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RetainDB vs Letta

RetainDB vs Letta: add memory to your existing agents, don't rebuild them

Letta handles agent lifecycle, memory, and execution in one platform. RetainDB handles memory, context, and knowledge base ingestion — and plugs into whatever you're already running. The question isn't just 'which memory is better' but 'do I want a platform or a layer that also handles my team's documents?'

88% Preference recall
79% Overall memory score
0% Hallucination rate
<40ms Retrieval latency
88%
Preference recall
LongMemEval · RetainDB
79%
Overall memory score
LongMemEval · RetainDB
0%
Hallucination rate
In benchmark testing · RetainDB
<40ms
Retrieval latency
Global average · RetainDB
TL;DR

Letta is a platform you build agents on. RetainDB is a library you drop into agents you already have. If you're adding memory to an existing product, RetainDB. If you're designing agents from scratch and want a full platform, Letta.

At a glance

RetainDB vs Letta

Feature
RetainDB
Letta
Preference recall (LongMemEval)
88%
Not published
Product type
Memory layer — drop-in
Agent platform — build on
Integration
3-line SDK, any framework
Agent logic moves into Letta
Time to first memory write
<30 min
Days to weeks
Memory taxonomy
13 typed categories
In-context memory blocks
Framework compatibility
Any — AI SDK, LangChain, LangGraph, raw API
Within the Letta framework
Benchmark proof
Published LongMemEval page
Not published
Knowledge base ingestion
22 connectors — Notion, PDF, Confluence, YouTube, arXiv, Playwright, GitHub, GitLab, Discord, Slack
Not a feature — Letta is focused on agent execution, not KB ingestion
Context layer
Hybrid retrieval assembles memory + knowledge per query
In-context memory blocks managed within the Letta platform
The specifics

Why the difference matters

01

Memory layer vs platform — a real distinction

When teams evaluate Letta for user-memory use cases, the scope often expands: 'add memory to our agent' becomes 'migrate our agents to a new platform'. RetainDB is explicitly designed to avoid that. Three lines of SDK, any framework, no platform adoption required.

02

88% preference recall vs unpublished numbers

RetainDB publishes LongMemEval scores at retaindb.com/benchmark: 88% preference recall, 79% overall. Letta doesn't publish equivalent scores for its memory subsystem. For teams that need internal validation before shipping, that gap matters.

03

Your stack stays yours

RetainDB adapters drop into Vercel AI SDK, LangChain, LangGraph, or plain REST. The wizard generates integration code for your existing setup. You're not adopting a new execution model — you're adding a memory layer to the one you have.

04

Memory and knowledge in the same system

Letta handles memory within its platform execution model — not your help center docs, Notion workspace, or PDFs. RetainDB ingests all of it: 22 built-in connectors for Notion, Confluence, PDFs, YouTube, arXiv, Playwright sessions, sitemaps. Your agents can recall what a user told them two sessions ago and what your product documentation says — in the same retrieval call.

Pick your fit

Who should use what

Choose RetainDB when
You have an existing AI product and want to add memory
You also need KB ingestion — Notion, PDFs, help docs, YouTube
You need production memory in under 30 minutes
You want typed categories (preference ≠ decision ≠ constraint)
You need a published benchmark for internal buy-in
Consider Letta when
You're building agents from scratch on a new platform
You want memory tightly integrated with agent execution
You prefer one system for agent lifecycle + memory
Common questions

What people ask before deciding

What's the core difference?

RetainDB is a library you add to agents you already have. Letta is a platform you build new agents on. If you have an existing product, the RetainDB path is measured in minutes. The Letta path is measured in sprint cycles.

Can I use RetainDB with my current framework?

Yes. Drop-in adapters for Vercel AI SDK, LangChain, LangGraph — or use the REST API directly. npx retaindb-wizard detects your stack and generates the integration code.

Does Letta have better memory inside its own platform?

Letta's memory integrates tightly with its execution model, which is an advantage if you're running inside Letta. Outside the platform, RetainDB's hybrid retrieval and 88% LongMemEval preference recall gives it the lead on pure memory quality.

Start today — free

Your agents deserve memory
that actually works.

88% preference recall on LongMemEval. Under 40ms retrieval. Most teams are in production in under 30 minutes — no infrastructure to manage.

88% preference recall·0% hallucination rate·<40ms retrieval·No training on your data