What Is the Best AI Agent Memory for Make Workflows?

The best AI agent memory for Make workflows depends on where your agents' context needs to travel. If everything lives inside Make, Data Stores or a stable Conversation ID per thread covers most cross-run state. If your agents also work outside Make, in Claude, Codex, or scheduled tools, you need a memory layer that follows them across tools, which no Make-native store can do.

What are the memory options for Make AI workflows?

Make workflows have seven practical memory options: Make Data Stores for simple key-value state, a stable Conversation ID on the AI agent module for within-thread continuity, Knowledge files for reference material the agent can search, a Google Sheet or Airtable base as a DIY state store, a vector database reached over HTTP for semantic recall, Mem0 for self-hosted memory over MCP, and Vilix AI for shared memory across every tool your agents run in.

Option 1: Make Data Stores

Data Stores are Make's built-in key-value storage. One module writes a value, a later module reads it, even in a different scenario run. It is native, and for the classic pattern, the last processed timestamp, a run counter, a deduplication flag, it takes minutes to wire up.

The limit is shape and scope. You look up values by keys you already know, so there is no retrieval by meaning, only by exact key. Schema discipline is yours: what gets written, when it goes stale, what overwrites what. And it is Make-only. The moment the same agent does work in Claude or a scheduled script, that memory is behind a wall it cannot cross.

Who should pick this: small, well-defined state that never leaves Make, like the last processed row ID or a retry counter.

Option 2: a stable Conversation ID on the AI agent module

Make's AI agent module takes a Conversation ID. Leave it blank and every run creates a fresh agent identity that knows nothing. Give each thread a stable ID, per customer, per ticket, per run series, and the agent keeps continuity within that thread: follow-ups reference what was said before, context carries across executions.

The honest catch: the continuity dies at the agent's boundary. A second scenario with its own agent and its own thread starts from zero again, even if it is working on the same customer. You also own the IDs: generating them, storing them, keeping them consistent. Blank field, blank memory is a sharp default.

Who should pick this: customer-facing agents where one thread equals one conversation, and the memory only needs to last within that conversation.

Option 3: Knowledge files

Make's AI agent supports knowledge files, a native RAG layer of uploaded documents the agent can search during a run. Great for stable reference material: the refund policy, the product catalog, the escalation rules, the tone-of-voice guide.

They are frozen, not living. A knowledge file cannot record what happened in a run, because a run's output is not an uploaded document. It is retrieval for what you knew before the run, not memory of what the agent learned during it.

Who should pick this: agents whose context is mostly static documentation, updated on your schedule rather than by the agent.

Option 4: Google Sheets or Airtable as the state store

The classic operator pattern: the scenario writes a row at the end of each run and reads the latest row at the start of the next. Human-readable, easy to audit, and the business already trusts the sheet. For state that people want to open and inspect, this is still the fastest path.

The tradeoff is retrieval discipline. The agent only gets what you explicitly look up, so the write policy, what to store, in what format, under what key, is the entire system. Sheets get slow and fragile past a few thousand rows. Airtable is nicer, but you still own every decision about what persists and what expires.

Who should pick this: teams that want a memory they can open and read, and whose state is small and structured.

Option 5: a vector database reached over HTTP

When the recall need is semantic, find the support answer most like this ticket, search past decisions by meaning, teams wire Pinecone, Weaviate, Qdrant, or Supabase into their scenarios with HTTP modules. The agent embeds and retrieves by meaning instead of exact keys.

The tradeoff is operational: embeddings, chunking, retrieval tuning, and the write policy are all yours. Most teams end up running two stores, an exact one for state and a vector one for knowledge, plus the discipline that keeps both from rotting. Inside Make this also means every recall is a module call you pay operations for.

Who should pick this: agents that must recall from large, messy knowledge bases rather than small structured state.

Option 6: Mem0

Mem0 is the open-source category leader for agent memory, with user and agent scoping and an MCP server. Its real strength is the self-host path: if memory must live inside your own infrastructure, Mem0 is the option the others cannot match. And because Make's AI agent module accepts MCP tools, a Mem0 server can plug in as tools the agent calls at runtime.

The tradeoff is that you run it: the deployment, the upgrades, the retrieval tuning. Pick it for the self-host capability, not to avoid operating a memory system.

Who should pick this: teams with a hard self-hosting requirement or an existing Mem0 deployment.

Option 7: Vilix AI

Vilix AI is a cloud-hosted memory layer that follows your agents across every tool, not just Make. Connect each AI client to the same Vilix AI account over MCP, and the same context, decisions, and full conversation history travel with you into Claude, Codex, Cursor, OpenClaw, Hermes, scheduled agents, and anything else that speaks MCP. Make's AI agent module accepts MCP tools, so the memory can sit one level above your scenarios: the agent reads recent memory at run start and writes a summary at run end, across scenarios, not inside one. Retrieval is semantic plus keyword search over a vector database, so recall finds what you meant, not just what you typed. There is a dashboard at app.vilix.ai, you can export everything in a portable format or wipe the account instantly, and data is isolated per user.

The honest positioning: if your agents live entirely inside Make, Data Stores or a stable Conversation ID are simpler and cheaper. Vilix AI earns its place when the same operator runs agents in several tools and is tired of re-briefing each one, because no Make-native store can carry context into Claude or a scheduled job. It is cloud-hosted, so you manage zero infrastructure.

Who should pick this: operators whose agents span Make plus other AI tools and who want one memory everywhere instead of a memory per tool.

So which one should you actually use?

Start with Data Stores for small state and a stable Conversation ID for customer-facing agents. Move to a sheet when the state needs eyes on it. Reach for Mem0 when compliance says self-host. Reach for a cross-tool layer when the pain stops being "my scenario forgot" and starts being "I re-brief every agent, every morning."

Frequently asked questions

Can Make scenarios remember data between runs?

Yes, with a store the next run reads: a Data Store, a Google Sheet, or an external memory service. Make scenario runs are stateless by default, so memory is always an explicit write-then-read pattern, never automatic.

Does the Conversation ID in Make's AI agent give the agent long-term memory?

It gives continuity within one thread, not durable cross-agent memory. A stable ID per thread lets follow-up runs reference what was said before. A second scenario, or a blank ID, starts from zero. For memory that spans scenarios and tools, you need a memory layer outside Make.

What is the best memory for Make agents that also work in other tools?

A cross-tool memory layer connected over MCP, so the same context follows the agent into Claude, Codex, and scheduled jobs. Make's AI agent module accepts MCP tools, which is exactly how such a layer attaches. No Make-native store can do this, because they are all scoped to Make.

Do I need a vector database for Make agent memory?

Only when recall is semantic rather than exact. For known values like the last processed timestamp, a Data Store is faster and cheaper. For fuzzy recall across a knowledge base, use a vector database.