kxkm_clown/docs/LOT203_MEMORY_BENCHMARK.md

LOT-203 — Memory Benchmark: KXKM Local Store vs Mem0

Date: 2026-03-26 Author: Agent (lot-203) Status: CONCLUDED — Recommendation: Keep local store

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1. Mem0 Overview

What it is

Mem0 (mem0ai/mem0) is an open-source "universal memory layer" for LLM applications and AI agents. At 37 000+ GitHub stars as of early 2026, it is the dominant OSS solution in this space. It ships both an open-source self-hosted variant and a cloud-hosted managed service.

Architecture

Mem0 OSS uses a multi-layer pipeline:

User message
     |
     v
[LLM extraction layer]     ← calls an LLM to detect facts / entities
     |
     +--> [Embedding model]  → embed extracted facts
     |         |
     |         v
     |    [Vector store]     ← Qdrant (default local), pgvector, Chroma, Weaviate…
     |
     +--> [Graph store]      ← Neo4j or Memgraph (optional, entity relationships)
     |
     +--> [SQLite]           ← conversation history tracking

Every add() call (write) triggers:

1. An LLM call to extract and deduplicate facts from the incoming text.
2. An embedding model call to vectorize each extracted fact.
3. A vector DB write.
4. Optionally a Neo4j graph DB write.

Every search() call (read) triggers:

1. An embedding model call to vectorize the query.
2. A vector DB similarity search.
3. A scoring/reranking pass (relevance × recency × importance).
4. Optionally a Neo4j graph traversal.

Default dependency stack (self-hosted)

Component	Default	Alternatives
LLM (extraction)	OpenAI GPT-4.1-nano	Anthropic Claude, Ollama (local)
Embedding model	OpenAI text-embedding-3-small	Ollama nomic-embed-text, bge-m3
Vector store	Qdrant (local)	pgvector, Chroma, Weaviate, Pinecone
Graph store	Neo4j (optional)	Memgraph, none
History DB	SQLite	—

Self-hosted requirements (Docker Compose reference setup)

• Minimum: t3.medium — 2 vCPU, 4 GB RAM (~$30/mo on EC2, or equivalent bare metal)
• Recommended for local LLM integration: t3.large — 8 GB RAM
• Services: FastAPI REST server + PostgreSQL/pgvector + Neo4j 5.x
• Storage: persistent volumes for two databases
• Network: no authentication by default — requires a reverse proxy (nginx/Caddy) for production
• Deployment time: 2–5 minutes initial pull (~500 MB images)
• VRAM: none required for cloud embeddings; if Ollama is used for embeddings/LLM, add ~4–8 GB VRAM per model loaded

Latency characteristics (published benchmarks)

Metric	Mem0 value	Source
p95 search latency	~200 ms	mem0.ai official benchmark
Token usage per conv.	~1 764 tokens	vs 26 031 for full context
Latency reduction vs full context	~91%	mem0.ai benchmark
LoCoMo benchmark accuracy	58–66%	2026 community benchmark (5-system comparison)

Note: the 200 ms figure is for cloud-hosted embeddings (OpenAI). Self-hosted with Ollama embeddings will be higher due to local inference overhead — community reports suggest 400–900 ms p95 for a medium GPU machine with bge-m3 embeddings.

Known limitations

• Every write requires an outbound LLM call (or local Ollama call) — memory writes are not atomic/fast.
• The default setup requires an external API key (OpenAI), making it non-offline by default.
• No authentication layer out of the box — allow_origins=["*"] CORS by default.
• Neo4j licensing: Community edition limits cluster features; Enterprise requires license.
• Python-only SDK for the full pipeline; Node.js SDK is thinner and less maintained.

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2. KXKM Local Store

Current implementation summary

The KXKM persona memory system consists of two TypeScript modules:

• /apps/api/src/persona-memory-store.ts — persistence layer (read/write/reset, v2-local JSON files)
• /apps/api/src/persona-memory-policy.ts — extraction logic, normalization, pruning, policy engine

Storage model (v2-local)

Each persona gets a single JSON file at data/v2-local/persona-memory/<personaId>.json.

The record schema (PersonaMemoryRecordV2) contains:

• workingMemory: hot facts (≤20), current summary, last source messages (≤10)
• archivalMemory: deduplicated historical facts (≤100) with first/last-seen timestamps; summaries ring buffer (≤50)
• compat block: backward-compatible legacy view (facts + summary + lastUpdated)

Policy engine

resolvePersonaMemoryPolicy() reads all limits from environment variables with safe integer clamping:

Parameter	Default	Env var
updateEveryResponses	5	KXKM_PERSONA_MEMORY_UPDATE_EVERY
recentMessagesWindow	10	KXKM_PERSONA_MEMORY_EXTRACTION_WINDOW
workingFactsLimit	20	KXKM_PERSONA_MEMORY_FACTS_LIMIT
archivalFactsLimit	100	KXKM_PERSONA_MEMORY_ARCHIVAL_FACTS_LIMIT
archivalSummariesLimit	50	KXKM_PERSONA_MEMORY_ARCHIVAL_SUMMARIES_LIMIT

Extraction (fact distillation) is handled by calling the local LLM with a structured prompt (buildPersonaMemoryExtractionPrompt) and merging results via applyPersonaMemoryExtraction. This LLM call happens every N responses (configurable), not on every message.

Read path

1. In-memory LRU cache (30 s TTL, Map keyed by personaId and nick).
2. If cache miss: readFile of the persona's JSON file.
3. If no v2 file: legacy migration path (reads old persona-memory/<nick>.json, promotes to v2).
4. If nothing found: returns an empty record (no I/O error).

Write path

1. normalizePersonaMemory() — deduplicates facts, trims to policy limits, upserts archival.
2. writeFile to data/v2-local/persona-memory/<personaId>.json.
3. Parallel writeFile to legacy compat path.
4. Updates in-memory cache.

Key properties

• Zero external services: pure Node.js fs + in-process Map cache.
• Zero network calls at read/write time (LLM call happens upstream, on extraction trigger only).
• Fully offline: no API keys, no vector DB, no embedding model at runtime.
• Deterministic latency: cache hit ~0 ms; disk read ~1–5 ms (local SSD); disk write ~2–10 ms.
• Language-native: TypeScript, same runtime as the API — no IPC, no subprocess.

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3. Comparison Matrix

Dimension	KXKM Local Store	Mem0 OSS (self-hosted)
Read latency (cache hit)	< 1 ms	N/A (no cache by default)
Read latency (disk / vector search)	1–5 ms	50–200 ms (cloud embeddings); 400–900 ms (Ollama)
Write latency	2–10 ms	300–2000 ms (LLM extraction + embed + DB write)
Offline capability	Full — zero external deps	Partial — requires Ollama config; still needs vector DB
VRAM requirement	0 (memory ops only)	0 if cloud; 4–8 GB per model if Ollama
RAM overhead	~50 MB (Node process + cache)	~2–4 GB (Neo4j) + ~512 MB (FastAPI) + pgvector
Infrastructure components	0 additional services	3 additional services (FastAPI, PostgreSQL/pgvector, Neo4j)
Semantic retrieval quality	None — exact/substring only	Vector similarity + graph traversal + reranking
Contradiction resolution	None (deduplication by text equality)	LLM-assisted on every write
Memory coherence (LoCoMo score)	Not benchmarked — structurally simpler	58–66% on LoCoMo
Fact extraction quality	Depends on local LLM (same as Mem0 with Ollama)	Same LLM dependency
Scalability (many personas)	O(1) per-persona files, linear scan fallback	Designed for multi-user at scale
Setup complexity	0 — embedded in API process	Medium — Docker Compose, 3 services, env config
Maintenance burden	Near-zero (pure TypeScript)	Medium (DB upgrades, Neo4j licensing, Python deps)
Node.js integration	Native TypeScript	REST HTTP calls (extra hop + serialization)
Audit / transparency	Full source in-repo, 318 LOC	External library, 37 k stars, active community
Portability	Git-tracked JSON files	PostgreSQL + Neo4j volumes (heavier export)

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4. Recommendation

Verdict: Keep the KXKM local store. Do not adopt Mem0 at this time.

Rationale

The KXKM use case is a multi-persona IRC-style chat system with ~10–50 concurrent personas on a single GPU machine (kxkm-ai). Memory access is per-message, low-volume, and latency-sensitive (each persona response cycle must not add perceptible delay for the user).

The local store achieves sub-5 ms reads and sub-10 ms writes with zero additional infrastructure. Mem0 OSS self-hosted introduces 2–3 additional Docker services consuming 3–5 GB RAM minimum, requires either an OpenAI key or a dedicated Ollama instance for embeddings, and adds 300–2000 ms per memory write — all for a use case where the persona count is bounded and does not need enterprise-scale vector search.

The only genuine advantage Mem0 provides is semantic retrieval (vector similarity search) and LLM-assisted contradiction resolution. These are real strengths for large-scale, long-horizon memory (hundreds of users, thousands of facts). For KXKM's current scale, the policy-based pruning and deduplication in persona-memory-policy.ts is sufficient.

Conditions under which this verdict should be revisited

• Persona count grows beyond ~100 with distinct long-term users (thousands of archival facts).
• A use case requires "find the most semantically relevant past fact" rather than "recall recent facts".
• The archival store exceeds the current 100-fact / 50-summary caps and accuracy degrades noticeably.
• The project moves to a hosted/cloud deployment where PostgreSQL/Neo4j are already provisioned.

Hybrid option (future, if needed)

If semantic retrieval becomes necessary without the full Mem0 stack, a lighter path is:

• Keep the current KXKM store for working memory (hot facts, recent messages).
• Add a local SQLite + sqlite-vss (vector similarity extension) for archival semantic search.
• This avoids Neo4j entirely, keeps everything in Node.js, and stays offline.

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5. Migration Cost (if Mem0 were adopted)

Provided here for completeness only — this migration is not recommended.

Task	Estimated effort
Replace persona-memory-store.ts read/write with Mem0 REST client	1 day
Adapt policy engine triggers to Mem0 add/search API surface	1 day
Migrate existing JSON persona memory files to Mem0 (import script)	0.5 day
Set up Docker Compose services (PostgreSQL/pgvector + Neo4j + FastAPI)	0.5 day
Configure Ollama as Mem0 embedding + LLM backend (offline mode)	0.5 day
Add authentication layer (reverse proxy + Mem0 API tokens)	0.5 day
Regression test suite update	1 day
Total	~5 days

Additional ongoing cost: maintenance of 3 extra Docker services, Neo4j version upgrades, Python dependency security patches in the Mem0 container.

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Sources

• GitHub — mem0ai/mem0
• Mem0 Open Source Overview
• Mem0 Self-Host Docker Guide
• AI Memory Benchmark: Mem0 vs OpenAI vs LangMem vs MemGPT
• 5 AI Agent Memory Systems Compared — 2026 Benchmark (DEV.to)
• AI Agent Memory Systems in 2026 — Medium
• Vector Store Providers — DeepWiki
• Mem0 InfoWorld overview