@copass/mastra

@copass/mastra

Copass retrieval as Mastra tools. The LLM decides whether to discover, interpret, or search — you don't write the tool-calling loop.

Prerequisites

Install the Copass CLI and bootstrap your account:

npm install -g @copass/cli
copass login                             # email OTP
copass setup                             # creates a sandbox, writes .olane/refs.json
copass apikey create --name my-app       # prints an olk_... key — shown once, save it

Output	Use as
olk_... key printed by copass apikey create	COPASS_API_KEY
sandbox_id in ./.olane/refs.json	COPASS_SANDBOX_ID
project_id in ./.olane/refs.json (optional)	COPASS_PROJECT_ID

Ingest some content so retrieval has something to return:

copass ingest path/to/file.md
# or pipe stdin:  echo "some decision or note" | copass ingest -

Install

npm install @copass/mastra @copass/core @mastra/core @ai-sdk/anthropic zod

Quickstart

import { CopassClient } from '@copass/core';
import { copassTools, createWindowTracker } from '@copass/mastra';
import { Agent } from '@mastra/core/agent';
import { anthropic } from '@ai-sdk/anthropic';

const copass = new CopassClient({
  auth: { type: 'bearer', token: process.env.COPASS_API_KEY! },
});
const sandbox_id = process.env.COPASS_SANDBOX_ID!;
const window = await copass.contextWindow.create({ sandbox_id });
const tracker = createWindowTracker({ window });
const tools = copassTools({ client: copass, sandbox_id, window });

const agent = new Agent({
  name: 'support-bot',
  instructions: 'Answer questions using the knowledge graph.',
  model: anthropic('claude-opus-4-7'),
  tools,
});

const userMessage = 'what do we know about checkout retry behavior?';
await tracker.recordUserTurn(userMessage);

const response = await agent.generate(userMessage, {
  onStepFinish: tracker.onStepFinish,
  maxSteps: 5,
});
console.log(response.text);

If it worked, the answer cites concepts from whatever you ingested. Keep the same window and tracker across turns — follow-up calls won't re-surface items the agent already used.

Window auto-tracking

Mastra's agent.generate() / agent.stream() fire onStepFinish after each internal step with response.messages — the assistant and tool messages generated during that step. createWindowTracker(...) returns a handler that mirrors those into the ContextWindow, de-duplicated against what's already there.

The user's initial message isn't in onStepFinish (it's the input going into the call), so capture it explicitly with tracker.recordUserTurn(text) before agent.generate(). Safe to call repeatedly — the tracker de-duplicates.

Tool results (role: 'tool') are skipped by default; opt in with createWindowTracker({ window, includeToolMessages: true }) if you want them tracked.

Why this, not the raw API

• LLM chooses the retrieval shape. Three tools; the model picks the right one per turn.
• Window-aware automatically — when paired with createWindowTracker. Without the tracker, retrieval sees an empty history.
• Mastra-native tool shape. Drop the returned { discover, interpret, search } object straight into any agent config.

Tools

Tool	When the LLM calls it
discover	"What's relevant?" — ranked menu of pointers
interpret	"Tell me about these specific items." — brief pinned to canonical_ids
search	"Answer this directly." — full synthesized answer

Related

• @copass/core — client SDK
• @copass/ai-sdk, @copass/langchain, copass-pydantic-ai — same shape for other frameworks
• @copass/mcp — standalone MCP server for Claude Code / Desktop / Cursor

License

MIT