listing_version.name,
                     description=store_listing_version.description,
@@ -1560,12 +1560,6 @@ async def review_store_submission(
                     categories=store_listing_version.categories or [],
                     tx=tx,
                 )
-                if not embedding_success:
-                    raise ValueError(
-                        f"Failed to generate embedding for listing {store_listing_version_id}. "
-                        "This is likely due to OpenAI API being unavailable. "
-                        "Please try again later or contact support if the issue persists."
-                    )
 
                 await prisma.models.StoreListing.prisma(tx).update(
                     where={"id": store_listing_version.StoreListing.id},

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

@@ -63,49 +63,42 @@ def build_searchable_text(
     return " ".join(parts)
 
 
-async def generate_embedding(text: str) -> list[float] | None:
+async def generate_embedding(text: str) -> list[float]:
     """
     Generate embedding for text using OpenAI API.
 
-    Returns None if embedding generation fails.
-    Fail-fast: no retries to maintain consistency with approval flow.
+    Raises exceptions on failure - caller should handle.
     """
-    try:
-        client = get_openai_client()
-        if not client:
-            logger.error("openai_internal_api_key not set, cannot generate embedding")
-            return None
+    client = get_openai_client()
+    if not client:
+        raise RuntimeError("openai_internal_api_key not set, cannot generate embedding")
 
-        # Truncate text to token limit using tiktoken
-        # Character-based truncation is insufficient because token ratios vary by content type
-        enc = encoding_for_model(EMBEDDING_MODEL)
-        tokens = enc.encode(text)
-        if len(tokens) > EMBEDDING_MAX_TOKENS:
-            tokens = tokens[:EMBEDDING_MAX_TOKENS]
-            truncated_text = enc.decode(tokens)
-            logger.info(
-                f"Truncated text from {len(enc.encode(text))} to {len(tokens)} tokens"
-            )
-        else:
-            truncated_text = text
-
-        start_time = time.time()
-        response = await client.embeddings.create(
-            model=EMBEDDING_MODEL,
-            input=truncated_text,
-        )
-        latency_ms = (time.time() - start_time) * 1000
-
-        embedding = response.data[0].embedding
+    # Truncate text to token limit using tiktoken
+    # Character-based truncation is insufficient because token ratios vary by content type
+    enc = encoding_for_model(EMBEDDING_MODEL)
+    tokens = enc.encode(text)
+    if len(tokens) > EMBEDDING_MAX_TOKENS:
+        tokens = tokens[:EMBEDDING_MAX_TOKENS]
+        truncated_text = enc.decode(tokens)
         logger.info(
-            f"Generated embedding: {len(embedding)} dims, "
-            f"{len(tokens)} tokens, {latency_ms:.0f}ms"
+            f"Truncated text from {len(enc.encode(text))} to {len(tokens)} tokens"
         )
-        return embedding
+    else:
+        truncated_text = text
 
-    except Exception as e:
-        logger.error(f"Failed to generate embedding: {e}")
-        return None
+    start_time = time.time()
+    response = await client.embeddings.create(
+        model=EMBEDDING_MODEL,
+        input=truncated_text,
+    )
+    latency_ms = (time.time() - start_time) * 1000
+
+    embedding = response.data[0].embedding
+    logger.info(
+        f"Generated embedding: {len(embedding)} dims, "
+        f"{len(tokens)} tokens, {latency_ms:.0f}ms"
+    )
+    return embedding
 
 
 async def store_embedding(
@@ -144,48 +137,45 @@ async def store_content_embedding(
 
     New function for unified content embedding storage.
     Uses raw SQL since Prisma doesn't natively support pgvector.
+
+    Raises exceptions on failure - caller should handle.
     """
-    try:
-        client = tx if tx else prisma.get_client()
+    client = tx if tx else prisma.get_client()
 
-        # Convert embedding to PostgreSQL vector format
-        embedding_str = embedding_to_vector_string(embedding)
-        metadata_json = dumps(metadata or {})
+    # Convert embedding to PostgreSQL vector format
+    embedding_str = embedding_to_vector_string(embedding)
+    metadata_json = dumps(metadata or {})
 
-        # Upsert the embedding
-        # WHERE clause in DO UPDATE prevents PostgreSQL 15 bug with NULLS NOT DISTINCT
-        # Use unqualified ::vector - pgvector is in search_path on all environments
-        await execute_raw_with_schema(
-            """
-            INSERT INTO {schema_prefix}"UnifiedContentEmbedding" (
-                "id", "contentType", "contentId", "userId", "embedding", "searchableText", "metadata", "createdAt", "updatedAt"
-            )
-            VALUES (gen_random_uuid()::text, $1::{schema_prefix}"ContentType", $2, $3, $4::vector, $5, $6::jsonb, NOW(), NOW())
-            ON CONFLICT ("contentType", "contentId", "userId")
-            DO UPDATE SET
-                "embedding" = $4::vector,
-                "searchableText" = $5,
-                "metadata" = $6::jsonb,
-                "updatedAt" = NOW()
-            WHERE {schema_prefix}"UnifiedContentEmbedding"."contentType" = $1::{schema_prefix}"ContentType"
-                AND {schema_prefix}"UnifiedContentEmbedding"."contentId" = $2
-                AND ({schema_prefix}"UnifiedContentEmbedding"."userId" = $3 OR ($3 IS NULL AND {schema_prefix}"UnifiedContentEmbedding"."userId" IS NULL))
-            """,
-            content_type,
-            content_id,
-            user_id,
-            embedding_str,
-            searchable_text,
-            metadata_json,
-            client=client,
+    # Upsert the embedding
+    # WHERE clause in DO UPDATE prevents PostgreSQL 15 bug with NULLS NOT DISTINCT
+    # Use unqualified ::vector - pgvector is in search_path on all environments
+    await execute_raw_with_schema(
+        """
+        INSERT INTO {schema_prefix}"UnifiedContentEmbedding" (
+            "id", "contentType", "contentId", "userId", "embedding", "searchableText", "metadata", "createdAt", "updatedAt"
         )
+        VALUES (gen_random_uuid()::text, $1::{schema_prefix}"ContentType", $2, $3, $4::vector, $5, $6::jsonb, NOW(), NOW())
+        ON CONFLICT ("contentType", "contentId", "userId")
+        DO UPDATE SET
+            "embedding" = $4::vector,
+            "searchableText" = $5,
+            "metadata" = $6::jsonb,
+            "updatedAt" = NOW()
+        WHERE {schema_prefix}"UnifiedContentEmbedding"."contentType" = $1::{schema_prefix}"ContentType"
+            AND {schema_prefix}"UnifiedContentEmbedding"."contentId" = $2
+            AND ({schema_prefix}"UnifiedContentEmbedding"."userId" = $3 OR ($3 IS NULL AND {schema_prefix}"UnifiedContentEmbedding"."userId" IS NULL))
+        """,
+        content_type,
+        content_id,
+        user_id,
+        embedding_str,
+        searchable_text,
+        metadata_json,
+        client=client,
+    )
 
-        logger.info(f"Stored embedding for {content_type}:{content_id}")
-        return True
-
-    except Exception as e:
-        logger.error(f"Failed to store embedding for {content_type}:{content_id}: {e}")
-        return False
+    logger.info(f"Stored embedding for {content_type}:{content_id}")
+    return True
 
 
 async def get_embedding(version_id: str) -> dict[str, Any] | None:
@@ -217,34 +207,31 @@ async def get_content_embedding(
 
     New function for unified content embedding retrieval.
     Returns dict with contentType, contentId, embedding, timestamps or None if not found.
+
+    Raises exceptions on failure - caller should handle.
     """
-    try:
-        result = await query_raw_with_schema(
-            """
-            SELECT
-                "contentType",
-                "contentId",
-                "userId",
-                "embedding"::text as "embedding",
-                "searchableText",
-                "metadata",
-                "createdAt",
-                "updatedAt"
-            FROM {schema_prefix}"UnifiedContentEmbedding"
-            WHERE "contentType" = $1::{schema_prefix}"ContentType" AND "contentId" = $2 AND ("userId" = $3 OR ($3 IS NULL AND "userId" IS NULL))
-            """,
-            content_type,
-            content_id,
-            user_id,
-        )
+    result = await query_raw_with_schema(
+        """
+        SELECT
+            "contentType",
+            "contentId",
+            "userId",
+            "embedding"::text as "embedding",
+            "searchableText",
+            "metadata",
+            "createdAt",
+            "updatedAt"
+        FROM {schema_prefix}"UnifiedContentEmbedding"
+        WHERE "contentType" = $1::{schema_prefix}"ContentType" AND "contentId" = $2 AND ("userId" = $3 OR ($3 IS NULL AND "userId" IS NULL))
+        """,
+        content_type,
+        content_id,
+        user_id,
+    )
 
-        if result and len(result) > 0:
-            return result[0]
-        return None
-
-    except Exception as e:
-        logger.error(f"Failed to get embedding for {content_type}:{content_id}: {e}")
-        return None
+    if result and len(result) > 0:
+        return result[0]
+    return None
 
 
 async def ensure_embedding(
@@ -272,46 +259,38 @@ async def ensure_embedding(
         tx: Optional transaction client
 
     Returns:
-        True if embedding exists/was created, False on failure
+        True if embedding exists/was created
+
+    Raises exceptions on failure - caller should handle.
     """
-    try:
-        # Check if embedding already exists
-        if not force:
-            existing = await get_embedding(version_id)
-            if existing and existing.get("embedding"):
-                logger.debug(f"Embedding for version {version_id} already exists")
-                return True
+    # Check if embedding already exists
+    if not force:
+        existing = await get_embedding(version_id)
+        if existing and existing.get("embedding"):
+            logger.debug(f"Embedding for version {version_id} already exists")
+            return True
 
-        # Build searchable text for embedding
-        searchable_text = build_searchable_text(
-            name, description, sub_heading, categories
-        )
+    # Build searchable text for embedding
+    searchable_text = build_searchable_text(name, description, sub_heading, categories)
 
-        # Generate new embedding
-        embedding = await generate_embedding(searchable_text)
-        if embedding is None:
-            logger.warning(f"Could not generate embedding for version {version_id}")
-            return False
+    # Generate new embedding
+    embedding = await generate_embedding(searchable_text)
 
-        # Store the embedding with metadata using new function
-        metadata = {
-            "name": name,
-            "subHeading": sub_heading,
-            "categories": categories,
-        }
-        return await store_content_embedding(
-            content_type=ContentType.STORE_AGENT,
-            content_id=version_id,
-            embedding=embedding,
-            searchable_text=searchable_text,
-            metadata=metadata,
-            user_id=None,  # Store agents are public
-            tx=tx,
-        )
-
-    except Exception as e:
-        logger.error(f"Failed to ensure embedding for version {version_id}: {e}")
-        return False
+    # Store the embedding with metadata using new function
+    metadata = {
+        "name": name,
+        "subHeading": sub_heading,
+        "categories": categories,
+    }
+    return await store_content_embedding(
+        content_type=ContentType.STORE_AGENT,
+        content_id=version_id,
+        embedding=embedding,
+        searchable_text=searchable_text,
+        metadata=metadata,
+        user_id=None,  # Store agents are public
+        tx=tx,
+    )
 
 
 async def delete_embedding(version_id: str) -> bool:
@@ -521,6 +500,24 @@ async def backfill_all_content_types(batch_size: int = 10) -> dict[str, Any]:
             success = sum(1 for result in results if result is True)
             failed = len(results) - success
 
+            # Aggregate unique errors to avoid Sentry spam
+            if failed > 0:
+                # Group errors by type and message
+                error_summary: dict[str, int] = {}
+                for result in results:
+                    if isinstance(result, Exception):
+                        error_key = f"{type(result).__name__}: {str(result)}"
+                        error_summary[error_key] = error_summary.get(error_key, 0) + 1
+
+                # Log aggregated error summary
+                error_details = ", ".join(
+                    f"{error} ({count}x)" for error, count in error_summary.items()
+                )
+                logger.error(
+                    f"{content_type.value}: {failed}/{len(results)} embeddings failed. "
+                    f"Errors: {error_details}"
+                )
+
             results_by_type[content_type.value] = {
                 "processed": len(missing_items),
                 "success": success,
@@ -557,11 +554,12 @@ async def backfill_all_content_types(batch_size: int = 10) -> dict[str, Any]:
     }
 
 
-async def embed_query(query: str) -> list[float] | None:
+async def embed_query(query: str) -> list[float]:
     """
     Generate embedding for a search query.
 
     Same as generate_embedding but with clearer intent.
+    Raises exceptions on failure - caller should handle.
     """
     return await generate_embedding(query)
 
@@ -594,40 +592,30 @@ async def ensure_content_embedding(
         tx: Optional transaction client
 
     Returns:
-        True if embedding exists/was created, False on failure
+        True if embedding exists/was created
+
+    Raises exceptions on failure - caller should handle.
     """
-    try:
-        # Check if embedding already exists
-        if not force:
-            existing = await get_content_embedding(content_type, content_id, user_id)
-            if existing and existing.get("embedding"):
-                logger.debug(
-                    f"Embedding for {content_type}:{content_id} already exists"
-                )
-                return True
+    # Check if embedding already exists
+    if not force:
+        existing = await get_content_embedding(content_type, content_id, user_id)
+        if existing and existing.get("embedding"):
+            logger.debug(f"Embedding for {content_type}:{content_id} already exists")
+            return True
 
-        # Generate new embedding
-        embedding = await generate_embedding(searchable_text)
-        if embedding is None:
-            logger.warning(
-                f"Could not generate embedding for {content_type}:{content_id}"
-            )
-            return False
+    # Generate new embedding
+    embedding = await generate_embedding(searchable_text)
 
-        # Store the embedding
-        return await store_content_embedding(
-            content_type=content_type,
-            content_id=content_id,
-            embedding=embedding,
-            searchable_text=searchable_text,
-            metadata=metadata or {},
-            user_id=user_id,
-            tx=tx,
-        )
-
-    except Exception as e:
-        logger.error(f"Failed to ensure embedding for {content_type}:{content_id}: {e}")
-        return False
+    # Store the embedding
+    return await store_content_embedding(
+        content_type=content_type,
+        content_id=content_id,
+        embedding=embedding,
+        searchable_text=searchable_text,
+        metadata=metadata or {},
+        user_id=user_id,
+        tx=tx,
+    )
 
 
 async def cleanup_orphaned_embeddings() -> dict[str, Any]:
@@ -854,9 +842,8 @@ async def semantic_search(
         limit = 100
 
     # Generate query embedding
-    query_embedding = await embed_query(query)
-
-    if query_embedding is not None:
+    try:
+        query_embedding = await embed_query(query)
         # Semantic search with embeddings
         embedding_str = embedding_to_vector_string(query_embedding)
 
@@ -907,24 +894,21 @@ async def semantic_search(
         """
         )
 
-        try:
-            results = await query_raw_with_schema(sql, *params)
-            return [
-                {
-                    "content_id": row["content_id"],
-                    "content_type": row["content_type"],
-                    "searchable_text": row["searchable_text"],
-                    "metadata": row["metadata"],
-                    "similarity": float(row["similarity"]),
-                }
-                for row in results
-            ]
-        except Exception as e:
-            logger.error(f"Semantic search failed: {e}")
-            # Fall through to lexical search below
+        results = await query_raw_with_schema(sql, *params)
+        return [
+            {
+                "content_id": row["content_id"],
+                "content_type": row["content_type"],
+                "searchable_text": row["searchable_text"],
+                "metadata": row["metadata"],
+                "similarity": float(row["similarity"]),
+            }
+            for row in results
+        ]
+    except Exception as e:
+        logger.warning(f"Semantic search failed, falling back to lexical search: {e}")
 
     # Fallback to lexical search if embeddings unavailable
-    logger.warning("Falling back to lexical search (embeddings unavailable)")
 
     params_lexical: list[Any] = [limit]
     user_filter = ""

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

@@ -298,17 +298,16 @@ async def test_schema_handling_error_cases():
             mock_client.execute_raw.side_effect = Exception("Database error")
             mock_get_client.return_value = mock_client
 
-            result = await embeddings.store_content_embedding(
-                content_type=ContentType.STORE_AGENT,
-                content_id="test-id",
-                embedding=[0.1] * EMBEDDING_DIM,
-                searchable_text="test",
-                metadata=None,
-                user_id=None,
-            )
-
-            # Should return False on error, not raise
-            assert result is False
+            # Should raise exception on error
+            with pytest.raises(Exception, match="Database error"):
+                await embeddings.store_content_embedding(
+                    content_type=ContentType.STORE_AGENT,
+                    content_id="test-id",
+                    embedding=[0.1] * EMBEDDING_DIM,
+                    searchable_text="test",
+                    metadata=None,
+                    user_id=None,
+                )
 
 
 if __name__ == "__main__":

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

@@ -80,9 +80,8 @@ async def test_generate_embedding_no_api_key():
     ) as mock_get_client:
         mock_get_client.return_value = None
 
-        result = await embeddings.generate_embedding("test text")
-
-        assert result is None
+        with pytest.raises(RuntimeError, match="openai_internal_api_key not set"):
+            await embeddings.generate_embedding("test text")
 
 
 @pytest.mark.asyncio(loop_scope="session")
@@ -97,9 +96,8 @@ async def test_generate_embedding_api_error():
     ) as mock_get_client:
         mock_get_client.return_value = mock_client
 
-        result = await embeddings.generate_embedding("test text")
-
-        assert result is None
+        with pytest.raises(Exception, match="API Error"):
+            await embeddings.generate_embedding("test text")
 
 
 @pytest.mark.asyncio(loop_scope="session")
@@ -173,11 +171,10 @@ async def test_store_embedding_database_error(mocker):
 
     embedding = [0.1, 0.2, 0.3]
 
-    result = await embeddings.store_embedding(
-        version_id="test-version-id", embedding=embedding, tx=mock_client
-    )
-
-    assert result is False
+    with pytest.raises(Exception, match="Database error"):
+        await embeddings.store_embedding(
+            version_id="test-version-id", embedding=embedding, tx=mock_client
+        )
 
 
 @pytest.mark.asyncio(loop_scope="session")
@@ -277,17 +274,16 @@ async def test_ensure_embedding_create_new(mock_get, mock_store, mock_generate):
 async def test_ensure_embedding_generation_fails(mock_get, mock_generate):
     """Test ensure_embedding when generation fails."""
     mock_get.return_value = None
-    mock_generate.return_value = None
+    mock_generate.side_effect = Exception("Generation failed")
 
-    result = await embeddings.ensure_embedding(
-        version_id="test-id",
-        name="Test",
-        description="Test description",
-        sub_heading="Test heading",
-        categories=["test"],
-    )
-
-    assert result is False
+    with pytest.raises(Exception, match="Generation failed"):
+        await embeddings.ensure_embedding(
+            version_id="test-id",
+            name="Test",
+            description="Test description",
+            sub_heading="Test heading",
+            categories=["test"],
+        )
 
 
 @pytest.mark.asyncio(loop_scope="session")

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

@@ -186,13 +186,12 @@ async def unified_hybrid_search(
 
     offset = (page - 1) * page_size
 
-    # Generate query embedding
-    query_embedding = await embed_query(query)
-
-    # Graceful degradation if embedding unavailable
-    if query_embedding is None or not query_embedding:
+    # Generate query embedding with graceful degradation
+    try:
+        query_embedding = await embed_query(query)
+    except Exception as e:
         logger.warning(
-            "Failed to generate query embedding - falling back to lexical-only search. "
+            f"Failed to generate query embedding - falling back to lexical-only search: {e}. "
             "Check that openai_internal_api_key is configured and OpenAI API is accessible."
         )
         query_embedding = [0.0] * EMBEDDING_DIM
@@ -464,13 +463,12 @@ async def hybrid_search(
 
     offset = (page - 1) * page_size
 
-    # Generate query embedding
-    query_embedding = await embed_query(query)
-
-    # Graceful degradation
-    if query_embedding is None or not query_embedding:
+    # Generate query embedding with graceful degradation
+    try:
+        query_embedding = await embed_query(query)
+    except Exception as e:
         logger.warning(
-            "Failed to generate query embedding - falling back to lexical-only search."
+            f"Failed to generate query embedding - falling back to lexical-only search: {e}"
         )
         query_embedding = [0.0] * EMBEDDING_DIM
         total_non_semantic = (

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

@@ -172,8 +172,8 @@ async def test_hybrid_search_without_embeddings():
         with patch(
             "backend.api.features.store.hybrid_search.query_raw_with_schema"
         ) as mock_query:
-            # Simulate embedding failure
-            mock_embed.return_value = None
+            # Simulate embedding failure by raising exception
+            mock_embed.side_effect = Exception("Embedding generation failed")
             mock_query.return_value = mock_results
 
             # Should NOT raise - graceful degradation
@@ -613,7 +613,9 @@ async def test_unified_hybrid_search_graceful_degradation():
             "backend.api.features.store.hybrid_search.embed_query"
         ) as mock_embed:
             mock_query.return_value = mock_results
-            mock_embed.return_value = None  # Embedding failure
+            mock_embed.side_effect = Exception(
+                "Embedding generation failed"
+            )  # Embedding failure
 
             # Should NOT raise - graceful degradation
             results, total = await unified_hybrid_search(

autogpt_platform/backend/backend/util/settings.py

@@ -666,12 +666,6 @@ class Secrets(UpdateTrackingModel["Secrets"], BaseSettings):
         default="https://cloud.langfuse.com", description="Langfuse host URL"
     )
 
-    # PostHog analytics
-    posthog_api_key: str = Field(default="", description="PostHog API key")
-    posthog_host: str = Field(
-        default="https://us.i.posthog.com", description="PostHog host URL"
-    )
-
     # Add more secret fields as needed
     model_config = SettingsConfigDict(
         env_file=".env",

autogpt_platform/backend/poetry.lock

@@ -4204,14 +4204,14 @@ strenum = {version = ">=0.4.9,<0.5.0", markers = "python_version < \"3.11\""}
 
 [[package]]
 name = "posthog"
-version = "7.6.0"
+version = "6.1.1"
 description = "Integrate PostHog into any python application."
 optional = false
-python-versions = ">=3.10"
+python-versions = ">=3.9"
 groups = ["main"]
 files = [
-    {file = "posthog-7.6.0-py3-none-any.whl", hash = "sha256:c4dd78cf77c4fecceb965f86066e5ac37886ef867d68ffe75a1db5d681d7d9ad"},
-    {file = "posthog-7.6.0.tar.gz", hash = "sha256:941dfd278ee427c9b14640f09b35b5bb52a71bdf028d7dbb7307e1838fd3002e"},
+    {file = "posthog-6.1.1-py3-none-any.whl", hash = "sha256:329fd3d06b4d54cec925f47235bd8e327c91403c2f9ec38f1deb849535934dba"},
+    {file = "posthog-6.1.1.tar.gz", hash = "sha256:b453f54c4a2589da859fd575dd3bf86fcb40580727ec399535f268b1b9f318b8"},
 ]
 
 [package.dependencies]
@@ -4225,7 +4225,7 @@ typing-extensions = ">=4.2.0"
 [package.extras]
 dev = ["django-stubs", "lxml", "mypy", "mypy-baseline", "packaging", "pre-commit", "pydantic", "ruff", "setuptools", "tomli", "tomli_w", "twine", "types-mock", "types-python-dateutil", "types-requests", "types-setuptools", "types-six", "wheel"]
 langchain = ["langchain (>=0.2.0)"]
-test = ["anthropic (>=0.72)", "coverage", "django", "freezegun (==1.5.1)", "google-genai", "langchain-anthropic (>=1.0)", "langchain-community (>=0.4)", "langchain-core (>=1.0)", "langchain-openai (>=1.0)", "langgraph (>=1.0)", "mock (>=2.0.0)", "openai (>=2.0)", "parameterized (>=0.8.1)", "pydantic", "pytest", "pytest-asyncio", "pytest-timeout"]
+test = ["anthropic", "coverage", "django", "freezegun (==1.5.1)", "google-genai", "langchain-anthropic (>=0.3.15)", "langchain-community (>=0.3.25)", "langchain-core (>=0.3.65)", "langchain-openai (>=0.3.22)", "langgraph (>=0.4.8)", "mock (>=2.0.0)", "openai", "parameterized (>=0.8.1)", "pydantic", "pytest", "pytest-asyncio", "pytest-timeout"]
 
 [[package]]
 name = "postmarker"
@@ -7512,4 +7512,4 @@ cffi = ["cffi (>=1.11)"]
 [metadata]
 lock-version = "2.1"
 python-versions = ">=3.10,<3.14"
-content-hash = "ee5742dc1a9df50dfc06d4b26a1682cbb2b25cab6b79ce5625ec272f93e4f4bf"
+content-hash = "18b92e09596298c82432e4d0a85cb6d80a40b4229bee0a0c15f0529fd6cb21a4"

autogpt_platform/backend/pyproject.toml

@@ -85,7 +85,6 @@ exa-py = "^1.14.20"
 croniter = "^6.0.0"
 stagehand = "^0.5.1"
 gravitas-md2gdocs = "^0.1.0"
-posthog = "^7.6.0"
 
 [tool.poetry.group.dev.dependencies]
 aiohappyeyeballs = "^2.6.1"

autogpt_platform/frontend/.env.default

@@ -30,7 +30,3 @@ NEXT_PUBLIC_TURNSTILE=disabled
 
 # PR previews
 NEXT_PUBLIC_PREVIEW_STEALING_DEV=
-
-# PostHog Analytics
-NEXT_PUBLIC_POSTHOG_KEY=
-NEXT_PUBLIC_POSTHOG_HOST=https://eu.i.posthog.com

autogpt_platform/frontend/package.json

@@ -34,7 +34,6 @@
     "@hookform/resolvers": "5.2.2",
     "@next/third-parties": "15.4.6",
     "@phosphor-icons/react": "2.1.10",
-    "@posthog/react": "1.7.0",
     "@radix-ui/react-accordion": "1.2.12",
     "@radix-ui/react-alert-dialog": "1.1.15",
     "@radix-ui/react-avatar": "1.1.10",
@@ -92,7 +91,6 @@
     "next-themes": "0.4.6",
     "nuqs": "2.7.2",
     "party-js": "2.2.0",
-    "posthog-js": "1.334.1",
     "react": "18.3.1",
     "react-currency-input-field": "4.0.3",
     "react-day-picker": "9.11.1",
@@ -122,6 +120,7 @@
   },
   "devDependencies": {
     "@chromatic-com/storybook": "4.1.2",
+    "happy-dom": "20.3.4",
     "@opentelemetry/instrumentation": "0.209.0",
     "@playwright/test": "1.56.1",
     "@storybook/addon-a11y": "9.1.5",
@@ -149,7 +148,6 @@
     "eslint": "8.57.1",
     "eslint-config-next": "15.5.7",
     "eslint-plugin-storybook": "9.1.5",
-    "happy-dom": "20.3.4",
     "import-in-the-middle": "2.0.2",
     "msw": "2.11.6",
     "msw-storybook-addon": "2.0.6",

autogpt_platform/frontend/pnpm-lock.yaml

@@ -23,9 +23,6 @@ importers:
       '@phosphor-icons/react':
         specifier: 2.1.10
         version: 2.1.10(react-dom@18.3.1(react@18.3.1))(react@18.3.1)
-      '@posthog/react':
-        specifier: 1.7.0
-        version: 1.7.0(@types/react@18.3.17)(posthog-js@1.334.1)(react@18.3.1)
       '@radix-ui/react-accordion':
         specifier: 1.2.12
         version: 1.2.12(@types/react-dom@18.3.5(@types/react@18.3.17))(@types/react@18.3.17)(react-dom@18.3.1(react@18.3.1))(react@18.3.1)
@@ -197,9 +194,6 @@ importers:
       party-js:
         specifier: 2.2.0
         version: 2.2.0
-      posthog-js:
-        specifier: 1.334.1
-        version: 1.334.1
       react:
         specifier: 18.3.1
         version: 18.3.1
@@ -1800,10 +1794,6 @@ packages:
   '@open-draft/until@2.1.0':
     resolution: {integrity: sha512-U69T3ItWHvLwGg5eJ0n3I62nWuE6ilHlmz7zM0npLBRvPRd7e6NYmg54vvRtP5mZG7kZqZCFVdsTWo7BPtBujg==}
 
-  '@opentelemetry/api-logs@0.208.0':
-    resolution: {integrity: sha512-CjruKY9V6NMssL/T1kAFgzosF1v9o6oeN+aX5JB/C/xPNtmgIJqcXHG7fA82Ou1zCpWGl4lROQUKwUNE1pMCyg==}
-    engines: {node: '>=8.0.0'}
-
   '@opentelemetry/api-logs@0.209.0':
     resolution: {integrity: sha512-xomnUNi7TiAGtOgs0tb54LyrjRZLu9shJGGwkcN7NgtiPYOpNnKLkRJtzZvTjD/w6knSZH9sFZcUSUovYOPg6A==}
     engines: {node: '>=8.0.0'}
@@ -1824,12 +1814,6 @@ packages:
     peerDependencies:
       '@opentelemetry/api': '>=1.0.0 <1.10.0'
 
-  '@opentelemetry/exporter-logs-otlp-http@0.208.0':
-    resolution: {integrity: sha512-jOv40Bs9jy9bZVLo/i8FwUiuCvbjWDI+ZW13wimJm4LjnlwJxGgB+N/VWOZUTpM+ah/awXeQqKdNlpLf2EjvYg==}
-    engines: {node: ^18.19.0 || >=20.6.0}
-    peerDependencies:
-      '@opentelemetry/api': ^1.3.0
-
   '@opentelemetry/instrumentation-amqplib@0.55.0':
     resolution: {integrity: sha512-5ULoU8p+tWcQw5PDYZn8rySptGSLZHNX/7srqo2TioPnAAcvTy6sQFQXsNPrAnyRRtYGMetXVyZUy5OaX1+IfA==}
     engines: {node: ^18.19.0 || >=20.6.0}
@@ -1968,18 +1952,6 @@ packages:
     peerDependencies:
       '@opentelemetry/api': ^1.3.0
 
-  '@opentelemetry/otlp-exporter-base@0.208.0':
-    resolution: {integrity: sha512-gMd39gIfVb2OgxldxUtOwGJYSH8P1kVFFlJLuut32L6KgUC4gl1dMhn+YC2mGn0bDOiQYSk/uHOdSjuKp58vvA==}
-    engines: {node: ^18.19.0 || >=20.6.0}
-    peerDependencies:
-      '@opentelemetry/api': ^1.3.0
-
-  '@opentelemetry/otlp-transformer@0.208.0':
-    resolution: {integrity: sha512-DCFPY8C6lAQHUNkzcNT9R+qYExvsk6C5Bto2pbNxgicpcSWbe2WHShLxkOxIdNcBiYPdVHv/e7vH7K6TI+C+fQ==}
-    engines: {node: ^18.19.0 || >=20.6.0}
-    peerDependencies:
-      '@opentelemetry/api': ^1.3.0
-
   '@opentelemetry/redis-common@0.38.2':
     resolution: {integrity: sha512-1BCcU93iwSRZvDAgwUxC/DV4T/406SkMfxGqu5ojc3AvNI+I9GhV7v0J1HljsczuuhcnFLYqD5VmwVXfCGHzxA==}
     engines: {node: ^18.19.0 || >=20.6.0}
@@ -1990,18 +1962,6 @@ packages:
     peerDependencies:
       '@opentelemetry/api': '>=1.3.0 <1.10.0'
 
-  '@opentelemetry/sdk-logs@0.208.0':
-    resolution: {integrity: sha512-QlAyL1jRpOeaqx7/leG1vJMp84g0xKP6gJmfELBpnI4O/9xPX+Hu5m1POk9Kl+veNkyth5t19hRlN6tNY1sjbA==}
-    engines: {node: ^18.19.0 || >=20.6.0}
-    peerDependencies:
-      '@opentelemetry/api': '>=1.4.0 <1.10.0'
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@@ -9961,19 +9801,6 @@ snapshots:
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@@ -11622,9 +11426,6 @@ snapshots:
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autogpt_platform/frontend/src/app/providers.tsx

@@ -1,62 +1,33 @@
 "use client";
 
 import { TooltipProvider } from "@/components/atoms/Tooltip/BaseTooltip";
-import {
-  PostHogPageViewTracker,
-  PostHogUserTracker,
-} from "@/components/monitor/PostHogUserTracker";
 import { SentryUserTracker } from "@/components/monitor/SentryUserTracker";
 import { BackendAPIProvider } from "@/lib/autogpt-server-api/context";
 import { getQueryClient } from "@/lib/react-query/queryClient";
 import CredentialsProvider from "@/providers/agent-credentials/credentials-provider";
 import OnboardingProvider from "@/providers/onboarding/onboarding-provider";
 import { LaunchDarklyProvider } from "@/services/feature-flags/feature-flag-provider";
-import { PostHogProvider as PHProvider } from "@posthog/react";
 import { QueryClientProvider } from "@tanstack/react-query";
 import { ThemeProvider, ThemeProviderProps } from "next-themes";
 import { NuqsAdapter } from "nuqs/adapters/next/app";
-import posthog from "posthog-js";
-import { Suspense, useEffect } from "react";
-
-function PostHogProvider({ children }: { children: React.ReactNode }) {
-  useEffect(() => {
-    if (process.env.NEXT_PUBLIC_POSTHOG_KEY) {
-      posthog.init(process.env.NEXT_PUBLIC_POSTHOG_KEY, {
-        api_host: process.env.NEXT_PUBLIC_POSTHOG_HOST,
-        defaults: "2025-11-30",
-        capture_pageview: false,
-        capture_pageleave: true,
-        autocapture: true,
-      });
-    }
-  }, []);
-
-  return <PHProvider client={posthog}>{children}</PHProvider>;
-}
 
 export function Providers({ children, ...props }: ThemeProviderProps) {
   const queryClient = getQueryClient();
   return (
     <QueryClientProvider client={queryClient}>
       <NuqsAdapter>
-        <PostHogProvider>
-          <BackendAPIProvider>
-            <SentryUserTracker />
-            <PostHogUserTracker />
-            <Suspense fallback={null}>
-              <PostHogPageViewTracker />
-            </Suspense>
-            <CredentialsProvider>
-              <LaunchDarklyProvider>
-                <OnboardingProvider>
-                  <ThemeProvider forcedTheme="light" {...props}>
-                    <TooltipProvider>{children}</TooltipProvider>
-                  </ThemeProvider>
-                </OnboardingProvider>
-              </LaunchDarklyProvider>
-            </CredentialsProvider>
-          </BackendAPIProvider>
-        </PostHogProvider>
+        <BackendAPIProvider>
+          <SentryUserTracker />
+          <CredentialsProvider>
+            <LaunchDarklyProvider>
+              <OnboardingProvider>
+                <ThemeProvider forcedTheme="light" {...props}>
+                  <TooltipProvider>{children}</TooltipProvider>
+                </ThemeProvider>
+              </OnboardingProvider>
+            </LaunchDarklyProvider>
+          </CredentialsProvider>
+        </BackendAPIProvider>
       </NuqsAdapter>
     </QueryClientProvider>
   );

autogpt_platform/frontend/src/components/monitor/PostHogUserTracker.tsx

@@ -1,64 +0,0 @@
-"use client";
-
-import { useSupabase } from "@/lib/supabase/hooks/useSupabase";
-import { usePathname, useSearchParams } from "next/navigation";
-import posthog from "posthog-js";
-import { useEffect, useRef } from "react";
-
-/**
- * PostHogUserTracker component identifies users in PostHog for analytics.
- * This component should be placed high in the component tree to ensure user
- * identification happens as soon as the user logs in.
- *
- * It automatically:
- * - Identifies the user when they log in (linking anonymous to authenticated)
- * - Resets PostHog when a user logs out
- * - Updates identification when user data changes
- */
-export function PostHogUserTracker() {
-  const { user, isUserLoading } = useSupabase();
-  const previousUserIdRef = useRef<string | null>(null);
-
-  useEffect(() => {
-    if (isUserLoading) return;
-
-    if (user) {
-      // Only identify if we haven't already identified this user
-      if (previousUserIdRef.current !== user.id) {
-        posthog.identify(user.id, {
-          email: user.email,
-          ...(user.user_metadata?.name && { name: user.user_metadata.name }),
-        });
-        previousUserIdRef.current = user.id;
-      }
-    } else if (previousUserIdRef.current !== null) {
-      // User logged out - reset PostHog to generate new anonymous ID
-      posthog.reset();
-      previousUserIdRef.current = null;
-    }
-  }, [user, isUserLoading]);
-
-  return null;
-}
-
-/**
- * PostHogPageViewTracker captures page views on route changes in Next.js App Router.
- * The default PostHog capture_pageview only works for initial page loads.
- * This component ensures soft navigations (client-side route changes) are also tracked.
- */
-export function PostHogPageViewTracker() {
-  const pathname = usePathname();
-  const searchParams = useSearchParams();
-
-  useEffect(() => {
-    if (pathname) {
-      let url = window.origin + pathname;
-      if (searchParams && searchParams.toString()) {
-        url = url + `?${searchParams.toString()}`;
-      }
-      posthog.capture("$pageview", { $current_url: url });
-    }
-  }, [pathname, searchParams]);
-
-  return null;
-}