How do I get started with claw.zip?

Run npx claw-zip init in your terminal. It will walk you through credential setup and configuration. You can also sign up at claw.zip and create your first API key from the dashboard.

Will compression change my model outputs?

Our compression is designed to preserve semantic meaning. For most use cases, outputs are identical. If you need guaranteed verbatim passthrough, you can disable compression per-key from the dashboard.

What happens if claw.zip goes down?

claw.zip runs on Cloudflare Workers across 300+ global edge locations. In the unlikely event of an outage, you can point your config back to the Anthropic API directly — no data is locked in our system.

Can I use claw.zip with non-Anthropic models?

Currently, claw.zip is optimized for the Anthropic API (Claude models). Support for additional providers is on our roadmap.

Integration Guide

Using claw.zip with LangChain

LangChain chains and agents can generate large prompts. claw.zip compresses them automatically, reducing OpenClaw API costs across your entire pipeline. This is especially valuable for OpenClaw users running multi-step agents or RAG pipelines — each intermediate prompt gets compressed too.

Difficulty:Easy

Time:3 minutes

Before You Start

Prerequisites

→Python 3.9+
→LangChain installed
→An OpenClaw API key
→A claw.zip account and API key

Step-by-Step

Integration Steps

Step 1

Install dependencies

Install LangChain with the Anthropic integration.

bash

pip install langchain langchain-anthropic

Step 2

Configure ChatAnthropic with claw.zip

Pass the claw.zip base URL and API key to the ChatAnthropic model.

python

from langchain_anthropic import ChatAnthropic

llm = ChatAnthropic(
    model="claude-sonnet-4-20250514",
    anthropic_api_key="your_openclaw_key",
    anthropic_api_url="https://api.claw.zip",
    default_headers={
        "x-api-key": "your_clawzip_key",
    },
)

Step 3

Use in a chain

Use the configured LLM in any LangChain chain. Compression is transparent.

python

from langchain_core.prompts import ChatPromptTemplate

prompt = ChatPromptTemplate.from_messages([
    ("system", "You are a helpful assistant that answers concisely."),
    ("human", "{input}"),
])

chain = prompt | llm

response = chain.invoke({
    "input": "What are the benefits of prompt compression?"
})
print(response.content)

Step 4

Use with agents

LangChain agents work the same way. claw.zip compresses the prompts that agents generate internally.

python

from langchain.agents import create_tool_calling_agent, AgentExecutor
from langchain_core.prompts import ChatPromptTemplate

prompt = ChatPromptTemplate.from_messages([
    ("system", "You are a helpful assistant."),
    ("human", "{input}"),
    ("placeholder", "{agent_scratchpad}"),
])

agent = create_tool_calling_agent(llm, tools, prompt)
executor = AgentExecutor(agent=agent, tools=tools)

result = executor.invoke({"input": "Search for AI cost optimization"})

Done

You Are All Set

All LangChain calls now go through claw.zip. This is especially valuable for OpenClaw users running agents and complex chains that generate many intermediate prompts — each of which gets compressed automatically.

Get Your API Key All Guides

More Guides

Other Integration Guides

[Easy]

Difficulty:Easy

Time:3 minutes

Integration Steps

Step 1

Install dependencies

Install LangChain with the Anthropic integration.

bash

pip install langchain langchain-anthropic

Step 2

Configure ChatAnthropic with claw.zip

Pass the claw.zip base URL and API key to the ChatAnthropic model.

python

from langchain_anthropic import ChatAnthropic

llm = ChatAnthropic(
    model="claude-sonnet-4-20250514",
    anthropic_api_key="your_openclaw_key",
    anthropic_api_url="https://api.claw.zip",
    default_headers={
        "x-api-key": "your_clawzip_key",
    },
)

Step 3

Use in a chain

Use the configured LLM in any LangChain chain. Compression is transparent.

python

from langchain_core.prompts import ChatPromptTemplate

prompt = ChatPromptTemplate.from_messages([
    ("system", "You are a helpful assistant that answers concisely."),
    ("human", "{input}"),
])

chain = prompt | llm

response = chain.invoke({
    "input": "What are the benefits of prompt compression?"
})
print(response.content)

Step 4

Use with agents

LangChain agents work the same way. claw.zip compresses the prompts that agents generate internally.

python

from langchain.agents import create_tool_calling_agent, AgentExecutor
from langchain_core.prompts import ChatPromptTemplate

prompt = ChatPromptTemplate.from_messages([
    ("system", "You are a helpful assistant."),
    ("human", "{input}"),
    ("placeholder", "{agent_scratchpad}"),
])

agent = create_tool_calling_agent(llm, tools, prompt)
executor = AgentExecutor(agent=agent, tools=tools)

result = executor.invoke({"input": "Search for AI cost optimization"})

Using claw.zip with LangChain

Prerequisites

Integration Steps

Install dependencies

Configure ChatAnthropic with claw.zip

Use in a chain

Use with agents

You Are All Set

Other Integration Guides

Using claw.zip with the Anthropic SDK

Using claw.zip with Docker

Using claw.zip with FastAPI

Using claw.zip with LangChain

Prerequisites

Integration Steps

Install dependencies

Configure ChatAnthropic with claw.zip

Use in a chain

Use with agents

You Are All Set

Other Integration Guides

Using claw.zip with the Anthropic SDK

Using claw.zip with Docker

Using claw.zip with FastAPI