kweaver-ai / kweaver

KWeaver 中文 | English KWeaver is an open-source ecosystem for building, deploying, and running decision intelligence AI applications. This ecosystem adopts business knowledge network (Business Knowledge Network) as its core methodology, aiming to provide elastic, agile, and reliable enterprise-grade decision intelligence to further unleash everyone's productivity. The KWeaver project includes KWeaver Core and KWeaver DIP. 📚 Quick Links • 🤝 Contributing - Guidelines for contributing to the project • 🚢 Deployment - One-click deploy to Kubernetes • 🚀 Release Guidelines - Version management and release process • 🏗️ Architecture - Architecture design specification • 🧾 Changelog - All notable changes • 📄 License - Apache License 2.0 • 🐛 Report Bug - Report a bug or issue • 💡 Request Feature - Suggest a new feature 🎬 Demo Video Click the image to watch the KWeaver demo on Bilibili 🚀 Quick Start • **Source deployment**: see the Deployment Guide. • **Prerequisites**: follow the prerequisites described in . • **Run installation scripts**: • **Verify the deployment**: • **Access the system**: • Deployment console: , account , initial password • KWeaver Studio: Platform Architecture Core Subsystems | Sub-project | Description | Repository | | --- | --- | --- | | **KWeaver SDK** | CLI and SDK (TypeScript/Python) for AI agents and developers to access KWeaver knowledge networks and Decision Agents programmatically | kweaver-sdk | | **KWeaver DIP** | Include AI application and component marketplace, DIP Studio - Visual development and management interface | AI Store DIP Studio| | **KWeaver Core** | AI-native platform foundation — Decision Agent, AI Data Platform (BKN Engine, VEGA Engine, Context Loader, Execution Factory), Info Security Fabric, Trace AI |ADP Decision Agent ISF Trace AI | KWeaver SDK **kweaver-sdk** gives AI agents (Claude Code, GPT, custom agents, etc.) access to KWeaver knowledge networks and Decision Agents via the CLI. It also provides Python and TypeScript SDKs for programmatic integration. AI Agent Skill The skill equips AI coding assistants with full knowledge of KWeaver's APIs and CLI conventions, so they can autonomously operate KWeaver on your behalf. **Before using the skill**, authenticate with your KWeaver instance: See skills/kweaver-core/SKILL.md for details. CLI TypeScript & Python SDK --- KWeaver Core **KWeaver Core** is the AI-native platform foundation for autonomous decision-making. It sits between AI Agents (above) and AI/Data infrastructure (below), with the **Business Knowledge Network (BKN)** at its center, providing unified data access, execution, and security governance for Agents. KWeaver Core solves two critical pain points when connecting proprietary data with autonomous AI Agents: Context Engineering — High-Quality Context for Agents In long-running agent scenarios, context inevitably faces explosion, decay, pollution, and high token costs. KWeaver Core addresses these through the Business Knowledge Network: • **Context explosion containment** — Multi-source candidates are first organized and aggregated via the BKN semantic network, then unified by Context Loader (recall → coarse ranking → fine ranking) to retain only key evidence and constraints, avoiding massive prompt fragments that cause decision drift. Overall accuracy reaches **93%+**. • **Context decay mitigation** — Replaces long-text stacking with "real-time facts + evidence citations", keeping reasoning grounded around specific objects and reducing forgetting and hallucination risks in long inputs. Accuracy improves **15%+** over baselines across scenario types. • **Context pollution isolation** — Builds precise enterprise digital twins through the BKN network, blocking unreliable content and potential injection risks outside the knowledge and execution boundary, ensuring a clean and controllable reasoning chain. • **Token cost compression** — Converts multi-source materials into structured object information fetched on demand (not full-text concatenation), improving information density within the same budget. Token consumption reduced **30%+** while improving accuracy. Harness Engineering — Safe & Controllable Execution Beyond "seeing more", Agents must "do it right". KWeaver Core provides constraint engineering capabilities for enterprise-grade safe execution: • **Explainable decisions** — Uses "Object → Action → Rule → Constraint" knowledge structures to express business intent graphs, grounding tool invocation and parameter selection to explicit semantic boundaries and rule dependencies, making it clear "why this action was taken". • **Traceable evidence chain** — From action intent → knowledge node → data source → mapping/operator → final invocation, full-chain tracing is supported. Entities and relationships can be traced back to source data and active rules, enabling audit and review. • **Controllable execution loop** — Unifies identity and access control down to knowledge network object/action permissions, with pre-execution validation, mid-execution policy interception, and post-execution audit logging, achieving "authorizable, approvable, revocable" security loops. • **Risk prevention mechanism** — Models risks as "Risk Types" linked to Action Types; performs risk assessment and simulation before execution, with automatic downgrade/blocking/secondary confirmation when thresholds are hit, blocking high-risk actions before they execute. Core Architecture | Component | Description | | --- | --- | | **AI Data Platform** | Non-intrusive access architecture — unified data access, unified execution, and unified security governance through the Business Knowledge Network | | **Decision Agent** | Goal-oriented autonomous task planning — acquires high-quality context from AI Data Platform, manages runtime effectively, suppresses hallucination and context decay, invokes tools and skills under permission control, forming a safe "reason → risk-assess → execute → f…