Building With MemContext

Mental model

MemContext is the memory layer for your AI product. Your app sends durable context to MemContext, then retrieves relevant context before generation. Your application does not need to manage retrieval logic, duplicate memories, or memory updates manually.

Integration pattern

A typical integration follows four steps:

Save durable user or project context via POST /api/memories
Search before generation or decision-making via GET /api/memories/search
Profile (optional) - fetch broad context in one call via GET /api/memories/profile
Feedback - record retrieval quality via POST /api/memories/:id/feedback

For larger notes, POST /api/memories may return 202 Accepted. This means extraction has started and can be tracked with the returned jobId. For multi-user applications, always pass a stable end-user or tenant ID in scope. project remains a secondary grouping field inside that scope.

Example: Support bot

User: "I'm having trouble with my subscription"

Bot workflow:
1. search_memory({ query: "user subscription plan and billing" })
   → finds: "User is on the Pro plan, subscribed via Stripe"
   → finds: "User had a billing issue resolved on March 15"

2. Generate response with memory context injected into prompt

3. memory_feedback({ memoryId: "...", type: "helpful" })

Good fit use cases

Use case	Memory role
Coding assistants	Stable preferences, project decisions, architecture context
Content generation	Brand rules, evolving strategy, audience insights
Support bots	Customer history, past issues, plan details
CRM memory	Relationship context, interaction history
Onboarding	User progress, preferences learned during setup
Personalization	Style preferences, feature usage patterns

Ownership boundary

Keep your application logic outside MemContext. MemContext owns:

Saving and retrieving memory
Handling duplicates and updates
Memory history
Expiry and temporal filtering
MCP tool access

Your application owns:

Business-specific prompts and logic
UI and user experience
Workflow orchestration
Downstream actions and integrations

API integration example

const MEMCONTEXT_API = "https://api.memcontext.in";

async function searchMemory(query: string, scope: string, project?: string) {
  const params = new URLSearchParams({ query });
  params.set("scope", scope);
  if (project) params.set("project", project);

  const res = await fetch(`${MEMCONTEXT_API}/api/memories/search?${params}`, {
    headers: { "X-API-Key": process.env.MEMCONTEXT_API_KEY },
  });

  return res.json();
}

async function saveMemory(
  content: string,
  category: string,
  scope: string,
  project?: string,
) {
  const res = await fetch(`${MEMCONTEXT_API}/api/memories`, {
    method: "POST",
    headers: {
      "Content-Type": "application/json",
      "X-API-Key": process.env.MEMCONTEXT_API_KEY,
    },
    body: JSON.stringify({ content, category, scope, project }),
  });

  return res.json();
}

If you submit a long note or transcript, check the returned status:

saved, updated, extended, duplicate → final synchronous result
accepted → asynchronous extraction in progress

TypeScript SDK example

Install the SDK:

npm install memcontext-sdk

import { MemContextClient } from "memcontext-sdk";

const client = new MemContextClient({
  apiKey: process.env.MEMCONTEXT_API_KEY!,
  scope: "user_123",
  project: "memcontext",
});

const profile = await client.profile();

await client.save({
  content: "User prefers TypeScript over JavaScript for type safety",
  category: "preference",
});

const results = await client.search({
  query: "What language does the user prefer?",
});

Tips

Search before saving - avoid duplicates by checking if the knowledge already exists
Use scope for hard per-user or per-tenant isolation
Use projects only to group memories inside that scope
Category matters - preference and fact go into profile static context; everything goes into dynamic
Do not save noise - only persist information with long-term value

Get Started

Concepts

Context Vault

Guides

SDK

API Reference

MCP Tools

Building With MemContext

Mental model

Integration pattern

Example: Support bot

Good fit use cases

Ownership boundary

API integration example

TypeScript SDK example

Tips

​Mental model

​Integration pattern

​Example: Support bot

​Good fit use cases

​Ownership boundary

​API integration example

​TypeScript SDK example

​Tips

Mental model

Integration pattern

Example: Support bot

Good fit use cases

Ownership boundary

API integration example

TypeScript SDK example

Tips