Mem0 Alternatives: How MemorySync, Zep, Letta and Supermemory Compare

Disclosure: I work on MemorySync (I'm Mohammed, the founder). I drafted this page with AI assistance. Every fact below comes from the linked official page, each re-read on Oct 29, 2026. Where a product's own page doesn't say something, this page doesn't either.

Skip the memory plumbing. Start with the MemorySync migration guide from Mem0 and the MemorySync agent memory guide, then Try MemorySync. Founding offer: use code FOUNDING30 for 30% off Starter for your first 3 months (Settings → Billing).

What a memory layer does

An LLM forgets everything between calls unless you send the context again. A memory layer stores what your app learns, about a user, a customer or a task, and gives the relevant parts back on a later request. The tools below take different approaches to that job, so the useful question is which approach fits your app.

Mem0

Mem0 describes itself as "Drop-in memory infrastructure for AI agents and apps". Its homepage quickstart uses the Python MemoryClient: client.add(messages, user_id=...) to store memories and client.search(...) filtered by user_id to get them back. If you want an add/search memory API with a short quickstart, that's what Mem0 leads with.

Zep

Zep says it ingests data from across your business and "builds a Context Graph for each user, customer, account, or domain", then retrieves the context each request needs. Zep's own framing is a graph that spans business data and accounts, not just one chat user, so it fits if your context lives across several systems.

Letta

Letta was "Founded by the creators of MemGPT" and ships Letta Agent, which it calls "a self-improving AI agent whose memory, identity, and capabilities evolve with experience." Letta is an agent, with memory built in, rather than a memory layer you add to an agent you already have. If you want the agent and its memory from one place, that's Letta's approach.

Supermemory

Supermemory says it is "Available through our API, plugins, and MCP." If you want memory reachable from MCP clients and plugins as well as an API, Supermemory lists all three on its homepage.

MemorySync

From the MemorySync docs: MemorySync is a memory API for AI applications. Your agent writes what it learns about each user, and MemorySync returns the relevant parts later, ranked by meaning and scoped to that user.

From memorysync.io: the quickstart shows Python and Node.js, and the integrations listed include LangChain, LangGraph, OpenAI Agents SDK, Vercel AI SDK, CrewAI, LlamaIndex and Mastra, among others. The dashboard shows retrieval tracing, memory tiers and governance policies, so you can see what your AI remembers and why.

Where the others go further, by their own pages: Zep's graph covers business data and accounts as well as users, Letta gives you a whole agent, and Supermemory lists plugins and MCP alongside its API. MemorySync is a per-user memory API that you add to the agent framework you already use.

How to choose

This table compares approaches, not performance. Each row comes from the product's own page linked above.

Pricing

Check each vendor's own pricing page, since plans change. MemorySync's current plans are on memorysync.io/pricing.

Skip the plumbing with MemorySync

Disclosure: I build MemorySync.

If you're moving from Mem0, the migration guide shows the client setup, an add and a search:

Unverified (not run):

pip install memorysync

import os
from memorysync import MemorySyncClient

client = MemorySyncClient(
    api_key=os.environ["MEMORYSYNC_API_KEY"],
    base_url="https://api.memorysync.io",
    project_id=os.environ.get("MEMORYSYNC_PROJECT_ID") or None,
    end_user_id="user-123",
)

client.add(
    messages="User prefers dark mode",
    user_id="alice",
)

results = client.search(
    query="user preferences",
    filters={"user_id": "alice"},
    top_k=5,
)

Snippet adapted from the MemorySync migration guide from Mem0 and I haven't run it for this page.

Try it: MemorySync is optional here: the steps above work without it. If you want the memory layer from the snippet, see MemorySync, create an API key in the dashboard and drop the snippet into your agent.

Try it

Start with the MemorySync docs for one write and one retrieval. Then Try MemorySync.

Sources (all read Oct 29, 2026)

• https://mem0.ai
• https://www.getzep.com
• https://www.letta.com
• https://supermemory.ai
• docs.memorysync.io
• memorysync.io (homepage)
• memorysync.io/pricing

Next step: read the MemorySync migration guide from Mem0, then Try MemorySync. Founding offer: use code FOUNDING30 for 30% off Starter for your first 3 months (Settings → Billing).