title: "10 Best Free AI Memory Tools in 2026 (Compared by Free Tier)" description: "Compare the 10 best free AI memory tools in 2026 by their free tiers: what you actually get, the limits, and which fits your project." date: 2026-09-16

10 Best Free AI Memory Tools in 2026 (Compared by Free Tier)

AI memory tools all promise to remember your conversations, projects, and preferences. The free tiers behind them look very different though. Some give you enough to run a real project. Some are barely a trial. This list compares ten tools by what their free tiers actually include, based on public pricing pages as of September 2026. If a number is not stated publicly, it says so rather than guessing.

How to read a free tier

Three numbers tell you almost everything. First, the unit: tokens, memories, episodes, and API calls are not interchangeable, and a big number in one unit can mean less than a small number in another. Second, the ceiling behavior: does the tool throttle, block writes, or start billing when you exceed the free tier? Third, the lock-in: how much of your architecture depends on proprietary features once you are past the prototype stage? A generous free tier on a tool you cannot leave is a loan, not a gift. Keep these three questions in mind as you read the list.

Quick comparison

1. MemoryLake

MemoryLake offers one of the most generous free tiers on this list: 300,000 tokens per month at no cost. That is enough for a real side project or a small team prototype without entering card details.

Pros: generous monthly allowance, managed so there is nothing to host, simple API-first design.

Cons: token-based pricing means heavy RAG workloads can burn through the allowance fast; check their pricing page for what happens when you exceed it.

Skip it if your workload is retrieval-heavy at scale; token meters punish read volume.

2. Mem0

Mem0 provides a managed memory layer with a free tier of 10,000 memories per month. It handles extraction and retrieval for you, which saves the work of building that pipeline yourself.

Pros: purpose-built for agent memory, the free tier covers real usage for a solo developer.

Cons: 10k memories is a monthly ceiling, not a pool; if your agent writes aggressively, you will hit it.

Skip it if your agent stores every turn as a separate memory; batch or summarize first.

3. Zep

Zep focuses on temporal memory for agents, tracking what happened and when. Its free tier includes 1,000 episodes per month, which is enough to prototype time-aware agents.

Pros: temporal modeling most vector stores do not offer, managed hosting on the free tier.

Cons: 1,000 episodes is a modest ceiling; production workloads will outgrow it quickly.

Skip it if you need plain semantic retrieval; you are paying for temporal modeling you will not use.

4. Chroma

Chroma is an open-source vector database that runs locally. There is no free tier because there is no bill: you host it, so usage is unlimited by definition.

Pros: free forever, runs in-process for local development, no account or network dependency.

Cons: "free" means you pay in ops time. Scaling, backups, and uptime are your job.

Skip it if you have no server to run; local-only Chroma does not help a deployed app.

5. Pinecone

Pinecone is the best-known managed vector database, with a free starter tier aimed at prototypes and hobby projects. Exact limits change often, so check their pricing page before building on it.

Pros: managed, fast, and familiar to most AI developers; good for proof of concepts.

Cons: the free tier is clearly positioned as a starting point. Production indexes and namespaces push you to paid plans.

Skip it if you want to stay free at scale; plan the migration path before you prototype.

6. Weaviate

Weaviate is an open-source vector search engine you can self-host for free, with a managed cloud offering that has its own trial and starter options.

Pros: self-hosted version is genuinely free with no usage cap; hybrid search is built in.

Cons: self-hosting a distributed search engine is real work; the managed cloud free options are limited.

Skip it if you only need simple vector lookup; Weaviate's power is wasted on basic use cases.

7. LlamaIndex

LlamaIndex is an open-source framework for connecting LLMs to your data. Being a framework rather than a hosted service, its free version is the full version, self-hosted.

Pros: the core library is free and actively maintained; pairs with any vector store on this list.

Cons: it is a toolkit, not a memory product. You still decide what gets stored, how it is retrieved, and where it lives.

Skip it if you want memory out of the box; LlamaIndex is a building material, not a finished product.

8. LangChain

LangChain is the open-source agent framework most developers reach for first. Like LlamaIndex, the framework itself is free; you pay for whatever models and infrastructure you plug into it.

Pros: huge ecosystem, memory primitives included, free to use in any project.

Cons: flexibility is the cost. A "memory" in LangChain is whatever you build it to be, which means design work falls on you.

Skip it if your team is small and the deadline is near; LangChain rewards investment, not shortcuts.

9. Flowise

Flowise is an open-source, no-code builder for LLM apps and agents. Self-hosting the community edition costs nothing beyond your server.

Pros: visual builder lowers the barrier for non-developers; memory nodes are built in.

Cons: you host it and maintain it; production hardening is on you.

Skip it if your builders need custom logic; visual nodes hit walls that code does not.

10. AutoGen

Microsoft's AutoGen is an open-source framework for multi-agent applications. Free to use, with memory handled through whatever store you attach.

Pros: strong for multi-agent research and experiments, no licensing cost.

Cons: it is a research-grade framework; expect to write substantial code around it for anything production-like.

Skip it if you need one agent with memory, not a society of them; AutoGen is overkill for single-agent apps.

The managed option: Vilix AI

If you want memory without hosting or quota math, Vilix AI is a shared memory layer that connects to your AI tools over MCP. It auto-saves conversation turns, retrieves them with semantic RAG search, and keeps the same memory across devices and clients. You can list, update, and delete memories from any connected AI or the dashboard, export your data anytime, and delete instantly. Data is isolated per user with last-write-wins on conflicts. It connects to Claude, Codex, Cursor, OpenClaw, Hermes, Manus, and Lovable via MCP, with setup in about ten minutes.

There is a free tier plus a 7-day Pro trial that does not ask for a credit card. For individuals who want cross-tool memory without running infrastructure, it is worth comparing against the self-hosted options above.

How to choose

Pick from this list based on your constraint, not the marketing. If your constraint is money and you have ops skills, go open source: Chroma, Weaviate, LlamaIndex, LangChain, Flowise, or AutoGen cost nothing but your time. If your constraint is time, the managed free tiers (MemoryLake, Mem0, Zep, Pinecone) buy you speed up to a ceiling. And when the ceiling arrives, either pay for managed or move to self-hosted. Knowing which constraint you are optimizing for is the whole decision.

A few concrete starting points. Building a weekend prototype with no server? MemoryLake or Mem0. Running a team project where data must stay in-house? Chroma or Weaviate self-hosted. Experimenting with agents that need to reason about time? Zep. Building a RAG pipeline you fully control? LlamaIndex plus any vector store. Non-technical team members building AI workflows? Flowise. Multi-agent research? AutoGen or LangChain. Match the tool to the situation and the free tier stops being a gamble.

I build Vilix AI, a shared memory layer for AI tools.