--- a/letta/agent.py
+++ b/letta/agent.py
@@ -1,3 +1,4 @@
+# Modified.
 import json
 import time
 import traceback
@@ -70,6 +71,33 @@
 logger = get_logger(__name__)
 
 
+def _fit_context_window(sequence: List[Message], new_messages: List[Message], limit: Optional[int]) -> List[Message]:
+    if not limit or len(sequence) <= 2 or not new_messages:
+        return sequence
+    counts = get_token_counts_for_messages(sequence)
+    if sum(counts) <= limit:
+        return sequence
+    n_new = len(new_messages)
+    room = limit - counts[0] - sum(counts[-n_new:])
+    middle = list(zip(sequence[1:-n_new], counts[1:-n_new]))
+    units, i = [], 0
+    while i < len(middle):
+        j = i + 1
+        if getattr(middle[i][0], "role", None) == "assistant" and middle[i][0].tool_calls:
+            while j < len(middle) and getattr(middle[j][0], "role", None) == "tool":
+                j += 1
+        units.append(middle[i:j])
+        i = j
+    kept = []
+    for unit in reversed(units):
+        size = sum(n for _, n in unit)
+        if size > room:
+            break
+        kept[:0] = [m for m, _ in unit]
+        room -= size
+    return [sequence[0], *kept, *new_messages]
+
+
 class BaseAgent(ABC):
     """
     Abstract class for all agents.
@@ -745,6 +773,23 @@
             elif max_chaining_steps is not None and counter > max_chaining_steps:
                 self.logger.info(f"Hit max chaining steps, stopping after {counter} steps")
                 break
+            elif (max_chaining_steps is not None and counter == max_chaining_steps
+                  and (heartbeat_request or function_failed or token_warning)
+                  and __import__("os").environ.get("MEMEBENCH_LETTA_FINAL_REPLY_AT_CAP") == "1"):
+                assert self.agent_state.created_by_id is not None
+                next_input_messages = [
+                    Message.dict_to_message(
+                        agent_id=self.agent_state.id,
+                        model=self.model,
+                        openai_message_dict={
+                            "role": "user",
+                            "content": get_heartbeat(
+                                "You have reached the maximum number of steps. Call send_message now "
+                                "with your final response."),
+                        },
+                    )
+                ]
+                continue
             # Chain handlers
             elif token_warning and summarizer_settings.send_memory_warning_message:
                 assert self.agent_state.created_by_id is not None
@@ -828,6 +873,8 @@
 
             in_context_messages = self.agent_manager.get_in_context_messages(agent_id=self.agent_state.id, actor=self.user)
             input_message_sequence = in_context_messages + messages
+            input_message_sequence = _fit_context_window(
+                input_message_sequence, messages, self.agent_state.llm_config.context_window)
 
             if (
                 len(input_message_sequence) > 1
--- a/letta/constants.py
+++ b/letta/constants.py
@@ -1,7 +1,8 @@
+# Modified.
 import os
 from logging import CRITICAL, DEBUG, ERROR, INFO, NOTSET, WARN, WARNING
 
-LETTA_DIR = os.path.join(os.path.expanduser("~"), ".letta")
+LETTA_DIR = os.environ.get("LETTA_DIR") or os.path.join(os.path.expanduser("~"), ".letta")
 LETTA_TOOL_EXECUTION_DIR = os.path.join(LETTA_DIR, "tool_execution_dir")
 
 ADMIN_PREFIX = "/v1/admin"
--- a/letta/functions/function_sets/base.py
+++ b/letta/functions/function_sets/base.py
@@ -1,3 +1,4 @@
+# Modified.
 from typing import Optional
 
 from letta.agent import Agent
@@ -43,7 +44,7 @@
         page = int(page)
     except:
         raise ValueError(f"'page' argument must be an integer")
-    count = RETRIEVAL_QUERY_DEFAULT_PAGE_SIZE
+    count = (self.agent_state.metadata or {}).get("retrieve_num", RETRIEVAL_QUERY_DEFAULT_PAGE_SIZE)
     # TODO: add paging by page number. currently cursor only works with strings.
     # original: start=page * count
     messages = self.message_manager.list_user_messages_for_agent(
@@ -104,7 +105,7 @@
         page = int(page)
     except:
         raise ValueError(f"'page' argument must be an integer")
-    count = RETRIEVAL_QUERY_DEFAULT_PAGE_SIZE
+    count = (self.agent_state.metadata or {}).get("retrieve_num", RETRIEVAL_QUERY_DEFAULT_PAGE_SIZE)
 
     try:
         # Get results using passage manager
--- a/letta/schemas/openai/chat_completion_request.py
+++ b/letta/schemas/openai/chat_completion_request.py
@@ -1,3 +1,4 @@
+# Modified.
 from typing import Any, Dict, List, Literal, Optional, Union
 
 from pydantic import BaseModel, Field, field_validator
@@ -124,6 +125,7 @@
     logprobs: Optional[bool] = False
     top_logprobs: Optional[int] = None
     max_tokens: Optional[int] = None
+    max_completion_tokens: Optional[int] = None
     n: Optional[int] = 1
     presence_penalty: Optional[float] = 0
     response_format: Optional[ResponseFormat] = None
--- a/letta/services/message_manager.py
+++ b/letta/services/message_manager.py
@@ -1,7 +1,8 @@
+# Modified.
 import json
 from typing import List, Optional, Sequence
 
-from sqlalchemy import delete, exists, func, select, text
+from sqlalchemy import String, cast, delete, exists, func, select, text
 
 from letta.log import get_logger
 from letta.orm.agent import Agent as AgentModel
@@ -304,7 +305,9 @@
                 query = query.filter(MessageModel.group_id == group_id)
 
             # If query_text is provided, filter messages using subquery + json_array_elements.
-            if query_text:
+            if query_text and session.bind is not None and session.bind.dialect.name == "sqlite":
+                query = query.filter(func.lower(cast(MessageModel.content, String)).like(f"%{query_text.lower()}%"))
+            elif query_text:
                 content_element = func.json_array_elements(MessageModel.content).alias("content_element")
                 query = query.filter(
                     exists(