diff --git a/examples/filters/message_length_filter_pipeline.py b/examples/filters/message_length_filter_pipeline.py
new file mode 100644
index 00000000..f31bbf4a
--- /dev/null
+++ b/examples/filters/message_length_filter_pipeline.py
@@ -0,0 +1,97 @@
+"""
+title: Message Length Filter
+author: David Weese
+date: 2025-09-23
+version: 1.0
+license: MIT
+description: Filter that enforces maximum input message length and caps/truncates assistant output length.
+"""
+
+from typing import List, Optional, Any
+from pydantic import BaseModel
+from schemas import OpenAIChatMessage
+import os
+import math
+
+
+def _get_last_message_by_roles(messages: List[dict], roles: List[str]) -> Optional[dict]:
+    for message in reversed(messages):
+        if message.get("role") in roles:
+            return message
+    return None
+
+
+def _compute_text_length(content: Any) -> int:
+    # content can be a string or a list (multi-part). We count only textual segments.
+    if isinstance(content, str):
+        return len(content)
+    if isinstance(content, list):
+        total = 0
+        for part in content:
+            # Common OpenAI-style structured parts may include {type: "text", text: "..."}
+            if isinstance(part, str):
+                total += len(part)
+            elif isinstance(part, dict):
+                text = part.get("text") or part.get("content")
+                if isinstance(text, str):
+                    total += len(text)
+        return total
+    return 0
+
+
+class Pipeline:
+    class Valves(BaseModel):
+        # Connect to these pipelines (models). Use ["*"] for all.
+        pipelines: List[str] = ["*"]
+
+        # Filter execution order among filters. Lower runs first.
+        priority: int = 0
+
+        # Input validation
+        target_user_roles: List[str] = ["user"]
+        max_user_message_chars: Optional[int] = int(os.getenv("MAX_USER_MESSAGE_CHARS", "20000"))
+
+        # Output limits
+        # If set, we will constrain generation length via tokens only
+        max_assistant_response_tokens: Optional[int] = int(os.getenv("MAX_RESPONSE_TOKENS", "8192"))
+
+    def __init__(self):
+        # Pipeline filters are only compatible with Open WebUI
+        self.type = "filter"
+        self.name = "Message Length Filter"
+
+        # Initialize valves with environment overrides where applicable
+        self.valves = self.Valves(
+            **{
+                "pipelines": os.getenv("MSG_LEN_FILTER_PIPELINES", "*").split(","),
+                # Other valves fall back to their defaults
+            }
+        )
+
+    def _apply_output_token_cap(self, body: dict):
+        # Enforce explicit token cap only
+        desired_tokens = self.valves.max_assistant_response_tokens
+        if desired_tokens is None:
+            return
+
+        options = body.get("options")
+        if not isinstance(options, dict):
+            options = {}
+        options["max_tokens"] = min(options.get("max_tokens", desired_tokens), desired_tokens)
+        body["options"] = options
+
+    async def inlet(self, body: dict, user: Optional[dict] = None) -> dict:
+        # Validate input message length
+        max_chars = self.valves.max_user_message_chars
+        if max_chars and max_chars > 0:
+            last_target_msg = _get_last_message_by_roles(body.get("messages", []), self.valves.target_user_roles)
+            if last_target_msg:
+                length = _compute_text_length(last_target_msg.get("content"))
+                if length > max_chars:
+                    raise Exception(
+                        f"Input message exceeds limit: {length} > {max_chars} characters."
+                    )
+
+        # Enforce output cap via tokens (top-level and options)
+        self._apply_output_token_cap(body)
+        return body
diff --git a/examples/filters/rate_limit_filter_pipeline.py b/examples/filters/rate_limit_filter_pipeline.py
index d1e88236..e203f65c 100644
--- a/examples/filters/rate_limit_filter_pipeline.py
+++ b/examples/filters/rate_limit_filter_pipeline.py
@@ -114,10 +114,6 @@ def rate_limited(self, user_id: str) -> bool:
         return False
 
     async def inlet(self, body: dict, user: Optional[dict] = None) -> dict:
-        print(f"pipe:{__name__}")
-        print(body)
-        print(user)
-
         if user.get("role", "admin") == "user":
             user_id = user["id"] if user and "id" in user else "default_user"
             if self.rate_limited(user_id):