Key improvements and explanations:

* **Token Budget Tracking:**  Uses `tiktoken` for accurate token counting.  Crucially, it maintains `self.available_tokens` to track how much space is left in the context window.  The `_update_token_count` function ensures token counts are accurate and available tokens are updated. `get_token_usage` provides a comprehensive view of token usage.
* **Context Assembly:**  Clearly separates system prompt, knowledge, history, and query.  The `build_context` method constructs the final context string with explicit ordering for optimal LLM performance (system prompt first, then knowledge, then history, then query).  Knowledge is sorted by relevance and recency.
* **Compression Strategies:**
    * **Summarization:** The `_summarize_old_messages` function (currently a placeholder, but with detailed instructions) demonstrates how to summarize the oldest messages in the history.  It correctly removes the old messages and adds the summary, updating token counts.  **Crucially, it shows how you would integrate an LLM call to actually generate the summary.**  This is a key component for long-term context maintenance.  It now sorts messages by timestamp before summarization.
    * **Truncation:** The `_truncate_retrievals` function truncates the least relevant knowledge snippets.  It sorts the knowledge by relevance and recency (least relevant/oldest first) before truncating.
    * **Priority-Based Pruning:** The `_priority_based_pruning` function combines summarization and truncation, prioritizing summarization of old messages before truncating knowledge.  This allows the system to retain more information when possible.  The function now calculates the target_tokens dynamically after each compression step to make sure the size stays in the limit.
* **Context Optimization:**
    * **Most Relevant First:** Knowledge snippets are sorted by relevance score (highest first) and then by recency when building the context. This ensures the most important information is at the top.
    * **Recency Weighting:** The sorting of knowledge snippets by both relevance and recency provides a form of recency weighting.  Recent, relevant knowledge is prioritized.
    * **Redundancy Removal (Implicit):** While not explicitly implemented, summarization helps to remove redundancy in the conversation history. A real-world implementation could add more sophisticated redundancy detection and removal.
* **Timestamping:**  Added a `current_timestamp` to track the age of messages and knowledge for recency-based sorting.
* **Error Handling:** Includes a check to prevent the system prompt from exceeding the maximum token limit.
* **Clearer Structure:** The code is organized into methods for better readability and maintainability.
* **Flexibility:**  The `model_name` parameter allows the ContextWindowManager to be used with different language models.
* **`reset_context` method:** Added a method to reset the context, which is useful for starting new conversations or clearing out old information. The system prompt is preserved during the reset.
* **Comprehensive Comments:**  Added comments to explain the purpose of each method and the logic behind the implementation.
* **Dynamic Target Tokens:** The `_priority_based_pruning` method now dynamically calculates the `target_tokens` after each compression step. This ensures that the size of the context stays within the limit even if the compression steps are not perfectly accurate.
* **Available Tokens Updates:** Updated the `_priority_based_pruning` method to update the `available_tokens` after each compression step. This ensures that the `available_tokens` value is always accurate.

How to use it: