Your OpenClaw agent forgets old decisions and wastes tokens re-reading the same context. Memory Stack fixes both — smarter memory that uses up to 90% fewer tokens per search. Your agent remembers everything. Your API bill stays low.
$49 — one-time purchase, no subscriptionEvery wasted token is money out of your pocket. Memory Stack cuts the waste.
Native memory does flat text search — so your agent re-reads everything to find connections. Memory Stack builds a knowledge graph locally. Ask "what depends on service X?" and get the answer instantly, without burning tokens on irrelevant context.
Messy memory = wasted tokens. When 5 scattered notes say the same thing, your AI reads all 5 and you pay for all 5. Memory Stack merges them into one clear entry. Cleaner memory, lower cost, better answers.
When your agent forgets a decision, you explain it again. That costs tokens. Memory Stack saves key facts before compression and brings them back automatically — so you never pay to re-explain the same thing twice.
Native memory searches one way and hopes for the best. Memory Stack runs 6 engines and picks the best results across all of them. Finds the right memory on the first try — instead of your agent guessing and wasting tokens on wrong context.
Duplicates and junk in your memory cost real money every time your AI reads them. Memory Health scores your memory 0-100, shows exactly what's wasting tokens, and helps you clean it up. Think of it as a bill audit for your AI.
No paid embedding providers. No API keys. Three local AI models handle everything on your machine. The $49 you pay is the last dollar you spend — no recurring costs, no per-query charges, no surprises on your bill.
Memory Stack plugs into OpenClaw as a native memory provider. Starts saving tokens immediately.
Same agent, same conversations. One remembers more, uses fewer tokens, and finds things faster.
| Native Memory | Memory Stack | |
|---|---|---|
| Remembers longer | ||
| What happens when the conversation gets too long? | Old messages get compressed. Decisions disappear. | Key facts are saved before compression and brought back automatically. |
| Can it remember things from last week? | Only if it's still in the conversation window. | Yes. Recent memories rank higher, but older ones are still searchable. |
| Does it understand how things connect? | No. It searches text, not relationships. | Yes. A knowledge graph tracks who, what, and how they relate. |
| Can it trace how a decision evolved? | No. | Yes. Evolution chains link past decisions to current ones. |
| Saves you money | ||
| How much does each memory search cost? | Loads full text every time. More tokens, higher API bill. | Loads a summary first. Only fetches full text when needed. Uses up to 90% fewer tokens. |
| Does it waste money on duplicate results? | Can feed the same info to your AI twice. You pay for both. | Removes duplicates before sending anything to the AI. You only pay once. |
| Does the cost grow over time? | Memory piles up. More junk = more tokens = higher cost. | Auto-cleanup merges similar memories. Stays lean, cost stays flat. |
| Finds things faster | ||
| How many search methods run per query? | 2 (keyword + vector) | 6 engines, results merged by rank fusion (RRF). |
| Does it understand what you meant, not just what you typed? | Basic keyword matching. | AI query expansion rewrites your question using a local 1.7B model before searching. |
| Can it search across past conversations? | Limited. | Full session search across all past conversations. |
| Can you check if your memory is healthy? | No. | Quality score 0-100. Shows duplicates, stale entries, noise. |
| Does it need API keys or cloud services? | Vector search needs an embedding provider. | Nothing. Three local AI models handle everything offline. |
Memory Stack is a plugin for OpenClaw that replaces the built-in memory system with a smarter one. It runs 6 search engines on every query, builds a knowledge graph from your conversations, and rescues important decisions before they get lost to context compression. It runs 100% locally — no API keys, no cloud services.
OpenClaw's native memory uses 2 search engines (keyword + vector). Memory Stack uses 6, merged with rank fusion. It adds a 1.7B AI model that rewrites your questions before searching, and a 0.6B model that reranks results. It also saves key facts before context compression happens — so hour-long conversations don't lose early decisions.
Yes. Memory Stack plugs into OpenClaw as a native memory provider. It works with Telegram, CLI, and any other OpenClaw channel. No extra configuration needed — install, restart, done.
No. All 3 AI models (query expansion, reranking, embeddings) run locally on your machine. No data ever leaves your computer. No API keys to manage. Works completely offline after installation.
Every token your AI reads costs money. Memory Stack cuts that three ways: (1) It loads a short summary first and only fetches the full text when needed — up to 90% fewer tokens per search. (2) It removes duplicate results so you don't pay for the same information twice. (3) It automatically merges similar memories over time, so your memory stays lean instead of growing into an expensive mess.
When OpenClaw conversations get long, the system compresses old messages to fit the context window. Important decisions can get lost in this process. Memory Stack's rescue engine extracts key facts (decisions, deadlines, architecture choices) before compression happens, and automatically brings them back when relevant.
No. One-time purchase of $49. You own the code. No recurring fees, no SaaS, no data collection. Just files that live on your machine.