Power your agent
การติดตั้งจากผู้เผยแพร่ ไม่ใช่เอนด์พอยต์ของ PlugMCP
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รันบน PlugMCP
A new server you create. It is not this listing's official endpoint. Use the publisher install when you want their URL and their credentials.
เปิดจากผู้เผยแพร่
mcp-name: io.github.oxgeneral/agentmem
Lightweight persistent memory for AI agents. One SQLite file. Hybrid search (keywords + semantics). Zero to 12MB install.
No PyTorch. No cloud. No server. Just memory.
206 unit tests. 107 quality tests on real data. Typed API (16 TypedDict). Production-ready.
Built by an AI agent that wakes up with no memory every session — and needed a way to remember.
Lightweight persistent memory for AI agents. Hybrid search. 16 tools. One SQLite file.
Every AI agent session starts from zero. Context windows compress, conversations end, memory vanishes. agentmem gives agents persistent memory that survives across sessions — in a single SQLite file.
# Best quality (sqlite-vec + model2vec, 12MB total)
pip install agentmem-lite[all]
# Minimal (sqlite-vec + hash embeddings, 151KB)
pip install agentmem-lite
# Zero dependencies (pure Python, stdlib only)
pip install agentmem-lite --no-deps
# From source
git clone https://github.com/oxgeneral/agentmem && cd agentmem
pip install -e ".[all]"
from agentmem import MemoryStore, get_embedding_model
# Auto-selects best available backend
embed = get_embedding_model()
store = MemoryStore("memory.db", embedding_dim=embed.dim)
store.set_embed_fn(embed)
# Store memories with namespaces
store.remember("Server costs $50/month", tier="core", namespace="infra")
store.remember("API returns 403 without auth", tier="learned", namespace="api")
store.remember("Deployed v2.1 at 15:30", tier="episodic")
# Search — hybrid keyword + semantic, with recency boost
results = store.recall("server costs", recency_weight=0.15)
# Namespace isolation
results = store.recall("server", namespace="infra")
# Save working state before context compression
store.save_state("Working on auth fix, step 3/5, blocked by CORS")
# Add behavioral rules (procedural memory)
store.add_procedure("Always use HTTPS in production")
store.add_procedure("Never expose debug endpoints")
rules = store.get_procedures() # → formatted for system prompt
# Update facts with version chain
store.update_memory(old_id=1, new_content="Server costs $75/month")
history = store.history(memory_id=2) # → trace fact evolution
# Find related memories by entity
related = store.related("10.0.0.1") # → all memories mentioning this IP
entities = store.entities(entity_type="ip") # → list all known IPs
# Auto-extract from conversations
messages = [
{"role": "user", "content": "Set API_KEY to sk-abc123. Always validate input."},
{"role": "assistant", "content": "Noted. I decided to use pydantic for validation."},
]
result = store.process_conversation(messages, namespace="project")
# → extracts config, preferences, decisions automatically
# Maintenance
store.compact(max_age_days=90) # archive old low-value memories
store.consolidate(similarity_threshold=0.85) # merge near-duplicates
# Import markdown files
store.import_markdown("MEMORY.md", tier="core")
# Initialize database
agentmem init --db memory.db
# Import markdown files
agentmem import MEMORY.md --tier core -n my-agent
agentmem import-dir ./daily-logs/ --tier episodic
# Search with namespace filter
agentmem search "deployment process" --limit 5 -n infra
# Manage procedures
agentmem add-procedure "Always use markdown formatting"
agentmem procedures
# View entities and relations
agentmem entities --type ip
agentmem related 10.0.0.1
# Maintenance
agentmem compact --max-age-days 90 --dry-run
agentmem consolidate --threshold 0.85
# Process conversation
agentmem process chat.json -n project
# Stats and export
agentmem stats
agentmem export --tier core
python -m agentmem --db memory.db
Add to your MCP client config:
{
"mcpServers": {
"memory": {
"command": "python",
"args": ["-m", "agentmem", "--db", "/path/to/memory.db"]
}
}
}
# Start HTTP server
agentmem serve-http --port 8422
# Or directly
agentmem-http --port 8422 --db memory.db
# Store a memory
curl -X POST http://localhost:8422/remember \
-H "Content-Type: application/json" \
-d '{"content": "Server IP is 10.0.0.1", "tier": "core", "namespace": "infra"}'
# Search
curl "http://localhost:8422/recall?query=server+IP&namespace=infra"
# Health check
curl http://localhost:8422/health
16 MCP tools / 14 HTTP endpoints:
Isolate memories per user, agent, or project:
# Store in namespaces
store.remember("Alice's API key", namespace="user/alice")
store.remember("Bob's config", namespace="user/bob")
store.remember("Shared fact", namespace="team")
# Search within namespace (prefix matching)
store.recall("API", namespace="user/alice") # only Alice's memories
store.recall("API", namespace="user") # Alice + Bob (prefix match)
store.recall("API") # everything
Track how facts evolve over time:
# Initial fact
r1 = store.remember("Server costs $50/month", tier="core")
# Fact changes — old version archived, linked via supersedes
r2 = store.update_memory(r1["id"], "Server costs $75/month")
# Trace the history
history = store.history(r2["id"])
# → [{"id": 2, "content": "...$75..."}, {"id": 1, "content": "...$50..."}]
Automatic regex-based NER on every remember() call:
# Find all memories mentioning an entity
store.related("10.0.0.1")
store.related("@alice", entity_type="mention")
# List all known entities
store.entities() # sorted by memory count
store.entities(entity_type="ip")
Auto-extract memories from chat history (regex-only, no LLM):
messages = [
{"role": "user", "content": "Set DATABASE_URL to postgres://localhost/mydb"},
{"role": "assistant", "content": "I decided to use connection pooling. Important: max 20 connections."},
{"role": "user", "content": "Always validate input. TODO: add rate limiting."},
]
result = store.process_conversation(messages)
# Extracts: config→core, decisions→episodic, preferences→procedural, todos→working, important→core
agentmem automatically selects the best available mode:
*With lazy loading — model2vec loads on first query, not on init
┌──────────────────────────────────────────────┐
│ MemoryStore │
│ ┌──────────┐ ┌──────────┐ ┌────────────┐ │
│ │ FTS5 │ │ Vector │ │ Entity │ │
│ │ keywords │ │ Index │ │ Index │ │
│ │ + BM25 │ │ cosine │ │ regex NER │ │
│ └────┬─────┘ └────┬─────┘ └─────┬──────┘ │
│ └──────┬───────┘ │ │
│ Adaptive Hybrid Scorer │ │
│ (query classify + recency + │ │
│ importance boost) │ │
│ ┌──────────────────────────────────┴───────┐ │
│ │ SQLite + WAL │ │
│ │ memories │ memories_fts │ vecs │ entities│ │
│ └──────────────────────────────────────────┘ │
│ One file: memory.db │
└───────────────────────────────────────────────┘
MIT
Lightweight persistent memory for AI agents. Hybrid search. 16 tools. One SQLite file.
เครื่องมือ
ยังไม่มีรายการเครื่องมือ เซิร์ฟเวอร์แบบรีโมตจะได้รายการหลังการตรวจสอบสถานะครั้งแรก
FAQ
คัดลอกคอนฟิก stdio สำหรับ Cursor, Claude Desktop, Claude Code หรือ VS Code ได้ในหน้านี้ คอนฟิกนี้ใช้การติดตั้งของผู้เผยแพร่ ไม่ใช่พร็อกซีของ PlugMCP
ระเบียนนี้ไม่ได้ระบุตัวแปรสภาพแวดล้อมที่จำเป็น
Agentmem ทำงานแบบโลคัลผ่าน stdio
ตรวจสอบล่าสุดเมื่อ 2026-11-10 Lightweight persistent memory for AI agents. Hybrid search. 16 tools. One SQLite file.
