KiloCode `max_tokens` Cache Issue With LiteLLM & Claude

Have you ever encountered a situation where your application stubbornly clings to outdated information, leading to frustrating errors and unexpected behavior? This is precisely the challenge faced by a KiloCode user who discovered a caching issue related to max_tokens after a model update in LiteLLM with Claude Sonnet 4. In this comprehensive article, we'll delve into the specifics of the problem, explore the steps taken to diagnose it, and discuss potential solutions to ensure your applications stay up-to-date with the latest model configurations. Let's dive in and unravel the mysteries of caching in KiloCode!

The Case of the Stubborn max_tokens Value

At the heart of the issue lies a caching mechanism within KiloCode that, under certain circumstances, fails to refresh the max_tokens value after a model's metadata is updated on the backend. This problem surfaces when using KiloCode with LiteLLM, a service that acts as a proxy for various language models, including Anthropic's Claude Sonnet 4. The user, deeply involved in the intricacies of model management and API interactions, encountered this snag while working with the claude-4-sonnet-20250514 model, mapped to claude-sonnet-4-20250514 through LiteLLM.

Initially, due to an upstream bug in LiteLLM, the model reported an incorrect max_output_tokens value of 1,000,000. This inflated value was then cached by KiloCode. Subsequently, after upgrading LiteLLM, the correct max_output_tokens value of 64,000 was reported. However, despite the user clicking the "Refresh Models" button in KiloCode, the cached value stubbornly remained at 1,000,000. This discrepancy led to a series of errors, as KiloCode continued to send requests with the outdated max_tokens value, exceeding the actual limit enforced by the model.

Diving Deeper: Verification and Symptoms

The user meticulously verified the issue by conducting a series of tests. Direct curl requests without the max_tokens parameter worked flawlessly, as did requests with a max_tokens value within the correct limit (e.g., 4000). However, requests originating from KiloCode consistently failed with a 400 Bad Request error, accompanied by the message: max_tokens: 1000000 > 64000, which is the maximum allowed number of output tokens for claude-sonnet-4-20250514. This starkly highlighted the caching issue, as KiloCode was clearly using the outdated value despite the backend reporting the correct limit. The "Refresh Models" functionality, intended to update the model list, proved ineffective in refreshing the cached metadata. The user's setup, relying on the provider list from LiteLLM, lacked a manual option to remove or re-add the model, further complicating the troubleshooting process. This situation underscores the importance of robust caching mechanisms and their impact on application behavior.

Expected Behavior vs. Reality

The user's expectations were straightforward and reasonable. They anticipated that KiloCode would either refresh the cached metadata, such as max_output_tokens, when the "Refresh Models" button was clicked, or provide a manual mechanism to clear or refresh the cache. In essence, the user expected the application to adapt to the updated backend metadata. However, the actual behavior deviated significantly from this expectation. The cached value persisted stubbornly, and the "Refresh Models" functionality only updated the model list, leaving the metadata untouched. This disconnect between expected and actual behavior highlights a critical gap in the application's caching strategy. The only workaround discovered was to introduce a new alias on the proxy, a less-than-ideal solution that adds complexity and potential maintenance overhead. This scenario emphasizes the need for clear and consistent caching behavior to avoid user frustration and application instability.

The Real-World Impact: Why This Matters

This caching issue is more than just a minor inconvenience; it has significant implications for application reliability and user experience. The situation creates a client-side lock, where the backend is correctly configured, but the client (KiloCode) continues to fail due to the outdated cached value. For providers like Anthropic, which strictly enforce output token caps, this translates to systematic 400 errors. The user is left with no recourse on their side, as KiloCode relentlessly sends requests with the excessive max_tokens value. This can lead to a complete breakdown in functionality, preventing users from effectively interacting with the model. The issue underscores the critical importance of accurate metadata management in applications that rely on external services. A failure to refresh cached data can have cascading effects, leading to widespread errors and a degraded user experience. This situation highlights the need for robust error handling and recovery mechanisms in applications that deal with cached data.

Potential Solutions and Future Directions

Addressing this caching issue requires a multifaceted approach that considers both the immediate problem and the long-term implications for application design. The user, in their detailed report, thoughtfully suggested several possible directions for resolution. One approach would be to ensure that metadata is reloaded whenever the "Refresh Models" button is clicked. This would provide a straightforward and intuitive way for users to update cached information. Another option would be to expose a manual mechanism for clearing the cache or refreshing metadata. This would give users more granular control over the caching behavior and allow them to resolve issues proactively. Ultimately, the most appropriate approach will depend on the overall architecture of KiloCode and the specific caching requirements of the application. It's crucial to strike a balance between performance optimization and data freshness, ensuring that cached data is both readily available and up-to-date. This requires careful consideration of caching strategies and their potential impact on application behavior.

Diving into Implementation Details

To effectively address this issue, developers need to delve into the implementation details of KiloCode's caching mechanism. This involves understanding how model metadata is stored, how it's retrieved, and how often it's refreshed. One potential area of focus is the "Refresh Models" functionality itself. It's crucial to ensure that this functionality not only updates the model list but also triggers a refresh of the associated metadata. This might involve modifying the code to fetch the latest metadata from LiteLLM whenever the "Refresh Models" button is clicked. Another area to consider is the caching strategy itself. KiloCode might be using a time-based caching mechanism, where data is cached for a certain period of time. If this is the case, it might be necessary to adjust the cache expiration time or implement a more sophisticated invalidation strategy. This could involve listening for events from LiteLLM that indicate a model's metadata has changed and invalidating the cache accordingly. Ultimately, the solution will depend on a deep understanding of KiloCode's architecture and caching implementation. This requires a collaborative effort between developers and users to identify the root cause of the issue and implement an effective solution.

User-Centric Solutions for a Seamless Experience

In addition to technical fixes, it's crucial to consider the user experience when addressing this caching issue. A user-centric approach would focus on providing clear feedback and intuitive controls for managing cached data. This could involve displaying the last updated timestamp for model metadata, allowing users to easily see when the data was last refreshed. It could also involve providing a clear and concise explanation of how caching works in KiloCode and how users can manage it. Another user-centric solution would be to provide proactive notifications when cached data is outdated. This could involve displaying a warning message when KiloCode detects that a model's metadata has changed on the backend. By providing users with clear feedback and intuitive controls, developers can empower them to manage caching issues effectively. This can significantly improve the overall user experience and reduce frustration.

Reproduction Steps: A Walkthrough

The user provided a detailed set of reproduction steps that clearly illustrate the issue. These steps are invaluable for developers attempting to diagnose and fix the problem. Let's walk through them:

1. Deploy a LiteLLM proxy that exposes the model claude-sonnet-4-20250514 (Anthropic).
2. Ensure the proxy reports (incorrectly) max_output_tokens = 1000000 (e.g., using an older LiteLLM version). This is the key to triggering the caching issue.
3. In KiloCode, add this LiteLLM provider and use "Refresh Models" to fetch the model list. This step causes KiloCode to cache the incorrect max_output_tokens value.
   • → KiloCode will display max_output_tokens = 1,000,000.
4. Upgrade LiteLLM so that it now correctly reports max_output_tokens = 64000. This simulates a backend update with the correct metadata.
5. In KiloCode, click "Refresh Models" again to reload the model list. This is where the issue surfaces, as the cached value is not updated.
   • → The model is refreshed in the list, but the cached max_output_tokens remains at 1,000,000.
6. Try to make a request through KiloCode. This triggers the error, as KiloCode sends the outdated max_tokens value.
   • → Anthropic returns a 400 Bad Request error because KiloCode still sends max_tokens=1000000.

These steps provide a clear and concise recipe for reproducing the issue, making it easier for developers to understand the problem and develop a solution. By following these steps, developers can quickly confirm the bug and begin the process of debugging and fixing it. This collaborative approach, involving both users and developers, is crucial for ensuring the quality and reliability of software applications.

A Deeper Look at the Technical Aspects of Reproduction

These reproduction steps are not just a simple checklist; they offer valuable insights into the technical nuances of the issue. The first step, deploying a LiteLLM proxy, highlights the importance of the interaction between KiloCode and external services. The second step, ensuring the proxy reports an incorrect max_output_tokens value, is crucial for triggering the caching issue. This emphasizes the need to test applications with a variety of data conditions, including incorrect or outdated values. The third step, adding the LiteLLM provider and refreshing the model list in KiloCode, demonstrates the caching behavior in action. The fourth and fifth steps, upgrading LiteLLM and refreshing the model list again, highlight the failure of KiloCode to update the cached value. The final step, making a request through KiloCode, demonstrates the practical consequences of the issue, resulting in a 400 Bad Request error. By understanding the technical aspects of these steps, developers can gain a deeper understanding of the issue and develop a more effective solution. This requires a thorough understanding of caching mechanisms, API interactions, and error handling.

Provider and Model Information

The issue was observed with the following provider and model:

• Provider: LiteLLM
• Model: claude-4-sonnet-20250514

This information is crucial for developers as it helps them narrow down the scope of the issue and focus their debugging efforts. Knowing the specific provider and model allows developers to examine the interactions between KiloCode and LiteLLM, as well as the specific metadata associated with the claude-4-sonnet-20250514 model. This can help identify any potential issues related to the provider integration or model-specific configurations. This information also highlights the importance of testing applications with a variety of providers and models to ensure compatibility and reliability. By providing this information, the user has significantly contributed to the troubleshooting process.

Conclusion: Towards a More Robust KiloCode

The caching issue reported by the user highlights the importance of robust caching mechanisms and their impact on application behavior. While caching is essential for performance optimization, it's crucial to ensure that cached data is up-to-date and that users have a way to manage it. The user's detailed report, including reproduction steps and potential solutions, provides a valuable starting point for addressing this issue. By implementing a fix that ensures metadata is refreshed when models are updated, KiloCode can become more reliable and user-friendly. This will not only resolve the immediate problem but also improve the overall user experience. The collaborative effort between users and developers is essential for building high-quality software applications. This case study serves as a reminder of the importance of careful attention to detail and a commitment to continuous improvement.

For further information on caching strategies and best practices, consider exploring resources from trusted sources like MDN Web Docs on Caching. This will provide a deeper understanding of the underlying concepts and techniques for effective caching management.