kibi
Health Warn
- License — License: AGPL-3.0
- Description — Repository has a description
- Active repo — Last push 0 days ago
- Low visibility — Only 5 GitHub stars
Code Fail
- rm -rf — Recursive force deletion command in .github/workflows/ci.yml
- rm -rf — Recursive force deletion command in .github/workflows/publish.yml
Permissions Pass
- Permissions — No dangerous permissions requested
This tool provides a repo-local, per-git-branch knowledge base for software projects. It allows AI agents to query and store requirements, tests, and architecture decisions via an MCP server, ensuring code traceability.
Security Assessment
The tool does not request dangerous system permissions or access inherently sensitive data. However, there is a prominent security flag: recursive force deletion commands (`rm -rf`) are present in the CI/CD workflow files (`.github/workflows/ci.yml` and `publish.yml`). While these are restricted to the automated build environment rather than the user's local runtime, they are generally considered a poor security practice and should be reviewed to ensure they don't accidentally delete critical system paths. There are no hardcoded secrets detected. Overall risk is rated as Medium.
Quality Assessment
The project appears to be actively maintained, with its last push occurring just today. It is fully open-source under the AGPL-3.0 license. However, the tool currently has very low community visibility, with only 5 GitHub stars. This means it has undergone minimal peer review and might lack the community support needed for rapid bug fixes or security patches.
Verdict
Use with caution — while the core functionality is useful for LLM context, the presence of force deletion scripts in its pipelines and lack of widespread community testing warrant a careful review before adopting it in sensitive or production environments.
Repo-local, per-git-branch, queryable knowledge base for LLM Agents.
Kibi is a repo-local, per-git-branch, queryable knowledge base for software projects. It stores requirements, scenarios, tests, architecture decisions, and more as linked entities, ensuring end-to-end traceability between code and documentation.
Why Kibi
Kibi is designed to boost AI agents' memory during software development. It maintains a living, verifiable project memory that:
- Tracks context across branches — Every git branch gets its own KB snapshot, preserving context as you switch between features
- Enforces traceability — Links code symbols to requirements, preventing orphan features and technical debt
- Validates automatically — Rules catch missing requirements, dangling references, and consistency issues
- Agent-friendly — LLM assistants can query and update knowledge base via MCP without risking file corruption
What You Get
Kibi provides concrete, day-to-day benefits for developers and teams:
Requirements Traceability — Track every code symbol back to its requirement. Know why code exists and what business need it addresses.
Test Coverage Visibility — See which requirements have tests, which don't, and what's covered at a glance. Ensure nothing slips through the cracks.
Architectural Constraints — Link code to ADRs. Know what constraints apply to each symbol and verify architecture decisions are honored.
Feature Flag Blast Radius — See what code depends on a runtime/config gate before toggling it. Understand the impact of enabling or disabling a feature.
Event-Driven Architecture — Map who publishes and consumes each domain event. Trace event flows and identify couplings across the system.
Branch-Local Memory — Every git branch keeps its own KB snapshot. Switch contexts without losing traceability or polluting other branches.
For OpenCode users, bootstrap an existing repo with /init-kibi.
Entity Modeling Note: Use
flagfor runtime/config gates only. Document bugs and workarounds asfactentities withfact_kind: observationormeta. See Entity Schema and AGENTS.md for the canonical guidance.
Key Components
- kibi-core — Prolog-based knowledge graph that tracks entities across branches
- kibi-cli — Command-line interface for automation and hooks
- kibi-mcp — Model Context Protocol server for LLM integration
- kibi-opencode — OpenCode plugin that injects Kibi guidance and runs background syncs
- kibi-vscode — VS Code extension for exploring the knowledge base
Prerequisites
- SWI-Prolog 9.0+ — Kibi's knowledge graph runs on Prolog
Installation
Kibi supports two common setups:
- Global install for normal use across repositories
- Repo-local dogfood workflow in this repository, where OpenCode and MCP use locally built artifacts
# Using npm (recommended)
npm install -g kibi-cli kibi-mcp
# Using bun
bun add -g kibi-cli kibi-mcp
After installation, verify that kibi is available:
kibi --version
OpenCode Plugin
Add kibi-opencode to your project opencode.json:
{
"$schema": "https://opencode.ai/config.json",
"plugin": ["kibi-opencode"]
}
OpenCode installs npm plugins declared in plugin automatically at startup.
VS Code Extension
The Kibi VS Code extension provides a TreeView explorer for your knowledge base and built-in MCP integration.
Download the latest .vsix from GitHub Releases, then install it:
- Command Palette:
Ctrl+Shift+P→Extensions: Install from VSIX...→ select the file - CLI:
code --install-extension kibi-vscode-x.x.x.vsix
Every GitHub release includes the latest VS Code extension build as a .vsix artifact.
Repo-local dogfood workflow (this repo)
This repository uses local built kibi-mcp and kibi-opencode artifacts during development. If you change package versions or local package wiring used by the OpenCode setup here, rebuild before testing:
bun run build
VS Code MCP
Create .vscode/mcp.json:
{
"servers": {
"kibi": {
"type": "stdio",
"command": "kibi-mcp"
}
}
}
If kibi-mcp is not on your PATH, replace command with the full executable path.
For complete installation steps and SWI-Prolog setup, see detailed installation guide.
Quick Start
Initialize kibi in your repository:
# Verify environment prerequisites
kibi doctor
# Initialize .kb/ and install git hooks
kibi init
# Parse markdown docs and symbols into branch KB
kibi sync
# Discover relevant knowledge before exact lookups
kibi search auth
# Inspect current branch snapshot and freshness
kibi status
# Run integrity checks
kibi check
Note:
kibi initinstalls git hooks by default. Hooks automatically sync your KB on branch checkout and merge.
Typical discovery workflow
# Explore the KB first
kibi search login
# Then follow up with exact/source-linked queries
kibi query req --source src/auth/login.ts --format table
# Check branch attachment and freshness when needed
kibi status
# Ask focused reporting questions
kibi gaps req --missing-rel specified_by,verified_by --format table
kibi coverage --by req --format table
Documentation
- Installation Guide — Prerequisites, SWI-Prolog setup, and verification steps
- CLI Reference — Complete command documentation with all flags and options
- Troubleshooting — Recovery procedures and common issues
- Entity Schema — Entity types, relationships, and examples
- Architecture — System architecture and component descriptions
- Inference Rules — Validation rules and constraint logic
- MCP Reference — MCP server documentation
- LLM Prompts — Ready-to-copy system prompts for agents
- AGENTS.md — Guidelines for AI agents working on kibi projects
- Contributing — Development setup and contributor workflow
Release and Versioning
All publishable npm packages in this repo (kibi-core, kibi-cli, kibi-mcp, kibi-opencode) follow the same Changesets workflow for versioning and changelog generation.
# Add release metadata for changed package(s)
bun run changeset
# Preview pending releases
bunx changeset status
# Apply version bumps and update package changelogs
bun run version-packages
⚠️ Alpha Status: Kibi is in early alpha. Expect breaking changes. Pin exact versions of kibi-cli, kibi-mcp, and kibi-opencode in your projects, and expect to occasionally delete and rebuild your .kb folder when upgrading.
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