Introduction

The context engineering layer for AI agents. Selects only the tools and skills relevant to each turn, recovering accuracy lost to tool overload and cutting what you pay per call. No vector DB, no infra.

Why

• Cost: Every tool schema, every skill, and a growing list of instructions in the system prompt are tokens you pay for on every call. Send them all up front and you pay for them all, every turn.
• Accuracy: Models get worse as that context grows. Crowd it with tools, skills, and instructions a turn doesn't need and the model picks the wrong option and drifts off task.
• Ratel fixes both: it indexes your tools and skills into a catalog the agent progressively discloses, searching for what each turn needs and injecting only the matching capabilities instead of loading everything up front. Constant grounding your agent always needs — a shop's address, a brand's voice — is registered as facts and pushed into context, re-injected only when it isn't already fresh in the transcript.

Across local, open-source, and frontier model setups, Ratel cuts token usage and recovers accuracy lost to tool overload, with no vector DB required. Full results: benchmark.ratel.sh

Quickstart

Guides: Quickstart · TypeScript SDK · Python SDK

Examples: Vercel AI SDK · Pydantic AI

Typescript

Install the SDK first:

pnpm add @ratel-ai/sdk

Then create and use your Catalogs:

import { readFile } from "node:fs/promises";
import {
  SkillCatalog,
  ToolCatalog,
  getSkillContentTool,
  invokeToolTool,
  searchCapabilitiesTool,
} from "@ratel-ai/sdk";

const catalog = new ToolCatalog();
catalog.register({
  id: "read_file",
  name: "read_file",
  description: "Read a file from local disk.",
  inputSchema: { type: "object", properties: { path: { type: "string" } } },
  outputSchema: { type: "object", properties: { contents: { type: "string" } } },
  execute: async ({ path }) => ({ contents: await readFile(path, "utf8") }),
});

const skills = new SkillCatalog();
skills.register({
  id: "inspect-local-file",
  name: "inspect-local-file",
  description: "Inspect a local file before answering questions about it.",
  tools: ["read_file"],
  body: "Read the requested file, then ground your answer in its contents.",
});

// use the following as tools in your agent framework
const search = searchCapabilitiesTool(catalog, skills);
const invoke = invokeToolTool(

... [View full README on GitHub](https://github.com/ratel-ai/ratel#readme)