Keep your agent context small and cheap by zipping large tool results out of the conversation and into storage. ctx-zip automatically persists bulky tool outputs (JSON/text) to a storage backend and replaces them in the message list with short, human- and model-friendly references. You control when and how compaction happens, including a simple "last-N messages" strategy for long-running loops.

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

• Context bloat: Tool calls often return large payloads (logs, search results, files). Keeping these in the message history quickly exhausts the model context window and raises costs.
• Slower iterations: Bigger histories mean slower prompts and higher latency.

• Persist large tool outputs to a storage backend (local filesystem or Vercel Blob) and replace them with concise references (e.g., Written to storage: blob://prefix/abc.txt).
• Out-of-the-box reader tools let the model follow references and read/search on demand (e.g., readFile, grepAndSearchFile).
• Configurable boundaries let you decide what to compact (entire history, since the last assistant/user text, or preserve the first N messages such as system/instructions).
• Works with AI SDK agent loops via prepareStep so you can also layer a simple, robust "last-N" message-retention strategy.

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

The example below shows how to keep only the last N messages while also compacting tool results to storage on each step. It follows the AI SDK prepareStep pattern for loop control.

After compaction, the model will see short references like Written to storage: blob://prefix/<key>. The agent can then retrieve or search that content using the built-in tools below. Add them to your tools map so the model can call them when it needs to re-open persisted outputs.

• readFile: Reads a full file by key from a storage URI (file://... or blob:).
• grepAndSearchFile: Runs a regex search over a file in storage.

Usage:

import { generateText, stepCountIs } from "ai";
import { openai } from "@ai-sdk/openai";
import {
  compactMessages,
  createReadFileTool,
  createGrepAndSearchFileTool,
} from "ctx-zip";

// Choose a storage backend (see Storage section below)
// - Local filesystem (default if omitted): file:///absolute/path
// - Vercel Blob: blob: (requires BLOB_READ_WRITE_TOKEN)
const storageUri = `file://${process.cwd()}`;

const result = await generateText({
  model: openai("gpt-4.1-mini"),
  tools: {
    // Built-in tools so the model can read/search persisted outputs
    readFile: createReadFileTool(),
    grepAndSearchFile: createGrepAndSearchFileTool(),

    // ... your other tools (zod-typed) ...
  },
  stopWhen: stepCountIs(6),
  prompt: "Use tools to research, summarize, and cite sources.",
  prepareStep: async ({ messages }) => {
    // 1. Writes the tool results of the first 20 messages to a local file
    // 2. Replaces those messages with a reference to that file
    const compacted = await compactMessages(messages, {
      storage: storageUri,
      boundary: { type: "first-n-messages", count: 20 },
    });

    return { messages: compacted };
  },
});

console.log(result.text);

Notes:

• The compactor recognizes reader/search tools like readFile and grepAndSearchFile so their outputs aren’t re-written; a friendly "Read from storage" reference is shown instead.
• You can pass your own storageReaderToolNames to extend this behavior for custom reader tools. If you provide additional reader tools, include them in the tools map and add their names to storageReaderToolNames so compaction treats their outputs as references rather than rewriting to storage.

Tool inputs (model-provided):

• readFile: { key: string; storage: string }
• grepAndSearchFile: { key: string; storage: string; pattern: string; flags?: string }

By default, compactMessages treats readFile and grepAndSearchFile as reader tools and will not re-write their results back to storage; instead it replaces them with a short reference to the source so the context stays lean.

compactMessages(messages, options) accepts:

interface CompactOptions {
  strategy?: "write-tool-results-to-storage" | string; // default
  storage?: string | StorageAdapter | undefined;        // e.g. "file:///..." | "blob:" | adapter instance
  boundary?:
    | "since-last-assistant-or-user-text"
    | "entire-conversation"
    | { type: "first-n-messages"; count: number };     // keep first N intact
  serializeResult?: (value: unknown) => string;         // default: JSON.stringify(v, null, 2)
  storageReaderToolNames?: string[];                    // tool names that read from storage
}

• strategy: Currently only write-tool-results-to-storage is supported.
• storage: Destination for persisted tool outputs. Provide a URI (file://..., blob:) or an adapter.
• boundary:
  • since-last-assistant-or-user-text (default): Compact only the latest turn.
  • entire-conversation: Re-compact the full history.
  • { type: "first-n-messages", count: N }: Preserve the first N messages (useful for system instructions) and compact the rest.
• serializeResult: Customize how non-string tool outputs are converted to text before writing.
• storageReaderToolNames: Tool names whose outputs will be replaced with a reference back to the source instead of being re-written.

ctx-zip supports local filesystem and Vercel Blob out of the box. Choose one via a URI in CompactOptions.storage or by passing a constructed adapter.

• URI form: file:///absolute/output/dir
• If omitted, ctx-zip writes under process.cwd().

Examples:

// Use a URI
await compactMessages(messages, { storage: "file:///var/tmp/ctx-zip" });

// Or construct an adapter
import { FileStorageAdapter } from "ctx-zip";
await compactMessages(messages, {
  storage: new FileStorageAdapter({ baseDir: "/var/tmp/ctx-zip" }),
});

• URI form: blob: (optionally blob://prefix)
• Env: set BLOB_READ_WRITE_TOKEN (this single token is sufficient)

Examples:

// Use a URI (requires BLOB_READ_WRITE_TOKEN)
await compactMessages(messages, { storage: "blob:" });

// Or construct an adapter with a prefix
import { VercelBlobStorageAdapter } from "ctx-zip";
await compactMessages(messages, {
  storage: new VercelBlobStorageAdapter({ prefix: "my-agent" }),
});

.env example:

# Required for Vercel Blob
BLOB_READ_WRITE_TOKEN=vcblt_rw_...

Adapters implement a minimal interface so you can persist anywhere (S3, Supabase, GCS, Azure Blob, databases, …):

export interface StorageAdapter {
  write(params: { key: string; body: string | Uint8Array; contentType?: string }): Promise<{ key: string; url?: string }>;
  readText?(params: { key: string }): Promise<string>;
  openReadStream?(params: { key: string }): Promise<NodeJS.ReadableStream>;
  resolveKey(name: string): string; // map a file name to a storage key/path
  toString(): string;                // human-readable URI (e.g., "blob://prefix")
}

Example: S3 (sketch):

import { S3Client, PutObjectCommand, HeadObjectCommand, GetObjectCommand } from "@aws-sdk/client-s3";
import type { StorageAdapter } from "ctx-zip";

class S3StorageAdapter implements StorageAdapter {
  constructor(private bucket: string, private prefix = "") {}

  resolveKey(name: string) {
    const safe = name.replace(/\\/g, "/").replace(/\.+\//g, "");
    return this.prefix ? `${this.prefix.replace(/\/$/, "")}/${safe}` : safe;
  }

  async write({ key, body, contentType }: { key: string; body: string | Uint8Array; contentType?: string }) {
    const s3 = new S3Client({});
    const Body = typeof body === "string" ? new TextEncoder().encode(body) : body;
    await s3.send(new PutObjectCommand({ Bucket: this.bucket, Key: key, Body, ContentType: contentType }));
    return { key, url: `s3://${this.bucket}/${key}` };
  }

  toString() {
    return `s3://${this.bucket}${this.prefix ? "/" + this.prefix : ""}`;
  }
}

// Usage
// await compactMessages(messages, { storage: new S3StorageAdapter("my-bucket", "agent-prefix") });

You can apply the same pattern to Supabase Storage, GCS, Azure Blob, or any other service.

• Pair compaction with AI SDK loop control to dynamically trim history and adjust models/tools per step. See: AI SDK – Loop Control: Context Management.
• When preserving long-lived system instructions, consider boundary: { type: "first-n-messages", count: N }.
• For debugging, use the file backend first (file://...) to inspect outputs locally, then switch to blob: for production.

From ctx-zip:

• Compaction: compactMessages(messages, options) and CompactOptions
• Strategies: detectWindowStart, messageHasTextContent (advanced)
• Storage Adapters: FileStorageAdapter, VercelBlobStorageAdapter, createStorageAdapter(uriOrAdapter)
• Utilities: resolveFileUriFromBaseDir, grepObject (advanced)
• Tools: createReadFileTool, createGrepAndSearchFileTool (recognized as reader tools by default)

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