Every conversation with an AI starts from zero. The model doesn't know your preferences, your projects, or your history. It can't build on past interactions because it has no memory of them.
This is the problem we set out to solve with takizen.
The Context Problem
Modern LLMs have impressive capabilities, but they're stateless. Each API call is independent. The "memory" in ChatGPT and Claude is a layer on top — a retrieval system that fetches relevant context from previous conversations.
That approach has limitations:
- Scope: Limited to what fits in context window
- Granularity: All-or-nothing; can't retrieve specific facts
- Semantics: Keyword-based retrieval misses meaning
- Persistence: Locked to one platform; not portable
We wanted something better. Memory that understands meaning. Memory that persists across sessions, platforms, and models. Memory that learns and adapts.
Our Approach
takizen treats memory as infrastructure, not an afterthought. Here's what that means:
Semantic Storage
Memories are stored as vector embeddings, not just text. This enables semantic search — finding memories by meaning, not keyword matching. Ask "What are my tech preferences?" and retrieve "User prefers Python for data science" even if the words don't overlap.
Namespace Isolation
Every user (or agent) gets an isolated namespace. Your memories never mix with others'. This isn't just privacy — it's clean architecture. Namespaces can have different settings, rate limits, and retention policies.
Strength and Decay
Not all memories are equally important. We track "strength" for each memory, which increases with recall and decays with disuse. Unused memories gradually fade (and eventually archive), while frequently-accessed knowledge stays strong.
Relationship Graphs
Memories connect to other memories. A project memory links to tech stack memories, which link to pattern memories. This graph structure enables discovery — finding related context even when it wasn't explicitly queried.
The MCP Standard
We built takizen on the Model Context Protocol (MCP) because we believe memory should be portable. Your takizen namespace works with Claude, Cursor, or any MCP-compatible client.
This matters. Your memory shouldn't be locked to one AI provider. It should be yours, portable across tools and platforms.
What's Next
takizen is just getting started. We're working on:
- CoT Feedback: Letting agents learn which memories work best in reasoning chains
- Memory Consolidation: Automatically summarizing and clustering related memories
- Multi-modal Memory: Supporting images, code, and structured data
The vision is an AI that truly remembers. That learns from every interaction. That gets better the more you use it.
That's takizen.