# AI Chat Slimmer: Context Storage Shield (`flowlockautomation/ai-chat-slimmer`) Actor

AI Memory Slimmer works like a lightweight coat-check ticket system for massive AI data logs. It intercepts massive tool outputs, stores the full text safely in an optimized cloud vault, and returns a tiny 16-character ticket code to your conversation history.

- **URL**: https://apify.com/flowlockautomation/ai-chat-slimmer.md
- **Developed by:** [Martin B.](https://apify.com/flowlockautomation) (community)
- **Stats:** 1 total users, 0 monthly users, 0.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $1.00 / 1,000 results

This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events.

Learn more: https://docs.apify.com/actors/running/actors-in-store.md#pay-per-event

## What's an Apify Actor?

An Actor is a serverless cloud program that runs on the Apify platform. It has two run modes.
In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours,
and optionally produces a well-defined JSON output, datasets with results, or files in key-value store.
In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server.

Apify vocabulary and the platform model are defined once, in the agent quickstart at https://apify.com/agents.md.

## How to integrate an Actor?

If asked about integration, you help developers integrate Actors into their projects.
You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready.

Do not guess an integration path. Every one of them is in the agent quickstart at https://apify.com/agents.md: the Apify MCP server, Agent Skills with the Apify CLI, the JavaScript and Python clients, the REST API, and the account-free path for an agent with no human to sign in. It also carries the rule on stating cost before the first paid run.

For examples already wired to this Actor's own input schema, see the [API](#api) section below.

Each client library has reference documentation the quickstart does not restate: [JavaScript/TypeScript](https://docs.apify.com/api/client/js/docs.md) (`npm install apify-client`) and [Python](https://docs.apify.com/api/client/python/docs.md) (`pip install apify-client`).

# README

## 🧠 AI Memory Slimmer - Stop Burning Tokens in Claude & Cursor

AI Memory Slimmer works like a lightweight coat-check ticket system for massive AI data logs. It intercepts massive tool outputs, stores the full text safely in an optimized cloud vault, and returns a tiny 16-character ticket code to your conversation history.

By keeping heavy data out of active turns, it provides instant **Context Window Optimization**, acts as a **RAG Cost Reducer**, and serves as an unbreakable **MCP State Saver** so your tools and AI agents stay fast and cheap.

***

### 🛠️ How It Works

- **Step 1 (SLIM):** Your AI agent gets a massive, messy data chunk from a local tool or web scraper.
- **Step 2 (PIN):** The text is sent to a persistent Apify key-value store and swapped for a tiny, clean ticket code. The ticket remains usable across separate Actor runs.
- **Step 3 (SAVE):** Your AI stays fast and cheap because it only has to read and remember the tiny ticket code.
- **Step 4 (HYDRATE):** When your workflow needs that specific data back, the system trades the ticket number for the original full text block automatically.

***

### 🎛️ Input Settings

The Actor UI features an incredibly simple configuration structure available in both the **Form** and **JSON** tabs:

- **Action:** Choose **SLIM** to store a massive data block and receive your ticket code. Choose **HYDRATE** to pass a ticket code and pull the original text block back down.
- **Payload:** The data string you want to pass. Enter your raw text or data chunk for `SLIM`, or enter your 16-character hexadecimal ticket ID for `HYDRATE`.

Tickets created by older builds that stored payloads in each run's temporary default store cannot be hydrated by this version. Slim those payloads again to create reusable tickets.

***

### 🚀 API Quick Start

#### Python SDK Integration

```python
from apify_client import ApifyClient

client = ApifyClient("YOUR_APIFY_API_TOKEN")

## Compress heavy tool lists or outputs
actor_input = {
    "action": "SLIM",
    "payload": "YOUR_MASSIVE_TEXT_OR_DATA_HERE"
}

run = client.actor("flowlockautomation/ai-chat-slimmer").call(actor_input=actor_input)
print(run.get("output"))
```

***

### 💳 Billing & Architecture

This infrastructure asset operates on a **custom Pay-Per-Event (PPE) billing matrix**. You are only charged precisely per event invocation when data is actively slimmmed or hydrated, keeping operational maintenance overhead next to zero.

# Actor input Schema

## `action` (type: `string`):

Choose SLIM to store large data and return a compact text token, or HYDRATE to look up a token.

## `payload` (type: `string`):

Enter the heavy text or JSON data to process. For HYDRATE, enter the token to look up.

## Actor input object example

```json
{
  "action": "SLIM"
}
```

# Actor output Schema

## `results` (type: `string`):

One result record with status, ticket ID, payload byte count, estimated tokens saved, and the active payload or ticket.

# API

You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup.

## JavaScript example

```javascript
import { ApifyClient } from 'apify-client';

// Initialize the ApifyClient with your Apify API token
// Replace the '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {};

// Run the Actor and wait for it to finish
const run = await client.actor("flowlockautomation/ai-chat-slimmer").call(input);

// Fetch and print Actor results from the run's dataset (if any)
console.log('Results from dataset');
console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`);
const { items } = await client.dataset(run.defaultDatasetId).listItems();
items.forEach((item) => {
    console.dir(item);
});

// 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs

```

## Python example

```python
from apify_client import ApifyClient

# Initialize the ApifyClient with your Apify API token
# Replace '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = {}

# Run the Actor and wait for it to finish
run = client.actor("flowlockautomation/ai-chat-slimmer").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}")
for item in client.dataset(run.default_dataset_id).iterate_items():
    print(item)

# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start

```

## CLI example

```bash
echo '{}' |
apify call flowlockautomation/ai-chat-slimmer --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,flowlockautomation/ai-chat-slimmer"
        }
    }
}
```

The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer <APIFY_API_TOKEN>` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations).

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/VAASzTqWebYaFiW6y/builds/TXf2sBISkKJRJQmBb/openapi.json