Without Long-Term Memory, No Agent Deserves to Be Called a Personal Assistant

I've been using OpenClaw since the first day it got traction in the Chinese-speaking community. The configuration headaches were manageable. But long-term memory was the one thing that kept breaking me — because it was the feature that drew me in. Local memory storage (not tied to any single AI client) plus persistent memory across sessions. After actually using it, even through the latest update (v2.19), the problem still wasn't solved. Until...

Part 1: What the Real Problem Is

Large language models have a fundamental flaw: no long-term memory across sessions. Every new conversation starts from zero. You spend two hours with Claude working through a business strategy — next time, it remembers nothing. You do market analysis in Gemini, switch to ChatGPT, and have to rebuild all the context from scratch.

The deeper problem: every AI is a data island.

Part 2: Three Stages of Finding a Solution

Stage 1: Pure Markdown Files

The earliest approach was minimal: write important content into .md files manually, keep them in one directory, paste them into conversations as needed.

Pros:

• Any AI can read it, zero lock-in
• Data is fully yours
• Zero cost, zero configuration

Fatal flaws:

• Entirely manual — one lazy day and the system breaks
• Once you have many files, you don't know which one to paste
• AI sees isolated documents with no awareness of relationships between them
• No intelligent retrieval — you have to remember where things are yourself

This held up for a while. As information volume grew, it became unworkable.

Stage 2: OpenViking — ByteDance's Open-Source Approach

ByteDance Volcengine open-sourced OpenViking (github.com/volcengine/OpenViking), a context database designed for AI agents. 2.9K GitHub stars. Its core innovation is a filesystem paradigm — instead of fragmented vector storage, it manages everything like a local file system:

• L0 — minimal summary (tens of tokens, always in context)
• L1 — structured outline (loaded on demand)
• L2 — full content (fetched only when needed)

Smart design — tiered loading saves a lot of tokens. It also supports recursive directory retrieval, combining semantic search with path-based location, with traceable retrieval paths (you can debug where things went wrong).

I used it for a day and gave up. Three reasons:

① Deep interface coupling. All operations go through proprietary CLI commands: viking_search / viking_add. System config files and skill files all embed this interface. If OpenViking breaks or you want to switch tools, everything is tangled together. When I cleaned up my config, just finding scattered viking_* references took half an hour.

② It's a developer tool, not a personal memory system. OpenViking solves "how developers manage context for agents" — not "how a user's knowledge flows across AI tools." Fundamentally different use cases.

③ Cross-AI sharing doesn't work natively. Getting Claude Code, Gemini, and ChatGPT to share the same memory requires separate integrations for each. Not how it was designed.

Stage 3: Nowledge Mem

After dropping OpenViking, I found Nowledge Mem (github.com/nowledge-co/nowledge-mem), built by an independent team. One line that nailed the problem: "Stop opening 5 AI apps to find that one conversation."

The core difference: knowledge graph vs file index

Dimension	OpenViking	Nowledge Mem
Storage	Filesystem + layered index	Knowledge graph (nodes + relationship edges)
Retrieval	Semantic + directory path	5-signal fusion scoring
Memory relationships	Implicit via directory structure	Explicit edges, cross-topic traversal
Cross-AI support	Requires separate integration per AI	Native multi-AI design
Data ownership	Semi-transparent proprietary format	Local-first, fully transparent
Time awareness	None	Dual temporal (event time + record time)

Nowledge Mem's 5-signal retrieval runs simultaneously on every search:

1. Semantic vector similarity
2. BM25 keyword exact match
3. Graph relationship propagation (neighboring nodes get score boost)
4. Community clustering (same-topic cluster prioritized)
5. Time decay + importance weighting

Result: I search "product direction" and it surfaces all related project memories from my past sessions — because those projects have edges connecting them in the graph. File indexing can't do that.

Part 3: The Final Architecture

Input sources
Claude Code / Gemini / ChatGPT / TARS
         ↓
   Nowledge Mem (unified memory layer)
   Local graph · 5-signal retrieval · auto-distillation
         ↓
   OpenClaw + TARS (Mac Mini, running 24/7)
   Read memory → respond → write back to memory

Four design principles:

Data sovereignty first — all memory stored in a local graph, no cloud lock-in. Switch AI tools tomorrow, your memory stays.

Cross-AI, not AI-bound — Claude, Gemini, ChatGPT all write to the same graph. Memory is a shared asset, not any tool's private property.

Don't over-engineer — dropped self-hosted Chroma vector DB, QMD, and other custom solutions. Use mature existing products. Don't reinvent the wheel.

Part 4: Why an Independent Team Beat ByteDance

ByteDance built OpenViking to solve developer agent engineering problems. Target user: developers integrating memory into their own products. So the design naturally skews toward tooling and APIs.

Nowledge Mem was built to let ordinary users' knowledge flow across AI tools. Target user: people like me who use 5 AI tools simultaneously. So the design is naturally cross-platform, local-first, and fully transparent about user data.

Different user profiles produce completely different products.

Remember: the memory is yours, not any AI's.