Stash makes your AI remember you. Every session. Forever. No more explaining yourself from scratch.
28
MCP tools
6
Pipeline stages
∞
Agents supported
Sound familiar?
😫 Without Stash
Hey, I'm building a SaaS for restaurants. Can you help?
Of course! Tell me about your project.
We talked about this last week... I already explained everything.
I'm sorry, I don't have access to previous conversations.
...again?
🔁 You just wasted 10 minutes re-explaining yourself. Again.
VS
😌 With Stash
Hey, continuing work on my project.
Welcome back! Last time we finalized the pricing model for your restaurant SaaS. You were about to work on the onboarding flow. Want to pick up there?
Yes! Exactly that.
Great. You also mentioned you wanted to avoid Stripe's complexity — I have that noted. Here's where we left off...
✓ Picked up instantly. Zero repetition. Full context.
New session
❌ "Who are you again?"
✓ Picks up where you left off
Your preferences
❌ Re-explain every time
✓ Already knows them
Past mistakes
❌ Repeats the same errors
✓ Remembers what didn't work
Long projects
❌ Loses track of goals
✓ Tracks goals across weeks
Token cost
❌ Grows every session
✓ Only recalls what matters
Switching models
❌ Start from zero again
✓ Memory is model-agnostic
What is Stash
Not just memory. A second brain.
Stash is a persistent cognitive layer that sits between your AI agent and the world. It doesn't replace your model — it makes your model continuous. Episodes become facts. Facts become patterns. Patterns become wisdom.
"Your AI is the brain. Stash is the life experience."
your agent
Claude, GPT, local model, anything
episodes
Raw observations, append-only
facts
Synthesized beliefs with confidence
relationships
Entity knowledge graph
patterns
Higher-order abstractions
goals · failures · hypotheses
Intent, learning, uncertainty
postgres + pgvector
Battle-tested infrastructure
Stash vs RAG
RAG gives your AI a search engine. Stash gives it a life.
You've probably heard of RAG — Retrieval Augmented Generation. It's clever. But it's not memory. Here's the difference, in plain English.
📚 RAG
"A very fast librarian"
You give it a pile of documents. When you ask a question, it searches those documents and hands you the relevant pages. That's it. It doesn't remember your conversation. It doesn't learn. It doesn't know you. Every question starts from scratch — it's just a smarter search engine over files you already wrote.
Only knows what's in your documents
Cannot learn from conversations
Cannot track goals or intentions
Cannot reason about cause and effect
Cannot notice contradictions over time
Stateless — no continuity whatsoever
You must write the knowledge first
VS
🧠 Stash
"A mind that grows"
Stash learns from everything your agent experiences — conversations, decisions, successes, failures. It synthesizes raw observations into facts, connects facts into a knowledge graph, detects contradictions, tracks goals, and builds an understanding of you that deepens over time. You don't write anything. It figures it out.
Learns from every conversation automatically
Builds a knowledge graph over time
Tracks your goals across weeks and months
Reasons about cause and effect
Self-corrects when beliefs contradict
Continuous — picks up exactly where you left off
Creates knowledge — you don't have to
📚
RAG is like...
A brilliant intern who reads your files perfectly — but forgets everything the moment they leave the room.
→
🧠
Stash is like...
A colleague who was there from day one, remembers every decision you ever made, and gets more valuable every single week.
Can you use both? Yes — RAG is great for searching documents. Stash is for remembering experience. They solve different problems. Stash just goes much, much further.
Why Stash is Different
Everyone gave AI a notepad. We gave it a mind.
Claude.ai has memory. ChatGPT has memory. They only work for themselves — locked to one platform, one model, one company. Stash works for everyone, everywhere, forever. And it goes far deeper than any of them.
Remembers you
✓
✓
✓
Works with any AI model
✗
✗
✓
Works with local / private models
✗
✗
✓
You own your data
✗
✗
✓
Open source
✗
✗
✓
Background consolidation
✗
✗
✓
Goals & intent tracking
✗
✗
✓
Learns from failures
✗
✗
✓
Causal reasoning
✗
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✓
Agent self-model
✗
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✓
What it gives your AI
A notepad
A notepad
A mind
The Problem
🧠
Brilliant brain, no experience
AI models reason brilliantly but remember nothing. Every session you re-explain who you are, what you need, and what you've already tried. You're training the same student every single day.
💸
Context windows are expensive
The workaround is stuffing full conversation history into every prompt. It's slow, expensive, and you still hit the limit. You're paying for tokens that repeat the same facts over and over.
🔄
Agents repeat their mistakes
Your agent tried something, it failed, and next session it tries the exact same thing again. There's no mechanism to carry lessons forward. Every failure is forgotten.
🔒
Memory is a platform privilege
Only a handful of AI platforms offer memory — and only for their own models. Your custom agent, your local LLM, your Cursor setup? They all start blind. Memory shouldn't be a premium feature.
Express Setup
Up and running in 3 commands.
No infrastructure to set up. No dependencies to install manually. Docker Compose handles everything — Postgres, pgvector, Stash, all wired together and ready.
1
Clone the repo
2
Copy .env.example → .env and set your API key + model preferences
3
Run docker compose up — that's it. Stash is live.
terminal
$git clone https://github.com/alash3al/stash
$cd stash
$cp .env.example .env
# edit .env with your API key,
# models and STASH_VECTOR_DIM
$docker compose up
✓ postgres + pgvector ready
✓ stash migrations applied
✓ mcp server listening
✓ consolidation running in background
$
⚠️Set STASH_VECTOR_DIM in your .env before first run. It cannot be changed after initialization.
01
📝
Episodes
Raw observations stored as they happen
02
💡
Facts
Clustered episodes synthesized by LLM
03
🕸️
Relationships
Entity edges extracted from facts
04
🔗
Causal Links
Cause-effect pairs between facts
05
🌀
Patterns
Abstract higher-order insights
06
⚖️
Contradictions
Self-correction and confidence decay
NEW
07
🎯
Goal Inference
Facts automatically tracked against active goals. Progress detected, contradictions surfaced.
NEW
08
💥
Failure Patterns
Detect repeated mistakes. Extract failure patterns as new facts. The agent stops repeating itself.
NEW
09
🔬
Hypothesis Scan
New evidence passively confirms or rejects open hypotheses. No manual intervention needed.
MCP Integration
Two commands. Any agent.
Stash speaks MCP natively. Drop it into Claude Desktop, Cursor, or any MCP-compatible agent in under 5 minutes. No SDK. No vendor lock-in. Your agent remembers you everywhere.
28 tools covering the full cognitive stack — from raw remember and recall all the way to causal chains, contradiction resolution, and hypothesis management.
Claude DesktopCursorOpenCodeCustom AgentsLocal LLMsAny MCP Client
Call init and Stash creates a /self namespace scaffold. The agent uses its own memory layer to build and maintain a model of its own capabilities, limits, and preferences.
/self/capabilities
What I can do well
The agent remembers where it excels and recalls these when planning how to approach a task.
/self/limits
What I struggle with
Recorded failures and known weaknesses. The anti-repeat mechanism. Never make the same mistake twice.
/self/preferences
How I work best
Learned preferences for how to operate. The agent develops a working style over time, not just facts.
Autonomous Loop
An agent that never stops learning.
Give your agent a 5-minute research loop. It orients from past memory, researches a topic it chooses itself, invents new connections, consolidates what it learned, and closes gracefully — ready to pick up next time.
Run it as a cron job. Every 5 minutes, your agent gets smarter.
Recall context, active goals, open hypotheses, past failures
02
Research
Search the web on a topic the agent chooses itself
03
Think
Surface tensions, gaps, contradictions in what it now knows
04
Invent
Generate something new — a hypothesis, pattern, or discovery
05
Consolidate
Run the pipeline. Synthesize raw episodes into structured knowledge
06
Reflect + Sleep
Write a session summary. Set context for next run. Stop.
⚡
Stash itself runs on OpenRouter. The author runs Stash locally pointed at OpenRouter — access to hundreds of models, one API key, zero infrastructure.
☁️
Cloud APIs
OpenRouter gives you access to hundreds of models — GPT, Claude, Gemini, Mistral — all behind one OpenAI-compatible endpoint. Point Stash at it and pick any model for embedding and reasoning.
🏠
Local Models
Running Ollama locally? Stash works out of the box. Use Qwen, Llama, Mistral, or any model you've pulled — your memory stays fully private, fully offline.
🔧
Self-Hosted
vLLM, LM Studio, llama.cpp server, Together AI, Groq — if it speaks the OpenAI API format, Stash speaks it back. Same provider serves both models.
⚠️
Set STASH_VECTOR_DIM before your first run and never change it. pgvector locks the embedding dimension at initialization — changing it later requires a full database reset. The default embedding model is openai/text-embedding-3-small with STASH_VECTOR_DIM=1536.
