ctx-zip

Keep your AI agent context small and cheap by managing tool bloat and large outputs. ctx-zip provides two complementary techniques: Tool Discovery (transform MCP servers and tools into explorable code) and Output Compaction (persist large results to storage with smart retrieval).

Works with the AI SDK for agents and loop control. See: AI SDK – Loop Control: Context Management.

Installation

npm i ctx-zip
# or
pnpm add ctx-zip

What It Does

ctx-zip tackles two major context problems:

1. Tool Discovery & Code Generation

Transforms MCP servers and AI SDK tools into inspectable TypeScript code in a sandbox. Instead of loading hundreds of tool schemas upfront, agents explore tools on-demand using filesystem operations.

The Problem:

Context Rot

Tool schemas consume thousands of tokens before your prompt
Every tool output is pulled into the model context
Every tool definition passes through model context
Agents can't inspect implementations to understand behavior

The Solution:

Tools are transformed to importable code that live in the sandbox filesystem (not in context)
Progressive Exploration: Agents explore the file system on-demand with sandbox_ls, sandbox_cat, sandbox_grep
Agents write and execute code that combines multiple tools
Result: Higher reliability, fast and ~80%+ token reduction for multi-tool workflows

2. Output Compaction with Smart Retrieval

Automatically persists large tool outputs to the sandbox filesystem and replaces them with concise references. Agents can retrieve content on-demand using the same sandbox exploration tools.

The Problem:

Context Rot

Large tool outputs (search results, file contents, API responses) bloat context
Conversation history accumulates thousands of unused tokens
Context windows exhaust, costs increase, performance degrades

The Solution:

Tool outputs are automatically written to the sandbox filesystem
Replaced with short references in conversation
Agents retrieve data on-demand with sandbox exploration tools (sandbox_ls, sandbox_cat, sandbox_grep, sandbox_find)
Result: ~60-90% token reduction for tool-heavy conversations

How It Works

Technique 1: Tool Discovery & Code Generation

Transform tools into explorable code that agents can inspect and execute:

import { generateText, tool } from "ai";
import { z } from "zod";
import { SandboxManager, E2BSandboxProvider } from "ctx-zip";

// Step 1: Create a sandbox
const sandboxProvider = await E2BSandboxProvider.create();
const manager = await SandboxManager.create({ sandboxProvider });

// Step 2: Register MCP servers and/or AI SDK tools
await manager.register({
  servers: [
    { name: "grep-app", url: "https://mcp.grep.app" },
    { 
      name: "linear", 
      url: "https://mcp.linear.app/mcp",
      headers: { Authorization: `Bearer ${process.env.LINEAR_TOKEN}` }
    },
  ],
  standardTools: {
    weather: tool({
      description: "Get the weather in a location",
      inputSchema: z.object({
        location: z.string(),
      }),
      async execute({ location }) {
        const temp = 72 + Math.floor(Math.random() * 21) - 10;
        return { location, temperature: temp, units: "°F" };
      },
    }),
  },
});

// Step 3: Get exploration and execution tools
const tools = manager.getAllTools();
// Available: sandbox_ls, sandbox_cat, sandbox_grep, sandbox_find, sandbox_exec

// Step 4: Agent explores and uses tools
const result = await generateText({
  model: "openai/gpt-4.1-mini",
  tools,
  prompt: "Search the codebase, check the weather, and create a Linear issue",
});

console.log(result.text);

// Cleanup
await manager.cleanup();

What Gets Generated:

The register() call creates a directory structure in the sandbox:

/workspace/
├── mcp/              # MCP tool implementations
│   ├── grep-app/
│   │   ├── search.ts
│   │   ├── index.ts
│   │   └── _types.ts
│   ├── linear/
│   │   ├── createIssue.ts
│   │   ├── searchIssues.ts
│   │   └── index.ts
│   └── _client.ts       # MCP routing client
├── local-tools/         # AI SDK tool implementations
│   ├── weather.ts
│   └── index.ts
├── user-code/          # Agent execution workspace
└── compact/            # Tool output storage (for compaction)

Exploration Tools:

Once tools are registered, agents can explore them:

sandbox_ls(path) - List directory contents
sandbox_cat(path) - Read file contents
sandbox_grep(pattern, path) - Search in files
sandbox_find(pattern, path) - Find files by name
sandbox_exec(code) - Execute TypeScript code

Example Agent Workflow:

// Agent explores available tools
await sandbox_ls("/workspace/mcp")
// → ["grep-app/", "linear/", "_client.ts"]

await sandbox_ls("/workspace/local-tools")
// → ["weather.ts", "index.ts"]

// Agent inspects a tool
await sandbox_cat("/workspace/mcp/grep-app/search.ts")
// → Full TypeScript source with types and documentation

// Agent writes code to use multiple tools together
await sandbox_exec(`
  import { search } from './mcp/grep-app/index.ts';
  import { createIssue } from './mcp/linear/index.ts';
  import { weather } from './local-tools/index.ts';
  
  const results = await search({ query: 'authentication bug' });
  const topResult = results[0];
  const weatherData = await weather({ location: 'San Francisco' });
  
  await createIssue({
    title: 'Fix auth bug from codebase search',
    description: \`Found issue: \${topResult.content}\nWeather: \${weatherData.temperature}\`,
  });
  
  return { created: true, result: topResult.file };
`);

API Reference:

// Create manager
const manager = await SandboxManager.create({
  sandboxProvider?: SandboxProvider,  // E2B, Vercel, or Local
  sandboxOptions?: LocalSandboxOptions, // If no provider
});

// Register tools
await manager.register({
  servers?: MCPServerConfig[],        // MCP servers to connect
  standardTools?: Record<string, Tool>, // AI SDK tools
  standardToolOptions?: {
    title?: string,
    outputDir?: string,
  },
});

// Get tools
manager.getAllTools()           // Exploration + execution tools
manager.getExplorationTools()   // ls, cat, grep, find (for MCP tools, defaults to mcp dir)
manager.getCompactionTools()    // ls, cat, grep, find (for compacted files, defaults to workspace root)
manager.getExecutionTool()      // Only exec

// Get paths
manager.getMcpDir()         // /workspace/mcp
manager.getLocalToolsDir()      // /workspace/local-tools
manager.getUserCodeDir()        // /workspace/user-code
manager.getCompactDir()         // /workspace/compact
manager.getWorkspacePath()      // /workspace

// Cleanup
await manager.cleanup()

Technique 2: Output Compaction with Smart Retrieval

Automatically reduce context size by managing large tool outputs. Two strategies available: write-to-file (persist to storage with on-demand retrieval) or drop-results (remove outputs entirely).

import { generateText, tool } from "ai";
import { z } from "zod";
import { compact, SandboxManager } from "ctx-zip";

// Create sandbox manager
const manager = await SandboxManager.create();
const fileAdapter = manager.getFileAdapter({ sessionId: "my-session" });

const result = await generateText({
  model: "openai/gpt-4.1-mini",
  tools: {
    // Compaction tools for reading compacted files using exact paths
    ...manager.getCompactionTools(),
    
    // Your data-generating tools
    fetchEmails: tool({
      description: "Fetch emails from inbox",
      inputSchema: z.object({ limit: z.number() }),
      async execute({ limit }) {
        const emails = await getEmails(limit);
        return { emails }; // This will be compacted
      },
    }),
  },
  prompt: "Check my latest emails and find any about 'budget'",
  prepareStep: async ({ messages }) => {
    // Compact outputs after each turn (default: write-tool-results-to-file)
    const compacted = await compact(messages, {
      strategy: "write-tool-results-to-file", // or "drop-tool-results"
      storage: fileAdapter, // Required for write-tool-results-to-file
      boundary: "all",
      sessionId: "my-session",
    });
    
    return { messages: compacted };
  },
});

await manager.cleanup();

Compaction Strategies:

ctx-zip provides two strategies for managing tool outputs:

Strategy 1: `write-tool-results-to-file` (Default)

Persists tool outputs to storage and replaces them with references. Agents can retrieve data on-demand.

How it works:

Agent calls a tool (e.g., fetchEmails(50))
Large output returned (50 emails = 10,000 tokens)

compact() runs in prepareStep:

Detects large tool output
Writes to storage: /compact/my-session/tool-results/fetchEmails.json

Replaces output with reference:

Written to file: file:///path/compact/my-session/tool-results/fetchEmails.json
Key: compact/my-session/tool-results/fetchEmails.json
Use the read/search tools to inspect its contents.

Agent can retrieve data:
- sandbox_ls(path) - List directory contents
- sandbox_cat(file) - Read entire file
- sandbox_grep(pattern, path) - Search within files
- sandbox_find(pattern, path) - Find files by name pattern

When to use: When you need agents to access historical tool outputs later in the conversation.

Strategy 2: `drop-tool-results`

Removes tool outputs entirely from the conversation, replacing them with a simple message indicating the output was dropped.

How it works:

Agent calls a tool (e.g., fetchEmails(50))
Large output returned (50 emails = 10,000 tokens)
compact() runs in prepareStep:
- Detects tool output
- Replaces output with: "Results dropped for tool: fetchEmails to preserve context"
- No storage required - outputs are permanently removed

When to use: When tool outputs are only needed for immediate processing and don't need to be referenced later. Maximum token savings, simplest setup.

Storage Location (write-tool-results-to-file only):

When using SandboxManager with write-tool-results-to-file strategy:

/workspace/
└── compact/
    └── {sessionId}/
        └── tool-results/
            ├── fetchEmails.json
            ├── searchGitHub.json
            └── getWeather.json

Each tool overwrites its own file on subsequent calls (one file per tool type).

Note: The drop-tool-results strategy doesn't use storage - outputs are removed from the conversation entirely.

Boundary Strategies:

Control which messages get compacted:


// 1. All messages - re-compact entire conversation
boundary: "all"

// 2. Keep first N - preserve system prompt, compact rest
boundary: { type: "keep-first", count: 5 }

// 3. Keep last N - preserve recent context, compact older
boundary: { type: "keep-last", count: 20 }

Sandbox Tools for Retrieval (write-tool-results-to-file only):

When using write-tool-results-to-file, use compaction-specific tools that default to the workspace root:

const manager = await SandboxManager.create();
const tools = manager.getCompactionTools();
// Available: sandbox_ls, sandbox_cat, sandbox_grep, sandbox_find
// These default to workspace root for easy use with compaction paths

// The compaction message tells you exactly how to read the file:
// "Written to file: sandbox://... To read it, use: sandbox_cat({ file: "compact/..." })"
// Just copy the path from the message!

Note: The drop-tool-results strategy doesn't require retrieval tools since outputs are permanently removed.

Example: Drop Strategy (No Storage Required)

import { generateText, tool } from "ai";
import { z } from "zod";
import { compact } from "ctx-zip";

const result = await generateText({
  model: "openai/gpt-4.1-mini",
  tools: {
    fetchEmails: tool({
      description: "Fetch emails from inbox",
      inputSchema: z.object({ limit: z.number() }),
      async execute({ limit }) {
        const emails = await getEmails(limit);
        return { emails }; // This will be dropped
      },
    }),
  },
  prompt: "Summarize my latest emails",
  prepareStep: async ({ messages }) => {
    // Drop tool outputs - no storage needed
    const compacted = await compact(messages, {
      strategy: "drop-tool-results",
      boundary: "all",
    });
    
    return { messages: compacted };
  },
});

API Reference:

// Compact messages
const compacted = await compact(messages, {
  strategy?: "write-tool-results-to-file" | "drop-tool-results", // Default: write-tool-results-to-file
  storage?: FileAdapter | string,    // Required for write-tool-results-to-file, ignored for drop-tool-results
  boundary?: Boundary,               // Which messages to compact
  sessionId?: string,                // Organize by session (write-tool-results-to-file only)
  fileReaderTools?: string[],        // Tools that read (not persisted, write-tool-results-to-file only)
});

Combining Both Techniques

Use tool discovery and compaction together for maximum efficiency:

import { generateText } from "ai";
import { 
  SandboxManager,
  compact,
  E2BSandboxProvider,
} from "ctx-zip";

// Step 1: Setup sandbox with tools
const sandboxProvider = await E2BSandboxProvider.create();
const manager = await SandboxManager.create({ sandboxProvider });

await manager.register({
  servers: [
    { name: "grep-app", url: "https://mcp.grep.app" },
  ],
});

// Step 2: Get file adapter for compaction
const fileAdapter = manager.getFileAdapter({
  sessionId: "combined-session",
});

// Step 3: Get all sandbox tools (exploration + execution)
// These same tools are used for both tool discovery AND accessing compacted outputs
const tools = manager.getAllTools();

// Step 4: Use in agent loop with compaction
const result = await generateText({
  model: "openai/gpt-4.1-mini",
  tools,
  prompt: "Search for authentication bugs in the codebase and summarize",
  prepareStep: async ({ messages }) => {
    const compacted = await compact(messages, {
      strategy: "write-tool-results-to-file", // or "drop-tool-results"
      storage: fileAdapter, // Required for write-tool-results-to-file
      boundary: "all",
      sessionId: "combined-session",
    });
    return { messages: compacted };
  },
});

console.log(result.text);
await manager.cleanup();

Benefits:

MCP tools explored on-demand (no upfront schema loading)
Large search results compacted to sandbox storage
Same exploration tools work for both tool discovery and compacted output retrieval
Maximum token efficiency and simplified API

Sandbox Providers

ctx-zip supports three sandbox environments:

Local Sandbox (Default)

import { SandboxManager, LocalSandboxProvider } from "ctx-zip";

// Option 1: Let SandboxManager create default local sandbox
const manager = await SandboxManager.create();

// Option 2: Explicit local provider
const provider = await LocalSandboxProvider.create({
  sandboxDir: "./.sandbox",
  cleanOnCreate: false,
});
const manager = await SandboxManager.create({ sandboxProvider: provider });

E2B Sandbox

import { SandboxManager, E2BSandboxProvider } from "ctx-zip";

const provider = await E2BSandboxProvider.create({
  apiKey: process.env.E2B_API_KEY,
  timeout: 1800000, // 30 minutes
});

const manager = await SandboxManager.create({ sandboxProvider: provider });

Vercel Sandbox

import { SandboxManager, VercelSandboxProvider } from "ctx-zip";

const provider = await VercelSandboxProvider.create({
  timeout: 1800000,
  runtime: "node22",
  vcpus: 4,
});

const manager = await SandboxManager.create({ sandboxProvider: provider });

Examples

See the examples/ directory:

examples/mcp/ - MCP server integration with grep.app
- local_mcp_search.ts - Local sandbox
- e2b_mcp_search.ts - E2B sandbox
- vercel_mcp_search.ts - Vercel sandbox
examples/tools/ - AI SDK tool transformation
- weather_tool_sandbox.ts - Transform and explore standard tools
examples/ctx-management/ - Full-featured compaction demo
- email_management.ts - Interactive email assistant with multi-environment support

API Overview

SandboxManager

class SandboxManager {
  // Create
  static async create(config?: {
    sandboxProvider?: SandboxProvider,
    sandboxOptions?: LocalSandboxOptions,
  }): Promise<SandboxManager>

  // Register tools
  async register(options: {
    servers?: MCPServerConfig[],
    standardTools?: Record<string, Tool>,
    standardToolOptions?: ToolCodeGenerationOptions,
  }): Promise<void>

  // Get tools
  getAllTools(): Record<string, Tool>
  getExplorationTools(): Record<string, Tool>
  getExecutionTool(): Record<string, Tool>

  // Get paths
  getMcpDir(): string
  getLocalToolsDir(): string
  getUserCodeDir(): string
  getCompactDir(): string
  getWorkspacePath(): string

  // File adapter for compaction
  getFileAdapter(options?: {
    prefix?: string,
    sessionId?: string,
  }): FileAdapter

  // Static helper
  static createLocalFileAdapter(options: LocalFileAdapterOptions): FileAdapter

  // Cleanup
  async cleanup(): Promise<void>
}

Compaction

function compact(
  messages: ModelMessage[],
  options: CompactOptions
): Promise<ModelMessage[]>

interface CompactOptions {
  strategy?: "write-tool-results-to-file" | "drop-tool-results",
  storage?: FileAdapter | string,  // Required for write-tool-results-to-file
  boundary?: Boundary,
  sessionId?: string,               // write-tool-results-to-file only
  fileReaderTools?: string[],        // write-tool-results-to-file only
}

type Boundary = 
  | "all"
  | { type: "keep-first"; count: number }
  | { type: "keep-last"; count: number }

TypeScript Support

Full TypeScript support with exported types:

import type {
  SandboxProvider,
  FileAdapter,
  CompactOptions,
  Boundary,
  E2BSandboxOptions,
  LocalSandboxOptions,
  VercelSandboxOptions,
} from "ctx-zip";

License

MIT

Contributing

Contributions welcome! Please open an issue or PR.

createVercelSandboxCodeMode

This tool is currently broken

Interactive Playground

Installation & Usage

1. Install the package

2. Import the tool

3. Use with AI SDK

AI Agent Integration

Use Case

Limitations

Examples

Parameters

README

ctx-zip

Installation

What It Does

1. Tool Discovery & Code Generation

2. Output Compaction with Smart Retrieval

How It Works

Technique 1: Tool Discovery & Code Generation

Technique 2: Output Compaction with Smart Retrieval

Strategy 1: `write-tool-results-to-file` (Default)

Strategy 2: `drop-tool-results`

Combining Both Techniques

Sandbox Providers

Local Sandbox (Default)

E2B Sandbox

Vercel Sandbox

Examples

API Overview

SandboxManager

Compaction

TypeScript Support

License

Contributing

Statistics

NPM Keywords

Maintainers

Links

Frameworks

createVercelSandboxCodeMode

This tool is currently broken

Interactive Playground

Installation & Usage

1. Install the package

2. Import the tool

3. Use with AI SDK

AI Agent Integration

Use Case

Limitations

Examples

Parameters

README

ctx-zip

Installation

What It Does

1. Tool Discovery & Code Generation

2. Output Compaction with Smart Retrieval

How It Works

Technique 1: Tool Discovery & Code Generation

Technique 2: Output Compaction with Smart Retrieval

Strategy 1: write-tool-results-to-file (Default)

Strategy 2: drop-tool-results

Combining Both Techniques

Sandbox Providers

Local Sandbox (Default)

E2B Sandbox

Vercel Sandbox

Examples

API Overview

SandboxManager

Compaction

TypeScript Support

License

Contributing

Statistics

NPM Keywords

Maintainers

Links

Frameworks

Strategy 1: `write-tool-results-to-file` (Default)

Strategy 2: `drop-tool-results`