mcp-memory
The mcp-memory server provides intelligent, decay-based memory management for AI agents, preventing one-time questions from becoming permanent obsessions by applying exponential decay and frequency-based relevance scoring.
Store memories (
remember): Save information with automatic content-based categorization (preference,fact,correction,context,one-time,question), semantic deduplication, and preference superseding. Supports optionaltagsand explicitcategoryoverride.Retrieve memories (
recall): Fetch memories ranked by a dynamic score combining decay × frequency × match quality. Automatically reinforces the top match and prunes dead memories. Acceptsquery,limit, andmin_relevanceparameters.Delete memories (
forget): Remove a memory byidor fuzzy content match (e.g., "VS Code" matches "User prefers VS Code for all editing").Reinforce topics (
reinforce): Boost a memory's relevance and reset its decay clock. After 5 mentions, aquestionautomatically upgrades tointerestwith a longer lifespan.Inspect memory state (
inspect): Debug view of all memories showing decay status, relevance scores, ages, and category breakdown.Prune decayed memories (
prune): Explicitly remove memories below a configurable relevancethreshold(also happens automatically duringrecall).Memory health overview (
stats): Summary of active, fading, and dead memories broken down by category.
Key behaviors: Each category has its own decay half-life (e.g., 7 days for one-time, 180 days for preferences). Active memories can be injected into AI agent system prompts. Older tool names from v0.1.0 are supported for backwards compatibility.
mcp-memory
Smart memory for AI agents. Memories decay, topics are frequency-weighted, one-time questions don't become obsessions.
Solves the Karpathy problem: "A single question from 2 months ago keeps coming up as a deep interest with undue mentions in perpetuity."
What's New in v0.2.0
Auto-categorization — no need to specify category, inferred from content
Semantic dedup — bigram similarity prevents duplicate memories
Preference supersede — "prefers dark mode" then "prefers light mode" updates, not duplicates
Recall auto-reinforces — searching for a topic counts as a mention
Recall auto-prunes — dead memories cleaned up on every read
System prompt injection — active memories provided via MCP prompts capability
Fuzzy forget — "VS Code" matches "User prefers VS Code for all editing"
7 tools → 4 tools — simpler API, higher adoption (backwards compatible)
In Action
Day 1: User asks 5 questions (Rust, dark mode, Python, job title, Haskell)
#1 [ACTIVE] rel=1.000 cat=preference "User prefers dark mode in all editors"
#2 [ACTIVE] rel=0.900 cat=fact "User works as a senior software engineer"
#3 [FADING] rel=0.500 cat=question "User is building a Python web scraper"
#4 [FADING] rel=0.300 cat=one-time "User asked about Rust programming"
#5 [FADING] rel=0.300 cat=one-time "User asked what Haskell monads are"
Day 2-5: User mentions Python 4 more times → auto-upgraded to "interest"
#1 [ACTIVE] rel=2.658 mentions=5 cat=interest "Python web scraper"
#2 [ACTIVE] rel=1.000 mentions=1 cat=preference "dark mode"
#3 [ACTIVE] rel=0.900 mentions=1 cat=fact "senior software engineer"
#4 [FADING] rel=0.300 mentions=1 cat=one-time "Rust" ← FADING, won't obsess
#5 [FADING] rel=0.300 mentions=1 cat=one-time "Haskell" ← FADING, won't obsess
After 60 days:
Rust: 0.3 × 0.5^(60/7) = 0.0008 → DEAD (gone, as it should be)
Python: 0.8 × 0.5^(60/60) × 3.32 = 1.329 → STILL ACTIVE (real interest)How It Fixes This
Current LLM Memory | mcp-memory |
Ask about Rust once → mentioned forever | Ask once → fades in 7 days |
All memories equal weight | Categories: one-time (7d), question (14d), interest (60d), preference (180d) |
No decay | Exponential decay — old memories naturally fade |
No frequency tracking | Mentioned 5+ times → auto-upgrades from "question" to "interest" |
Keyword matching | Bigram similarity + relevance scoring |
Agent must decide to remember | Auto-categorizes from content patterns |
Contradicting preferences coexist | New preference supersedes old one |
Manual cleanup required | Auto-prunes dead memories on recall |
Install
"mcpServers": {
"memory": {
"command": "npx",
"args": ["-y", "mcp-memory"]
}
}Tools
Tool | What it does |
| Store a memory. Auto-categorizes from content. Auto-deduplicates via bigram similarity. Supersedes conflicting preferences. |
| Retrieve memories ranked by relevance. Auto-reinforces top match. Auto-prunes dead memories. |
| Delete a memory by ID or fuzzy content match. |
| Debug view: all memories with decay status, relevance scores, category breakdown, health. |
Auto-Categorization
No need to specify category — it's inferred from content:
Content Pattern | Auto-Category | Decay |
"prefers X", "likes X", "always uses X" |
| 180 days |
"works as X", "is a X", "lives in X" |
| 365 days |
"actually X", "meant X", "wrong" |
| 365 days |
"currently building", "working on" |
| 30 days |
"what is X", "how to X" |
| 7 days |
anything else |
| 14 days |
You can still override: remember(content: "...", category: "preference")
Examples
Auto-categorized preference:
remember(content: "User prefers TypeScript over JavaScript")
→ Auto-detected as "preference". Persists 180 days.Semantic dedup:
remember(content: "Works as data scientist at Google")
remember(content: "Works as senior data scientist at Google")
→ Second call reinforces first (80% similar). Keeps longer version.Preference supersede:
remember(content: "User prefers dark mode")
remember(content: "User prefers light mode")
→ Superseded: "dark mode" → "light mode". One memory, not two.Recall auto-reinforces:
recall(query: "MCP servers")
→ Returns matching memories AND counts this as a mention.
mention_count goes from 1 → 2 automatically.Fuzzy forget:
forget(content: "VS Code")
→ Matches and removes "User prefers VS Code for all editing"The Math
relevance = base_weight × decay × frequency_boost
where:
base_weight = category-specific (0.3 for one-time, 1.0 for preference)
decay = 0.5 ^ (age_days / halflife_days)
freq_boost = 1 + log2(mention_count)A one-time question from 2 months ago:
0.3 × 0.5^(60/7) × 1.0 = 0.0003 → effectively zero. Won't surface.
A preference mentioned 8 times, last week:
1.0 × 0.5^(7/180) × 4.0 = 3.89 → top of every recall.
Backwards Compatibility
v0.2.0 still accepts the old v0.1.0 tool names (reinforce, prune, stats). They map to the new tools internally. No breaking changes.
License
MIT
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