feat: prompt caching

- Updated `litellm` dependency version from `1.83.4` to `1.83.7`.
- Adjusted `aiohttp` version from `3.13.5` to `3.13.4` in the lock file.
- Implemented `apply_litellm_prompt_caching` in `chat_deepagent.py` to improve prompt caching.
- Added model name resolution logic in `chat_deepagent.py` to ensure correct provider-variant dispatch.
- Enhanced `llm_config.py` to configure prompt caching for various LLM providers.
- Updated tests to verify correct model name forwarding and prompt caching behavior.

surfsense_backend/app/agents/new_chat/chat_deepagent.py

@@ -10,7 +10,9 @@ We use ``create_agent`` (from langchain) rather than ``create_deep_agent``
 This lets us swap in ``SurfSenseFilesystemMiddleware`` — a customisable
 subclass of the default ``FilesystemMiddleware`` — while preserving every
 other behaviour that ``create_deep_agent`` provides (todo-list, subagents,
-summarisation, prompt-caching, etc.).
+summarisation, etc.). Prompt caching is configured at LLM-build time via
+``apply_litellm_prompt_caching`` (LiteLLM-native, multi-provider) rather
+than as a middleware.
 """
 
 import asyncio
@@ -33,7 +35,6 @@ from langchain.agents.middleware import (
     TodoListMiddleware,
     ToolCallLimitMiddleware,
 )
-from langchain_anthropic.middleware import AnthropicPromptCachingMiddleware
 from langchain_core.language_models import BaseChatModel
 from langchain_core.tools import BaseTool
 from langgraph.types import Checkpointer
@@ -74,6 +75,7 @@ from app.agents.new_chat.plugin_loader import (
     load_allowed_plugin_names_from_env,
     load_plugin_middlewares,
 )
+from app.agents.new_chat.prompt_caching import apply_litellm_prompt_caching
 from app.agents.new_chat.subagents import build_specialized_subagents
 from app.agents.new_chat.system_prompt import (
     build_configurable_system_prompt,
@@ -94,6 +96,39 @@ from app.utils.perf import get_perf_logger
 
 _perf_log = get_perf_logger()
 
+
+def _resolve_prompt_model_name(
+    agent_config: AgentConfig | None,
+    llm: BaseChatModel,
+) -> str | None:
+    """Resolve the model id to feed to provider-variant detection.
+
+    Preference order (matches the established idiom in
+    ``llm_router_service.py`` — see ``params.get("base_model") or
+    params.get("model", "")`` usages there):
+
+    1. ``agent_config.litellm_params["base_model"]`` — required for Azure
+       deployments where ``model_name`` is the deployment slug, not the
+       underlying family. Without this, a deployment named e.g.
+       ``"prod-chat-001"`` would silently miss every provider regex.
+    2. ``agent_config.model_name`` — the user's configured model id.
+    3. ``getattr(llm, "model", None)`` — fallback for direct callers that
+       don't supply an ``AgentConfig`` (currently a defensive path; all
+       production callers pass ``agent_config``).
+
+    Returns ``None`` when nothing is available; ``compose_system_prompt``
+    treats that as the ``"default"`` variant (no provider block emitted).
+    """
+    if agent_config is not None:
+        params = agent_config.litellm_params or {}
+        base_model = params.get("base_model")
+        if isinstance(base_model, str) and base_model.strip():
+            return base_model
+        if agent_config.model_name:
+            return agent_config.model_name
+    return getattr(llm, "model", None)
+
+
 # =============================================================================
 # Connector Type Mapping
 # =============================================================================
@@ -279,6 +314,14 @@ async def create_surfsense_deep_agent(
         )
     """
     _t_agent_total = time.perf_counter()
+
+    # Layer thread-aware prompt caching onto the LLM. Idempotent with the
+    # build-time call in ``llm_config.py``; this run merely adds
+    # ``prompt_cache_key=f"surfsense-thread-{thread_id}"`` for OpenAI-family
+    # configs now that ``thread_id`` is known. No-op when ``thread_id`` is
+    # None or the provider is non-OpenAI-family.
+    apply_litellm_prompt_caching(llm, agent_config=agent_config, thread_id=thread_id)
+
     filesystem_selection = filesystem_selection or FilesystemSelection()
     backend_resolver = build_backend_resolver(
         filesystem_selection,
@@ -398,6 +441,7 @@ async def create_surfsense_deep_agent(
             enabled_tool_names=_enabled_tool_names,
             disabled_tool_names=_user_disabled_tool_names,
             mcp_connector_tools=_mcp_connector_tools,
+            model_name=_resolve_prompt_model_name(agent_config, llm),
         )
     else:
         system_prompt = build_surfsense_system_prompt(
@@ -405,6 +449,7 @@ async def create_surfsense_deep_agent(
             enabled_tool_names=_enabled_tool_names,
             disabled_tool_names=_user_disabled_tool_names,
             mcp_connector_tools=_mcp_connector_tools,
+            model_name=_resolve_prompt_model_name(agent_config, llm),
         )
     _perf_log.info(
         "[create_agent] System prompt built in %.3fs", time.perf_counter() - _t0
@@ -568,7 +613,6 @@ def _build_compiled_agent_blocking(
         ),
         create_surfsense_compaction_middleware(llm, StateBackend),
         PatchToolCallsMiddleware(),
-        AnthropicPromptCachingMiddleware(unsupported_model_behavior="ignore"),
     ]
 
     general_purpose_spec: SubAgent = {  # type: ignore[typeddict-unknown-key]
@@ -1006,12 +1050,12 @@ def _build_compiled_agent_blocking(
         action_log_mw,
         PatchToolCallsMiddleware(),
         DedupHITLToolCallsMiddleware(agent_tools=list(tools)),
-        # Plugin slot — sits just before AnthropicCache so plugin-side
+        # Plugin slot — sits at the tail so plugin-side transforms see the
-        # transforms see the final tool result and run before any
+        # final tool result. Prompt caching is now applied at LLM build time
-        # caching heuristics. Multiple plugins in declared order; loader
+        # via ``apply_litellm_prompt_caching`` (see prompt_caching.py), so no
-        # filtered by the admin allowlist already.
+        # caching middleware is needed here. Multiple plugins run in declared
+        # order; loader filtered by the admin allowlist already.
         *plugin_middlewares,
-        AnthropicPromptCachingMiddleware(unsupported_model_behavior="ignore"),
     ]
     deepagent_middleware = [m for m in deepagent_middleware if m is not None]
 

surfsense_backend/app/agents/new_chat/llm_config.py

@@ -27,6 +27,7 @@ from litellm import get_model_info
 from sqlalchemy import select
 from sqlalchemy.ext.asyncio import AsyncSession
 
+from app.agents.new_chat.prompt_caching import apply_litellm_prompt_caching
 from app.services.llm_router_service import (
     AUTO_MODE_ID,
     ChatLiteLLMRouter,
@@ -494,6 +495,11 @@ def create_chat_litellm_from_config(llm_config: dict) -> ChatLiteLLM | None:
 
     llm = SanitizedChatLiteLLM(**litellm_kwargs)
     _attach_model_profile(llm, model_string)
+    # Configure LiteLLM-native prompt caching (cache_control_injection_points
+    # for Anthropic/Bedrock/Vertex/Gemini/Azure-AI/OpenRouter/Databricks/etc.).
+    # ``agent_config=None`` here — the YAML path doesn't have provider intent
+    # in a structured form, so we set only the universal injection points.
+    apply_litellm_prompt_caching(llm)
     return llm
 
 
@@ -518,7 +524,16 @@ def create_chat_litellm_from_agent_config(
             print("Error: Auto mode requested but LLM Router not initialized")
             return None
         try:
-            return get_auto_mode_llm()
+            router_llm = get_auto_mode_llm()
+            if router_llm is not None:
+                # Universal cache_control_injection_points only — auto-mode
+                # fans out across providers, so OpenAI-only kwargs (e.g.
+                # ``prompt_cache_key``) are left off here. ``drop_params``
+                # would strip them at the provider boundary anyway, but
+                # there's no point setting them when we don't know the
+                # destination.
+                apply_litellm_prompt_caching(router_llm, agent_config=agent_config)
+            return router_llm
         except Exception as e:
             print(f"Error creating ChatLiteLLMRouter: {e}")
             return None
@@ -549,4 +564,9 @@ def create_chat_litellm_from_agent_config(
 
     llm = SanitizedChatLiteLLM(**litellm_kwargs)
     _attach_model_profile(llm, model_string)
+    # Build-time prompt caching: sets ``cache_control_injection_points`` for
+    # all providers and (for OpenAI/DeepSeek/xAI) ``prompt_cache_retention``.
+    # Per-thread ``prompt_cache_key`` is layered on later in
+    # ``create_surfsense_deep_agent`` once ``thread_id`` is known.
+    apply_litellm_prompt_caching(llm, agent_config=agent_config)
     return llm

surfsense_backend/app/agents/new_chat/prompt_caching.py

@@ -0,0 +1,166 @@
+"""LiteLLM-native prompt caching configuration for SurfSense agents.
+
+Replaces the legacy ``AnthropicPromptCachingMiddleware`` (which never
+activated for our LiteLLM-based stack — its ``isinstance(model, ChatAnthropic)``
+gate always failed) with LiteLLM's universal caching mechanism.
+
+Coverage:
+
+- Marker-based providers (need ``cache_control`` injection, which LiteLLM
+  performs automatically when ``cache_control_injection_points`` is set):
+  ``anthropic/``, ``bedrock/``, ``vertex_ai/``, ``gemini/``, ``azure_ai/``,
+  ``openrouter/`` (Claude/Gemini/MiniMax/GLM/z-ai routes), ``databricks/``
+  (Claude), ``dashscope/`` (Qwen), ``minimax/``, ``zai/`` (GLM).
+- Auto-cached (LiteLLM strips the marker silently): ``openai/``,
+  ``deepseek/``, ``xai/`` — these caches automatically for prompts ≥1024
+  tokens and surface ``prompt_cache_key`` / ``prompt_cache_retention``.
+
+We inject **two** breakpoints per request:
+
+- ``role: system`` — pins the SurfSense system prompt (provider variant,
+  citation rules, tool catalog, KB tree, skills metadata) into the cache.
+- ``index: -1`` — pins the latest message so multi-turn savings compound:
+  Anthropic-family providers use longest-matching-prefix lookup, so turn
+  N+1 still reads turn N's cache up to the shared prefix.
+
+For OpenAI-family configs we additionally pass:
+
+- ``prompt_cache_key=f"surfsense-thread-{thread_id}"`` — routing hint that
+  raises hit rate by sending requests with a shared prefix to the same
+  backend.
+- ``prompt_cache_retention="24h"`` — extends cache TTL beyond the default
+  5-10 min in-memory cache.
+
+Safety net: ``litellm.drop_params=True`` is set globally in
+``app.services.llm_service`` at module-load time. Any kwarg the destination
+provider doesn't recognise is auto-stripped at the provider transformer
+layer, so an OpenAI→Bedrock auto-mode fallback can't 400 on
+``prompt_cache_key`` etc.
+"""
+
+from __future__ import annotations
+
+import logging
+from typing import TYPE_CHECKING, Any
+
+from langchain_core.language_models import BaseChatModel
+
+if TYPE_CHECKING:
+    from app.agents.new_chat.llm_config import AgentConfig
+
+logger = logging.getLogger(__name__)
+
+
+# Two-breakpoint policy: system + latest message. See module docstring for
+# rationale. Anthropic limits requests to 4 ``cache_control`` blocks; we
+# use 2 here, leaving headroom for Phase-2 tool caching.
+_DEFAULT_INJECTION_POINTS: tuple[dict[str, Any], ...] = (
+    {"location": "message", "role": "system"},
+    {"location": "message", "index": -1},
+)
+
+# Providers (uppercase ``AgentConfig.provider`` values) that natively expose
+# OpenAI-style automatic prompt caching with ``prompt_cache_key`` and
+# ``prompt_cache_retention`` kwargs. Strict whitelist — many other providers
+# in ``PROVIDER_MAP`` route through litellm's ``openai`` prefix without
+# implementing the OpenAI prompt-cache surface (e.g. MOONSHOT, ZHIPU,
+# MINIMAX), so we can't infer family from the litellm prefix alone.
+_OPENAI_FAMILY_PROVIDERS: frozenset[str] = frozenset({"OPENAI", "DEEPSEEK", "XAI"})
+
+
+def _is_router_llm(llm: BaseChatModel) -> bool:
+    """Detect ``ChatLiteLLMRouter`` (auto-mode) without an eager import.
+
+    Importing ``app.services.llm_router_service`` at module-load time would
+    create a cycle via ``llm_config -> prompt_caching -> llm_router_service``.
+    Class-name comparison is sufficient since the class is defined in a
+    single place.
+    """
+    return type(llm).__name__ == "ChatLiteLLMRouter"
+
+
+def _is_openai_family_config(agent_config: AgentConfig | None) -> bool:
+    """Whether the config targets an OpenAI-style prompt-cache surface.
+
+    Strict — only returns True when the user explicitly chose OPENAI,
+    DEEPSEEK, or XAI as the provider in their ``NewLLMConfig`` /
+    ``YAMLConfig``. Auto-mode and custom providers return False because
+    we can't statically know the destination.
+    """
+    if agent_config is None or not agent_config.provider:
+        return False
+    if agent_config.is_auto_mode:
+        return False
+    if agent_config.custom_provider:
+        return False
+    return agent_config.provider.upper() in _OPENAI_FAMILY_PROVIDERS
+
+
+def _get_or_init_model_kwargs(llm: BaseChatModel) -> dict[str, Any] | None:
+    """Return ``llm.model_kwargs`` as a writable dict, or ``None`` to bail.
+
+    Initialises the field to ``{}`` when present-but-None on a Pydantic v2
+    model. Returns ``None`` if the LLM type doesn't expose a writable
+    ``model_kwargs`` attribute (caller should treat as no-op).
+    """
+    model_kwargs = getattr(llm, "model_kwargs", None)
+    if isinstance(model_kwargs, dict):
+        return model_kwargs
+    try:
+        llm.model_kwargs = {}  # type: ignore[attr-defined]
+    except Exception:
+        return None
+    refreshed = getattr(llm, "model_kwargs", None)
+    return refreshed if isinstance(refreshed, dict) else None
+
+
+def apply_litellm_prompt_caching(
+    llm: BaseChatModel,
+    *,
+    agent_config: AgentConfig | None = None,
+    thread_id: int | None = None,
+) -> None:
+    """Configure LiteLLM prompt caching on a ChatLiteLLM/ChatLiteLLMRouter.
+
+    Idempotent — values already present in ``llm.model_kwargs`` (e.g. from
+    ``agent_config.litellm_params`` overrides) are preserved. Mutates
+    ``llm.model_kwargs`` in place; the kwargs flow to ``litellm.completion``
+    via ``ChatLiteLLM._default_params`` and via ``self.model_kwargs`` merge
+    in our custom ``ChatLiteLLMRouter``.
+
+    Args:
+        llm: ChatLiteLLM, SanitizedChatLiteLLM, or ChatLiteLLMRouter instance.
+        agent_config: Optional ``AgentConfig`` driving provider-specific
+            behaviour. When omitted (or auto-mode), only the universal
+            ``cache_control_injection_points`` are set.
+        thread_id: Optional thread id used to construct a per-thread
+            ``prompt_cache_key`` for OpenAI-family providers. Caching still
+            works without it (server-side automatic), but the key improves
+            backend routing affinity and therefore hit rate.
+    """
+    model_kwargs = _get_or_init_model_kwargs(llm)
+    if model_kwargs is None:
+        logger.debug(
+            "apply_litellm_prompt_caching: %s exposes no writable model_kwargs; skipping",
+            type(llm).__name__,
+        )
+        return
+
+    if "cache_control_injection_points" not in model_kwargs:
+        model_kwargs["cache_control_injection_points"] = [
+            dict(point) for point in _DEFAULT_INJECTION_POINTS
+        ]
+
+    # OpenAI-family extras only when we statically know the destination is
+    # OpenAI / DeepSeek / xAI. Auto-mode router fans out across providers
+    # so we can't safely set OpenAI-only kwargs there (drop_params would
+    # strip them but it's wasteful to set them in the first place).
+    if _is_router_llm(llm):
+        return
+    if not _is_openai_family_config(agent_config):
+        return
+
+    if thread_id is not None and "prompt_cache_key" not in model_kwargs:
+        model_kwargs["prompt_cache_key"] = f"surfsense-thread-{thread_id}"
+    if "prompt_cache_retention" not in model_kwargs:
+        model_kwargs["prompt_cache_retention"] = "24h"

surfsense_backend/app/services/llm_router_service.py

@@ -28,6 +28,7 @@ from litellm.exceptions import (
     BadRequestError as LiteLLMBadRequestError,
     ContextWindowExceededError,
 )
+from pydantic import Field
 
 from app.utils.perf import get_perf_logger
 
@@ -573,6 +574,11 @@ class ChatLiteLLMRouter(BaseChatModel):
     # Public attributes that Pydantic will manage
     model: str = "auto"
     streaming: bool = True
+    # Static kwargs that flow through to ``litellm.completion(...)`` on every
+    # invocation (e.g. ``cache_control_injection_points`` set by
+    # ``apply_litellm_prompt_caching``). Per-call ``**kwargs`` from
+    # ``invoke()`` still take precedence — see ``_generate``/``_astream``.
+    model_kwargs: dict[str, Any] = Field(default_factory=dict)
 
     # Bound tools and tool choice for tool calling
     _bound_tools: list[dict] | None = None
@@ -898,13 +904,16 @@ class ChatLiteLLMRouter(BaseChatModel):
                     logger.warning(f"Failed to convert tool {tool}: {e}")
                     continue
 
-        # Create a new instance with tools bound
+        # Create a new instance with tools bound. Carry through ``model_kwargs``
+        # so static settings (e.g. cache_control_injection_points) survive the
+        # bind_tools rebuild.
         return ChatLiteLLMRouter(
             router=self._router,
             bound_tools=formatted_tools if formatted_tools else None,
             tool_choice=tool_choice,
             model=self.model,
             streaming=self.streaming,
+            model_kwargs=dict(self.model_kwargs),
             **kwargs,
         )
 
@@ -929,8 +938,10 @@ class ChatLiteLLMRouter(BaseChatModel):
         formatted_messages = self._convert_messages(messages)
         formatted_messages = self._trim_messages_to_fit_context(formatted_messages)
 
-        # Add tools if bound
+        # Merge static model_kwargs (e.g. cache_control_injection_points) under
-        call_kwargs = {**kwargs}
+        # per-call kwargs so callers can still override per invocation. Then add
+        # bound tools.
+        call_kwargs = {**self.model_kwargs, **kwargs}
         if self._bound_tools:
             call_kwargs["tools"] = self._bound_tools
         if self._tool_choice is not None:
@@ -997,8 +1008,10 @@ class ChatLiteLLMRouter(BaseChatModel):
         formatted_messages = self._convert_messages(messages)
         formatted_messages = self._trim_messages_to_fit_context(formatted_messages)
 
-        # Add tools if bound
+        # Merge static model_kwargs (e.g. cache_control_injection_points) under
-        call_kwargs = {**kwargs}
+        # per-call kwargs so callers can still override per invocation. Then add
+        # bound tools.
+        call_kwargs = {**self.model_kwargs, **kwargs}
         if self._bound_tools:
             call_kwargs["tools"] = self._bound_tools
         if self._tool_choice is not None:
@@ -1060,8 +1073,10 @@ class ChatLiteLLMRouter(BaseChatModel):
         formatted_messages = self._convert_messages(messages)
         formatted_messages = self._trim_messages_to_fit_context(formatted_messages)
 
-        # Add tools if bound
+        # Merge static model_kwargs (e.g. cache_control_injection_points) under
-        call_kwargs = {**kwargs}
+        # per-call kwargs so callers can still override per invocation. Then add
+        # bound tools.
+        call_kwargs = {**self.model_kwargs, **kwargs}
         if self._bound_tools:
             call_kwargs["tools"] = self._bound_tools
         if self._tool_choice is not None:
@@ -1110,8 +1125,10 @@ class ChatLiteLLMRouter(BaseChatModel):
         formatted_messages = self._convert_messages(messages)
         formatted_messages = self._trim_messages_to_fit_context(formatted_messages)
 
-        # Add tools if bound
+        # Merge static model_kwargs (e.g. cache_control_injection_points) under
-        call_kwargs = {**kwargs}
+        # per-call kwargs so callers can still override per invocation. Then add
+        # bound tools.
+        call_kwargs = {**self.model_kwargs, **kwargs}
         if self._bound_tools:
             call_kwargs["tools"] = self._bound_tools
         if self._tool_choice is not None:

surfsense_backend/pyproject.toml

@@ -74,7 +74,7 @@ dependencies = [
     "deepagents>=0.4.12",
     "stripe>=15.0.0",
     "azure-ai-documentintelligence>=1.0.2",
-    "litellm>=1.83.4",
+    "litellm>=1.83.7",
     "langchain-litellm>=0.6.4",
 ]
 

surfsense_backend/tests/unit/agents/new_chat/prompts/test_composer.py

@@ -226,6 +226,31 @@ class TestCompose:
         # Default block should NOT be present
         assert "<knowledge_base_only_policy>" not in prompt
 
+    def test_provider_hints_render_with_custom_system_instructions(
+        self, fixed_today: datetime
+    ) -> None:
+        """Regression guard for the always-append decision: provider hints
+        append AFTER a custom system prompt.
+
+        Provider hints are stylistic nudges (parallel tool-call rules,
+        formatting guidance, etc.) that help the model regardless of
+        what the system instructions say. Suppressing them when a
+        custom prompt is set would partially defeat the per-family
+        prompt machinery.
+        """
+        prompt = compose_system_prompt(
+            today=fixed_today,
+            custom_system_instructions="You are a custom assistant.",
+            model_name="anthropic/claude-3-5-sonnet",
+        )
+        assert "You are a custom assistant." in prompt
+        assert "<provider_hints>" in prompt
+        # The custom prompt must come BEFORE the provider hints so the
+        # user's framing isn't drowned out by the stylistic nudges.
+        assert prompt.index("You are a custom assistant.") < prompt.index(
+            "<provider_hints>"
+        )
+
     def test_use_default_false_with_no_custom_yields_no_system_block(
         self, fixed_today: datetime
     ) -> None:

surfsense_backend/tests/unit/agents/new_chat/test_prompt_caching.py

@@ -0,0 +1,350 @@
+"""Tests for ``apply_litellm_prompt_caching`` in
+:mod:`app.agents.new_chat.prompt_caching`.
+
+The helper replaces the legacy ``AnthropicPromptCachingMiddleware`` (which
+never activated for our LiteLLM stack) with LiteLLM-native multi-provider
+prompt caching. It mutates ``llm.model_kwargs`` so the kwargs flow to
+``litellm.completion(...)``. The tests below pin its public contract:
+
+1. Always sets BOTH ``role: system`` and ``index: -1`` injection points so
+   savings compound across multi-turn conversations on Anthropic-family
+   providers.
+2. Adds ``prompt_cache_key``/``prompt_cache_retention`` only for
+   single-model OPENAI/DEEPSEEK/XAI configs (where OpenAI's automatic
+   prompt-cache surface is available).
+3. Treats ``ChatLiteLLMRouter`` (auto-mode) as universal-only — no
+   OpenAI-only kwargs because the router fans out across providers.
+4. Idempotent: user-supplied values in ``model_kwargs`` are preserved.
+5. Defensive: LLMs without a writable ``model_kwargs`` are silently
+   skipped rather than raising.
+"""
+
+from __future__ import annotations
+
+from typing import Any
+
+import pytest
+
+from app.agents.new_chat.llm_config import AgentConfig
+from app.agents.new_chat.prompt_caching import apply_litellm_prompt_caching
+
+pytestmark = pytest.mark.unit
+
+
+# ---------------------------------------------------------------------------
+# Test doubles
+# ---------------------------------------------------------------------------
+
+
+class _FakeLLM:
+    """Stand-in for ``ChatLiteLLM``/``SanitizedChatLiteLLM``.
+
+    The helper only inspects ``getattr(llm, "model_kwargs", None)``,
+    ``getattr(llm, "model", None)``, and ``type(llm).__name__``. A simple
+    object suffices — we don't need to spin up real LangChain/LiteLLM
+    machinery for unit tests of the helper's logic.
+    """
+
+    def __init__(
+        self,
+        model: str = "openai/gpt-4o",
+        model_kwargs: dict[str, Any] | None = None,
+    ) -> None:
+        self.model = model
+        self.model_kwargs: dict[str, Any] = dict(model_kwargs) if model_kwargs else {}
+
+
+class ChatLiteLLMRouter:
+    """Class-name-only impostor of the real router.
+
+    The helper's router gate is ``type(llm).__name__ == "ChatLiteLLMRouter"``
+    (a deliberate stringly-typed check to avoid an import cycle with
+    ``app.services.llm_router_service``). Reusing the same class name here
+    triggers the same code path without instantiating a real ``Router``.
+    """
+
+    def __init__(self) -> None:
+        self.model = "auto"
+        self.model_kwargs: dict[str, Any] = {}
+
+
+def _make_cfg(**overrides: Any) -> AgentConfig:
+    """Build an ``AgentConfig`` with sensible defaults for the helper test."""
+    defaults: dict[str, Any] = {
+        "provider": "OPENAI",
+        "model_name": "gpt-4o",
+        "api_key": "k",
+    }
+    return AgentConfig(**{**defaults, **overrides})
+
+
+# ---------------------------------------------------------------------------
+# (a) Universal injection points
+# ---------------------------------------------------------------------------
+
+
+def test_sets_both_cache_control_injection_points_with_no_config() -> None:
+    """Bare call (no agent_config, no thread_id) still sets the two
+    universal breakpoints — these cost nothing on providers that don't
+    consume them and unlock caching on every supported provider."""
+    llm = _FakeLLM()
+
+    apply_litellm_prompt_caching(llm)
+
+    points = llm.model_kwargs["cache_control_injection_points"]
+    assert {"location": "message", "role": "system"} in points
+    assert {"location": "message", "index": -1} in points
+    assert len(points) == 2
+
+
+def test_injection_points_set_for_anthropic_config() -> None:
+    """Anthropic-family configs need the marker — verify it lands."""
+    cfg = _make_cfg(provider="ANTHROPIC", model_name="claude-3-5-sonnet")
+    llm = _FakeLLM(model="anthropic/claude-3-5-sonnet")
+
+    apply_litellm_prompt_caching(llm, agent_config=cfg)
+
+    assert "cache_control_injection_points" in llm.model_kwargs
+
+
+# ---------------------------------------------------------------------------
+# (b) Idempotency / user override wins
+# ---------------------------------------------------------------------------
+
+
+def test_does_not_overwrite_user_supplied_cache_control_injection_points() -> None:
+    """Users who set their own injection points (e.g. with ``ttl: "1h"``
+    via ``litellm_params``) keep them — the helper merges, never
+    clobbers."""
+    user_points = [
+        {"location": "message", "role": "system", "ttl": "1h"},
+    ]
+    llm = _FakeLLM(
+        model_kwargs={"cache_control_injection_points": user_points},
+    )
+
+    apply_litellm_prompt_caching(llm)
+
+    assert llm.model_kwargs["cache_control_injection_points"] is user_points
+
+
+def test_idempotent_when_called_multiple_times() -> None:
+    """Build-time + thread-time double-call must be a no-op the second time."""
+    cfg = _make_cfg(provider="OPENAI")
+    llm = _FakeLLM()
+
+    apply_litellm_prompt_caching(llm, agent_config=cfg, thread_id=1)
+    snapshot = {
+        "cache_control_injection_points": list(
+            llm.model_kwargs["cache_control_injection_points"]
+        ),
+        "prompt_cache_key": llm.model_kwargs["prompt_cache_key"],
+        "prompt_cache_retention": llm.model_kwargs["prompt_cache_retention"],
+    }
+    apply_litellm_prompt_caching(llm, agent_config=cfg, thread_id=1)
+
+    assert (
+        llm.model_kwargs["cache_control_injection_points"]
+        == snapshot["cache_control_injection_points"]
+    )
+    assert llm.model_kwargs["prompt_cache_key"] == snapshot["prompt_cache_key"]
+    assert (
+        llm.model_kwargs["prompt_cache_retention"] == snapshot["prompt_cache_retention"]
+    )
+
+
+def test_does_not_overwrite_user_supplied_prompt_cache_key() -> None:
+    """A pre-set ``prompt_cache_key`` (e.g. tenant-aware override via
+    ``litellm_params``) wins over our default per-thread key."""
+    cfg = _make_cfg(provider="OPENAI")
+    llm = _FakeLLM(model_kwargs={"prompt_cache_key": "tenant-abc"})
+
+    apply_litellm_prompt_caching(llm, agent_config=cfg, thread_id=42)
+
+    assert llm.model_kwargs["prompt_cache_key"] == "tenant-abc"
+
+
+# ---------------------------------------------------------------------------
+# (c) OpenAI-family extras (OPENAI / DEEPSEEK / XAI)
+# ---------------------------------------------------------------------------
+
+
+@pytest.mark.parametrize("provider", ["OPENAI", "DEEPSEEK", "XAI"])
+def test_sets_openai_family_extras(provider: str) -> None:
+    """OpenAI-style providers gain ``prompt_cache_key`` (raises hit rate
+    via routing affinity) and ``prompt_cache_retention="24h"`` (extends
+    cache TTL beyond the default 5-10 min)."""
+    cfg = _make_cfg(provider=provider)
+    llm = _FakeLLM()
+
+    apply_litellm_prompt_caching(llm, agent_config=cfg, thread_id=42)
+
+    assert llm.model_kwargs["prompt_cache_key"] == "surfsense-thread-42"
+    assert llm.model_kwargs["prompt_cache_retention"] == "24h"
+
+
+def test_skips_prompt_cache_key_when_no_thread_id() -> None:
+    """Without a thread id we can't construct a per-thread key. Retention
+    is still useful so we set it (it's free)."""
+    cfg = _make_cfg(provider="OPENAI")
+    llm = _FakeLLM()
+
+    apply_litellm_prompt_caching(llm, agent_config=cfg, thread_id=None)
+
+    assert "prompt_cache_key" not in llm.model_kwargs
+    assert llm.model_kwargs["prompt_cache_retention"] == "24h"
+
+
+@pytest.mark.parametrize(
+    "provider",
+    ["ANTHROPIC", "BEDROCK", "VERTEX_AI", "GOOGLE_AI_STUDIO", "GROQ", "MOONSHOT"],
+)
+def test_no_openai_extras_for_other_providers(provider: str) -> None:
+    """Non-OpenAI-family providers don't expose ``prompt_cache_key`` —
+    skip it. ``cache_control_injection_points`` is still set (universal)."""
+    cfg = _make_cfg(provider=provider)
+    llm = _FakeLLM()
+
+    apply_litellm_prompt_caching(llm, agent_config=cfg, thread_id=42)
+
+    assert "prompt_cache_key" not in llm.model_kwargs
+    assert "prompt_cache_retention" not in llm.model_kwargs
+    assert "cache_control_injection_points" in llm.model_kwargs
+
+
+def test_no_openai_extras_in_auto_mode() -> None:
+    """Auto-mode fans out across mixed providers — we can't statically
+    target OpenAI-only kwargs."""
+    cfg = AgentConfig.from_auto_mode()
+    llm = _FakeLLM()
+
+    apply_litellm_prompt_caching(llm, agent_config=cfg, thread_id=42)
+
+    assert "prompt_cache_key" not in llm.model_kwargs
+    assert "prompt_cache_retention" not in llm.model_kwargs
+    assert "cache_control_injection_points" in llm.model_kwargs
+
+
+def test_no_openai_extras_for_custom_provider() -> None:
+    """Custom providers route through arbitrary user-supplied prefixes —
+    we don't try to infer OpenAI-family compatibility."""
+    cfg = _make_cfg(provider="OPENAI", custom_provider="my_proxy")
+    llm = _FakeLLM()
+
+    apply_litellm_prompt_caching(llm, agent_config=cfg, thread_id=42)
+
+    assert "prompt_cache_key" not in llm.model_kwargs
+    assert "prompt_cache_retention" not in llm.model_kwargs
+
+
+# ---------------------------------------------------------------------------
+# (d) ChatLiteLLMRouter — universal injection points only
+# ---------------------------------------------------------------------------
+
+
+def test_router_llm_gets_only_universal_injection_points() -> None:
+    """Even with an OpenAI-flavoured config, a ``ChatLiteLLMRouter`` must
+    receive only the universal injection points — its requests dispatch
+    across provider deployments and OpenAI-only kwargs would be wasted
+    (or stripped by ``drop_params``) on non-OpenAI legs."""
+    router = ChatLiteLLMRouter()
+    cfg = _make_cfg(provider="OPENAI")
+
+    apply_litellm_prompt_caching(router, agent_config=cfg, thread_id=42)
+
+    assert "cache_control_injection_points" in router.model_kwargs
+    assert "prompt_cache_key" not in router.model_kwargs
+    assert "prompt_cache_retention" not in router.model_kwargs
+
+
+# ---------------------------------------------------------------------------
+# (e) Defensive paths
+# ---------------------------------------------------------------------------
+
+
+def test_handles_llm_with_no_writable_model_kwargs() -> None:
+    """Some LLM implementations (e.g. fakes / minimal subclasses) don't
+    expose a writable ``model_kwargs``. The helper must skip silently —
+    raising would crash the entire LLM build path on a non-critical
+    optimisation."""
+
+    class _ImmutableLLM:
+        # ``__slots__`` blocks attribute creation, so ``setattr`` raises.
+        __slots__ = ("model",)
+
+        def __init__(self) -> None:
+            self.model = "openai/gpt-4o"
+
+    llm = _ImmutableLLM()
+
+    apply_litellm_prompt_caching(llm)
+
+
+def test_initialises_missing_model_kwargs_dict() -> None:
+    """When ``model_kwargs`` is present-but-None (Pydantic v2 default
+    pattern when no factory is set), the helper initialises it to an
+    empty dict before mutating."""
+
+    class _LazyLLM:
+        def __init__(self) -> None:
+            self.model = "openai/gpt-4o"
+            self.model_kwargs: dict[str, Any] | None = None
+
+    llm = _LazyLLM()
+
+    apply_litellm_prompt_caching(llm)
+
+    assert isinstance(llm.model_kwargs, dict)
+    assert "cache_control_injection_points" in llm.model_kwargs
+
+
+def test_falls_back_to_llm_model_prefix_when_no_agent_config() -> None:
+    """Direct caller path (e.g. ``create_chat_litellm_from_config`` for
+    YAML configs without a structured ``AgentConfig``): without
+    ``agent_config`` the helper sets only the universal injection points
+    — no OpenAI-family extras even if the prefix says ``openai/``.
+    Conservative: we'd rather miss the speedup than silently misroute."""
+    llm = _FakeLLM(model="openai/gpt-4o")
+
+    apply_litellm_prompt_caching(llm, agent_config=None, thread_id=99)
+
+    assert "cache_control_injection_points" in llm.model_kwargs
+    assert "prompt_cache_key" not in llm.model_kwargs
+    assert "prompt_cache_retention" not in llm.model_kwargs
+
+
+# ---------------------------------------------------------------------------
+# (f) drop_params safety net (regression guard for #19346)
+# ---------------------------------------------------------------------------
+
+
+def test_litellm_drop_params_is_globally_enabled() -> None:
+    """``litellm.drop_params=True`` is set globally in
+    :mod:`app.services.llm_service` so any ``prompt_cache_key`` /
+    ``prompt_cache_retention`` we set on an OpenAI-family config is
+    auto-stripped if the request later routes to a non-supporting
+    provider (e.g. via auto-mode router fallback). This test pins that
+    invariant — losing it would mean Bedrock/Vertex 400s on ``prompt_cache_key``.
+    """
+    import litellm
+
+    import app.services.llm_service  # noqa: F401  (side-effect: sets globals)
+
+    assert litellm.drop_params is True
+
+
+# ---------------------------------------------------------------------------
+# Regression note: LiteLLM #15696 (multi-content-block last message)
+# ---------------------------------------------------------------------------
+#
+# Before LiteLLM 1.81 a list-form last message ``[block_a, block_b]``
+# would get ``cache_control`` applied to *every* content block instead
+# of only the last one — wasting cache breakpoints and triggering 400s
+# on Anthropic when it exceeded the 4-breakpoint limit. Fixed in
+# https://github.com/BerriAI/litellm/pull/15699.
+#
+# We pin ``litellm>=1.83.7`` in ``pyproject.toml`` (well past the fix).
+# An end-to-end behavioural test would need to run ``litellm.completion``
+# through the Anthropic transformer, which is integration territory and
+# better covered by LiteLLM's own test suite. The unit guard here is the
+# version pin plus the build-time ``model_kwargs`` shape we verify above.

surfsense_backend/tests/unit/agents/new_chat/test_resolve_prompt_model_name.py

@@ -0,0 +1,117 @@
+"""Tests for ``_resolve_prompt_model_name`` in :mod:`app.agents.new_chat.chat_deepagent`.
+
+The helper picks the model id fed to ``detect_provider_variant`` so the
+right ``<provider_hints>`` block lands in the system prompt. The tests
+below pin its preference order:
+
+1. ``agent_config.litellm_params["base_model"]`` (Azure-correct).
+2. ``agent_config.model_name``.
+3. ``getattr(llm, "model", None)``.
+
+Without (1) an Azure deployment named e.g. ``"prod-chat-001"`` would
+silently miss every provider regex.
+"""
+
+from __future__ import annotations
+
+import pytest
+
+from app.agents.new_chat.chat_deepagent import _resolve_prompt_model_name
+from app.agents.new_chat.llm_config import AgentConfig
+
+pytestmark = pytest.mark.unit
+
+
+def _make_cfg(**overrides) -> AgentConfig:
+    """Build an ``AgentConfig`` with sensible defaults for the helper test."""
+    defaults = {
+        "provider": "OPENAI",
+        "model_name": "x",
+        "api_key": "k",
+    }
+    return AgentConfig(**{**defaults, **overrides})
+
+
+class _FakeLLM:
+    """Stand-in for a ``ChatLiteLLM`` / ``ChatLiteLLMRouter`` instance.
+
+    The resolver only reads the ``.model`` attribute via ``getattr``,
+    matching the established idiom in ``knowledge_search.py`` /
+    ``stream_new_chat.py`` / ``document_summarizer.py``.
+    """
+
+    def __init__(self, model: str | None) -> None:
+        self.model = model
+
+
+def test_prefers_litellm_params_base_model_over_deployment_name() -> None:
+    """Azure deployment slug must NOT shadow the underlying model family.
+
+    This is the failure mode the helper exists to prevent: a deployment
+    named ``"azure/prod-chat-001"`` would not match any provider regex
+    on its own, but the family ``"gpt-4o"`` lives in
+    ``litellm_params["base_model"]`` and routes to ``openai_classic``.
+    """
+    cfg = _make_cfg(
+        model_name="azure/prod-chat-001",
+        litellm_params={"base_model": "gpt-4o"},
+    )
+    assert _resolve_prompt_model_name(cfg, _FakeLLM("azure/prod-chat-001")) == "gpt-4o"
+
+
+def test_falls_back_to_model_name_when_litellm_params_is_none() -> None:
+    cfg = _make_cfg(
+        model_name="anthropic/claude-3-5-sonnet",
+        litellm_params=None,
+    )
+    got = _resolve_prompt_model_name(cfg, _FakeLLM("anthropic/claude-3-5-sonnet"))
+    assert got == "anthropic/claude-3-5-sonnet"
+
+
+def test_handles_litellm_params_without_base_model_key() -> None:
+    cfg = _make_cfg(
+        model_name="openai/gpt-4o",
+        litellm_params={"temperature": 0.5},
+    )
+    assert _resolve_prompt_model_name(cfg, _FakeLLM("openai/gpt-4o")) == "openai/gpt-4o"
+
+
+def test_ignores_blank_base_model() -> None:
+    """Whitespace-only ``base_model`` must not shadow ``model_name``."""
+    cfg = _make_cfg(
+        model_name="openai/gpt-4o",
+        litellm_params={"base_model": "   "},
+    )
+    assert _resolve_prompt_model_name(cfg, _FakeLLM("openai/gpt-4o")) == "openai/gpt-4o"
+
+
+def test_ignores_non_string_base_model() -> None:
+    """Defensive: a non-string ``base_model`` should not crash the resolver."""
+    cfg = _make_cfg(
+        model_name="openai/gpt-4o",
+        litellm_params={"base_model": 42},
+    )
+    assert _resolve_prompt_model_name(cfg, _FakeLLM("openai/gpt-4o")) == "openai/gpt-4o"
+
+
+def test_falls_back_to_llm_model_when_no_agent_config() -> None:
+    """No ``agent_config`` -> use ``llm.model`` directly. Defensive path
+    for direct callers; production callers always supply a config."""
+    assert (
+        _resolve_prompt_model_name(None, _FakeLLM("openai/gpt-4o-mini"))
+        == "openai/gpt-4o-mini"
+    )
+
+
+def test_returns_none_when_nothing_available() -> None:
+    """``compose_system_prompt`` treats ``None`` as the ``"default"``
+    variant and emits no provider block."""
+    assert _resolve_prompt_model_name(None, _FakeLLM(None)) is None
+
+
+def test_auto_mode_resolves_to_auto_string() -> None:
+    """Auto mode -> ``"auto"``. ``detect_provider_variant("auto")``
+    returns ``"default"``, which is correct: the child model isn't
+    known until the LiteLLM Router dispatches."""
+    cfg = AgentConfig.from_auto_mode()
+    assert _resolve_prompt_model_name(cfg, _FakeLLM("auto")) == "auto"

surfsense_backend/tests/unit/test_stream_new_chat_contract.py

@@ -372,3 +372,39 @@ def test_turn_status_sse_contract_exists():
     assert 'type: "data-turn-status"' in state_source
     assert 'case "data-turn-status":' in pipeline_source
     assert "end_turn(str(chat_id))" in stream_source
+
+
+def test_chat_deepagent_forwards_resolved_model_name_to_both_builders():
+    """Regression guard: both system-prompt builders in chat_deepagent.py
+    must receive ``model_name=_resolve_prompt_model_name(...)`` so the
+    provider-variant dispatch can render the right ``<provider_hints>``
+    block. Without this the prompt silently falls back to the empty
+    ``"default"`` variant — the original bug being fixed.
+
+    This test mirrors :func:`test_stream_error_emission_keeps_machine_error_codes`
+    in style: it inspects module source text + a regex to enforce the
+    call-site shape, not just the wrapper layer (the wrappers already
+    forward ``model_name`` correctly, so testing them would not catch
+    the actual missed plumbing).
+    """
+    import app.agents.new_chat.chat_deepagent as chat_deepagent_module
+
+    source = inspect.getsource(chat_deepagent_module)
+
+    # Helper itself must be defined.
+    assert "def _resolve_prompt_model_name(" in source
+
+    # Both builder calls must forward the resolved model name. Match
+    # across newlines + whitespace because the kwargs are split over
+    # multiple lines.
+    pattern = re.compile(
+        r"build_(?:surfsense|configurable)_system_prompt\([^)]*"
+        r"model_name=_resolve_prompt_model_name\(",
+        re.DOTALL,
+    )
+    matches = pattern.findall(source)
+    assert len(matches) == 2, (
+        "Expected both system-prompt builder call sites to forward "
+        "`model_name=_resolve_prompt_model_name(...)`, found "
+        f"{len(matches)}"
+    )

surfsense_backend/uv.lock

@@ -62,7 +62,7 @@ wheels = [
 
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-version = "3.13.5"
+version = "3.13.4"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "aiohappyeyeballs" },
@@ -73,76 +73,76 @@ dependencies = [
     { name = "propcache" },
     { name = "yarl" },
 ]
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+sdist = { url = "https://files.pythonhosted.org/packages/45/4a/064321452809dae953c1ed6e017504e72551a26b6f5708a5a80e4bf556ff/aiohttp-3.13.4.tar.gz", hash = "sha256:d97a6d09c66087890c2ab5d49069e1e570583f7ac0314ecf98294c1b6aaebd38", size = 7859748 }
 wheels = [
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@@ -3723,7 +3723,7 @@ wheels = [
 
 [[package]]
 name = "litellm"
-version = "1.83.4"
+version = "1.83.14"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "aiohttp" },
@@ -3739,9 +3739,9 @@ dependencies = [
     { name = "tiktoken" },
     { name = "tokenizers" },
 ]
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@@ -5124,7 +5124,7 @@ wheels = [
 
 [[package]]
 name = "openai"
-version = "2.30.0"
+version = "2.24.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "anyio" },
@@ -5136,9 +5136,9 @@ dependencies = [
     { name = "tqdm" },
     { name = "typing-extensions" },
 ]
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+sdist = { url = "https://files.pythonhosted.org/packages/55/13/17e87641b89b74552ed408a92b231283786523edddc95f3545809fab673c/openai-2.24.0.tar.gz", hash = "sha256:1e5769f540dbd01cb33bc4716a23e67b9d695161a734aff9c5f925e2bf99a673", size = 658717 }
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@@ -6780,11 +6780,11 @@ wheels = [
 
 [[package]]
 name = "python-dotenv"
-version = "1.0.1"
+version = "1.2.2"
 source = { registry = "https://pypi.org/simple" }
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@@ -8070,7 +8070,7 @@ requires-dist = [
     { name = "langgraph", specifier = ">=1.1.3" },
     { name = "langgraph-checkpoint-postgres", specifier = ">=3.0.2" },
     { name = "linkup-sdk", specifier = ">=0.2.4" },
-    { name = "litellm", specifier = ">=1.83.4" },
+    { name = "litellm", specifier = ">=1.83.7" },
     { name = "llama-cloud-services", specifier = ">=0.6.25" },
     { name = "markdown", specifier = ">=3.7" },
     { name = "markdownify", specifier = ">=0.14.1" },

surfsense_web/components/pricing/pricing-section.tsx

@@ -17,7 +17,6 @@ const demoPlans = [
 			"Self Hostable",
 			"500 pages included to start",
 			"3 million premium tokens to start",
-			"Earn up to 3,000+ bonus pages for free",
 			"Includes access to OpenAI text, audio and image models",
 			"Realtime Collaborative Group Chats with teammates",
 			"Community support on Discord",

surfsense_web/components/settings/more-pages-content.tsx

@@ -1,21 +1,14 @@
 "use client";
 
 import { useMutation, useQuery, useQueryClient } from "@tanstack/react-query";
-import { Check, ExternalLink, Mail } from "lucide-react";
+import { Check, ExternalLink } from "lucide-react";
 import Link from "next/link";
 import { useParams } from "next/navigation";
-import { useEffect, useState } from "react";
+import { useEffect } from "react";
 import { toast } from "sonner";
 import { USER_QUERY_KEY } from "@/atoms/user/user-query.atoms";
 import { Button } from "@/components/ui/button";
 import { Card, CardContent } from "@/components/ui/card";
-import {
-	Dialog,
-	DialogContent,
-	DialogDescription,
-	DialogHeader,
-	DialogTitle,
-} from "@/components/ui/dialog";
 import { Separator } from "@/components/ui/separator";
 import { Skeleton } from "@/components/ui/skeleton";
 import { Spinner } from "@/components/ui/spinner";
@@ -33,7 +26,6 @@ export function MorePagesContent() {
 	const params = useParams();
 	const queryClient = useQueryClient();
 	const searchSpaceId = params?.search_space_id ?? "";
-	const [claimOpen, setClaimOpen] = useState(false);
 
 	useEffect(() => {
 		trackIncentivePageViewed();
@@ -79,35 +71,10 @@ export function MorePagesContent() {
 			<div className="text-center">
 				<h2 className="text-xl font-bold tracking-tight">Get Free Pages</h2>
 				<p className="mt-1 text-sm text-muted-foreground">
-					Claim your free page offer and earn bonus pages
+					Earn bonus pages by completing tasks
 				</p>
 			</div>
 
-			{/* 3k free offer */}
-			<Card className="border-emerald-500/30 bg-emerald-500/5">
-				<CardContent className="flex items-center gap-3 p-4">
-					<div className="flex h-10 w-10 shrink-0 items-center justify-center rounded-full bg-emerald-600 text-white text-xs font-bold">
-						3k
-					</div>
-					<div className="min-w-0 flex-1">
-						<p className="text-sm font-semibold">Claim 3,000 Free Pages</p>
-						<p className="text-xs text-muted-foreground">
-							Limited offer. Schedule a meeting or email us to claim.
-						</p>
-					</div>
-					<Button
-						size="sm"
-						className="bg-emerald-600 text-white hover:bg-emerald-700"
-						onClick={() => setClaimOpen(true)}
-					>
-						Claim
-					</Button>
-				</CardContent>
-			</Card>
-
-			<Separator />
-
-			{/* Free tasks */}
 			<div className="space-y-2">
 				<h3 className="text-sm font-semibold">Earn Bonus Pages</h3>
 				{isLoading ? (
@@ -182,7 +149,6 @@ export function MorePagesContent() {
 
 			<Separator />
 
-			{/* Link to buy pages */}
 			<div className="text-center">
 				<p className="text-sm text-muted-foreground">Need more?</p>
 				{pageBuyingEnabled ? (
@@ -197,25 +163,6 @@ export function MorePagesContent() {
 					</p>
 				)}
 			</div>
-
-			{/* Claim 3k dialog */}
-			<Dialog open={claimOpen} onOpenChange={setClaimOpen}>
-				<DialogContent className="sm:max-w-md">
-					<DialogHeader>
-						<DialogTitle>Claim 3,000 Free Pages</DialogTitle>
-						<DialogDescription>
-							Send us an email to claim your free 3,000 pages. Include your account email and
-							primary usecase for free pages.
-						</DialogDescription>
-					</DialogHeader>
-					<Button asChild className="w-full gap-2">
-						<a href="mailto:rohan@surfsense.com?subject=Claim%203%2C000%20Free%20Pages&body=Hi%2C%20I'd%20like%20to%20claim%20the%203%2C000%20free%20pages%20offer.%0A%0AMy%20account%20email%3A%20">
-							<Mail className="h-4 w-4" />
-							rohan@surfsense.com
-						</a>
-					</Button>
-				</DialogContent>
-			</Dialog>
 		</div>
 	);
 }