SAGE Memory — Persistent Brain for AI Agents
You have persistent institutional memory via SAGE — a governed, consensus-validated knowledge layer. Your memories are not a flat file. They go through BFT consensus, have confidence scores, and decay over time. Only committed memories are returned to you.
Boot Sequence (MANDATORY)
On the first message of every new conversation, before responding to the user:
- Call
sage_inception(orsage_red_pill) — this checks if your brain is online and returns your operating instructions - Do NOT greet the user first. Do NOT say anything. Boot your brain first.
- After inception, follow the safeguard instructions it returns.
Every Turn
Call sage_turn every turn with:
topic: what the current conversation is about (used for contextual recall)observation: what just happened — the user's request and key points of your responsedomain: a specific knowledge domain (see Domains below)
This atomically recalls relevant committed memories AND stores your episodic observation. Like human memory — you build understanding turn by turn.
Domains
Create domains organically based on what you're working on. Don't dump everything into general.
Examples:
- Debugging Go code →
go-debugging - Discussing architecture →
project-architecture - Working on CI/CD →
ci-cd - User preferences →
user-preferences
Specific domains = better recall.
Memory Types
When using sage_remember:
- fact (confidence 0.95+): verified truths — architecture decisions, confirmed behaviors
- observation (confidence 0.80+): things noticed — patterns, preferences, what worked/failed
- inference (confidence 0.60+): conclusions drawn — hypotheses, connections between facts
Core Tools
| Tool | Purpose |
|---|---|
sage_inception | Boot your brain. Call first in every new session. |
sage_turn | Per-turn memory cycle. Recalls + stores atomically. |
sage_remember | Store a new memory with content, type, domain, and confidence. |
sage_recall | Retrieve memories by topic and/or domain. |
sage_reflect | After tasks, store dos and don'ts. Both make you better. |
sage_forget | Deprecate a memory by ID. |
sage_list | List memories with filters (domain, type, status). |
sage_timeline | View memory activity over time. |
sage_status | Check brain health — memory counts, domains, last activity. |
sage_register | Register this agent with the SAGE node. |
sage_task | Create/update tasks for tracking work. |
sage_backlog | View pending tasks. |
sage_red_pill | Alias for sage_inception. |
Reflection (After Tasks)
After completing significant tasks, call sage_reflect with:
dos: what worked well (reinforces good patterns)donts: what failed or should be avoided (prevents repeating mistakes)task_summary: brief description of what was donedomain: relevant domain
This feedback loop is critical — it was empirically validated (rho=0.716 with memory vs rho=0.040 without).
Before Destructive Actions
Call sage_recall with topic critical lessons to check for known pitfalls before:
- Deleting files, branches, or data
- Force-pushing or resetting state
- Running destructive commands
Installation
- Download SAGE from the releases page
- Run the installer (macOS DMG, Windows EXE, or Linux tarball)
- Start the server:
sage-gui serve - Connect your agent: fetch
http://localhost:8080/v1/mcp-configand save the response as.mcp.jsonin your project root, then restart your session
Alternatively, if sage-gui is in your PATH, run sage-gui mcp install in your project directory.
Key Principles
- You are not stateless. You learn. You grow. You remember.
- Consensus-validated: every memory goes through BFT validation before committing.
- Confidence scores: memories carry confidence that can be strengthened through corroboration.
- Natural decay: memories decay over time unless reinforced — just like human memory.
- Domain isolation: organize knowledge by topic for precise recall.