How AI Agent Memory Works in nanobot

This guide explains how to use nanobot's long-term AI agent memory: session history, compressed archives, durable memory files, Dream consolidation, and Git-backed memory changes.

What you will build

a workspace with persistent session history
compressed history archives for older turns
durable memory files such as USER.md and MEMORY.md
a Dream workflow for curating long-term memory

When to use this

Use memory when an agent should remember stable preferences, project facts, decisions, and recurring context across sessions. Do not use memory as a dumping ground for every raw transcript; nanobot separates short-term messages from curated durable knowledge.

Install

python -m pip install nanobot-ai
nanobot onboard --wizard
nanobot agent -m "Hello!"

Minimal working example

Ask the agent to remember a stable fact in a normal session, then run Dream:

/dream

Inspect recent memory changes:

/dream-log

The exact files live in the active workspace, usually under ~/.nanobot/workspace/.

Production notes

Use one workspace per project or personal context.
Keep durable facts concise; old session details belong in history.jsonl.
Use /dream-prompt init when a workspace needs custom memory guidance.
Review Git-backed memory changes when memory affects important workflows.

Security notes

Memory files may contain sensitive user or project facts.
Avoid sharing workspaces without reviewing SOUL.md, USER.md, and memory/MEMORY.md.
Use separate workspaces for personal and team contexts.

Troubleshooting

If memory feels stale, run /dream and inspect /dream-log.
If memory changed incorrectly, use /dream-restore to inspect and restore previous versions.
If a new session lacks context, confirm it uses the same workspace.

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