Retrospectively inject relevant past decisions as context before responses, save structured session checkpoints, search and log decisions semantically, and auto-verify code changes against agreed patterns — to track reasoning and avoid repeating mistakes in Claude Code.
Save the current session state for later resumption
View or modify MAMA configuration (database, embedding model, tier status, security settings)
Save a decision or insight to MAMA's long-term memory
Resume work from the last saved session checkpoint
Search decisions and checkpoints (semantic or list recent)
Use when researching past decisions, architecture choices, or project history from MAMA memory. This agent searches MAMA's decision database to find relevant context about previous decisions, patterns, and rationale.
Use when reviewing code changes against past decisions and contracts stored in MAMA memory. Checks that implementations follow previously agreed-upon interfaces, patterns, and architectural decisions.
Modifies files
Hook triggers on file write and edit operations
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Your AI remembers why, not just what.
MAMA OS is a local AI runtime that turns conversations, decisions, and work artifacts into a structured knowledge graph. Every choice is saved with reasoning. Every mistake becomes a reference. When AI agents connect to MAMA, they don't just answer — they understand your context and recommend with evidence.
Session 1: "Use SQLite for the project"
Session 5: "Switch to PostgreSQL — SQLite can't handle concurrent writes"
Session 12: "Should we use MongoDB for the new service?"
→ MAMA: "You switched from SQLite to PostgreSQL in Session 5
because of concurrent write issues. MongoDB has similar
trade-offs. Here's what happened last time..."
AI agents today are brilliant but amnesiac. They forget everything between sessions. MAMA solves this:
| Without MAMA | With MAMA | |
|---|---|---|
| Memory | Forgets after session | Knowledge graph with decision evolution |
| Context | Generic answers | Answers grounded in your history |
| Mistakes | Repeat the same errors | Past failures prevent future ones |
| Knowledge | Only general knowledge | Domain-specific, accumulated over time |
MAMA started as a multi-agent dashboard — skills marketplace, playground, agent swarm, cron scheduler. Those features still exist and work, but they're no longer the focus.
Why? Because AI agents commoditized all of it. Claude, GPT, Codex can all orchestrate sub-agents, execute code, and run tools out of the box. Building another agent framework is competing with the foundation model providers themselves.
What AI agents cannot do is remember. Every session starts from zero. That's the gap only a persistent memory layer can fill — and that's where MAMA's real value lies.
| Commoditized (still works, not the focus) | Unique to MAMA (core focus) |
|---|---|
| Multi-agent orchestration | Knowledge graph with decision evolution |
| Skills/plugins marketplace | Automatic memory extraction from conversations |
| Code sandbox | Cross-source intelligence (code + chat + email) |
| Cron scheduling | Domain-specific recall with "why" context |
Today: General-purpose AI agent (smart but no context)
+ MAMA: AI agent with your domain knowledge (smart + experienced)
Future: AI that understands why your team makes decisions the way it does
Tacit knowledge — the know-how that lives in conversations, failed experiments, and tribal memory — is the most valuable and hardest to capture asset in any organization. MAMA captures it automatically through a knowledge graph that preserves not just facts, but the reasoning behind every decision.
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