OpenViking

7 Viral AI Agent Repos: 4 Checks Before You Install One Seven popular AI agent repos, four checks before you install any of them (checked 20-21 Oct 2026; star counts change daily). 1. Who made it? Anthropic Cybersecurity Skills is an independent community project by @mukul975. Its README says "Not affiliated with Anthropic PBC". 2. Is it the right repo? Two separate curated lists are both called Awesome Harness Engineering (ai-boost and walkinglabs), and several repos use "Agent Memory"-style names. Match the GitHub owner, not just the name. 3. The licence. OpenViking is AGPL-3.0, which has conditions if you offer a modified version as a service. Browser Use, Scientific Agent Skills and Diagram Design are MIT; agentmemory and Anthropic Cybersecurity Skills are Apache-2.0. Read the licence before business use. 4. The real cost. Browser Use's code is MIT and free, but you pay for the AI model you connect to it. Its optional Browser Use Cloud starts from $0.02 per browser-hour (the project's own figure). Also: agentmemory's "95.2% retrieval" and Scientific Agent Skills' "250,000+ scientists" are the projects' own claims. Diagram Design has a Claude Code plugin and also works with other agents. Repos (all 7, public on GitHub): Browser Use: browser-use/browser-use agentmemory: rohitg00/agentmemory Scientific Agent Skills: K-Dense-AI/scientific-agent-skills Diagram Design: cathrynlavery/diagram-design Anthropic Cybersecurity Skills (community project, not made by Anthropic): mukul975/Anthropic-Cybersecurity-Skills Awesome Harness Engineering (two lists share the name): ai-boost/awesome-harness-engineering and walkinglabs/awesome-harness-engineering OpenViking (AGPL-3.0): volcengine/OpenViking Browser Use Cloud pricing: browser-use.com Which check would have caught you out? #AI #AIagents #GitHub #shorts Made with AI-generated visuals and voice.

I built OptMem plugin for Hermes. It's one of the 24 in the official catalog. Zero dependencies. No database, no embeddings, no API key. An append-only log: summaries keep context small, the raw record is never deleted. OptMem from @VictorTaelin rarf/optmem-hermes-plugin

6 открытых репозиториев для ИИ-агентов 6 открытых репозиториев для ИИ-агентов, которые экономят месяцы работы. Один превращает Claude в учёного. 1. Браузер для агента: browser-use/browser-use 2. Память агента: rohitg00/agentmemory 3. Наука, 177 навыков: K-Dense-AI/scientific-agent-skills 4. Схемы: cathrynlavery/diagram-design 5. База памяти и знаний: volcengine/OpenViking 6. Обвязка агентов: ai-boost/awesome-harness-engineering Открой Claude Code, вставь нужную ссылку и напиши: «Поставь это и покажи на примере». Ставь по одному, а не все сразу. Нужна подписка Claude Pro или Max. В терминал ничего вводить не нужно. Бесплатная игра и материалы из всех роликов: ig.onai.academy #shorts #claude #ииагенты #github

Fixed a data-loss bug in OpenViking's agent memory: a failed upsert was still deleting the original memory file. My PR skips the delete when the write fails, verified with the issue's repro. OpenViking#6218 #OpenSource #AIMemory #RAG

OpenViking: The Context Database for AI Agents (40k stars) What it does All text below is quoted directly from the project README. >Self-evolving Context Database for AI Agents. Unify Agent Memory, Knowledge RAG and Skills. Why OpenViking: - One filesystem for knowledge, memory, and skills. Resources hold documents and code; memories retain user preferences and experience; skills define how to perform tasks, not just extracted facts, but the full context, each with a viking:// URI for browsing and retrieval. - Search a directory, not the whole index. Scope semantic search to a project or memory subtree instead of scanning a flat vector pool. find runs a query directly; search plans retrieval from session context. - Read the summary before the source. Generated directory abstracts (L0) and overviews (L1) let agents judge relevance before opening full content (L2). - Sessions become files you can read. Committing a session archives the conversation and extracts memories as Markdown you can inspect, edit, and merge. With VikingBot enabled, ov compile organizes source material into a wiki, knowledge graph, or report. - L0 (Abstract): a one-sentence summary for quick relevance checks. - L1 (Overview): core information and usage scenarios for planning. - L2 (Details): the full original data, read only when needed. >Install OpenViking and run the setup wizard, which configures the models: uv tool install openviking --upgrade && openviking-server init Repository volcengine/OpenViking Repository stats |Metric|Value| |:-|:-| |Stars|40,055| |Forks|3,187| |Last release|v0.5.0 (2026-10-09)| |Languages|Python 72.9%, TypeScript 11.9%, Rust 11.6%| |Contributors|324| |AI contributor accounts (Claude / Codex / Cursor / Copilot)|cursoragent: 4, codex: 7, claude: 208| |AI co-authored commits|Claude: 222, Codex: 3, Cursor: 4| Main contributor |Field|Value| |:-|:-| |Name|Qin Haojie| |GitHub|@qin-ctx| |Commits to this repo|434 (top contributor)| |Bio|Be the CEO of Agents !| |Company / Location|Bytedance / beijing| |Website|x.com| |Followers|145| |Public repos|15| |On GitHub since|2025-09-26| ------------------------------------------------------------------------------------------------- Discover the best GitHub repos without the endless scrolling. Get top repos organized by category (AI, DevOps, Databases & more), plus key takeaways from developers' most useful comments - all in one free, twice-weekly newsletter. 👉 Get the best repos & community insights - Free

OpenViking's Self-Evolving Context Database: How Agent Memory, RAG, and Skills Unify Under One Storage Layer Most agent frameworks bolt memory onto RAG, then bolt skills onto both. OpenViking flips the problem: it treats agent context (memory, knowledge, and skills) as a unified database that evolves its own schema based on agent query patterns. Instead of three separate systems, you get one storage layer with self-evolution primitives. The project, backed by Volcengine (ByteDance's cloud arm), has 40K+ stars and positions itself as a "Context Database for AI Agents." The DSH-plugin architecture and self-evolving indexing suggest production-scale deployment patterns, not a wrapper around existing vector stores. What OpenViking Actually Does OpenViking provides a single storage abstraction for three agent context types: - Agent Memory: Episodic and semantic memory of past interactions, decisions, and outcomes. - Knowledge RAG: Document embeddings, retrieval strategies, and reranking logic. - Skills: Callable tools, plugins, and their execution history. The "self-evolving" mechanism monitors query patterns, retrieval latency, and agent feedback to adjust indexing strategies, schema fields, and embedding models without manual intervention. When an agent repeatedly queries a specific knowledge domain, OpenViking can promote those embeddings to a hot tier or refine chunking boundaries. DSH-Plugin Architecture The DSH-plugin model separates three concerns: - Data layer (D): Stores raw context, embeddings, and metadata in a versioned schema. - Schema layer (S): Defines how context is indexed, chunked, and retrieved. This layer can mutate based on usage patterns. - Handler layer (H): Exposes retrieval, insertion, and evolution APIs to agent runtimes. Plugins extend the handler layer with domain-specific logic (e.g., code search, customer support memory, financial document RAG). Each plugin can register its own schema evolution rules. Why This Matters Traditional agent stacks treat memory and RAG as separate databases. You end up with: - Duplicate embeddings (one for memory, one for RAG). - Inconsistent versioning when an agent's behavior changes. - No shared indexing strategy across context types. OpenViking's unified layer means a single embedding model, one versioning strategy, and shared evolution logic. When an agent's retrieval patterns shift, the entire context database adapts. Self-Evolution Primitives The self-evolution mechanism tracks four signals: 1. Query latency: Slow retrievals trigger index restructuring or hot-tier promotion. 2. Retrieval accuracy: Low precision/recall scores prompt embedding model swaps or chunking adjustments. 3. Agent feedback: Explicit thumbs-up/down or implicit task success rates. 4. Schema drift: New context types (e.g., a new skill plugin) trigger schema expansion. Evolution decisions are logged as versioned migrations. You can roll back to a previous schema if agent performance degrades. Example Evolution Flow # Agent repeatedly queries customer support tickets from Q4 2025 # OpenViking detects high query frequency for date range [2025-10-01, 2025-12-31] evolution_event = { "trigger": "query_frequency", "context_type": "knowledge_rag", "pattern": "date_range_filter", "action": "create_temporal_index", "params": { "field": "ticket_date", "range": ["2025-10-01", "2025-12-31"], "index_type": "btree" }, "version": "schema_v47" } # OpenViking applies migration, logs rollback path # Next queries hit the new index, latency drops 60% Storage Layer Trade-Offs | Aspect | OpenViking Approach | Traditional Vector Store | |--------|---------------------|--------------------------| | Schema flexibility | Self-evolving, versioned migrations | Static schema, manual updates | | Context unification | Memory, RAG, skills in one layer | Separate databases per type | | Indexing strategy | Adaptive based on query patterns | Pre-configured, rarely changed | | Rollback support | Built-in version history | Manual backups, risky restores | | Embedding duplication | Single model across context types | Often duplicated per use case | | Observability | Evolution events logged as first-class | Metrics bolted on after the fact | The downside: complexity. Self-evolution adds a control plane that must be monitored. If evolution logic misfires, you can degrade retrieval quality across all agents. Rollback paths are critical. Deployment Shape OpenViking runs as a standalone service with three components: 1. Context API: REST/gRPC endpoints for agent reads/writes. 2. Evolution Engine: Background process that analyzes query logs and applies schema migrations. 3. Plugin Registry: Manages DSH-plugin lifecycle (install, update, rollback). Agents connect via a thin client library. The context database handles all storage, indexing, and evolution logic. Failure Modes - Evolution thrashing: Rapid schema changes if query patterns are noisy. Mitigation: rate-limit migrations, require minimum sample size. - Rollback cascades: Rolling back one schema version may invalidate dependent plugins. Mitigation: dependency graph for migrations. - Hot-tier exhaustion: Promoting too many embeddings to fast storage. Mitigation: LRU eviction with agent priority hints. - Plugin conflicts: Two plugins evolve the same schema field in incompatible ways. Mitigation: schema namespacing per plugin. Observability Hooks OpenViking exposes evolution events as structured logs: { "event_type": "schema_migration", "version": "schema_v48", "trigger": "retrieval_accuracy_drop", "context_type": "agent_memory", "action": "reembed_with_new_model", "model": "text-embedding-3-large", "affected_records": 142000, "rollback_path": "schema_v47", "timestamp": "2026-10-16T14:22:00Z" } You can pipe these to your existing observability stack (Prometheus, Grafana, Datadog). Key metrics: - Evolution frequency per context type. - Rollback rate (high rollback rate signals unstable evolution logic). - Query latency before/after migration. - Embedding model swap frequency. Security Boundaries Because OpenViking unifies memory, RAG, and skills, access control becomes critical. A compromised agent could leak memory from other agents or inject malicious skills. Recommended boundaries: - Namespace isolation: Each agent gets a dedicated context namespace. Cross-namespace reads require explicit grants. - Plugin sandboxing: Skills run in isolated containers with limited syscall access. - Audit logs: All context writes and evolution events are immutable, signed logs. - Encryption at rest: Embeddings and raw context are encrypted. Evolution engine operates on encrypted data where possible. When to Use OpenViking Good fit: - Multi-agent systems where memory, RAG, and skills overlap (e.g., customer support bots that learn from ticket history and call external APIs). - Long-running agents that need adaptive indexing without manual tuning. - Teams that want a single source of truth for agent context instead of stitching together vector stores, SQL databases, and plugin registries. Avoid if: - You have a single-purpose agent with static retrieval patterns. The evolution overhead is wasted. - Your compliance regime forbids automated schema changes. OpenViking's self-evolution is its core value. - You need sub-millisecond retrieval latency. The evolution engine adds a control plane layer that introduces some overhead. Technical Verdict OpenViking solves a real problem: agent context fragmentation. By treating memory, RAG, and skills as a unified database with self-evolution primitives, it reduces duplication and manual tuning. The DSH-plugin architecture provides clean separation between storage, schema, and handlers. The trade-off is operational complexity. Self-evolution requires monitoring, rollback strategies, and careful plugin management. If your agents have stable retrieval patterns, a simpler vector store may suffice. But for multi-agent systems with shifting context needs, OpenViking's unified layer is worth the overhead. The Volcengine backing suggests production-scale deployment patterns are already in play. If you're building agent infrastructure that will evolve over months or years, OpenViking's self-adapting storage layer is a strong foundation. Source Links - OpenViking GitHub Repository - OpenViking Documentation - OpenViking Live Demo

40,000 stars. new logo. same direction ⛵ Explore what's next with viking:// volcengine/OpenViking t.co

40K stars on OpenViking! 🌟 To everyone who starred the repo, filed an issue, sent a PR, answered a question in the group, or just tried it and told us what broke: this sail is yours. Let's keep building ⛵ volcengine/OpenViking t.co

Architectural Convergence or Borrowed Design? A Timeline Comparison Between OpenViking Gateway and TencentDB Agent Memory Did OpenViking’s new Gateway take inspiration from TencentDB Agent Memory? A timeline comparison I’ve been comparing OpenViking and TencentDB Agent Memory, and I’m wondering whether OpenViking’s recent Gateway design was influenced by TencentDB’s earlier work. I initially noticed three overlaps: a model API gateway, a memory dashboard, and context compression. After checking the history, the gateway seems like the strongest lead. The other two don’t support the same conclusion. 1. Gateway: a specific architectural overlap TencentDB’s July 22 commit snapshot already includes a MemoryProxy that sits between an agent and its model provider. It forwards OpenAI/Anthropic requests, injects memory and skills, and writes conversations back to the memory service: github.com/TencentCloud/TencentDB-Agent-Memory/bl… OpenViking’s Context Gateway PR was opened in early October and merged on October 6. It introduces a similar approach: change the client’s base URL and key, inject profile/memory/skill context, forward model requests, and capture conversations: OpenViking#5625 The overlap is more specific than “both support agent memory”: Agent → model API proxy → memory/context injection → upstream model → conversation capture There’s also a detail I’d like to understand better. TencentDB’s July 26 proposal describes an HMAC message-history chain for session identification. OpenViking’s Gateway describes a prefix hash chain for replay records: TencentDB-Agent-Memory#584 Those mechanisms serve different purposes, so I’m not claiming they’re equivalent. The TencentDB PR was also closed without merging—it’s evidence of a public proposal, not a shipped feature. 2. Dashboard: the timeline doesn’t support my initial suspicion OpenViking already had a Console merged in March, followed by Web Studio in May: OpenViking#383 OpenViking#2128 So “OpenViking now has a dashboard too” isn’t persuasive evidence of borrowing from TencentDB. 3. Compression: similar goals, different mechanisms OpenViking had tool-output compression proposals in April. One explicitly says it references Claude Code’s tool-result management strategy, although that PR wasn’t merged: OpenViking#1743 Its newer Gateway compaction summarizes older conversation history while keeping the original history searchable. That differs from TencentDB’s Mermaid-based task-memory approach. I don’t think “both compress context” establishes influence by itself. What I’m trying to establish My current suspicion is specifically about the Gateway integration pattern, not OpenViking’s entire architecture. TencentDB has an earlier implementation snapshot, and OpenViking later introduced a similar combination of capabilities. That establishes overlap and a potential timeline—not direct borrowing. I haven’t found copied code or an acknowledgment of influence, and a commit timestamp alone doesn’t prove when the code became public. Has anyone compared their session identification, injection placement, prompt-cache preservation, or conversation capture implementations? Is this independent convergence on an obvious architecture, a shared predecessor, or possible borrowing? I’d appreciate specific code references, earlier examples, or corrections to the timeline. Keywords: agent memory / agent context

Projetos open source do GitHub: #10 a #6 | Vol. 6 #shorts Os projetos #10 a #6 do Top 10 open source do GitHub, Vol. 6, 10 segundos para cada um. #10 Terminal Code: zenbu-labs/terminal-code #9 CyberStrike: CyberStrikeus/CyberStrike (só em sistemas autorizados) #8 Android Reverse Engineering: SimoneAvogadro/android-reverse-engineering-skill #7 OpenViking: volcengine/OpenViking #6 TestSprite: TestSprite/testsprite-cli Vídeo completo com os 10 no canal Hallen Tech. Oferecimento: Hallen Tech, hallentech.com #shorts #opensource #github