OpenViking

Volcengine's context database: memories, resources and skills as one `viking://` filesystem agents ls, tree and grep — L0/L1/L2 tiers, traceable retrieval, sessions distilled into memory.

31,798 2,430 Python AGPL-3.0updated today

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Curator's take

Use it when opaque vector recall has burned you: every query keeps the directory-browsing trajectory that produced it, so a wrong answer is debuggable, and the L0/L1/L2 tiers let an agent judge relevance before paying for full content. It pays off most for long-lived agents with heavy reference material — repos, docs, per-user preferences — and there are documented hooks for Claude Code, Codex, Cursor, OpenCode, LangChain/LangGraph and plain MCP clients. Costs: it's a server plus semantic pre-processing on every write, so ingest is slow and not free; AGPLv3 rules it out of many closed products; and there's an obvious managed-SaaS path on Volcano Engine behind it. Wrong tool if you just want an in-process vector index.

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README.md

OpenViking: The Context Database for AI Agents

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What is OpenViking

OpenViking is an open-source context database for AI agents. It stores memories, resources, and skills as one virtual filesystem under the viking:// protocol, so an agent browses its own context with ls, tree, and find instead of querying a black-box vector store. Content is processed into three tiers — L0 abstract, L1 overview, L2 details — and loaded on demand. Every retrieval leaves a trajectory you can watch and debug. Full introduction: Getting started.

The OpenViking Studio playground — a live demo you can open in the browser, no installation required.

Why OpenViking

One filesystem for all context. Memories, resources, and skills each get a viking:// URI. Agents locate and manipulate context deterministically, like a developer working with files. → Viking URI · Context types
Tiered loading cuts token spend. Every entry is processed into L0 (abstract), L1 (overview), and L2 (details) on write, then loaded only as deep as the task requires. → Context layers
Directory recursive retrieval. Vector search first locates the highest-scoring directory, then drills down layer by layer, so results arrive with their surrounding context intact. → Retrieval
Observable retrieval. Each query preserves its directory-browsing trajectory. When a result looks wrong, you can see exactly which path produced it. → [Retrieval](https://docs.openviking.ai/en/

Continue your stack

What teams reach for next — and why each earns a place beside OpenViking. Ranked by curator confidence.

Pairs well with

LangGraph

Build stateful, multi-actor LLM apps as graphs — durable execution, human-in-the-loop, streaming.

Why it fitsDocumented LangChain/LangGraph integration: OpenViking supplies the persistent context and post-session memory extraction that LangGraph's own checkpointing deliberately doesn't cover.

Alternative to See all + compare →

MemMachine

Long-term memory layer for AI agents — episodic (graph), profile (SQL) and working memory behind Python/TS SDKs, REST and MCP; ships LangChain, LangGraph, CrewAI and LlamaIndex integrations.

Why it fitsBoth are long-term memory backends for agents; MemMachine exposes episodic, profile and working memory through SDKs, OpenViking makes the entire context a browsable namespace with tiered summaries. Pick by whether you want an API or a filesystem agents explore.

Acontext

Skill memory layer for agents: auto-captures learnings from runs into plain Markdown skill files you can read, edit, git and share across frameworks — memory without an opaque store.

Why it fitsShared premise — agent memory should be inspectable artifacts, not an opaque store. acontext keeps it as plain Markdown skill files in git; OpenViking runs a server with semantic tiering and vector-guided directory retrieval. acontext for git-native simplicity, OpenViking for scale and traceable recall.

Chroma

Open-source embedding database for building AI apps with retrieval.

Why it fitsOpenViking is explicitly a reaction to querying a black-box vector store: Chroma hands you the embedding index as a library primitive, OpenViking layers an opinionated filesystem, tiering and session distillation on top. Take Chroma when you want to design retrieval yourself.