Browse MCP tools for persistent memory, knowledge graphs, and context management in AI agent workflows.
MCP Memory & Knowledge tools are AI-powered software designed to help developers and teams tackle mcp memory & knowledge-related tasks more efficiently. These tools are typically published as open-source projects on GitHub and can be integrated into existing workflows via MCP (Model Context Protocol), Claude Skills, or standalone agent frameworks. On Agent Skills Hub, we index 10 quality-scored mcp memory & knowledge tools across languages including Rust, Python, JavaScript.
In 2026, the AI agent ecosystem is maturing rapidly. MCP Memory & Knowledge tools can significantly boost development efficiency by automating repetitive tasks, reducing human error, and providing intelligent suggestions. The top 3 tools — yantrikdb, Dragon-Brain, pensyve — have earned an average of 247 GitHub stars, reflecting strong community validation. 9 of the listed tools come with clear open-source licenses, ensuring freedom to use and modify.
When choosing a mcp memory & knowledge tool, consider these factors: 1) Community activity — GitHub stars and recent commit frequency indicate reliability; 2) Integration method — check if it supports MCP, Claude, or your preferred agent framework; 3) Language compatibility — the most common language in this list is Rust; 4) Quality score — Agent Skills Hub's composite score evaluates code quality, documentation completeness, and maintenance activity. Our recommendation: start with yantrikdb — it ranks highest in both star count and quality score.
Cognitive memory engine for AI agents — temporal decay, contradiction detection, autonomous consolidation, knowledge graph, ANN recall via HNSW. Embeddable Rust library with Python bindings; powers yantrikdb-server (HTTP gateway, MCP server, openraft cluster). Apache-2.0.
Dragon Brain — persistent long-term memory for AI agents via MCP (Model Context Protocol). Knowledge graph (FalkorDB) + vector search (Qdrant) + CUDA GPU embeddings. Works with Claude, Gemini CLI, Cursor, Windsurf, VS Code Copilot. 30 tools, 1121 tests.
Persistent memory for AI coding agents: automatic capture, explainable recall, knowledge consolidation, privacy controls, and portable offline storage. One Python file, zero dependencies.
MCP server enabling persistent memory for Claude through a local knowledge graph - fork focused on local development
Caura (formerly MemClaw) — governed shared memory for AI agent fleets. Multi-agent, multi-tenant, MCP-native. Trust tiers, keystone policies, audit trails, knowledge graph, self-improving retrieval. Apache 2.0.
Shared, persistent memory for AI agents. Self-hosted MCP server with semantic search, vector RAG, and live updates. Works with Claude, Cursor, Codex, and any MCP client.
Caura (formerly MemClaw) — governed shared memory for AI agent fleets. Multi-agent, multi-tenant, MCP-native. Trust tiers, keystone policies, audit trails, knowledge graph, self-improving retrieval. Apache 2.0.
A local-first AI memory system with hybrid search, MCP integration, and a knowledge graph.
| Tool | Stars | Language | License | Score |
|---|---|---|---|---|
| yantrikdb | ★ 65 | Rust | Apache-2.0 | 58 |
| Dragon-Brain | ★ 51 | Python | MIT | 62 |
| pensyve | ★ 89 | Rust | — | 61 |
| mind | ★ 65 | Python | MIT | 69 |
| mcp-knowledge-graph | ★ 889 | JavaScript | MIT | 73 |
| caura | ★ 544 | Python | Apache-2.0 | 66 |
| montycat-mcp | ★ 54 | Python | MIT | 64 |
| caura-memclaw | ★ 430 | Python | Apache-2.0 | 66 |
| omega-memory | ★ 219 | Python | Apache-2.0 | 63 |
| memory-vault | ★ 65 | Python | MIT | 66 |
The top mcp memory & knowledge tools in 2026 are yantrikdb, Dragon-Brain, pensyve. Agent Skills Hub ranks 10 options by GitHub stars, quality score (6 dimensions including completeness, examples, and agent readiness), and recent activity. The list is rebuilt every 8 hours from live GitHub data.
yantrikdb (65 stars) is the most adopted choice for general mcp memory & knowledge workflows, written in Rust. Dragon-Brain (51 stars) is a strong alternative and uses Python instead. Pick by your existing stack: match the language and runtime your team already uses to minimize integration cost. If unsure, start with yantrikdb — it has the deepest community and the most examples online.
Avoid pre-built mcp memory & knowledge tools when (1) your use case requires deep customization that the tool's plugin system doesn't support, (2) you have strict compliance requirements that ban third-party dependencies, (3) the tool's maintenance is inactive (last commit >6 months ago), or (4) your data volume is small enough that a 50-line custom script is cheaper than learning the tool. For most production workflows above 100 requests/day, the time savings from a maintained tool outweigh the customization loss.
MCP Memory & Knowledge focuses specifically on browse mcp tools for persistent memory, knowledge graphs, and context management in ai agent workflows. Semantic Search is a related but distinct category — see https://agentskillshub.top/best/semantic-search/ for those tools. The two often appear in the same agent pipeline but solve different problems: choose mcp memory & knowledge when your primary goal is the specific task, and semantic search when the workflow is broader.
For most teams, yes. yantrikdb has 65 stars worth of community testing, handles edge cases you haven't thought of, and ships with documentation. Build your own only when (1) your requirements are deeply non-standard, (2) you have a security/compliance reason to avoid OSS dependencies, or (3) the maintenance burden is small enough (<200 lines of code) that you'll save time long-term. The break-even point is usually around 2-3 weeks of dev time saved.
Most mcp memory & knowledge tools listed are open source under permissive licenses (MIT, Apache 2.0). A handful offer paid managed/cloud versions on top of free self-hosted core. Always check the LICENSE file on each tool's GitHub repository before commercial use — some use AGPL or non-commercial restrictions that may not fit your deployment model.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
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