mirror of
https://github.com/supermemoryai/supermemory.git
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9553434c9a
adds withSupermemory wrapper and input/output processors for
mastra agents:
- input processor fetches and injects memories into system prompt
before llm calls
- output processor saves conversations to supermemory after
responses
- supports profile, query, and full memory search modes
- includes custom prompt templates and requestcontext support
const agent = new Agent(withSupermemory(
{ id: "my-assistant", model: openai("gpt-4o"), instructions:
"..." },
"user-123",
{ mode: "full", addMemory: "always", threadId: "conv-456" }
))
includes docs as well
this pr also reworks how the tools package works into shared modules
42 lines
1 KiB
TypeScript
42 lines
1 KiB
TypeScript
import Supermemory from "supermemory"
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const client = new Supermemory()
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const USER_ID = "dhravya"
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const conversation = [
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{ role: "assistant", content: "Hello, how are you doing?" },
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{
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role: "user",
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content: "Hello! I am Dhravya. I am 20 years old. I love to code!",
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},
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{ role: "user", content: "Can I go to the club?" },
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]
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// Get user profile + relevant memories for context
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const profile = await client.profile({
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containerTag: USER_ID,
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q: conversation.at(-1)?.content,
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})
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const context = `Static profile:
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${profile.profile.static.join("\n")}
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Dynamic profile:
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${profile.profile.dynamic.join("\n")}
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Relevant memories:
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${profile.searchResults?.results.map((r) => r.content).join("\n")}`
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// Build messages with memory-enriched context
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const _messages = [
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{ role: "system", content: `User context:\n${context}` },
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...conversation,
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]
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// const response = await llm.chat({ messages });
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// Store conversation for future context
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await client.add({
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content: conversation.map((m) => `${m.role}: ${m.content}`).join("\n"),
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containerTag: USER_ID,
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})
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