OpenClaw: The Open-Source Agent Framework for LLM Workflows

OpenClaw is an open-source framework for building structured LLM agent workflows. It occupies the same space as LangChain, CrewAI, and AutoGen — but focuses on simplicity, explicit control flow, and lower token overhead.

Note: OpenClaw is an emerging project under active development. Check the current documentation for the latest API.

What OpenClaw does

• Workflow definition — define agent pipelines as explicit graphs with typed inputs and outputs
• Multi-model support — works with OpenAI, Anthropic, and any OpenAI-compatible endpoint
• Tool integration — connect agents to external APIs, databases, and services via MCP
• State management — persistent state across agent steps without manual context handling
• Observability hooks — built-in logging of each agent step's inputs, outputs, and token usage

OpenClaw vs the alternatives

The token overhead problem with agent frameworks

Most agent frameworks add significant token overhead through built-in prompts, memory management, and orchestration boilerplate. As covered in our LangChain guide, this overhead can reach 200-300% above your actual prompt content. OpenClaw's explicit control flow reduces this by giving you direct access to what goes into each agent call.

Tracking OpenClaw token costs

Since OpenClaw uses standard LLM clients under the hood, wrap the underlying client with Tokoscope first:

from tokoscope import wrap
from openai import OpenAI

# Wrap first, then pass to OpenClaw
tracked_client = wrap(OpenAI(), api_key="ts_live_...")

# Every OpenClaw step is now tracked with cost and token attribution