Feature Request: Improve context management

[nissa-seru]

IIUC, the current logic garbage-collects the first-half of the conversation (except for the original task content) upon hitting ~80% model context length. A few optimizations to consider:

• Consider either:
• Setting a cache checkpoint after the task + environment details + contents of @mentioned files; or
• Use the opportunity from the cache being broken to update the environment details, and contents of @mentioned files.

I would recommend doing one or the other as a "free" optimization, especially as the original task + context can be quite bulky in terms of tokens.

Having multiple versions of the same file in context:

• Easily happens if a file is read multiple times over the course of work (especially in the event if an edit-miss requiring file re-read)
• Consumes a massive amount of tokens
• Greatly decreases output quality because of the relative textual similarity of the file versions simultaneously coexisting in memory.

When context is being garbage-collected, I would recommend:

1. Cleanup of any duplicate file contents in context (favoring the newest one); and
2. Prioritizing retention of user text -> model text -> read-file results -> write-file results, to limit the amount of abrupt amnesia that impacts the model at the garbage collection point.

[vijaykrishna00]

https://github.com/GreatScottyMac/roo-code-memory-bank

i found this and seem to be helpful to me
we can implement an improved version of this by default

[dmanresa-saes]

I'm wondering if anyone knows whether the Cline Memory Bank (remembering that Roo Code is a fork of Cline) is compatible with Roo Code. The Cline Memory Bank looks simpler or at least more straightforward compared to the memory bank available at https://github.com/GreatScottyMac/roo-code-memory-bank . This one might work better, but I haven't had the time to read through the prompts to fully understand them.

[vitalii4reva]

Hi everyone!

Looking at this discussion, I see we're all facing similar challenges with context management. The current memory bank solutions seem focused on storing predefined context, but I think we need something more dynamic.

What if, instead of just managing what we already have in context, the AI could proactively discover what context it needs?

My proposal builds on these ideas but takes a different approach:

1. Self-guided discovery - When Claude detects it's missing context (like a function it references but doesn't have), it automatically searches the codebase to find it

2. Smart prioritization - Beyond just cleaning up duplicates, it would rank files by relevance to the current query and adaptively manage the context window

3. Integrated context management - Rather than requiring manual @mentions of files, it would intelligently determine what should be in context

I've been experimenting with LangChain for implementing this kind of system, and it provides good tools for memory management that could address the exact issues @nissa-seru mentioned about garbage collection priorities and duplicates.

This would go beyond what the current memory banks provide by being adaptive rather than static.

Would this direction be interesting to explore? I'd be happy to elaborate more on the technical approach or even work on a proof of concept if there's interest!

[ebynnerlys]

🌐 Versión en Español

📂 Solicitud: Mejoras en la Gestión de Archivos Duplicados y Contexto en RooCode

Descripción:

1. 🗂️ Detección automática de archivos duplicados:

   • Identificar y eliminar automáticamente versiones repetidas de un mismo archivo, conservando solo la más reciente.
   • Notificar al usuario cuando se detecten duplicados para mayor transparencia.

2. 📊 Panel de contexto mejorado:

   • Mostrar una lista clara y organizada de los archivos en contexto, resaltando aquellos que están activos y los que han sido descartados por el garbage collection.
   • Permitir al usuario excluir manualmente archivos específicos del contexto, incluso si no son duplicados.

3. 🧠 Priorización inteligente de retención:

   • Implementar un sistema que priorice la retención de:
     • Texto del usuario (instrucciones).
     • Respuestas del modelo.
     • Resultados de archivos leídos (@mentioned).
   • Esto reduciría la "amnesia abrupta" durante el garbage collection.

Beneficios:

• 🎯 Optimización de tokens: Evitar el desperdicio de tokens por archivos duplicados.
• ⏱ Mayor eficiencia: Mejorar la calidad de las respuestas al mantener solo la información relevante.
• 🧠 Control y transparencia: Permitir al usuario gestionar activamente el contexto.

---

🌐 English Version

📂 Request: Improvements in Duplicate File Management and Context Handling in RooCode

Description:

1. 🗂️ Automatic detection of duplicate files:

   • Identify and automatically remove repeated versions of the same file, keeping only the most recent one.
   • Notify the user when duplicates are detected for greater transparency.

2. 📊 Enhanced context panel:

   • Display a clear and organized list of files in context, highlighting active files and those discarded by garbage collection.
   • Allow the user to manually exclude specific files from the context, even if they are not duplicates.

3. 🧠 Smart retention prioritization:

   • Implement a system that prioritizes retaining:
     • User text (instructions).
     • Model responses.
     • Results from read files (@mentioned).
   • This would reduce "abrupt amnesia" during garbage collection.

Benefits:

• 🎯 Token optimization: Avoid wasting tokens on duplicate files.
• ⏱ Greater efficiency: Improve response quality by keeping only relevant information.
• 🧠 Control and transparency: Allow users to actively manage the context.

[HobbesSR]

I'm currently workshopping a design for a ContextManager that organizes messages as a DAG of content addressable messages. The idea is to allow the context history to be manipulated, supporting functions like updating older messages to have the most up to date content, like files, summarizing past messages and thinking (using a local or remote LLM), providing tool usage to the LLM for managing context itself, like collapsing and expanding messages (similar to a summary, but more concise and with more meta data for the LLM), and other uses I haven't thought of. While it's intended to support a wide variety of manipulations, the "demo" use case would be to provide a block of messages after the system block that acts as a file browser and manager. The context for each message in the block is real time up to date with each prompt, and only has open the details the LLM has requested, collapsing the rest.

Here's the design overview so far:

100-199: Overview

1. Introduction

1.1 Purpose

The primary purpose of the ContextManagement system is to strategically manage and manipulate message histories within AI interactions to optimize both cost and performance. By intelligently structuring and operating on conversational context, the system aims to reduce context size, eliminate redundancy, and address staleness in chat histories. This will lead to more efficient and effective AI applications, enabling them to maintain coherent conversations while minimizing computational overhead and API costs. Furthermore, the system is designed to maintain a complete history of each prompt and its corresponding LLM response, ensuring traceability and the ability to selectively modify and restore specific points in the conversation flow.

1.2 Scope

The ContextManagement system is designed to provide a flexible and powerful framework for managing conversational context, primarily focused on enabling efficient manipulation and optimization rather than inherent persistent storage. Key capabilities and scope considerations include:

• ContextGraph Structure: Organizing message history within a ContextGraph to represent message relationships and enable targeted operations.
• Core Context Operations Support: Providing support for core operations such as:
  • Update: Selectively modifying message content or metadata.
  • Summarize: Condensing parts of the message history to reduce context size.
  • Elide: Removing less relevant messages from the active context to improve focus and performance.
  • Collapse: Merging related message nodes to simplify context representation.
  • Caching of Operations: Caching the results of context operations to enhance performance and avoid redundant computations.
• Policy and Strategy Flexibility: Facilitating the implementation of diverse policies and strategies for context management, allowing AI developers to tailor context handling to specific application needs. The system is not limited to the core operations and is intended to be extensible.
• Serialization Capability: Providing the capability to serialize and unserialize the ContextGraph, enabling persistence if required by the application, although persistent storage itself is considered outside the core scope of this component.
• History and Auditability: Maintaining a complete history of prompts and LLM responses, along with modifications made through operations, to ensure auditability and support selective restoration of context states.
• Anthropic API Integration: Integration with Anthropic's API to leverage external AI capabilities for message processing

1.3 Definitions, Acronyms, and Abbreviations

• ContextManagement system: The overall system being developed to manage conversational context, using the ContextGraph as its core data structure.
• ContextGraph: A fundamental data structure in the ContextManagement system, used to organize MessageNodes and their relationships, ensuring no cyclical dependencies.
• MessageNode: A fundamental unit in the ContextGraph, representing a message in the conversation history.
• Logical Identifier: A user-defined, mutable identifier assigned to a MessageNode in the ContextGraph, allowing AI Developers to reference and retrieve specific messages or conversational branches independent of the unique Message ID. Logical Identifiers can be updated to point to different messages as the ContextGraph evolves through operations.
• DAG: Directed Acyclic Graph, the data structure used to organize MessageNodes and their relationships within the ContextGraph.
• Anthropic API: Refers to the Application Programming Interface provided by Anthropic for interacting with their AI models and services.
• Operation: A function performed on the ContextGraph to modify or analyze the message history, such as update, summarize, elide, and collapse.

1.4 References

• Anthropic API Documentation: [Link to Anthropic API documentation will be added here]

[HobbesSR]

Fundamental to any content management design I have floating around in my head is first a refactor that maintains functional parity with existing behavior. It's intent is to encapsulate the structures that maintain the message histories for the LLM and UI.

Proposal: Encapsulate Conversation History in ClineContextManager

Introduction

This document proposes the encapsulation of apiConversationHistory and clineMessages within a dedicated ClineContextManager class. Currently, these lists are members of the Cline class in Cline.ts. This refactoring aims to improve code organization, maintainability, and robustness by centralizing the management of conversation context.

Analysis of Current Usage and Invariants

Based on the analysis of Cline.ts and ClineProvider.ts, here are the key findings regarding the usage and invariants of apiConversationHistory and clineMessages:

apiConversationHistory

• Purpose: Primarily used for managing the conversation context sent to the Anthropic API. Stores messages in the format expected by the API (Anthropic.MessageParam).
• Persistence: Saved to api_conversation_history.json in task-specific directories.
• Operations:
  • Adding messages (addToApiConversationHistory)
  • Overwriting history (overwriteApiConversationHistory)
  • Loading from storage (getSavedApiConversationHistory)
  • Truncating for context window management (truncateConversationIfNeeded)
• Usage Context: Primarily accessed and modified within Cline.ts for API interactions and task lifecycle management.

clineMessages

• Purpose: Serves as the source of truth for the UI, storing messages in a format suitable for rendering in the chat interface (ClineMessage). Includes UI-specific metadata.
• Persistence: Saved to ui_messages.json in task-specific directories.
• Operations:
  • Adding messages (addToClineMessages)
  • Overwriting messages (overwriteClineMessages)
  • Updating messages (updateClineMessage)
  • Loading from storage (getSavedClineMessages)
• Usage Context: Used extensively throughout the project, including:
  • Cline.ts and Cline.js (core logic and message handling)
  • ClineProvider.ts and ClineProvider.js (webview communication, state management, task lifecycle)
  • webview-ui/src/components/chat/ChatView.tsx, webview-ui/src/components/chat/__tests__/ChatView.test.tsx, etc. (webview UI components for rendering chat messages)
  • src/core/webview/__tests__/ClineProvider.test.ts and src/core/__tests__/Cline.test.ts (testing)

Relationship Between Lists

Analysis of the codebase reveals that operations on apiConversationHistory and clineMessages don't always happen together, though they're often related:

1. Separate Operations: While there are many cases where both lists are updated in sequence, they're often handled separately:

   • In startTask(), both lists are reset together
   • In resumeTaskFromHistory(), they're loaded separately and sometimes modified independently
   • In message deletion/forking, both are modified but with different logic specific to each list's purpose

2. Type Conversion: There's no direct conversion between Anthropic.MessageParam and ClineMessage types. They represent the same conversation but serve different purposes:

   • apiConversationHistory is formatted for API consumption
   • clineMessages contains additional UI metadata and formatting

3. External Invariants: Several invariants are currently managed externally:

   • Message ordering is maintained by the calling code
   • Synchronization between the lists is handled manually in various places
   • Complex operations like deletion maintain consistency through separate but parallel operations

Invariants

1. Task Scope: Both lists are scoped to a single task, identified by taskId.
2. Message Order: The order of messages is significant, representing conversation chronology.
3. Persistence: Both lists are persisted for task resumption.
4. Conceptual Synchronization: clineMessages reflects apiConversationHistory but with UI-specific enhancements.

Architectural Considerations

When designing the ClineContextManager, we considered two architectural approaches:

Approach 1: Combined Encapsulation (Selected Approach)

This approach encapsulates both apiConversationHistory and clineMessages within a single class.

Justification:

1. Conceptual Cohesion: Both structures represent the same conversation, just in different formats for different consumers (API vs UI). They're two views of the same underlying data.

2. Temporal Coupling: Operations on these structures frequently happen in close proximity, even if not always simultaneously. When they do diverge, it's usually temporary (during streaming) or for specific edge cases.

3. Shared Invariants: Both structures share critical invariants like message ordering and task scoping. Having a single class enforce these invariants reduces the risk of inconsistencies.

4. Simplified Coordination: Complex operations like deletion and forking require coordinated changes to both structures. A single class can manage this coordination more reliably.

5. Future Evolution: As we move toward more sophisticated operations (like graph-based conversation management), having a unified conversation manager will provide a cleaner foundation.

Approach 2: Separate Encapsulation with Synchronizer (Alternative Considered)

This approach would encapsulate each list in its own class, with a third component responsible for synchronization.

Considerations:

This approach would make sense if:

• The structures were evolving in significantly different directions
• They had few shared invariants
• They were rarely modified together
• The synchronization logic was trivial

From the code analysis, none of these conditions appear to be true. The structures are tightly coupled conceptually, share important invariants, are frequently modified together, and require non-trivial synchronization logic.

Decision:

The combined encapsulation approach was selected because it better reflects the conceptual relationship between the structures and provides a more robust foundation for future development.

Refined Plan for ClineContextManager

The refactoring will be implemented in two phases:

Phase 1: Basic Encapsulation

1. Create ClineContextManager Class:

   • Create a new file src/core/ClineContextManager.ts.
   • Define a class ClineContextManager to encapsulate apiConversationHistory and clineMessages as private members.
   • Define a clear interface for the class to ensure all necessary operations are covered.

2. Move List Members:

   • Move the declarations of apiConversationHistory and clineMessages from Cline.ts to ClineContextManager.ts.

3. Implement Management Methods in ClineContextManager:

   • Implement methods to manage the lists, including:
     • Basic operations: addClineMessage, overwriteClineMessages, updateClineMessage, addToApiConversationHistory, overwriteApiConversationHistory, getApiConversationHistory, getClineMessages, saveContext, loadContext.
     • Crucial: Implement complex logic for message deletion and forking, mirroring the existing logic in ClineProvider.ts to maintain data consistency and invariants during these operations.
     • Add validation methods to verify consistency between apiConversationHistory and clineMessages.
     • Implement transaction-like patterns for operations that update both lists to ensure atomicity.

4. Integrate ClineContextManager into Cline:

   • In Cline.ts, create a private instance of ClineContextManager.
   • Modify Cline methods to use ClineContextManager methods for list operations.
   • Ensure event emission for message creation/updates is maintained.

5. Update ClineProvider and Tests:

   • Modify ClineProvider.ts and tests to interact with ClineContextManager through Cline.

6. Maintain Invariants:

   • Implement logic within ClineContextManager to enforce the identified invariants, especially during complex operations like deletion and forking.
   • Document edge cases in message deletion and forking to ensure proper handling.

7. Testing:

   • Update and add tests to validate ClineContextManager functionality and invariant maintenance, with a focus on testing the message deletion and forking logic.
   • Add specific test cases for edge conditions in message deletion and forking.
   • Include tests for backward compatibility with saved task data.

8. Checkpoint Integration:

   • Ensure proper integration with the checkpointing functionality in Cline.ts.
   • Update checkpoint-related methods to work with the new ClineContextManager.

Phase 2: Enhanced Synchronization

In the second phase, we'll focus on better encapsulating the invariants and synchronization logic within the ClineContextManager class:

1. Identify Synchronization Points:

   • Analyze where and how apiConversationHistory and clineMessages are kept in sync.
   • Document the transformation logic between Anthropic.MessageParam and ClineMessage types.

2. Implement Higher-Level Methods:

   • Create methods that manage both lists together, such as:
     • addUserMessage(text: string, images?: string[]): Promise<void> - Adds a user message to both lists with appropriate formatting
     • addAssistantMessage(content: string | Anthropic.Messages.ContentBlockParam[]): Promise<void> - Adds an assistant message to both lists
     • addSystemMessage(content: string): Promise<void> - Adds a system message to both lists if supported

3. Encapsulate Transformation Logic:

   • Implement methods to convert between Anthropic.MessageParam and ClineMessage types:
     • convertToApiMessage(clineMessage: ClineMessage): Anthropic.MessageParam
     • convertToClineMessage(apiMessage: Anthropic.MessageParam): ClineMessage

4. Implement Consistency Checks:

   • Add methods to verify and repair consistency between the lists:
     • ensureConsistency(): Promise<void> - Checks and repairs any inconsistencies between the lists
     • validateMessagePair(clineIndex: number, apiIndex: number): boolean - Validates that a pair of messages represent the same content

5. Refactor Complex Operations:

   • Refactor complex operations like deletion and forking to use the new higher-level methods:
     • Update deleteMessage to use transformation logic
     • Update forkFromMessage to maintain consistency using the new methods

6. Update API:

   • Deprecate direct access to individual list operations in favor of the higher-level methods
   • Update Cline class to use the new methods

Implementation Details

Interface Definition (Phase 1)

interface IClineContextManager {
  // Basic operations - Return readonly copies to prevent unintended modifications
  getClineMessages(): ReadonlyArray<ClineMessage>;
  getApiConversationHistory(): ReadonlyArray<Anthropic.MessageParam>;
  
  // Message operations
  addClineMessage(message: ClineMessage): Promise<void>;
  overwriteClineMessages(newMessages: ClineMessage[]): Promise<void>;
  updateClineMessage(partialMessage: ClineMessage): Promise<void>;
  addToApiConversationHistory(message: Anthropic.MessageParam): Promise<void>;
  overwriteApiConversationHistory(newHistory: Anthropic.MessageParam[]): Promise<void>;
  
  // Persistence
  saveContext(): Promise<void>;
  loadContext(): Promise<void>;
  
  // Complex operations
  deleteMessage(timestamp: number, deleteSubsequent: boolean): Promise<void>;
  forkFromMessage(timestamp: number): Promise<{
    clineMessages: ReadonlyArray<ClineMessage>;
    apiConversationHistory: ReadonlyArray<Anthropic.MessageParam>;
  }>;
  
  // Validation
  validateConsistency(): boolean;
  
  // Event handling
  on(event: 'messageCreated' | 'messageUpdated', listener: (message: ClineMessage) => void): void;
  off(event: 'messageCreated' | 'messageUpdated', listener: (message: ClineMessage) => void): void;
}

Enhanced Interface (Phase 2)

interface IClineContextManager {
  // Phase 1 methods...
  
  // Higher-level operations (Phase 2)
  addUserMessage(text: string, images?: string[]): Promise<void>;
  addAssistantMessage(content: string | Anthropic.Messages.ContentBlockParam[]): Promise<void>;
  addSystemMessage(content: string): Promise<void>;
  
  // Transformation methods
  convertToApiMessage(clineMessage: ClineMessage): Anthropic.MessageParam;
  convertToClineMessage(apiMessage: Anthropic.MessageParam): ClineMessage;
  
  // Consistency management
  ensureConsistency(): Promise<void>;
  validateMessagePair(clineIndex: number, apiIndex: number): boolean;
  
  // Enhanced complex operations
  truncateConversation(options: TruncateOptions): Promise<void>;
}

Data Encapsulation and Immutability

To ensure proper encapsulation and prevent unintended modifications to internal state:

1. Return Immutable References: Methods that return internal lists should return them as readonly arrays:

// Implementation
private apiConversationHistory: Anthropic.MessageParam[] = [];
private clineMessages: ClineMessage[] = [];

// Getter methods
getClineMessages(): ReadonlyArray<ClineMessage> {
  return this.clineMessages;
}

getApiConversationHistory(): ReadonlyArray<Anthropic.MessageParam> {
  return this.apiConversationHistory;
}

2. Deep Copies for Complex Operations: For operations that need to return modified versions of the lists, return deep copies to prevent confusion about whether operations on these copies will affect the internal state:

async forkFromMessage(timestamp: number): Promise<{
  clineMessages: ReadonlyArray<ClineMessage>;
  apiConversationHistory: ReadonlyArray<Anthropic.MessageParam>;
}> {
  // Perform forking logic...
  
  // Return deep copies as readonly arrays
  return {
    clineMessages: [...forkedClineMessages] as ReadonlyArray<ClineMessage>,
    apiConversationHistory: [...forkedApiHistory] as ReadonlyArray<Anthropic.MessageParam>
  };
}

3. Defensive Copying for Inputs: When accepting arrays as inputs, make defensive copies to prevent external code from modifying the arrays after they've been passed to the manager:

async overwriteClineMessages(newMessages: ClineMessage[]): Promise<void> {
  // Create a defensive copy
  this.clineMessages = [...newMessages];
  await this.saveClineMessages();
}

Synchronization Logic (Phase 2)

The key to successful synchronization is understanding the mapping between message types. Here's how the transformation logic would work:

// Converting ClineMessage to Anthropic.MessageParam
convertToApiMessage(clineMessage: ClineMessage): Anthropic.MessageParam {
  // Determine role based on message type
  const role = clineMessage.type === "say" ? "assistant" : "user";
  
  // Convert content based on message type and format
  let content: string | Anthropic.Messages.ContentBlockParam[];
  
  if (clineMessage.images && clineMessage.images.length > 0) {
    // Handle messages with images
    content = [
      { type: "text", text: clineMessage.text || "" },
      ...clineMessage.images.map(img => ({ type: "image", source: { type: "base64", data: img } }))
    ];
  } else {
    // Text-only messages
    content = clineMessage.text || "";
  }
  
  return { role, content };
}

// Converting Anthropic.MessageParam to ClineMessage
convertToClineMessage(apiMessage: Anthropic.MessageParam): ClineMessage {
  const ts = Date.now();
  const type = apiMessage.role === "assistant" ? "say" : "ask";
  
  // Extract text and images from content
  let text: string | undefined;
  let images: string[] | undefined;
  
  if (typeof apiMessage.content === "string") {
    text = apiMessage.content;
  } else if (Array.isArray(apiMessage.content)) {
    // Extract text blocks
    const textBlocks = apiMessage.content.filter(block => block.type === "text");
    text = textBlocks.map(block => (block as any).text).join("\n");
    
    // Extract image blocks
    const imageBlocks = apiMessage.content.filter(block => block.type === "image");
    if (imageBlocks.length > 0) {
      images = imageBlocks.map(block => (block as any).source.data);
    }
  }
  
  return {
    ts,
    type,
    say: type === "say" ? "text" : undefined,
    ask: type === "ask" ? "followup" : undefined,
    text,
    images,
  };
}

Error Handling

The implementation will maintain the current error handling approach for file operations:

async saveApiConversationHistory(): Promise<void> {
  try {
    const filePath = path.join(await this.ensureTaskDirectoryExists(), GlobalFileNames.apiConversationHistory);
    await fs.writeFile(filePath, JSON.stringify(this.apiConversationHistory));
  } catch (error) {
    // Log error but don't stop task execution
    console.error("Failed to save API conversation history:", error);
  }
}

Transaction Safety

For operations that modify both lists, implement transaction-like patterns:

async deleteMessage(timestamp: number, deleteSubsequent: boolean): Promise<void> {
  // Create copies of the current state for rollback if needed
  const originalClineMessages = [...this.clineMessages];
  const originalApiHistory = [...this.apiConversationHistory];
  
  try {
    // Perform the deletion operation on both lists
    // ...
    
    // Save both lists
    await this.saveContext();
  } catch (error) {
    // Rollback to original state
    this.clineMessages = originalClineMessages;
    this.apiConversationHistory = originalApiHistory;
    throw error;
  }
}

Backward Compatibility

Ensure the new implementation can read task data saved by the old implementation:

async loadContext(): Promise<void> {
  // Load clineMessages
  this.clineMessages = await this.getSavedClineMessages();
  
  // Load apiConversationHistory
  this.apiConversationHistory = await this.getSavedApiConversationHistory();
  
  // Check for old format and convert if necessary
  if (this.needsFormatConversion()) {
    await this.convertFromOldFormat();
  }
}

Migration Strategy

The migration will be implemented in two phases to reduce risk:

1. Phase 1: Basic Encapsulation

   • Implement the ClineContextManager class with basic operations.
   • Add a feature flag to toggle between old and new implementations.
   • Run both implementations in parallel in development to verify equivalence.

2. Phase 2: Enhanced Synchronization

   • Implement higher-level methods that manage both lists together.
   • Refactor complex operations to use the new methods.
   • Gradually update calling code to use the new methods.
   • Deprecate direct access to individual list operations.

Implications and Considerations

• This refactoring will require updating many methods in Cline.ts and potentially some parts of ClineProvider.ts and tests to use the new ClineContextManager.
• Critical: The complex logic for message deletion and forking in ClineProvider.ts must be carefully moved and adapted within ClineContextManager to ensure continued correct behavior and data consistency.
• Thorough testing will be essential to ensure the refactoring is successful and doesn't introduce regressions, especially for message deletion and forking scenarios.
• The refactoring introduces an additional layer of abstraction, which could have a minor performance impact, but this is likely negligible compared to the benefits of improved code organization.
• Phase 2 will require a deeper understanding of the transformation logic between message types, but will result in more robust code with better encapsulation of invariants.
• Returning immutable references (ReadonlyArray) from getter methods ensures that callers cannot inadvertently modify the internal state of the ClineContextManager.

Next Steps

1. Request feedback on this updated proposal from the team.
2. Refine the plan based on feedback.
3. Implement Phase 1 with feature flag.
4. Test thoroughly, focusing on complex operations and edge cases.
5. Implement Phase 2 for enhanced synchronization.
6. Proceed with implementation in code mode.

[ElemTran]

The current gemini's official context window is too big, is it possible to set it manually, it affects model response.Of course I know it's possible to set it with openai compatible format but it's too much of a hassle.

[atototenten]

could using AST as intermediate be useful ?

like in http://x.com/itechnologynet/status/1908450412119597326

thanks

[atototenten]

i think long context is the future .

recently meta-corp's LLM released with 10M context window

[JabolDev]

i think long context is the future .

recently meta-corp's LLM released with 10M context window

To run 10m context they need 10xh100, which is not efficient for 0.2 per million, and llama maverick is low quality as fuck.

[atototenten]

if we talk about efficiency ,

why are we even using floats ,instead of fixed-point math. on integers ,which are natural for binary-compluters ,instead of bruteforcing decimal numbers on binary computers .

isnt it ?

[Yikai-Liao]

Thanks for all the insightful ideas in this thread! I’d like to propose an extension that goes beyond deduplication and prioritization, aiming for more cost-effective and intelligent context management, especially for users with a limited budget who cannot afford frequent, large-context API calls.

---

Proposal: Active Summarization & Embedding-Based Context Compression

Core Goal:
Reduce the high API costs caused by long, uncompressed context windows—especially for users who cannot use expensive, cacheless, large-context models—by actively summarizing and compressing low-value or redundant content.

Key Points

• Active Summarization, Not Just Deduplication:
  Instead of only removing duplicates, the system should use a lightweight LLM (like Gecko, GTE, E5, Llama3-3B, etc.) to summarize repetitive or low-quality content, such as:

  • Multiple failed CLI calls and their outputs (summarized as “several failed attempts to run X”)
  • Repeated, lengthy command outputs (summarized after several rounds)
  • Outdated file contents after many edits (summarized as “file X has changed N times, latest diff: ...”)

• Parallel Summarization with Lightweight Models:
  Summarization can be performed in parallel with a small, cost-effective model, so the main model’s context is kept clean and focused, minimizing token usage.

• Embedding & Caching of Pruned Content:
  Any pruned or summarized content is embedded and cached. If the main model needs to recall this information, it can retrieve it via embedding search (e.g., through MCP protocol or similar).

• Optional & Configurable:
  This feature can be toggled on/off and customized by the user, balancing cost, latency, and context quality.

Why This Matters

• Significantly reduces API costs for users who cannot afford to use large-context models without caching.
• Maximizes the value of every token sent to the main model, ensuring only the most relevant information is included.
• Historical information is not lost—it can be retrieved on demand via embedding search.
• Better support for long, iterative, or agentic conversations without breaking the bank.

How This Complements Existing Ideas

• Deduplication and prioritization are great first steps for cleaning up context.
• Active summarization and embedding-based recall take it further, enabling the system to handle not just duplicates, but all kinds of redundant or low-value content.
• This approach is especially valuable for budget-conscious users and for future RAG/agent scenarios, where context length and quality are critical.

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Would love to hear your thoughts! If there’s interest, I’m happy to help elaborate on the technical approach.

[BradKML]

Also check on Task Master and "memory bank" and their graph equivalents, maybe context can be more modular, while not losing relational context?

[Yikai-Liao]

Sub-task scheduling would also be benificial to this feature as I discussed in #1574 (comment)

[qdrddr]

Interesting idea of prompt compression/optimization to reduce tokens used.
https://github.com/ruvnet/SynthLang

[Yikai-Liao]

This project looks amazing！

[Yikai-Liao]

But it is strange that this project has no issue

[atototenten]

whats "amazing" about it ?

its just structured-text .

any sensible technical person would do this .

[BradKML]

Anything that eliminates redundancy and increases clarity is good

[dsent]

SynthLang looks incredible. I'd love to have this implemented in Roo Code! This would save so many tokens with each request 🤤

[BradKML]

@dosu could you check if Claude Flow is compatible with RooCode or not? If not, what steps are needed to make them as such, and accelerate RooCode/Kilocode architecture development and debugging? https://github.com/ruvnet/claude-flow

[dosubot[bot]]

Claude Flow is not directly integrated with RooCode, but both platforms support the Model Context Protocol (MCP), which makes integration possible. RooCode can connect to any external MCP server, and Claude Flow exposes a suite of MCP tools for orchestration, so the main requirement is to configure Claude Flow as an MCP server and ensure its tool definitions are clear and descriptive for RooCode to discover and use them effectively. There are no official or community-supported Claude Flow MCP servers listed for RooCode, so you’ll need to set up the connection manually and may need to develop adapters or clarify tool schemas if there are mismatches.

For integration, follow these steps: verify that Claude Flow exposes an MCP server endpoint, configure RooCode to connect to it, and test tool/resource discovery from within RooCode. RooCode’s documentation has detailed guides on MCP setup, tool definition best practices, and troubleshooting (see here, here). If you need to build a custom MCP server or adapter, the MCP SDK is recommended.

Integrating Claude Flow could accelerate RooCode/Kilocode architecture development and debugging by leveraging Claude Flow’s orchestration, memory, and agent features alongside RooCode’s Orchestrator mode, Boomerang Tasks, and custom modes for complex, multi-step workflows (details, details).

If you run into issues, check for data model mismatches or missing tool descriptions, and consider prototyping the connection or reaching out to maintainers for guidance.

To reply, just mention @dosu.

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