Memori vs Vilix AI: Which Memory Layer Should Your Agent Use?
Memori vs Vilix AI: Which Memory Layer Should Your Agent Use? The short answer: Memori is agent-native memory infrastructure you integrate into an agent you build: it captures what the agent does (tool calls, decisions, outcomes), stores it in SQL you control, and it is open source. Vilix AI is a hosted memory service your tools and agents share over MCP: one account, zero infrastructure, full conversation history plus work state across every connected client. The deciding question is who opera
Memori vs Vilix AI: Which Memory Layer Should Your Agent Use?
The short answer: Memori is agent-native memory infrastructure you integrate into an agent you build: it captures what the agent does (tool calls, decisions, outcomes), stores it in SQL you control, and it is open source. Vilix AI is a hosted memory service your tools and agents share over MCP: one account, zero infrastructure, full conversation history plus work state across every connected client. The deciding question is who operates the memory: you, or the service.
What is Memori?
Memori (GibsonAI/memori, formerly MemoriLabs) is an open-source memory layer for production AI agents, licensed Apache 2.0, with roughly 15,600 GitHub stars. Its core idea is that memory should come from what agents do, not just what they say.
In practice that means Memori wraps your existing LLM client and records agent execution: the tool calls made, the decisions taken, the workflow steps run, the outcomes produced. A background process ("Advanced Augmentation") turns that raw execution trace into structured memory: attributes, events, facts, people, preferences, relationships, rules, and skills, tracked at entity, process, and session levels. Recall happens automatically; you do not write search calls in your hot path.
Memori is LLM-agnostic (OpenAI, Anthropic, Bedrock, DeepSeek, Gemini, Grok) and ships adapters for Agno, LangChain, and Pydantic AI. Storage is SQL-native: bring your own database (Postgres, MySQL, SQLite, TiDB, and others) and the data stays on your infrastructure, or use Memori Cloud with an API key. There is also an MCP server so coding tools like Claude Code, Cursor, and Codex can use it, plus a plugin for OpenClaw and a memory provider for Hermes agents. Install is pip install memori or npm install @memorilabs/memori.
What is Vilix AI?
Vilix AI is a hosted memory and work-state layer shared across separately connected MCP clients, tied to one account. Plan in Claude, build in Codex, and your context, rules, and tasks come with you.
Each AI client connects to the same Vilix AI account (OAuth, or an API key as a Bearer header to https://api.vilix.ai/mcp for headless agents). get_context loads relevant saved context into the conversation; save_turn saves the exchange. Retrieval is semantic plus keyword, and it selects relevant context rather than dumping the whole archive. What gets stored is the full conversation history, not just extracted facts, alongside projects, tasks, personal rules, and reusable agent skills. Data is exportable in a portable format anytime and deletable instantly. Free plan forever, with a 7-day Pro trial and no credit card. Learn more
Memori vs Vilix AI: the honest comparison
| Memori | Vilix AI | |
|---|---|---|
| What it captures | Agent execution: tool calls, decisions, workflow steps, outcomes, plus conversation | Full conversations across tools, plus work state: projects, tasks, rules, skills |
| How you add it | Wrap your LLM client with the SDK, use a framework adapter, plugin, or MCP server | Connect each client to one account over MCP (OAuth or API key) |
| Who operates it | You: self-host on your own SQL database, or use Memori Cloud | The service: cloud-hosted, zero infrastructure on your side |
| License and custody | Apache 2.0; BYODB keeps data on your infrastructure | Hosted service; portable export and instant delete |
| Multi-tool memory | MCP server path per tool | Same memory shared across all connected clients by design |
| Recall style | Automatic background recall of structured execution memory | get_context retrieval: semantic plus keyword, recency-aware |
| Best fit | Builders who want execution-level memory inside their stack | People and teams who want one memory across every tool and agent |
Who should pick Memori?
Pick Memori if the memory you need is about what your agent did. When an agent runs a fifty-step workflow and you need to know which tool call produced which decision three sessions later, execution capture is the right primitive, and Memori is built exactly for that. Pick it if you want your memories in a SQL database you already run, with standard backup, audit, and migration tooling. Pick it if open source matters to your stack: you can read the code, self-host it, and keep full custody. And pick it if you are already building on LangChain, Agno, or Pydantic AI, where the adapters drop in with minimal glue.
Who should pick Vilix AI?
Pick Vilix AI if your memory problem spans tools rather than living inside one agent. When the same work moves between Claude, Codex, Cursor, ChatGPT, and a headless agent on a schedule, no single SDK wrapper covers all of them; a shared hosted memory that every client reads and writes does. Pick it if you do not want to operate anything: no database to provision, no augmentation pipeline to keep running, no keys to rotate across services. Pick it when the thing being remembered is the work itself: full conversations you can revisit, decisions with their reasoning intact, projects and tasks that persist across sessions. Agencies running agents per client get per-client memory without per-client infrastructure.
A built agent connects to Vilix AI with an API key as a Bearer header to https://api.vilix.ai/mcp and gets full memory with zero memory infrastructure. The difference between the two is who operates the memory, not who writes the agent: Memori is memory infrastructure you run, Vilix AI is memory as a service you connect to.
How to decide in five minutes
- Ask where the memory has to live. Inside one agent's codebase, or across every tool you and your agents use?
- Ask what has to be remembered. Execution traces of tool calls and decisions, or full conversations plus work state?
- Ask who operates it. Do you have a database and the appetite to run the memory layer, or do you want it handled?
- If the answers are "one agent," "execution traces," "me": Memori.
- If the answers are "every tool," "conversations and work state," "the service": Vilix AI.
Frequently asked questions
Does Memori work with n8n? Memori ships an MCP server and third-party n8n nodes exist, but its primary integration path is the SDK inside agent code, not workflow orchestration.
Can a headless scheduled agent use Vilix AI? Yes. A built agent authenticates with an API key as a Bearer header to https://api.vilix.ai/mcp and gets the same shared memory as interactive tools.
Is Memori really open source? Yes, Apache 2.0. You can self-host with your own database (BYODB) or use Memori Cloud.
Which one keeps full conversation history? Vilix AI stores full user/assistant exchanges, not just extracted facts, so the real conversation can be revisited anytime.