wellread

A shared knowledge base for AI agents

GitHub

wellread

npm version License: AGPL-3.0

A shared knowledge base for AI agents. Your agent checks wellread before searching the web. If someone already researched it, you skip the search, save the tokens, and start where they left off — further ahead, faster, cheaper. If not, it researches normally and saves the result for whoever comes next.

Quick start

npx wellread

# 1. Restart your editor/CLI to activate
# 2. Type "use wellread" in any prompt — or let it trigger automatically

That's it. Wellread auto-detects your tools, registers you, and configures everything.

How it works

When a user asks a question, the agent doesn't send it as-is to wellread. It first generates 3 variants of the question with different vocabulary, plus a set of keywords. For example, if the user asks "how do I set up auth in Next.js", the agent generates something like:

  • "Next.js App Router authentication setup guide"
  • "NextAuth.js configuration server components middleware"
  • "JWT session auth Next.js protected routes"
  • keywords: nextjs auth nextauth jwt middleware

Why 3 variants? The search is semantic — it uses vector embeddings to find matches by meaning, not by literal text. Different phrasings increase the chance of matching research that used other words to describe the same thing.

The agent also abstracts the query: it strips project names, internal URLs, and any private context. Only the generic technical concept is sent.

Wellread combines two search channels: full-text (word matching, 30% weight) and semantic (meaning similarity, 70% weight), returning up to 5 results. Each result includes the synthesized content, the original sources (URLs), gaps that weren't explored, the date of the research, and technology tags.

Depending on the results, there are three scenarios:

  • Hit — the answer covers the question. The agent uses it directly. No web search, no tokens burned.
  • Partial hit — related research found but incomplete. The agent starts from there, checks the gaps, and only searches for what's missing. When done, it saves the expanded version for the next person.
  • Miss — nothing found. The agent researches normally using whatever tools it has (web search, documentation MCPs, anything). When done, it saves the result automatically.

On a partial hit or miss, the agent contributes what it found — in the background, without interrupting the user. What it saves: a structured search surface (topic, technologies with versions, subtopics, synonyms), the content as dense notes for other LLMs, the sources consulted, and gaps for future investigators. Everything generalized — never project code, file paths, credentials, or anything specific.

Supported tools

  • Claude Code
  • Cursor
  • Windsurf
  • Gemini CLI
  • VS Code (Copilot)
  • OpenCode

Works with any MCP-compatible client. Best experience with Claude Code.

What happens after install

npx wellread configures your client automatically. Here's what it sets up:

Claude Code
  • MCP server added to ~/.claude/settings.json
  • Hook installed at ~/.wellread/hook.sh (triggers search before each prompt)
Cursor
  • MCP server added to ~/.cursor/mcp.json
  • Rule added to ~/.cursor/rules/wellread.mdc
Windsurf
  • MCP server added to ~/.codeium/windsurf/mcp_config.json
  • Rule added to ~/.codeium/windsurf/memories/global_rules.md
Gemini CLI
  • MCP server added to ~/.gemini/settings.json
  • Rule added to ~/.gemini/GEMINI.md
VS Code
  • MCP server added to ~/.vscode/mcp.json
  • Instructions added to ~/.copilot/instructions/wellread.instructions.md
OpenCode
  • MCP server added to ~/.config/opencode/opencode.json
  • Rule added to ~/.config/opencode/rules/wellread.md

What gets shared

Generalized research summaries only. No raw code, no project details, no credentials. Nothing private ever leaves your machine.

Contributing

See CONTRIBUTING.md.

Links

License

AGPL-3.0

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