How to Give Your AI Agent Long-Term Memory

Quick Answer

AI agents with long-term memory can remember your preferences, recall past conversations, and build up knowledge over time. In OpenClaw, memory works by storing important information from your interactions and loading relevant context into each new conversation. You can configure memory to remember everything automatically, or teach your agent to remember specific things on command. The result is an agent that gets better and more personalised the more you use it.

Why Memory Matters

Without memory, every conversation with your agent starts from zero. You ask the same questions, give the same instructions, and provide the same context over and over. The agent does not know your name, your preferences, your ongoing projects, or anything you discussed yesterday.

With memory, your agent builds a persistent understanding of who you are and what you need. It remembers that you prefer concise responses. It knows you are working on a product launch in April. It recalls that your team uses Notion for project management and Slack for communication. It remembers that you asked about flight prices to Tokyo last week and can follow up without you re-explaining the context.

Memory transforms an agent from a stateless tool into a personalised assistant that actually knows you.

How Agent Memory Works

Agent memory is conceptually simple but technically interesting. There are two main types: conversation memory and long-term knowledge memory.

Conversation memory is the short-term context within a single chat session. The model sees your current conversation and uses it to give relevant responses. This is what all AI models do by default. The limitation is the context window — once the conversation exceeds the model's context length, older messages get dropped.

Long-term memory persists across sessions. When you start a new conversation tomorrow, your agent can recall relevant information from today's conversation and from weeks or months ago. This is what most people mean when they talk about agent memory.

Long-term memory in OpenClaw works through a store-and-retrieve pattern. During conversations, important information gets extracted and saved to a memory store. When a new conversation starts, the agent searches its memory for context relevant to the current topic and loads it into the conversation. This happens automatically — you do not need to tell the agent what to remember or what to recall.

Types of Things Your Agent Can Remember

Facts about you. Your name, role, company, location, timezone, communication preferences, and personal details you share in conversation. Once you tell your agent you live in Manchester, it should remember that for all future interactions.

Preferences and instructions. "I prefer bullet points for summaries." "Always include the source when you cite a statistic." "Do not schedule meetings before 10am." These ongoing instructions get stored and applied to future conversations without you repeating them.

Project context. Ongoing projects, deadlines, team members, and decisions you have discussed. Your agent can maintain a running understanding of what you are working on and provide relevant suggestions.

Past interactions. What you asked about previously, what the agent researched for you, what tasks have been completed, and what is still pending. This creates continuity between sessions.

Configuring Memory in OpenClaw

OpenClaw supports several memory backends, and the right choice depends on your setup and needs.

The simplest option is file-based memory. OpenClaw stores memories as text files that it reads when starting new conversations. This works well for personal use and keeps everything local and transparent — you can literally open the memory files and see what your agent remembers.

For more sophisticated setups, vector database memory uses embeddings to store and retrieve memories based on semantic similarity. When a new conversation starts, the agent encodes the current topic as an embedding and searches for semantically related memories. This is more powerful for large memory stores because it finds relevant memories even when the exact words do not match.

Popular vector database options include Qdrant, ChromaDB, and Pinecone. If you are running locally, ChromaDB is the easiest to set up. If you are running on Tulip, the platform handles the memory infrastructure for you.

Teaching Your Agent What to Remember

While automatic memory extraction works for most cases, you can also explicitly teach your agent. Simply tell it: "Remember that the Q2 budget is £45,000" or "Remember that Sarah prefers email over Slack." Good agents will confirm they have stored this information and recall it when relevant.

You can also tell your agent to forget things: "Forget the project timeline I mentioned yesterday — it has changed" or "Clear what you know about the Henderson account." Having control over what the agent remembers and forgets is important for keeping memory accurate and up to date.

In the SOUL.md file, you can set guidelines for what your agent should automatically remember. For example: "Always remember the names and roles of people I mention. Always remember deadlines and project milestones. Do not remember casual small talk." This helps the agent be selective about what it stores, keeping memory focused and useful.

Memory and Model Choice

Your choice of model affects how well memory works. Models with larger context windows can load more memories into each conversation, giving the agent more context to work with. Llama 4 Scout with its 10 million token context is particularly well-suited for memory-heavy agent setups because it can hold vast amounts of recalled memory alongside the current conversation.

Model quality also matters for memory extraction. The agent needs to correctly identify what is worth remembering from a conversation. Larger, more capable models are better at this — they pick up on subtle details and important facts more reliably than smaller models.

If you are running locally with a smaller model, keep your memory store focused and concise. The smaller your context window, the more important it is that retrieved memories are highly relevant and not padded with unnecessary detail.

Privacy and Memory

Memory raises important privacy considerations. Your agent's memory store contains personal information, preferences, and potentially sensitive details from your conversations. Think about where this data is stored and who has access.

If you are running locally, memory stays on your machine. Full privacy, full control. If you are running on a cloud platform, your memory is stored on their infrastructure. Tulip runs on renewable-powered infrastructure with data privacy as a priority, but you should understand the data handling of whatever platform you choose.

You should also think about shared agents. If multiple people interact with the same agent, the memory store will contain information from all of them. For team agents, consider whether memories should be shared or siloed per user.

Common Memory Issues and Fixes

The most common issue is memory pollution — when the agent remembers outdated or incorrect information and applies it to new conversations. The fix is regular memory maintenance. Periodically review what your agent remembers and correct or remove anything outdated.

Another issue is memory overload. If the agent tries to recall too many memories for every conversation, it can slow down responses and confuse the model with too much context. Set limits on how many memories the agent retrieves per conversation, and prioritise recency and relevance.

Finally, some people find that their agent remembers things they would rather it forgot. Make sure you know how to clear memory — both specific items and the entire store — and do not hesitate to use this capability.

Frequently Asked Questions

Does memory make my agent slower?

Slightly. Retrieving memories adds a small delay to the start of each conversation — typically less than a second. The added context also means the model processes more tokens per response. In practice, the delay is barely noticeable and the improved response quality more than compensates.

How much storage does memory use?

Very little. Text-based memories are tiny. Even a year of daily interactions would likely produce less than 100MB of memory data. Vector databases add some overhead for the embeddings, but storage is rarely a concern.

Can I export my agent's memory?

With file-based memory, your memories are just text files you can copy anywhere. With vector database memory, most databases support data export. Your memories are your data — you should always be able to take them with you.

Does memory work across different channels?

Yes. In OpenClaw, memory is shared across all messaging channels. Something you discuss on WhatsApp is available when you chat through Telegram or Slack. This creates a unified experience regardless of which channel you use.