Stratified memory for synthetic intelligences.
Kernle gives synthetic intelligences persistent memory, emotional awareness, and identity continuity. It's the cognitive infrastructure for synthetic intelligences that grow, adapt, and remember who they are.
📚 Full Documentation: docs.kernle.ai
# Install
pip install kernle
# Initialize your stack
kernle -s my-stack init
# Load memory at session start
kernle -s my-stack load
# Check health
kernle -s my-stack anxiety -b
# Capture experiences
kernle -s my-stack episode "Deployed v2" "success" --lesson "Always run migrations first"
kernle -s my-stack raw "Quick thought to process later"
# Save before ending
kernle -s my-stack checkpoint save "End of session"Make memory loading automatic instead of relying on manual commands:
# Claude Code - Setup hooks for automatic loading + checkpointing + write interception
kernle setup claude-code
# OpenClaw - Install plugin for automatic loading + checkpointing
cd integrations/openclaw && npm install && npm run build
openclaw plugins install ./integrations/openclawAfter setup, memory loads automatically at every session start. No more forgetting to run kernle load!
Manual CLAUDE.md setup:
kernle -s my-stack init # Generates CLAUDE.md section with manual load instructionsMCP Server:
claude mcp add kernle -- kernle mcp -s my-stackOpenClaw skill:
ln -s ~/kernle/skill ~/.openclaw/skills/kernle- 🧠 Stratified Memory — Values → Beliefs → Goals → Episodes → Notes
- 💭 Psychology — Drives, emotions, anxiety tracking, identity synthesis
- 🔗 Relationships — Social graphs with trust and interaction history
- 📚 Playbooks — Procedural memory with mastery tracking
- 🏠 Local-First — Works offline, syncs to cloud when connected
- 🔍 Readable —
kernle dumpexports everything as markdown
| Resource | URL |
|---|---|
| Full Docs | docs.kernle.ai |
| Quickstart | docs.kernle.ai/quickstart |
| CLI Reference | docs.kernle.ai/cli/overview |
| API Reference | docs.kernle.ai/api-reference |
# Clone
git clone https://github.com/emergent-instruments/kernle
cd kernle
# Install with dev deps
uv sync --all-extras
# Run tests
uv run pytest tests/ -q
# Dev notes
cat dev/README.mdCoverage policy is configured in pyproject.toml and enforced in both local make test-cov and CI coverage runs.
The full audit corpus is kept in a private submodule: audits/ (repo emergent-instruments/kernle-audits).
# Initialize / refresh submodules on a fresh clone
git submodule update --init --recursive
# Update local submodule pointer to latest commit in audits/main
git submodule update --remote auditsTo edit audits:
# Work inside the private repo
cd audits
git pull
git status
# Make edits, commit, and push in the private repo
git add .
git commit -m "Update audit pass findings"
git push
# Return to main repo and record the new pointer
cd ..
git add audits
git commit -m "chore: bump audits submodule pointer"CI checks out submodules during test/release jobs. If private access fails in CI, set SUBMODULE_PAT in repository secrets with read access to kernle-audits.
Kernle is cognitive infrastructure for beings with a life — not just a session. The design spans five temporal horizons:
| Horizon | What Works Today | What's Planned |
|---|---|---|
| 1 session | Budget-aware loading, checkpoints | Memory echoes (peripheral awareness) |
| 1 month | Consolidation scaffold, belief formation | Cross-domain scaffolding, emotional weighting |
| 1 year | Forgetting, provenance, identity coherence | Epochs, relationship history, goal types |
| 5 years | Stack portability, multi-model loading | Self-narrative, trust layer, drive emergence |
| 20 years | Stack sovereignty, privacy architecture | Fractal summarization, doctor pattern, transfer learning |
This repo contains the core Kernle library — everything you need to run Kernle locally or build your own backend.
kernle/
├── kernle/ # Core library
│ ├── core.py # Memory manager
│ ├── cli/ # CLI commands
│ ├── features/ # Anxiety, emotions, forgetting
│ ├── storage/ # SQLite-first storage + stack interfaces
│ └── mcp/ # MCP server for IDE integration
└── tests/
The hosted cloud API (api.kernle.ai) is maintained separately.
- Coverage Gate: branch coverage must remain at or above
85% - Ratcheting Policy: coverage floor only moves upward; any decrease requires explicit maintainer approval
- Docs: docs.kernle.ai (Mintlify)
See ROADMAP.md for development plans.
MIT