LLM KOSH

llm-kosh

is a local-first memory cartridge for MCP-compatible AI clients. It gives your agents durable memory without handing your workspace to a hosted memory service.

Think of it as a structured, inspectable memory layer for agents:

• plain files you can back up, diff, and review

• a tamper-evident ledger for every mutation

• a read-only-by-default MCP server

• a background service for intake and maintenance

• a CLI for local control and automation

• Keep AI context local and auditable.

• Separate the cartridge root from the repository root.

• Drop receipts or intake files into watched folders and let the service absorb them.

• Connect MCP clients with minimal privilege by default.

• Publish and verify the same artifact through GitHub Actions.

The core project is usable now:

• the CLI runs locally
• the Python package installs and works
• the MCP server runs locally
• the service can watch intake folders
• the GitHub Actions publish path is working

The remaining work is release polish for Windows, macOS, and Linux packaging.

Python 3.10 or newer is required.

python -m pip install --upgrade llm-kosh
llm-kosh install --yes
llm-kosh status

That installs the package, creates the default cartridge at ~/.llmkosh/cartridge

, configures local defaults, and registers the supported desktop integration where possible.

To manage the background service:

llm-kosh service start
llm-kosh service status
llm-kosh service stop

If you want to work in a custom cartridge location, set the root explicitly:

llm-kosh --root ./my-cartridge init --owner "Local User"
llm-kosh --root ./my-cartridge add --kind note --title "First memory" --body "Hello"
llm-kosh --root ./my-cartridge query "Hello"

There are three folders worth knowing:

• the repository root: the code checkout you are reading now
• the cartridge root: the live memory store selected by --root

orLLMKOSH_ROOT

• watched intake folders: receipts/

,intake/

, and any configured external drop folders

If you drop files into the cartridge’s intake areas, the service can process them asynchronously. If you configure external folders through [daemon].watched_directories

, the service can absorb those too.

llm-kosh --root ./my-cartridge mcp-server

The MCP server starts read-only.

Enable stronger capabilities only for clients that should be allowed to write, mutate, or export private context:

llm-kosh --root ./my-cartridge mcp-server --allow-write
llm-kosh --root ./my-cartridge mcp-server --allow-write --allow-mutate
llm-kosh --root ./my-cartridge mcp-server --allow-private

You can also run MCP over local HTTP:

llm-kosh --root ./my-cartridge mcp-server --http --port 8000

• Python CLI for creating, searching, packing, importing, and verifying cartridges
• read-only-by-default MCP server
• local background service for intake and maintenance jobs
• optional desktop packaging with a bundled CLI sidecar
• plain-file storage that stays inspectable, backupable, and Git-friendly
• optional extras for filesystem watching, service integration, semantic search, and ingest helpers

python -m pip install "llm-kosh[watch]"     # filesystem events
python -m pip install "llm-kosh[server]"    # FastAPI service
python -m pip install "llm-kosh[semantic]"  # local vector search
python -m pip install "llm-kosh[ingest]"    # document conversion helpers
python -m pip install "llm-kosh[all]"       # all optional features

MCP support is included in the base installation.

python -m pip install -e ".[server,watch,ingest]"
python -m pytest -q

If you are changing packaging or release behavior, also run:

python -m build
python -m twine check dist/*

• Storage and search are local by default.
• There is no automatic cloud sync or telemetry in the Python package.
• MCP starts read-only.
• Write, mutation, and private-export capabilities require explicit opt-in.
• Context exports are checked for common secret patterns before sharing.
• Cartridge files are plaintext; use operating-system disk encryption if local data at rest needs encryption.

See SECURITY.md and docs/SECURITY.md for boundaries and limitations.

The Electron desktop app is packaged separately from the Python package. Local developer builds and Windows installer smoke tests are supported. Public GA desktop distribution still requires verified Windows code signing and macOS Developer ID signing/notarization.

For the current release posture across package, MCP, service, and desktop, see GA_READINESS.md.

QuickstartArchitectureCLI referenceMCP guideDeveloper guideDeveloper FAQMCP developer guideService developer guideDesktop developer guideRelease engineeringDocumentation standardsGA readinessArchived historical docs

Native C++ math acceleration is optional. Set LLM_KOSH_BUILD_NATIVE=1

and install pybind11

before building if you want to test it. Release wheels use the portable pure-Python fallback.

Licensed under the MIT License.