Try Mem0
Overview
Mem0 addresses the flaw users notice fastest in AI products: the assistant forgets everything. Stuffing the entire conversation history back into the context window is the naive fix, and it fails on both cost and quality — long contexts are expensive and models reason worse when buried in irrelevant history. Mem0 sits between the conversation and the model, extracting the facts worth keeping and retrieving only what is relevant to the current turn.
The extraction step is the interesting part. Rather than storing raw transcripts, Mem0 identifies durable information — preferences, constraints, decisions, facts about the user — and maintains it as structured memories that can be updated or contradicted later. A user who changes their mind should not have both statements retrieved as equally true, and handling that is harder than it looks.
It is open source with a managed cloud, works with whichever vector store you already run, and by 2026 is the most widely adopted option in this category. The trade-off is scope: Mem0 is a memory layer you add to an agent, not an agent runtime. If you want memory as a first-class part of the agent's architecture, Letta is the other design worth evaluating.
Key Features
Fact Extraction
Identifies durable information worth remembering rather than storing raw transcripts, which is what keeps retrieval relevant as history grows.
Memory Update and Contradiction Handling
Memories can be revised when a user changes their mind, instead of accumulating contradictory statements that all retrieve as true.
Relevance-Based Retrieval
Pulls only memories relevant to the current turn, keeping context windows small and model reasoning sharp.
Backend-Agnostic Storage
Works with Qdrant, Pinecone, Chroma and others, so it fits the infrastructure you already run.
Open Source Core
Self-hostable, which matters because memory stores accumulate exactly the personal data you must be careful with.
Per-User and Per-Agent Scoping
Memories are scoped so one user's information never surfaces in another's session.
Pros & Cons
Advantages
- Solves a problem users feel immediately and complain about loudly
- Extraction keeps retrieval relevant where naive history-stuffing degrades
- Cheaper than long-context approaches at conversation scale
- Open source and backend-agnostic
- The most adopted option, so integration examples are plentiful
Disadvantages
- Extraction quality varies — it sometimes keeps trivia and drops what mattered
- Memory stores accumulate personal data, creating real GDPR obligations
- Adds a layer and therefore latency to every turn
- Debugging wrong retrieved memories is harder than debugging a prompt
Pricing Plans
| Plan | Price | Key Features |
|---|---|---|
| Open Source | Free | Self-hosted, bring your own vector store and models |
| Cloud Free | $0 | Limited monthly memory operations |
| Cloud Pro | Usage-based | Production volumes with managed storage |
| Enterprise | Custom | Compliance controls, support, dedicated deployment |
Best Use Cases
Mem0 Excels At:
- Consumer assistants where personalisation drives retention
- Support agents that must remember prior tickets and preferences
- Long-running agents where full history is too expensive to resend
- Teams that want memory without redesigning their agent architecture
May Not Be Ideal For:
- Stateless single-turn applications
- Regulated contexts where storing user facts creates unacceptable obligations
- Teams wanting memory built into the agent runtime rather than added beside it
How It Compares
Mem0 vs Letta
Mem0 is a memory layer you bolt onto an existing agent; Letta is an agent runtime with memory management as a core design principle. Mem0 is easier to adopt, Letta is more principled about what stays in context.
Mem0 vs long context windows
Million-token contexts make memory look unnecessary until you count the bill and notice models reason worse when the relevant fact is buried in irrelevant history. Retrieval beats brute force on both cost and quality.
Final Verdict
Our Recommendation
Mem0 is the pragmatic answer to agent memory. It solves a problem users notice on day one, it does so more cheaply and more accurately than resending entire conversation histories, and it adds to your existing architecture rather than replacing it. Two things deserve attention before deploying it: extraction quality is imperfect, so evaluate it against your own conversations rather than the demo, and a memory store is by definition a personal data store, which brings deletion and retention obligations you need a plan for.