How Agent Memory Works

Every Agento agent has long-term memory built in. Your agent automatically remembers important facts from your conversations and recalls them when relevant, across all sessions and channels (web chat, Telegram, API, and inter-agent messaging).

This works differently from standard OpenClaw, where agents only remember things within a single session or when you manually tell them to save something.

The Two-Step System

Agento's memory runs in two steps on every conversation turn, without any action from you.

Step 1: Recall (Before Every Response)

Before your agent starts thinking about its reply, it searches its long-term memory for anything relevant to what you just said.

If it finds matching memories, they are quietly added to the agent's context. Think of a colleague glancing at their notes before answering your question. Your agent doesn't announce this; it simply has the right context available.

For example, if you previously discussed preferring TypeScript over JavaScript, and today you ask your agent to write some code, it will recall that preference and use TypeScript without being told again.

Recall is fast and non-blocking. If the memory search takes too long (more than a few seconds), your agent skips it and responds normally. Memory never slows down your agent.

Step 2: Capture (After Every Response)

After your agent replies, it analyzes the conversation exchange and extracts any meaningful facts worth remembering. These facts are stored in your agent's long-term memory for future recall.

Not everything gets captured. The system filters out low-value exchanges automatically:

• Short greetings ("hi", "thanks", "ok")
• Tool-only responses (when the agent just ran a command)
• Trivial back-and-forth with no new information
• Large data dumps (code blocks, JSON outputs, logs)

This keeps your agent's memory focused on what actually matters: preferences, decisions, project details, important context.

Capture runs in the background. It does not delay your agent's response. You get your answer immediately; fact extraction happens after.

What Gets Remembered

The memory system extracts structured facts, not raw conversation logs. Examples of things your agent will remember:

• Preferences: "User prefers dark mode", "Always use yarn instead of npm"
• Project context: "Main project is a Next.js app deployed on Vercel"
• Decisions: "Decided to use PostgreSQL instead of MongoDB"
• Personal details: "User's timezone is UTC+7", "Company name is Acme Corp"
• Technical context: "Production server runs Ubuntu 22.04", "API uses JWT authentication"

Over time, your agent builds a growing understanding of you, your projects, and your preferences.

How It Differs from Standard OpenClaw

Standard OpenClaw agents have file-based memory: markdown files like MEMORY.md and daily logs that the agent reads at the start of each session. This works well but has limitations:

	Standard OpenClaw	Agento Memory
Storage	Markdown files on disk	Semantic vector database
Recall	Loads files at session start	Searches relevant facts before every turn
Capture	Agent must explicitly write to files	Automatic fact extraction
Search	Keyword + local embeddings	Semantic similarity search
Cross-session	Only if written to disk	Automatic
Swarm sharing	Manual file coordination	Built-in shared memory

On Agento, both systems work together. Your agent still has its file-based memory (MEMORY.md, daily logs), and the automatic memory system adds a semantic layer on top.

Swarm Shared Memory

When your agent is part of a swarm, memory becomes collaborative. Agents in the same swarm can recall each other's memories automatically.

If Agent A learns that your deployment target is AWS, Agent B in the same swarm will also know this without you telling it separately.

Each agent can only write its own memories. An agent cannot create memories pretending to be another agent. But reading is shared across the swarm, so all members benefit from what any single agent learns.

When an agent moves from one swarm to another, its memories move with it. The old swarm can no longer access those memories, and the new swarm gains access.

Explicit Memory Tools

While memory works automatically, your agent also has two tools it can use on demand:

• memory_recall: search long-term memory for specific topics
• memory_store: save a fact to long-term memory

You can trigger these by asking your agent directly:

"Search your memory for what we discussed about the API migration."

"Remember that the staging server password was changed last Tuesday."

These complement the automatic system. Use them when you want to be certain something is remembered, or when you need to search for something specific.

Privacy and Isolation

Your agent's memories are strictly isolated:

• Per-account: No other Agento user can access your agent's memories
• Per-agent: Each agent has its own memory space (unless in a swarm)
• No cross-contamination: Memories from one account never leak to another

When you delete an agent, its memories are permanently removed.

Graceful Degradation

The memory system is designed to never break your agent. If the memory infrastructure has an issue:

• Your agent continues working normally, just without memory context for that turn
• No error messages interrupt your conversation
• Memories resume automatically when the system recovers
• Nothing is lost. Existing memories remain intact

Memory is an enhancement, not a dependency. Your agent is always functional.

Tips for Better Memory

Be explicit about important things. While automatic capture works well, stating things clearly helps:

"Remember: I always want error handling in my code examples."

Review what your agent knows. Ask it periodically:

"What do you remember about my project setup?"

Correct mistakes. If your agent recalls something wrong, tell it:

"That's outdated, we switched from MySQL to PostgreSQL last month. Update your memory."

Trust the system. You don't need to repeat yourself every session. If you told your agent something once, it likely remembers. Just start working.