Knowledge · Memory · Tools — one control plane, every model
VeriTeknik builds plugged.in — a model-agnostic layer that gives any AI system unified access to your:
- 📚 Knowledge — Document ingestion, metadata, and retrieval (RAG) so answers cite your sources.
- 🧠 Memory — Durable context that travels across tools and sessions (focus → short-term → long-term).
- Our memory philosophy: causality-aware, tiered retention, and promotion/TTL policies.
Read the essay: Building Digital Consciousness: A Memory Architecture Inspired by Human Cognition →
https://medium.com/@cem.karaca/building-digital-consciousness-a-memory-architecture-inspired-by-human-cognition-437412791044
- Our memory philosophy: causality-aware, tiered retention, and promotion/TTL policies.
- 🔧 Tools — Universal MCP (Model Context Protocol) orchestration: install once, use everywhere.
Works with Claude, ChatGPT, Cursor/Cline, VS Code, LM Studio, and more — without vendor lock-in.
- Standards-first: MCP • OAuth2/OIDC • JSON-RPC/JSON Schema
- Governance: workspaces, permissions, audit trails, export
- Interoperability: one tool/knowledge/memory layer across all clients
Click the thumbnail to see plugged.in in action.
One Platform. All AI Models. Unlimited Possibilities.
The hub for managing Knowledge, Memory, and Tools across any AI model.
- Tech: TypeScript, Next.js, PostgreSQL
- Core Features: universal RAG, cross-model memory, MCP orchestration, collaboration
- License: AGPL-3.0
One MCP connection. Infinite tool access.
- Tech: TypeScript
- Benefits: unified tool management, policy & governance, model-agnostic integration
- License: Apache-2.0
A community-driven registry for MCP servers.
- Tech: TypeScript
- License: Apache-2.0
Integrate plugged.in into your apps with official SDKs:
- Plugged.inKit JS — JavaScript/TypeScript for Node.js & browser
- Plugged.inKit Go — high-performance Go SDK
- Plugged.inKit Python — Python for AI/ML workflows
Full guides, APIs, and tutorials → docs.plugged.in
We welcome contributions across the stack:
- New features & MCP integrations
- Bug fixes & testing
- Documentation & translations
See each repo for contribution guidelines.
Building the future of AI data exchanges — one protocol at a time.