hindsight vs OpenViking
Two of the top memory, side by side: score, setup, license, activity and what each review found.
hindsight
Agent memory server with retain, recall and reflect operations
OpenViking
Context database exposing agent memory, knowledge and skills as a filesystem
| What we compare | hindsight | OpenViking |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 81, widely used | 73, popular |
| Freshness | 100, active | 100, active |
| Maintenance | 93, healthy | 87, healthy |
| Easy to run | 50, easy | 67, easy |
| Agent-ready | 85, ready | 0, none |
| Facts from GitHub and the README | ||
| Stars | 48.3k | 39.6k |
| License | MIT (permissive) | AGPL-3.0 (copyleft) |
| Last commit | Oct 2026 | Oct 2026 |
| Last release | Oct 2026 | Oct 2026 |
| Language | Python | Python |
| Docker | Yes | Yes |
| GPU | Not stated | Not needed |
| arm64 or Apple Silicon | Mentioned | Mentioned |
hindsight
Hindsight stores agent memories in banks and extracts facts, entities and timestamps from retained text using an LLM. Recall runs semantic, BM25, graph and temporal retrieval in parallel, then reranks the merged results. Background jobs consolidate facts into observations and mental models. It exposes a REST API, Python, Node.js and Go clients, a CLI, and a per-bank MCP endpoint.
Who it is for: Developers adding long-term memory to LLM agents and coding assistants
Strengths
- Four parallel retrieval strategies merged with reciprocal rank fusion and cross-encoder reranking
- Works with 25+ LLM providers, including local ollama, lmstudio and llamacpp
- Built-in MCP endpoint per bank, plus 60+ listed integrations
- Embedded mode runs in-process via pip with a bundled pg0 database
Weaknesses
- Every retain call requires an LLM, adding cost and latency
- Accuracy claims are the vendor's own benchmarks; independent reproduction is partial
- Managed Cloud and Enterprise tiers exist; feature differences from self-hosted are unclear
- Minimum RAM and GPU needs are not stated in the README
- Docker
- Needs LLM provider (hosted or local), PostgreSQL (embedded pg0 by default), Oracle AI Database (optional)
- Models: openai, anthropic, gemini, groq, bedrock
- port 9999
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