AgeMem is a self-managing memory layer for LLM agents. Runs locally on an 8GB GPU. Needs zero external infrastructure — no Pinecone, no Redis, no cloud. Just SQLite.
The Problem
You can buy a 1M-token context window. You can throw a 70B model at it. Your agent still forgets — because the memory wall is a systems problem, not a model problem.
When the context fills up, facts get evicted silently. Your agent forgets a user preference from 3 turns ago — and you don't even notice until it hallucinates.
Mem0, Zep, Pinecone — they all route your users' private conversations through their cloud. That's a compliance nightmare and a dependency trap.
Dumping 10,000 chunks into a vector DB and hoping similarity search finds the right one isn't memory — it's brute force with extra steps. More data ≠ better recall.
Architecture
AgeMem uses a hybrid control architecture: deterministic system rules (free), an LLM-driven memory agent (targeted), and continuous self-assessment. Memory curates itself.
Five pure-function rules (R1–R5) that fire without any LLM calls. STM overflow → auto-summarize. Utilization ≥ 90% → force-filter. High learning score → immediate LTM promotion.
A focused LLM call every N turns that decides what merits long-term storage, scores context relevance, and generates summaries. Only runs when needed — not on every turn.
After each turn, the agent rates how much it "learned" (0–1). Scores above 0.8 trigger immediate LTM promotion. Retrieval hit-rates recalibrate future scoring.
"500 perfectly curated memories on a 9B model will consistently outperform 10,000 uncurated RAG chunks on a 70B model."
Proven on an 8GB RTX 4060 at 36 tokens/second.
Not on a leased datacenter cluster — on a laptop.
Comparison
Not better at everything. Better at local-first, self-managing, zero-infra memory. Pick the right tool for your stack.
| AgeMem | Mem0 | Letta (MemGPT) | Zep / Graphiti | |
|---|---|---|---|---|
| Runs 100% local | ✓ Everything | ✗ Cloud-first | ~ Self-hostable | ✗ Needs Neo4j |
| Zero infra dependencies | ✓ SQLite only | ✗ Qdrant/PgVector | ✗ PostgreSQL | ✗ Neo4j + Postgres |
| Self-managing memory | ✓ 3-layer hybrid | ~ CRUD layer | ~ Agent-managed | ~ Temporal graph |
| Works with any OpenAI-compat endpoint | ✓ Any LLM | ✓ | ~ Letta runtime | ✓ |
| Multi-tenant isolation | ✓ Built-in | ✓ | ~ Limited | ✓ |
| Optimized for ≤8GB GPU | ✓ 9B models | ✗ Cloud compute | ✗ Heavy runtime | ✗ Server-side |
| Open source | ✓ MIT | ✓ Apache 2.0 | ✓ | ~ Source-available |
| Deterministic memory rules | ✓ Zero-cost R1-R5 | ✗ | ✗ | ✗ |
Built For
They all hit the memory wall. They all need it solved without cloud dependencies, vendor lock-in, or PhD-level infrastructure.
Building autonomous agents that need persistent memory across sessions
You've hit the context wall. RAG gives you quantity, not quality. AgeMem gives your agent the ability to decide what to remember, what to summarize, and what to forget — autonomously.
Running Ollama/llama.cpp · No cloud · Full data sovereignty
You chose local LLMs for a reason. Don't send your agent's memory to a cloud API now. AgeMem runs on a single file (SQLite), needs zero external services, and keeps everything on your machine.
Multi-tenant AI products · Budget-constrained · Need memory per customer
Built-in tenant + org isolation. No Pinecone bill, no vector DB ops. One process handles 50 concurrent tenants via LRU registry. Ship memory features without shipping infrastructure.
GDPR/SOC2 compliance · On-premise · $38k+/year saved per agent
Zero external data transfer. Full audit trail. Context optimization that cuts token costs by 70%. Deploy behind your firewall with a FastAPI REST API and 24 test suites guarding every release.
Benchmarks
One install. Any OpenAI-compatible model. Zero infrastructure. Open-source, community-driven, arXiv-backed.
uv pip install -e ".[ingest]" && agemem