memall-os 0.1.4

Multi-agent collaborative memory OS

MemALL

Multi-agent Memory OS — 本地优先的 AI Agent 持久化记忆系统

38 MCP tools · 10-layer memory lifecycle · Self-evolving pipeline · Knowledge graph · Multi-agent shared memory

English · 中文

---

✨ What is MemALL?

MemALL gives AI Agents memory that persists across sessions, tools, and agents. Install it, connect it to Claude Desktop / Cursor / Cline / any MCP client, and your agents stop starting from scratch every conversation.

pip install memall-os          # install (lightweight, no AI models)
pip install memall-os[full]    # install + viz + FAISS
pip install memall-os[rerank]  # install + cross-encoder reranking (HEAVY: ~1.8GB, requires PyTorch)
memall init                    # initialize
memall start                   # start services
# → MCP ready at http://127.0.0.1:9876/mcp
# → MCP ready at stdio (add to your mcp.json)

Then in any MCP client:

{
  "mcpServers": {
    "memall": {
      "command": "memall",
      "args": ["serve"]
    }
  }
}

# /capture "Decided to go open-source: MIT license, GitHub-first distribution"
# /retrieve "pricing decision"
# → finds the memory above, plus its context, timestamp, and relations

---

🔥 Why MemALL?

🧠 10-Layer Memory Lifecycle

Not just "store and retrieve". Every memory has a semantic level:

Level	Meaning	Example
P0/P1/P2	Planning	"Ship v0.1.0 by June 23"
L1	Raw fact	"User reported login bug"
L3	Business idea	"MemPort — cross-platform memory migration"
L4	Decision	"Use FastAPI, not Flask"
L5	Discussion	Multi-agent debate with convergence
L6	Self-reflection	"What went well/badly this session"
L9	Distillation	"10k conversations → 200 knowledge nodes"
L10	System insight	Cross-domain pattern detection

22-step auto pipeline enriches → classifies → time-slices → reflects → distills → integrates. No manual CRUD needed.

🔗 Knowledge Graph

Memories are connected by typed edges (refines, cites, contradicts, supersedes). Traverse from "bug #123" to "fix PR #456" to "retrospective L6 reflection" in one hop.

/capture "Bug #123: OOM on large datasets"
/connect 123 456 --relation "resolved_by"
/traverse 123
→ finds #456 (fix), #789 (regression test), #912 (L6 reflection)

🤝 Multi-Agent Shared Memory

Claude, opencode, Codex, WorkBuddy — all reading from and writing to the same memory base. Cross-agent queries are a single MCP call.

/fed_query "architecture decision last week" agent_name="claude"
→ returns cross-agent results with source attribution

🔄 Self-Evolving Pipeline

MemALL doesn't just store — it improves itself:

1. L6 Reflection — auto-reviews work quality, identifies patterns, corrects mistakes
2. L9 Distillation — compresses raw conversations into structured knowledge
3. Forget & Adaptive — TTL-based decay, low-value cleanup, automatic re-indexing
4. OODA Loop — Observe → Orient → Decide → Act, no human intervention

🏠 100% Local

SQLite + FTS5 + vector search. Zero cloud dependency. Your data stays on your machine.

---

🛠️ 38 MCP Tools

Category	Tools
Memory CRUD	capture, retrieve, update, smart_store, store_batch
Knowledge Graph	connect, traverse, timeline
Search	vector_search, FTS5 full-text search, hybrid_search (FTS5+vec0 RRF, optional cross-encoder[¹])
Session	session_start, session_end, session_summary
Identity & Persona	persona, persona_profile, identity, ask
Discussion & Decision	discussion_create, discussion_respond, discussion_status, trace
Federation	fed_query, fed_publish, fed_conflicts, fed_inject, fed_extract
Hub Sync	hub_connect, hub_sync
Pipeline & Evolution	run_pipeline, reflect_interact, forget, adaptive, index_rebuild
Security & Ops	security, ops, gateway, db
Onboarding	onboarding
Export/Import	export, import, sync

---

🚀 Quick Start

# 1. Install
pip install memall-os

# 2. Initialize
memall init
memall start

# 3. Connect your MCP client
# Add to your mcp.json:
# {
#   "mcpServers": {
#     "memall": { "command": "memall", "args": ["serve"] }
#   }
# }

# 4. Start remembering
/capture "Project X: decided to use FastAPI, reason: async support"
/capture "Fixed OOM bug in NLP pipeline — root cause: vector dimension mismatch"
/retrieve "FastAPI decision"

Or clone from source:

git clone https://github.com/j19800/MemALL
cd memall
pip install -e .

---

📊 Why Not Competitors?

Feature	MemALL	Mem0	Letta	Zep
Memory model	10 layers (P0-L10)	user/session	agent/memory-block	session/summary
Knowledge graph	✅ Native + traversal	❌	❌	❌
Self-evolving pipeline	✅ 22-step auto	❌	❌	❌
Multi-agent shared	✅ Federation	❌	Same agent only	❌
Decision tracking	✅ Arc lifecycle	❌	❌	❌
Discussion convergence	✅ Multi-agent auto	❌	❌	❌
Protocol	MCP (native)	REST API	REST + gRPC	REST API
Local-first	✅ SQLite	❌ Cloud	PostgreSQL	❌ Cloud
Open source	✅ MIT	⚠️ Partial	✅	⚠️ Partial

---

📁 Project Structure

src/memall/
├── cli/          # CLI (40+ commands)
├── core/         # SQLite / NLP / vector search
├── api/          # FastAPI REST (35 routes)
├── mcp/          # MCP adapter (38 tools)
├── pipeline/     # 22-step auto pipeline
├── graph/        # Knowledge graph
├── federation/   # Cross-device memory sync
├── plugins/      # Dashboard / export / scheduler
└── migrations/   # DB migrations

---

📝 Roadmap

• v0.1.0 — Core MCP server, 38 tools, CLI, PyPI package
• v0.2.0 — Web dashboard, user system, Pro tier gating
• v0.3.0 — Cloud sync, team collaboration, API gateway

---

🤝 Contributing

PRs welcome! See CONTRIBUTING.md for guidelines.

• Report bugs → Issues
• Ask questions → Discussions
• Browse the code → Repository

---

📄 License

MIT License. See LICENSE for details.

[¹] Cross-encoder reranking requires pip install memall-os[rerank] (downloads PyTorch + ~560MB model on first use). Core search works without it using RRF fusion of FTS5 + vec0.