Mem0 is a memory service you add to an existing AI agent or app: it extracts and stores facts from conversations, then retrieves the relevant ones later, with a compression engine to keep context small. Pick it when you need memory added to an agent, not a whole new agent platform. The Hobby tier is free (10,000 add / 1,000 retrieval requests a month); Starter is $19/month, Pro $249/month.
Other good fits
Starting free to test whether persistent memory actually improves your specific use case before paying for higher volume.
+ Pros
Read pros
Mem0 is a memory layer you add to an AI agent or application: it "adds" data from conversations, extracts and updates memories from it, and "retrieves" the relevant ones when needed later, so your agent doesn't have to re-establish context every session. A memory-compression engine condenses chat history into compact memories to cut tokens and latency while keeping relevant context. It's used through a Python or Node.js SDK (pip install mem0ai), with simple add() and search() calls, and can deploy portably across Kubernetes, private cloud or air-gapped environments.
Pick it when…
You already have an agent or application and just need it to remember things across sessions, not a full new platform.
You want to try memory on a free tier before committing budget to see if it actually improves your results.
You need a compression step so accumulated chat history doesn't blow up your context window and cost over time.
− Cons
Read cons
Mem0 solves memory specifically; it isn't a full agent platform with tools, scheduling or permissions, so you still need your own agent logic around it. Automatic extraction of what's "relevant" to remember is a judgement call the system makes for you, and it's worth periodically auditing what's actually stored, since an over-eager extraction step could retain something you didn't intend to keep.
Pick something else when…
You need a complete stateful-agent platform, not just memory.Letta bundles memory with tools, permissions and scheduling in one product.
You want application-level composition and broad integrations.LangChain operates at that layer, with memory as one of many concerns.
Your state needs are simple enough to track in your own database. Skip a dedicated memory service if a plain lookup table already does the job.
Avoid if
Your application's state is simple enough that you can track it yourself without a dedicated memory service.
Workflow
Send a conversation or fact to add() → let Mem0 extract and store what's relevant → call search() with the current context to retrieve what matters for this turn → feed the retrieved memories into your agent's prompt → review periodically what's actually being stored and retrieved to catch anything wrong or unwanted.
Usage-based pricing is also available for teams whose traffic doesn't fit a fixed tier. Running effort is mostly reviewing what gets stored (to avoid keeping something you shouldn't) and monitoring add/retrieval volume against your plan's limits.
Restrictions
You bring your own model provider costs on top of the platform price; Mem0 itself doesn't include model inference. Usage-based pricing beyond the fixed tiers is available for teams whose traffic doesn't map cleanly to one plan.
First project
Documentation-based suggestion — not run here.
Give a simple assistant a persistent preference.
Build a small test script using the Mem0 SDK and a fictional user ID.
Add one fact through add(), for example a stated preference like "prefers short answers".
In a separate script run (simulating a new session), call search() with a related query and check that the stored preference comes back.
Feed that retrieved memory into a test prompt and confirm the response actually reflects it.
Done when a simulated second session correctly recalls and uses the one fact you stored, with nothing invented or leaked from other test users.
Links & evidence
Documentation-based · not hands-on · Sep 23, 2026
Rests on Mem0's homepage, pricing page and documentation, checked 23 September 2026. No API key was used and no add/search calls were made for this review.
Give assistants retained facts across separate sessions.
Store and retrieve memories with Python or Node SDK calls.
Choose managed hosting or self-host the service with Docker.
Mem0 · free-tier · cloud · self-host
agent memory
long-term memory
memory api
personalisation
sdk
write-code
View sourced imageOfficial Mem0 playground screenshot showing conversational memory extraction. This illustrates the documented playground, not a standalone interface supplied by the Python library.
Mem0 documentation — Playground add-memory example
Owner-approved use of public official interface screenshots, as recorded in public-visuals-batch-1.json, 13 September 2026. Copyright retained by Mem0 and its contributors; attribution retained. Editorial product illustration; no open licence asserted. Converted on 15 October 2026 to a 1600px-wide WebP (quality 80) for faster page loads; the original image remains in the media library.