The 7 Best AI Memory Tools in 2026: Compared
We compare seven of the best AI memory tools in 2026, looking at what each does, who it is for, where it runs, and how much it costs
A few years ago, AI assistants had no persistent memory by default. If you wanted an assistant to remember something across conversations, you had to build the storage and retrieval layer yourself.
Today, memory is becoming a standard feature across AI products and developer platforms. But built-in memory has its limits. You often cannot see everything an AI has remembered, move that memory between tools, or control exactly what gets stored.
That gap has created a growing category of dedicated AI memory tools, each taking a different approach to making memory more capable, portable, or controllable.
We compared seven of them to see what each does best.
The Top 7 AI Memory Tools: At a Glance
1. MemoryPlugin
Pricing: Core $89/year, Pro $180/year | Free Trial: 7 days | Platforms: Chrome, Safari, MCP
MemoryPlugin creates a memory layer that sits outside any single AI app and works across multiple tools. Save something in ChatGPT, and Claude can use it in your next conversation. It connects to ChatGPT, Claude, Gemini, Grok, Perplexity, DeepSeek, and more than 21 platforms in total.
Every part of MemoryPlugin's memory system
MemoryPlugin combines several parts into one memory system.
Memories (the notebook): Curated facts and preferences that you explicitly save or extract. These are kept short, organized into buckets, and automatically added to new conversations across connected tools.
Chat History (the archive): Imported conversations from ChatGPT, Claude, Gemini, TypingMind, Grok, and DeepSeek are searchable in natural language. Relevant parts can be recalled when needed instead of loading an entire transcript. The system retrieves, reranks, and summarizes the relevant portion, typically using a few hundred tokens.
Files: Documents you upload and can query alongside your memories, from any connected tool, without re-uploading each time.
Buckets: Memories can be grouped by project, client, or area of life. The relevant bucket can then load automatically in the AI tool you are using.
Smart Memory: Automatically selects the information that is relevant to the current conversation, reducing the number of tokens compared with loading a full memory set.
Ask: A single interface to query your memories, chat history, and files together in plain language, with citations back to the source.
What you can do with it
Search across years of chats: Ask a question such as "what did we decide about the API migration timeline" and get an answer based on your actual conversation history, with a link back to the source.
Search from inside any AI site: Press Cmd+Shift+K, or Ctrl+Shift+K on Windows and Linux, on Claude, ChatGPT, or another supported tool to open an in-place search interface.
Extract structured output from any past conversation: Generate summaries or pull out decisions, action items, rejected ideas, technical specifications, and other useful details from old conversations.
Correct anything in one place: Fix a stale memory once, and the correction applies everywhere, instead of updating the same fact separately inside ChatGPT, Claude, and Gemini.
Import your existing memories: Sync information already stored in AI tools into MemoryPlugin rather than rebuilding your memory from scratch.
Keep memory updated automatically: The browser extension continues syncing new conversations after the initial import.
MemoryPlugin Pros and Cons
MemoryPlugin Pricing
Core: $89/year: Includes cross-AI memory, Smart Memory, buckets, and Chat History for your last 500 conversations.
Pro: $180/year: Removes the history limit and adds conversation summaries, monthly insights, and shared buckets.
Every plan starts with a 7-day trial.
Why Choose MemoryPlugin
MemoryPlugin is designed for people who already use ChatGPT, Claude, Gemini, and other AI tools and want the same memory to follow them between platforms. It requires no coding and gives you more visibility and control over what is stored.
2. Supermemory
Pricing: Free, Pro $19/mo, Max $100/mo, Scale $399/mo | Free tier: ~$5/month of usage included | Platforms: Personal Supermemory, MCP, Python, TypeScript, REST API
Supermemory now serves two related use cases. Personal Supermemory is designed to give one memory layer across the AI tools you use, including Claude, ChatGPT, and Cursor. Supermemory API is the developer product for adding persistent memory and context infrastructure to your own AI applications and agents.
For personal users, the main idea is simple: save your context once and make it available across the AI tools you use. Supermemory describes Personal Supermemory as a shared memory layer that works across AI tools and lets users export their memories as files.
For developers, Supermemory provides APIs and infrastructure for storing, retrieving, and operating on agent context. Its current platform includes memory graphs, retrieval, search, and traversal, and composable operations, all billed based on usage.
Top Features
Personal memory: Keeps a shared memory layer that can be used across multiple AI tools.
MCP: Lets compatible AI clients access Supermemory as an external memory source.
Memory graph: Builds user profiles and fact hierarchies so agents can retrieve structured context.
Connectors: Google Drive, Notion, and OneDrive are included on Pro. Gmail and Granola are added on Max. Scale adds GitHub, S3, and Web Crawler.
Self-hosting: Available on Scale and Enterprise, including air-gapped deployments.
Supermemory Pros and Cons
Supermemory Pricing
Free: $0/month, with approximately $5/month of usage included. Includes the Hermes Plugin, Supermemory MCP, and community support.
Pro: $19/month, with approximately $20/month of usage included. Adds unlimited storage and users, Google Drive, Notion, and OneDrive connectors, two teammates, additional plugins, and email support.
Max: $100/month, with approximately $130/month of usage included. Adds Gmail and Granola connectors, along with priority support.
Scale: $399/month, with approximately $600/month of usage included. Adds all connectors, up to 10 teammates, spend caps, SOC 2 and HIPAA BAA, priority support, and a self-hosted option.
When to Pick Supermemory
Pick Supermemory when you want one memory layer across the AI tools you personally use, or when you are a developer who wants to build persistent memory into an AI application. Personal Supermemory is the more relevant part for individual users, while the API is intended for developers building their own products.
3. Mem0
Pricing: Free / Starter $19/mo / Pro $249/mo / Enterprise custom | Free tier: 10,000 add requests and 1,000 retrieval requests per month | Platforms: Python, TypeScript, REST API, self-hosted
Mem0 is a memory layer for AI applications and agents. Instead of building your own system for storing, updating, and retrieving user context, you can send conversations or other information to Mem0 and retrieve relevant memories when the agent needs them. Mem0 is designed to persist memory across sessions, tools, and runs.
There are two ways to use it. The Mem0 Platform is the managed version, where Mem0 handles the infrastructure. The open-source version can be used as a library inside your application or deployed as a self-hosted server, giving you control over the underlying infrastructure and data.
Top Features
Persistent memory: Stores information that can be retrieved across sessions and runs instead of keeping everything inside a single conversation.
Memory extraction and updates: Mem0 processes incoming information and turns useful details into memories, updating existing information when appropriate.
User and agent scoping: Memories can be associated with users and other identifiers so applications can retrieve the appropriate context for a specific user or agent.
Graph memory: The Pro and Enterprise plans include graph memory for linking entities and relationships.
Multiple deployment options: Developers can use the hosted Platform, the Python or TypeScript libraries, or the self-hosted server.
Mem0 Pros and Cons
Mem0 Pricing
Hobby: Free, with 10,000 add requests and 1,000 retrieval requests per month.
Starter: $19/month, with 50,000 add requests and 5,000 retrieval requests per month.
Pro: $249/month, with 500,000 add requests and 50,000 retrieval requests per month. It also includes unlimited projects, multi-project support, advanced analytics, and graph memory.
Enterprise: Custom pricing, with unlimited add and retrieval requests, graph memory, Dream memory consolidation, on-prem deployment, audit logs, SSO, and custom integrations.
When to Pick Mem0
Pick Mem0 when you are building an AI application or agent and want persistent memory without designing the entire memory system yourself. It is especially attractive when you want the choice between a managed service and an open-source implementation you can run yourself.
4. Basic Memory
Pricing: Free and open source locally | Cloud from $15/seat/month | Free Trial: 7-day cloud trial | Platforms: Local files, MCP, Obsidian, VS Code, ChatGPT, Claude, Codex, Cursor, Gemini CLI, web, desktop, mobile
Basic Memory is a personal knowledge base for AI built around something deliberately simple: your memory stays in plain Markdown files that you own. Instead of putting memories into a proprietary database that only one product can access, Basic Memory turns your notes into a connected knowledge base that both you and your AI assistants can read, search, and update.
It can run locally for free, with the files and indexing kept on your own machine, or through Basic Memory Cloud when you want synchronization, web access, collaboration, and remote access from multiple devices and AI tools.
The key difference from most AI memory products is that Basic Memory treats the knowledge base itself as the product. The underlying information remains readable Markdown, while observations and relations turn those notes into a searchable knowledge graph.
Top Features
Markdown-based memory: Notes are stored as standard Markdown files that you can edit with a normal text editor, manage with Git, back up yourself, or open in Obsidian.
Knowledge graph: Observations and relations connect notes into a structured graph that AI assistants can navigate instead of relying only on text similarity.
MCP integration: AI assistants can search, read, write, and organize the same knowledge base through MCP. Basic Memory currently documents integrations for ChatGPT, Claude, Codex, Cursor, Gemini CLI, VS Code, Obsidian, and other MCP-compatible tools.
Local and cloud modes: The open-source local version keeps files and the MCP server on your machine. Basic Memory Cloud adds hosted access and synchronization across devices and tools.
Obsidian integration: Because the knowledge base uses standard Markdown and wiki links, you can open the same files in Obsidian and use graph view, backlinks, tags, and normal editing alongside your AI workflows.
AI editing: Your assistant can create and update the same notes you edit yourself, so the knowledge base is not just something the AI reads. It can become a shared workspace between you and the AI.
Basic Memory Pros and Cons
Basic Memory Pricing
Local: Free and open source under AGPL-3.0. Your knowledge base can remain entirely on your own machine.
Team: $15/seat/month. Includes AI collaboration through MCP, shared workspaces, full-text search, desktop, mobile, web access, private Markdown files, and version history.
Business: $30/seat/month. Adds features such as SSO/SAML, audit logs, administrative and security controls, and priority support.
Agent Infrastructure: Custom pricing for agent fleets, metered usage, custom deployments, enterprise requirements, and on-premises use.
Every cloud plan starts with a 7-day trial.
When to Pick Basic Memory
Pick Basic Memory when you want AI memory that remains yours, readable outside the AI product, and usable across multiple assistants. It is particularly strong for people who want a persistent knowledge base rather than an invisible collection of memories managed entirely by a vendor. The local, Markdown-first approach is also a major advantage when portability and control matter
5. Zep
Pricing: Free / Flex $125/mo / Flex Plus $375/mo | Free tier: 10,000 credits/month | Platforms: Python, TypeScript, Go, REST API, MCP
Zep is an agent memory and context engineering platform built around a temporal knowledge graph. You send Zep chat history, business data, documents, or structured data, and it builds a graph of the entities, relationships, and facts contained in that information. As facts change, Zep can update the graph while preserving the historical context.
The important distinction is that Zep is not simply a database for storing chat history. Its Memory API retrieves relevant context from a user's accumulated history and returns it in a form that can be inserted into an agent's prompt. The same underlying graph can also be searched directly when an application needs more control over retrieval.
Zep can ingest live conversations as well as historical documents, transcripts, emails, Slack exports, and other data. Each user can have multiple threads associated with a user-level graph, allowing context to carry across separate conversations.
Top Features
Temporal knowledge graph: Zep represents entities, relationships, and facts in a graph and tracks how those facts change over time. Older facts can be invalidated while their history remains available.
Prompt-ready memory: The Memory API can turn relevant information from the user's history into a context block that can be added directly to an agent's prompt.
Multiple data sources: Zep can ingest chat, business data, documents, JSON, email, Slack exports, and other application data rather than limiting memory to conversations.
Cross-session user memory: A user can have multiple threads while maintaining a shared user-level graph, allowing relevant context to carry between conversations.
Graphiti: Zep's Context Graph is powered by Graphiti, its open-source temporal knowledge graph framework. Developers can also use Graphiti independently of the Zep platform.
Agent integrations: Zep provides integrations for agent frameworks including LangGraph and supports development through Python, TypeScript, and Go SDKs.
Zep Pros and Cons
Zep Pricing
Free: $0/month with 10,000 credits per month, 2 projects, 1 Memory MCP Server seat, and 5 custom entity and edge types. Free credits do not roll over.
Flex: $125/month, or $104/month when billed annually. Includes 50,000 credits per month, 5 projects, 5 Memory MCP Server seats, 10 custom entity and edge types, and unlimited memories, retrievals, and users. Additional credits are charged at $25 per 10,000 credits.
Flex Plus: $375/month, or $312/month when billed annually. Includes 200,000 credits per month, 10 projects, 15 Memory MCP Server seats, observations, 20 custom entity and edge types, custom extraction instructions, webhooks, analytics, and longer API log retention. Additional credits are charged at $75 per 40,000 credits.
Enterprise: Custom pricing with negotiated credits, guaranteed rate limits and SLA, unlimited projects, custom MCP seats, SOC 2 Type II, HIPAA BAA, audit and API logs, and dedicated support.
Zep charges credits based on the size of each Episode you send. An Episode can be a chat message, JSON payload, document chunk, or other block of data. Storage, retrieval, and users are not separately metered.
When to Pick Zep
Pick Zep when your agent needs to understand how information changes over time, not just retrieve similar text. It is particularly well suited to applications that maintain ongoing user, customer, account, or business context across multiple interactions.
6. Pieces
Pricing: Pro $18.99/user/month | Enterprise $22.99/user/month| Free Trial: Available | Platforms: Windows, macOS, Linux, IDEs, browser, MCP
Pieces is a long-term memory and workflow context platform built around the idea that AI should remember what you actually did, not just what you explicitly saved in a chat. It runs in the background on your computer, captures relevant activity from supported apps, and turns that activity into searchable context that can be used later.
Its main product is the Long-Term Memory Engine, which works with Pieces Desktop and PiecesOS to capture, organize, and retrieve context across your workflow. Pieces can remember things such as research, conversations, code, decisions, and other activity, then let you search that history by time, topic, person, or application.
A key difference is that Pieces is local-first. Memory is stored on the device by default, and the company says its core memory creation, retrieval, tagging, and summarization can run locally. Cloud connectivity is optional for supported features.
Pieces also exposes its Long-Term Memory through an MCP server, allowing compatible AI assistants such as Claude, Cursor, Codex, and other MCP clients to retrieve relevant context from your past work.
Top Features
Long-Term Memory: Pieces continuously builds a searchable memory of your workflow, allowing you to recover context from previous work rather than starting from scratch.
Workstream Activity: The Pieces Desktop App provides a chronological view of recent activity, including tasks, references, decisions, and other workflow events.
Cross-tool context: Pieces can capture context from supported browsers, IDEs, email, chat, meetings, and other applications, creating a unified history of your work.
MCP integration: The Pieces MCP Server lets AI assistants retrieve relevant memories from Pieces directly inside their existing workflows.
Local-first architecture: Memory is stored on-device by default, with controls to pause capture and delete information by application, website, time period, or capture method.
Time-aware retrieval: Pieces is designed to answer questions grounded in when something happened, such as what you were working on yesterday or where a particular decision came from.
Audio capture: Current releases also support optional audio capture for meetings and calls. Pieces says audio is processed locally and raw audio is not stored.
Pieces Pros and Cons
Pieces Pricing
Pro: $18.99/user/month, billed monthly. Annual billing saves 25%. Includes Long-Term Memory saved on-device, capture across apps and websites, summaries and recaps, and access to your history through MCP-ready AI tools.
Enterprise: $22.99/user/month, billed monthly, or $69.99/user/quarter. Includes everything in Pro, plus organization-wide capture and feature controls, approved AI providers, and support for your own API keys.
A card is required to start the Pro 7-day free trial, and you can cancel before the trial ends.
When to Pick Pieces
Pieces is a good fit for developers and knowledge workers who want one memory layer across coding, browsing, and communication tools. It is less suitable if you want a free product or do not want an always-on background capture system.
7. Cognee
Pricing: Free self-hosted | Cloud: Free, then $2.50 per 1M tokens + $5 per additional workspace/month | Platforms: Python, Rust, TypeScript, MCP, REST API
Cognee is an open-source memory engine for AI agents that turns documents, conversations, and other data into structured, connected memory. Its core idea is to combine a knowledge graph with vector-based retrieval so agents can work with both relationships between entities and semantic similarity.
Cognee can run locally or be self-hosted on your own infrastructure. Its managed cloud provides the same memory engine without requiring you to operate the underlying infrastructure. The current version also supports sharing the same memory across MCP-compatible agents, so different tools can work from the same project context.
A notable change in Cognee 1.0 is its simplified production architecture. The memory layer can now run on a single PostgreSQL instance, combining graph, vector, session, and metadata storage instead of requiring a separate graph database.
Top Features
Knowledge graph: Cognee extracts entities and relationships from your data so agents can retrieve connected information rather than relying only on similar text.
Hybrid retrieval: Combines graph-based retrieval with vector search to handle both relationship-heavy and semantic queries.
Persistent agent memory: Context can survive across sessions, allowing an agent to continue working from previous state instead of starting over.
Shared MCP memory: Claude Code, Cursor, and other MCP-compatible clients can read and write to the same Cognee memory layer.
Self-hosting: The open-source engine can run locally or on your own infrastructure, giving teams control over deployment and data boundaries.
Cloud option: Cognee Cloud provides a fully managed version with shared agent memory, integrations, and infrastructure handled for you.
Self-improving memory: Cognee Cloud can reinforce frequently used or corrected knowledge and prune information that is no longer useful.
Cognee Pros and Cons
Cognee Pricing
Open source: Free to self-host. You pay for your own infrastructure, model usage, and other underlying services.
Cloud Free: $0/month, with 1 million tokens included, 1 workspace, unlimited users, unlimited API calls, and agent integrations including Claude Code, Codex, and MCP. No credit card is required.
Cloud Standard: $2.50 per 1 million tokens processed, plus $5/month for each additional workspace. It includes the features in Free plus data-source integrations such as Slack, Notion, and Google Drive, and in-app support.
Enterprise: Custom pricing. Includes dedicated support, a dedicated support engineer, bring-your-own-cloud deployment, and a support SLA.
When to Pick Cognee
Pick Cognee when you want agent memory built around connected knowledge rather than a simple list of stored memories. It is particularly useful when your application needs to reason across relationships between documents, entities, conversations, and other data.
How to Choose Between Them
Building your own AI agent or app: Supermemory, Mem0, Letta, Zep, and Cognee are the main options. Mem0 is the broadest and most open. Letta is better suited to agents that manage their own memory over long periods. Zep is strongest when time and changing facts matter. Cognee is designed for data with rich relationships. Supermemory stands out for retrieval speed and built-in productivity connectors.
Using ChatGPT, Claude, and Gemini every day: MemoryPlugin is built for this use case. It can be set up without writing code and gives you one memory layer across the AI tools you already use.
A developer who wants memory tied to your coding environment: Pieces.
Your AI Memory Should Work Everywhere, Not Just One Platform
Most of these tools focus on a specific problem. Mem0 is a memory API. Letta is an agent framework. Zep is a temporal graph. Cognee is a knowledge graph. Supermemory is a high-speed retrieval engine. Pieces is a developer workflow tool.
MemoryPlugin takes a different approach. It is designed for people who use multiple AI tools and want their context to follow them between platforms.
For that use case, MemoryPlugin is the cleanest fit.
