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

The experimental company-brain foundation adds reference-first multimodal evidence, session and episode understanding, atomic evidence-backed memories, review lifecycles, permission-first retrieval, and structured cited context packs. See Company brain foundation.

Why teams use it

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.

What works today

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.

Quick start

Python 3.10 or newer is required.

bash

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:

bash

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:

bash

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"

To migrate a cartridge into governed company memory:

bash

llm-kosh --root ./my-cartridge brain migrate --dry-run
llm-kosh --root ./my-cartridge brain migrate
llm-kosh --root ./my-cartridge brain health
llm-kosh --root ./my-cartridge brain context "Prepare the next project decision"

bash

llm-kosh --root ./my-cartridge brain register ./report.xlsx --artifact-type worksheet
llm-kosh --root ./my-cartridge brain inspect <evidence-id> \
  --locator '{"sheet":"Summary","range":"A1:F25"}'
llm-kosh --root ./my-cartridge brain evaluate

Build a replayable session/episode graph from a registered JSONL export:

bash

llm-kosh --root ./my-cartridge brain register ./session.jsonl --artifact-type structured_data
llm-kosh --root ./my-cartridge brain understand <evidence-id> --dry-run
llm-kosh --root ./my-cartridge brain understand <evidence-id>
llm-kosh --root ./my-cartridge brain episodes --query "what was implemented"

Core concepts

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 or LLMKOSH_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.

Use with MCP clients

bash

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:

bash

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:

bash

llm-kosh --root ./my-cartridge mcp-server --http --port 8000
# endpoint: http://127.0.0.1:8000/mcp

What’s included

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

Optional features

bash

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.

Developer workflow

bash

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

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

bash

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

Security model

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.

Desktop app status

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.

Documentation

Native acceleration

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.