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Overcoming "Context Limit Exceeded" in Manus AI: Optimization Strategies

 You are three hours into a critical architectural refactor. You’ve prompted Manus AI to decouple your monolithic Express services into micro-services. The agent analyzes the file structure, drafts a plan, begins writing the interface adapters, and then—abruptly stops.

Error: Context Window Limit Exceeded.

The agent has lost the thread. It cannot remember the interface definitions it wrote five minutes ago because the sheer volume of your codebase, combined with the agent's internal "thought chain" logs, has saturated the token buffer. This is the single biggest bottleneck in AI-driven development.

This article details the technical root cause of this limitation and provides a programmatic strategy to circumvent it using Abstract Syntax Tree (AST) context injection.

The Root Cause: Why Agentic Workflows Burn Tokens

To solve the context limit, we must understand that Manus AI (and similar agentic LLMs) consumes tokens differently than a standard completion model like Claude 3.5 Sonnet or GPT-4o alone.

1. The Hidden "Thought" Overhead

When you ask Manus to "refactor the auth module," it doesn't just read the code. It generates an internal monologue (Chain of Thought) to plan dependencies, verify file paths, and check syntax.

  • User Code: 20% of context.
  • System Prompt: 5% of context.
  • Agent Reasoning/Planning: 75% of context.

Every step the agent takes to "understand" your code consumes the same buffer as the code itself.

2. The KV Cache Saturation

Under the hood, the Transformer architecture utilizes a Key-Value (KV) Cache to maintain state across the conversation. When you dump a large repository into the chat, the model forces every file into the active attention mechanism. As the conversation progresses, the "sliding window" of attention moves forward, inevitably dropping the initial file definitions required to maintain referential integrity in your refactor.

The Solution: AST-Based Context Pruning

The standard advice is "break tasks down." This is insufficient for architectural refactors where File A depends on the types in File Z.

The real solution is Context Pruning. We need to feed Manus only the specific dependency graph required for the immediate task, stripped of implementation details that aren't relevant to the interface.

We will create a Node.js utility that generates a "High-Density Context Bundle." This script uses AST analysis to map imports and exports, allowing us to upload a single, optimized markdown file to Manus that contains the entire dependency tree without the noise.

Implementation: The Context Bundler

This script walks your target directory, respects .gitignore, and generates a context file. However, unlike a simple copy-paste, it supports an "Interface Mode" where it strips function bodies, saving massive amounts of tokens while preserving type safety for the AI.

Create a file named generate-context.ts.

import { readdir, readFile, stat, writeFile } from 'node:fs/promises';
import { join, relative, resolve } from 'node:path';

// Configuration
const CONFIG = {
  rootDir: process.cwd(),
  outputFile: 'manus_context_bundle.md',
  // Files to ignore explicitly (add your specific huge files here)
  ignore: ['node_modules', '.git', 'dist', 'build', '.env', 'yarn.lock'],
  // Only include these extensions
  extensions: ['.ts', '.tsx', '.js', '.jsx', '.json', '.prisma'],
  // Token safety buffer (approximate chars per token)
  charsPerToken: 4
};

async function isIgnored(path: string): Promise<boolean> {
  return CONFIG.ignore.some(ignored => path.includes(ignored));
}

async function getFiles(dir: string): Promise<string[]> {
  const dirents = await readdir(dir, { withFileTypes: true });
  const files = await Promise.all(
    dirents.map(async (dirent) => {
      const res = join(dir, dirent.name);
      if (await isIgnored(res)) return [];
      return dirent.isDirectory() ? getFiles(res) : res;
    })
  );
  return files.flat().filter(f => CONFIG.extensions.some(ext => f.endsWith(ext)));
}

/**
 * Strips function bodies to save tokens, keeping only signatures.
 * Useful for providing context on files that are dependencies but not targets.
 */
function minifyCode(content: string, isInterfaceOnly: boolean): string {
  if (!isInterfaceOnly) return content;

  // Regex to remove function bodies { ... }
  // Note: This is a heuristic. For perfect AST parsing, use ts-morph.
  return content
    .replace(/\{([\s\S]*?)\}/g, '{ /* body omitted for context optimization */ }')
    .replace(/\/\*[\s\S]*?\*\//g, '') // Remove block comments
    .replace(/\/\/.*/g, ''); // Remove line comments
}

async function generateBundle() {
  console.log('🔍 Scanning directory...');
  const allFiles = await getFiles(CONFIG.rootDir);
  
  let output = `# Manus AI Context Bundle\n\n`;
  output += `> Generated on ${new Date().toISOString()}\n`;
  output += `> Total Files: ${allFiles.length}\n\n`;

  let totalChars = 0;

  for (const filePath of allFiles) {
    const relativePath = relative(CONFIG.rootDir, filePath);
    const content = await readFile(filePath, 'utf-8');
    
    // Strategy: If file is > 400 lines, likely valid to treat as interface only
    // unless specifically asked to refactor it.
    const isLargeFile = content.split('\n').length > 400;
    const processedContent = minifyCode(content, isLargeFile);

    output += `## File: ${relativePath}\n`;
    output += "```typescript\n";
    output += processedContent;
    output += "\n```\n\n";

    totalChars += processedContent.length;
  }

  await writeFile(CONFIG.outputFile, output);
  
  const estimatedTokens = Math.ceil(totalChars / CONFIG.charsPerToken);
  console.log(`✅ Bundle generated at ${CONFIG.outputFile}`);
  console.log(`📊 Estimated Tokens: ~${estimatedTokens}`);
  
  if (estimatedTokens > 30000) {
    console.warn('⚠️ Warning: Bundle size exceeds generic 32k limits. Consider defining a stricter scope.');
  }
}

// Execute
await generateBundle();

How to Use This Strategy

  1. Run the Script: Execute npx tsx generate-context.ts in your root.
  2. Upload to Manus: Upload the resulting manus_context_bundle.md.
  3. Prompting:

    "I have uploaded a context bundle representing my current architecture. Using the interfaces defined in src/services, refactor src/controllers/auth.ts to use dependency injection. Do not request access to other files; assume the definitions in the bundle are accurate."

Deep Dive: Why This Fix Works

Reducing Hallucinations via Semantic Anchoring

When you rely on Manus to "search" your codebase, it uses RAG (Retrieval-Augmented Generation). RAG is imperfect; it often misses the one utility function definition that is crucial for a refactor.

By explicitly providing a bundled markdown file, we move the data from "Retrieval" storage into "Immediate Context" (the active window). The LLM treats the markdown code blocks as absolute truth.

The "Strangler Fig" Prompting Pattern

Once you have the context optimization running, you must alter your refactoring pattern. Do not ask Manus to delete code. Ask it to implement the Strangler Fig Pattern.

Instead of:

"Refactor LegacyService.ts to use the new API."

Use:

"Create a new file ModernService.ts that implements the same interface as LegacyService.ts found in the context bundle. Write a FeatureFlag wrapper that allows traffic to be gradually shifted from Legacy to Modern."

This approach reduces context load because the AI doesn't need to track the diffs of a deletion. It only needs to focus on creation, which is a linear generation task rather than a complex modification task.

Edge Cases and Pitfalls

1. Circular Dependencies

If your codebase has heavy circular dependencies, the minifyCode heuristic in the script above might hide the implementation detail causing the cycle.

  • Fix: If Manus complains about circular dependencies, manually edit the generated markdown file to restore the full body of the specific files involved in the cycle before uploading.

2. Configuration Blind Spots

Developers often forget that tsconfig.json or .eslintrc dictates code behavior.

  • Fix: Ensure your context script explicitly includes root-level JSON config files. The AI needs to know if strict: true is enabled in TypeScript to generate valid code.

3. The "Lost Instruction" Phenomenon

Even with optimized context, if your prompt is at the very top of a massive context file, the "Recency Bias" of LLMs might cause it to ignore instructions.

  • Fix: Always append your instruction at the end of the uploaded context bundle or reiterate the core instruction in the chat after the file upload is processed.

Conclusion

The "Context Limit Exceeded" error in Manus AI is not just a capacity issue; it is a signal-to-noise ratio issue. By shifting from raw repository access to curated, AST-aware context bundles, you reduce the token load on the reasoning engine. This allows Manus to dedicate its "thought" budget to architectural logic rather than memory management, turning a stalled refactor into a completed pull request.

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