agentmemory vs OpenViking
Two of the top memory, side by side: score, setup, license, activity and what each review found.
agentmemory
Persistent memory server for coding agents, exposed over MCP and REST
OpenViking
Context database exposing agent memory, knowledge and skills as a filesystem
| What we compare | agentmemory | OpenViking |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 48, known | 73, popular |
| Freshness | 100, active | 100, active |
| Maintenance | 81, healthy | 87, healthy |
| Easy to run | 83, very easy | 67, easy |
| Agent-ready | 30, minimal | 0, none |
| Facts from GitHub and the README | ||
| Stars | 29.3k | 39.6k |
| License | Apache-2.0 (permissive) | AGPL-3.0 (copyleft) |
| Last commit | Oct 2026 | Oct 2026 |
| Last release | Oct 2026 | Oct 2026 |
| Language | TypeScript | Python |
| Docker | Yes | Yes |
| GPU | Not needed | Not needed |
| arm64 or Apple Silicon | Mentioned | Mentioned |
agentmemory
agentmemory captures what a coding agent does across sessions, stores it as searchable memory, and injects relevant context at the start of the next session. It runs as a local Node.js server on the pinned iii engine and connects to agents through hooks, MCP, or REST, with 20 adapters listed. Keyless mode uses BM25 search; vector embeddings need a provider or the local Xenova/all-MiniLM-L6-v2 model.
Who it is for: Developers using coding agents who want memory kept across sessions
Strengths
- No external database; state lives in a local iii engine data directory
- Works with 20 listed agents through hooks, MCP, or REST
- Keyless BM25 mode works without any API key
- Local embeddings via EMBEDDING_PROVIDER=local after a one-time model download
Weaknesses
- Keyless mode has no vector search, so semantic queries can return nothing
- Pinned to iii-engine v0.22.1; refuses to attach to other engine versions
- Native Windows needs manual iii.exe install; WSL2 or Docker recommended
- Uses four local ports (3111, 3112, 3113, 49134)
- no GPU
- Docker + Compose
- Needs Node.js 20+, iii-engine v0.22.1
- Models: Xenova/all-MiniLM-L6-v2 (local embeddings)
- port 3113
OpenViking
OpenViking organises everything an agent knows as a viking:// virtual filesystem of resources, memories and skills, browsed with ls, tree, read and grep, with search scoped to a subtree. Each directory carries generated summaries so agents read full content only when needed. The server needs Python 3.10+ plus an embedding model and a VLM; plugins cover Claude Code, Codex, Cursor and OpenClaw.
Who it is for: Teams wanting transparent, file-like memory shared across coding agents
Strengths
- Memory is inspectable and editable as Markdown files under viking:// URIs
- LoCoMo accuracy 80 to 83% for OpenClaw, Hermes and Claude Code at far fewer tokens
- Python, Go and TypeScript SDKs plus HTTP API; multi-tenant accounts and ACLs
- Hosted Studio playground at openviking.ai/studio; Railway one-click deploy
Weaknesses
- AGPL-3.0 license
- Needs both an embedding model and a vision-language model from a provider
- Memory plugin installer is macOS/Linux only; Windows uses the beta desktop app
- Benchmarks were run with Volcengine Doubao models
- no GPU
- Docker + Compose
- Models: Volcengine, OpenAI, Codex OAuth, Kimi, GLM