WisdomForge

Created By
hadva year ago
A powerful knowledge management system that forges wisdom from experiences, insights, and best practices. Built with Qdrant vector database for efficient knowledge storage and retrieval.
Overview

What is WisdomForge?

WisdomForge is a powerful knowledge management system designed to forge wisdom from experiences, insights, and best practices, utilizing the Qdrant vector database for efficient knowledge storage and retrieval.

How to use WisdomForge?

To use WisdomForge, clone the repository from GitHub, install the necessary dependencies, configure your environment variables in a .env file, and then build and start the server. You can deploy it locally or on the Smithery.ai cloud platform.

Key features of WisdomForge?

  • Intelligent knowledge management and retrieval
  • Support for multiple knowledge types (best practices, lessons learned, insights, experiences)
  • Configurable database selection via environment variables
  • Uses Qdrant's FastEmbed for efficient embedding generation
  • Domain knowledge storage and retrieval
  • Deployable to Smithery.ai platform

Use cases of WisdomForge?

  1. Storing and retrieving domain-specific knowledge.
  2. Managing best practices and lessons learned in organizations.
  3. Integrating with AI IDEs for enhanced knowledge management.

FAQ from WisdomForge?

  • What databases does WisdomForge support?

WisdomForge supports Qdrant and Chroma vector databases.

  • Is there a cloud deployment option?

Yes, WisdomForge can be deployed on the Smithery.ai cloud platform.

  • How do I configure the environment variables?

You can configure the environment variables in the .env file based on the provided template.

Project Info
Created At
a year ago
Updated At
a year ago
Author Name
hadv
Star
3
Language
TypeScript
License
MIT license
Tags

Recommend Servers

View All
Bring your real authenticated browser session to AI coding agents. Local-first MCP server + Chrome MV3 extension. No cloud. No telemetry.
@Cubenest

peek records the user's actual logged-in browser (DOM via rrweb, console events, network metadata, optional response bodies via opt-in Deep capture) through a Chrome MV3 extension. The extension ships events through a native-messaging stdio bridge to a local MCP server (peek-mcp), which persists them to a SQLite database at ~/.peek/sessions.db. AI coding agents (Claude Code, Cursor, Cline, Windsurf) read sessions from the database via 10 MCP tools: Tool What it does list_recent_sessions List recently recorded sessions (id, origin, ts, event count). get_session_summary LLM-readable narrative summary of a session. get_session_console_errors Console errors recorded in a session. get_session_network_errors Failed/notable network requests in a session. get_user_action_before_error Last N user actions before a console error. generate_playwright_repro Generate a runnable Playwright test from a session. get_dom_snapshot Reconstruct the DOM at a given timestamp. query_dom_history Timeline of attribute/text changes for a selector. request_authorization Side-panel consent for write actions (Level 3). execute_action Dispatch a UI action (gated by permission level + destructive blocklist). Why local-first matters Every other "browser session for AI" tool ships to a vendor cloud. peek's SQLite + extension live on the user's machine — no remote endpoints, no telemetry. The privacy policy (docs/peek/PRIVACY_POLICY.md) is the source of truth. Install # 1. Add the MCP server to Claude Code claude mcp add peek -- npx -y @peekdev/mcp # 2. Install the Chrome extension from the Chrome Web Store # (link added once the CWS listing is approved)

a day ago