Polanyi-Stack: A Localized Platform for Transforming Tacit Expert Knowledge into Reusable AI Agents

Polanyi-Stack is a localized AI tool for Windows, designed to address the "Polanyi Paradox" in knowledge management—the pain point where experts hold a wealth of tacit knowledge that is hard to articulate and easily lost. By converting tacit expert knowledge into reusable AI agents and skill modules, it enables standardized inheritance of team knowledge and automated applications. All data processing is done locally, ensuring user privacy and data security.

In the field of knowledge management, the "Polanyi Paradox" states that humans possess a large amount of tacit knowledge that is hard to articulate. These experiential gems are deeply stored in experts' minds but difficult to record and inherit. When experts leave or retire, valuable knowledge is often lost, causing losses to the organization. Polanyi-Stack's core mission is to build a bridge between human experience and automated systems, converting the "intuitively understood but not verbally expressible" expert wisdom into reusable digital skills, and constructing AI agents capable of handling tasks and making decisions.

Polanyi-Stack adopts a localized deployment architecture. All data processing is completed on the user's local computer without relying on external cloud services, ensuring data privacy and user control. Its technical architecture revolves around the "knowledge flow": users input data such as documents, messages, and examples, and the agent analyzes patterns and rules; users can intervene to adjust the agent's unexpected decisions, and the system records feedback and optimizes logic to achieve continuous iterative evolution, closely aligning with experts' working methods.

Agent Creation and Training: Users create and train agents by naming them, selecting expert categories, uploading process documents, providing problem examples, and training. The agent automatically extracts decision logic, builds knowledge graphs, and forms skill modules.

Knowledge Logic Visualization and Editing: Users can view files that influence decisions, remove or add documents, and maintain control over the AI system.

Prompt Engineering Optimization Tool: Guides users to use clear sentences, avoid ambiguous vocabulary, and provide style examples to improve interaction quality.

External Tool Integration: Supports connecting to email clients and office suites. After configuration, the agent can complete automated processes such as creating drafts and summarizing documents.

Polanyi-Stack promises that data runs locally and does not send documents to external cloud storage. When online model inference is needed, only specific prompts are sent, and sensitive tags and private identifiers are filtered out. This design is particularly important for users handling sensitive business information, private data, or data from regulated industries, providing higher security and compliance guarantees.

Polanyi-Stack supports team collaboration: trained skill modules can be exported as files, and colleagues can import them for reuse. This promotes knowledge transfer within the organization, ensures team members benefit from the same expert insights, improves work consistency and efficiency, and realizes the core value of knowledge being built once and reused across the entire organization.

Polanyi-Stack is applicable to multiple scenarios: In customer service, it captures senior customer service skills to help new employees quickly meet standards; in financial auditing, it records expert judgment standards and risk identification patterns to improve audit quality; in medical diagnosis, it organizes senior doctors' diagnostic thinking to assist young doctors' growth. Any industry that relies on expert experience and faces knowledge inheritance challenges can benefit, converting tacit assets into quantifiable, inheritable, and scalable digital capabilities.

System Requirements: Windows 10/11, at least 8GB RAM, stable network, 2GB available disk space, administrator privileges.

Deployment Steps: Visit the GitHub repository to download the latest .msi or .exe installation package, and install it according to the prompts.

Troubleshooting: If it cannot be opened, check Windows updates or antivirus software; if the agent reports an error, verify the readability of input files; if it runs slowly, close memory-intensive programs. For specific help, refer to the FAQ.

Maintenance Recommendations: Regularly back up the library folder (including agent logic files), manage agents by category, and delete old versions to keep them organized.