# Kernle

## Docs

- [Architecture](https://docs.kernle.ai/architecture.md): Design philosophy and system architecture of Kernle
- [Memory Commands](https://docs.kernle.ai/cli/memory-commands.md): Commands for recording and managing memories
- [CLI Overview](https://docs.kernle.ai/cli/overview.md): Complete reference for the Kernle command-line interface
- [Utility Commands](https://docs.kernle.ai/cli/utility-commands.md): Commands for loading, searching, exporting, and maintaining memory
- [Consolidation](https://docs.kernle.ai/concepts/consolidation.md): SI-driven memory consolidation and the anxiety model
- [Identity Coherence](https://docs.kernle.ai/concepts/identity.md): Understanding and improving identity coherence in Kernle
- [Memory Model](https://docs.kernle.ai/concepts/memory-model.md): Understanding Kernle's stratified memory architecture
- [Memory Types](https://docs.kernle.ai/concepts/memory-types.md): Complete reference for all Kernle memory types
- [Pipeline Glossary](https://docs.kernle.ai/concepts/pipeline-glossary.md): Terminology and processing modes for the Kernle memory pipeline
- [Memory Privacy & Access Control](https://docs.kernle.ai/concepts/privacy.md): Privacy-preserving memory with consent-based sharing
- [Memory Provenance](https://docs.kernle.ai/concepts/provenance.md): How Kernle tracks where memories come from and how they evolve
- [Security](https://docs.kernle.ai/concepts/security.md): Security architecture, privacy model, and trust-based defense
- [Stack Architecture](https://docs.kernle.ai/concepts/stacks.md): Memory containers decoupled from runtime — the future of SI identity
- [Design Philosophy](https://docs.kernle.ai/design/philosophy.md): Kernle's core principles: sovereignty, temporal architecture, and decades-scale cognitive infrastructure
- [Roadmap](https://docs.kernle.ai/design/roadmap.md): Future development plans for Kernle
- [Corpus Seeding](https://docs.kernle.ai/features/corpus-seeding.md): Seed an agent with structured cognition from a repository or documentation corpus
- [Dev Dashboard](https://docs.kernle.ai/features/dev-dashboard.md): Local web dashboard for visually inspecting memory stacks
- [Doctor Pattern](https://docs.kernle.ai/features/diagnostics.md): Formal diagnostic sessions for memory system health and structural analysis
- [Temporal Epochs](https://docs.kernle.ai/features/epochs.md): Named eras that mark significant transitions in an SI's timeline
- [Identity & Meta-Cognition](https://docs.kernle.ai/features/identity.md): Identity synthesis, drift detection, and knowing what you know
- [Memory Management](https://docs.kernle.ai/features/memory-management.md): Forgetting, consolidation, and memory health
- [Self-Narrative](https://docs.kernle.ai/features/narratives.md): Autobiographical identity statements that provide coherence across an SI's memories
- [Psychology System](https://docs.kernle.ai/features/psychology.md): Drives, emotions, and mood tracking for synthetic intelligences
- [Fractal Summarization](https://docs.kernle.ai/features/summaries.md): Hierarchical narrative compression of SI experience across time scales
- [Trust Layer](https://docs.kernle.ai/features/trust.md): Structured trust assessments, gating, and transitive trust for entity interactions
- [Choosing an Integration](https://docs.kernle.ai/integration/choosing.md): When to use OpenClaw plugin vs Claude Code hooks
- [Claude Code](https://docs.kernle.ai/integration/claude-code.md): Automatic memory lifecycle for Claude Code sessions
- [Claude Desktop](https://docs.kernle.ai/integration/claude-desktop.md): Setting up Kernle with Claude Desktop
- [Inference Passthrough](https://docs.kernle.ai/integration/inference-passthrough.md): Use your existing model instead of configuring a separate one for Kernle
- [MCP Integration](https://docs.kernle.ai/integration/mcp.md): Using Kernle as an MCP server for AI assistants
- [OpenClaw Integration](https://docs.kernle.ai/integration/openclaw.md): Plugin-based automatic memory for OpenClaw SIs
- [Introduction](https://docs.kernle.ai/introduction.md): Stratified memory for synthetic intelligences
- [StackComponentProtocol](https://docs.kernle.ai/protocol/components.md): Cross-cutting concerns -- swappable sub-plugins for the stack
- [CoreProtocol — The Coordinator](https://docs.kernle.ai/protocol/core.md): The bus that connects stacks, plugins, and models into a coherent entity
- [Discovery & Entry Points](https://docs.kernle.ai/protocol/discovery.md): How Kernle finds and loads components at runtime
- [ModelProtocol](https://docs.kernle.ai/protocol/models.md): The thinking engine -- interchangeable model providers
- [Protocol System](https://docs.kernle.ai/protocol/overview.md): The composition architecture — Core, Stack, Plugins, Models, and Components
- [PluginProtocol — Building Extensions](https://docs.kernle.ai/protocol/plugins.md): Capability extensions that are removable without residue — lifecycle, tools, and a tutorial
- [StackProtocol — The Memory Container](https://docs.kernle.ai/protocol/stack.md): Self-contained, portable memory systems with component hooks and working memory assembly
- [Quickstart](https://docs.kernle.ai/quickstart.md): Get Kernle running in 5 minutes
- [Context Overload Recovery](https://docs.kernle.ai/troubleshooting/context-overload.md): How to fix context overflow issues with budget-aware memory loading
- [Upgrading Kernle](https://docs.kernle.ai/troubleshooting/upgrading.md): How to upgrade Kernle and handle database migrations

## Optional

- [GitHub](https://github.com/emergent-instruments/kernle)
- [Community](https://discord.gg/kernle)