Knowns

Turn stateless AI into a project-aware engineering partner.

Warning

Knowns is under active development. APIs, database schemas, and configuration formats may change between releases. Review the known limitations and security considerations before deploying to production.

Important

v0.13+: Rewritten in Go. To support AI Agent Workspaces (process management, live terminal, git worktree isolation), Knowns has been rewritten in Go as a native binary. CLI commands and .knowns/ data format are fully backward-compatible. Install via npm i -g knowns still works (auto-downloads platform binary).

Knowns is the memory layer for AI-native software development — enabling AI to understand your project instantly.

Instead of starting from zero every session, AI works with structured, persistent project context.

No repeated explanations.
No pasted docs.
No lost architectural knowledge.

Just AI that already understands your system.

⭐ If you believe AI should truly understand software projects, consider giving Knowns a star.

Table of Contents

• Why Knowns?
• What is Knowns?
• Core Capabilities
• How It Works
• Installation
• What You Can Build
• Quick Reference
• Claude Code Skills Workflow
• Documentation
• Roadmap
• Development
• Links

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Why Knowns?

AI is powerful — but fundamentally stateless.

Every session forces developers to:

• Re-explain architecture
• Paste documentation
• Repeat conventions
• Clarify past decisions
• Rebuild context

This breaks flow and limits AI’s effectiveness.

AI doesn't lack intelligence.

It lacks the right context.

Knowns fixes that.

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What is Knowns - Really?

Knowns provides persistent, structured project understanding so AI can operate with full awareness of your software environment.

Think of it as your project's external brain.

Knowns connects:

• Specs
• Tasks
• Documentation
• Decisions
• Team knowledge

So AI doesn’t just generate code — it understands what it’s building.

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Core Capabilities

🧠 Persistent Project Memory

Give AI long-term understanding of your codebase and workflows.

🔗 Structured Knowledge

Connect specs, tasks, and docs into a unified context layer.

⚡ Smart Context Delivery

Automatically provide relevant context to AI — reducing noise and token usage.

🤝 AI-Native Workflow

Transform AI from a tool into a true engineering collaborator.

🔐 Self-Hostable

Keep your knowledge private and fully under your control.

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How It Works

Knowns sits above your existing tools and makes them readable by AI.

Your stack stays the same.

But now:

• Specs → understood
• Tasks → connected
• Docs → usable
• Decisions → remembered

AI stops guessing — and starts contributing.

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Installation

Pre-built binaries

# Homebrew (macOS/Linux)
brew install knowns-dev/tap/knowns

# Shell installer (macOS/Linux)
curl -fsSL https://knowns.sh/script/install | sh

# Or with wget
wget -qO- https://knowns.sh/script/install | sh

# PowerShell installer (Windows)
irm https://knowns.sh/script/install.ps1 | iex

Uninstall

# Shell uninstaller (macOS/Linux)
curl -fsSL https://knowns.sh/script/uninstall | sh

# PowerShell uninstaller (Windows)
irm https://knowns.sh/script/uninstall.ps1 | iex

The uninstall scripts only remove installed CLI binaries and PATH entries added by the installer. They leave project .knowns/ folders untouched.

# npm — installs platform-specific binary automatically
npm install -g knowns

# npx (no install)
npx knowns

From source (Go 1.24.2+)

# Install to GOPATH/bin
go install github.com/howznguyen/knowns/cmd/knowns@latest

# Or clone and build
git clone https://github.com/knowns-dev/knowns.git
cd knowns
make build        # Output: dist/knowns
make install      # Install to GOPATH/bin

Get started

knowns init
knowns browser --open   # Start Web UI and open browser

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What You Can Build With Knowns

Feature	Description
Task Management	Create, track tasks with acceptance criteria
Documentation	Nested folders with markdown + mermaid support
Semantic Search	Search by meaning with local AI models (offline)
Time Tracking	Built-in timers and reports
Context Linking	@task-42 and @doc/patterns/auth references
Validation	Check broken refs with knowns validate
Template System	Code generation with Handlebars (.hbs) templates
Import System	Import docs/templates from git, npm, or local
AI Integration	Full MCP Server with AC/plan/notes operations
AI Workspaces	Multi-phase agent orchestration with live terminal
Web UI	Kanban board, doc browser, mermaid diagrams

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Quick Reference

# Tasks
knowns task create "Title" -d "Description" --ac "Criterion"
knowns task list --plain
knowns task <id> --plain                    # View task (shorthand)
knowns task edit <id> -s in-progress -a @me
knowns task edit <id> --check-ac 1          # Mark AC as done

# Documentation
knowns doc create "Title" -d "Description" -f "folder"
knowns doc "doc-name" --plain               # View doc (shorthand)
knowns doc "doc-name" --smart --plain       # Auto-handle small/large docs
knowns doc "doc-name" --section "2" --plain # Read specific section

# Templates
knowns template list                        # List available templates
knowns template run <name> --name "X"       # Generate from template
knowns template create <name>               # Create new template

# Imports
knowns import add <name> <source>           # Add import
knowns import sync                          # Sync all imports
knowns import list                          # List imports

# Time, Search & Validate
knowns time start <id> && knowns time stop
knowns search "query" --plain
knowns validate                             # Check broken refs

# AI Guidelines
knowns agents --sync                        # Sync/generate instruction files
knowns sync                                 # Sync skills + instruction files

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Claude Code Skills Workflow

When using Claude Code with Knowns, use skills (slash commands) for a guided workflow:

/kn-init     → Read project docs, understand context
/kn-plan     → Take task, gather context, create plan (wait for approval)
/kn-implement → Follow plan, check ACs, track progress
/kn-commit   → Commit with conventional format

Example Session

You: /kn-init
Claude: [Reads README, ARCHITECTURE, checks task backlog]
        "Project uses NestJS with CQRS pattern. 3 tasks in-progress..."

You: /kn-plan 42
Claude: [Takes task, reads refs, searches docs]
        "Task #42: Add user authentication

         ## Implementation Plan
         1. Review @doc/patterns/auth
         2. Create AuthModule with JWT strategy
         3. Add login/register endpoints
         4. Write unit tests

         Approve this plan?"

You: Yes

You: /kn-implement 42
Claude: [Follows plan, generates code, checks ACs]
        "✓ AC1: User can login - DONE
         ✓ AC2: JWT token returned - DONE
         ✓ AC3: Tests pass - DONE

         All acceptance criteria complete. Ready to commit?"

You: /kn-commit
Claude: [Creates conventional commit]
        "feat(auth): add JWT authentication

         - Add AuthModule with passport JWT strategy
         - Add login/register endpoints
         - Add unit tests (94% coverage)"

All Skills

Skill	Description
/kn-init	Initialize session - read docs, understand project
/kn-plan <id>	Take task, gather context, create implementation plan
/kn-implement <id>	Execute plan, track progress, check acceptance criteria
/kn-research	Search codebase, find patterns, explore before coding
/kn-commit	Create conventional commit with verification
/kn-spec	Create specification document for features (SDD)
/kn-verify	Run SDD verification and coverage report
/kn-doc	Create or update documentation
/kn-extract	Extract reusable patterns into docs/templates
/kn-template	List, run, or create code templates

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Documentation

Guide	Description
Command Reference	All CLI commands with examples
Workflow Guide	Task lifecycle from creation to completion
Reference System	How @doc/ and @task- linking works
Semantic Search	Setup and usage of AI-powered search
Templates	Code generation with Handlebars
Web UI	Kanban board and document browser
MCP Integration	Claude Desktop setup with full MCP tools
Configuration	Project structure and options
Developer Guide	Technical docs for contributors
User Guide	Getting started and daily usage
Multi-Platform	Cross-platform build and distribution

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Roadmap

AI Agent Workspaces ✅ (Active)

Multi-phase agent orchestration — assign tasks to AI agents with git worktree isolation, live terminal streaming, and automatic phase progression (research → plan → implement → review).

Self-Hosted Team Sync 🚧 (Planned)

Optional self-hosted sync server for shared visibility without giving up local-first workflows.

• Real-time visibility — See who is working on what
• Shared knowledge — Sync tasks and documentation across the team
• Full data control — Self-hosted, no cloud dependency

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Development

Requires Go 1.24.2+ and optionally Node.js + pnpm for UI development.

make build              # Build binary → dist/knowns
make dev                # Build with race detector
make test               # Run unit tests
make test-e2e           # Run CLI + MCP E2E tests
make test-e2e-semantic  # E2E tests including semantic search
make lint               # Run golangci-lint
make cross-compile      # Build for all 6 platforms
make ui                 # Rebuild embedded Web UI (requires pnpm)

Project structure

cmd/knowns/          # CLI entry point
internal/
  cli/               # Cobra commands
  models/            # Domain models
  storage/           # File-based storage (.knowns/)
  server/            # HTTP server, SSE, WebSocket
    routes/          # REST API handlers
    workspace/       # Agent orchestrator, process manager, worktree
  mcp/               # MCP server (stdio)
  search/            # Semantic search (ONNX)
ui/                  # Embedded React UI (built assets)
tests/               # E2E tests

Links

• npm
• GitHub
• Discord
• Changelog

For design principles and long-term direction, see Philosophy.

For technical details, see Architecture and Contributing.

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What your AI should have knowns.
Built for dev teams who pair with AI.