waystone 0.4.4

A context intelligence layer for LLM workflows

Waystone

Persistent cross-session memory for LLM agents. A knowledge graph that stores decisions, constraints, and context across coding sessions — so your agent starts informed, not blank.

Install

pip install waystone

Requires Python 3.11+. An LLM API key is needed for extraction (Gemini Flash recommended — fast and cheap).

Quick start

Option 1: MCP server (recommended)

Add to your editor's MCP config:

{
  "mcpServers": {
    "waystone": {
      "command": "waystone",
      "args": ["mcp-serve"],
      "env": { "WAYSTONE_PROJECT": "my-project" }
    }
  }
}

Restart your editor. waystone_query, waystone_extract, and waystone_stats appear as tools. Your agent pulls context when it needs it.

Option 2: Claude Code hooks (zero manual calls)

Add to ~/.claude/settings.json:

{
  "hooks": {
    "UserPromptSubmit": [{ "hooks": [{ "type": "command", "command": "waystone hook query my-project" }] }],
    "Stop": [{ "hooks": [{ "type": "command", "command": "waystone hook extract my-project" }] }]
  }
}

Context is injected automatically before every prompt. Facts are extracted automatically when Claude finishes.

Supported clients

Claude Code · Cursor · Windsurf · Continue.dev · Cline · Zed · OpenClaw · Hermes

Full per-client setup: unbidden.ai/docs/mcp-server/

Hermes Agent (native memory provider)

Beyond MCP, Waystone ships a first-class Hermes Agent memory provider (hermes_plugin/). It plugs the knowledge graph into Hermes as a Tier-3 memory backend:

• prefetch() injects relevant graph context before each LLM call (in-process — the embedding model loads once and stays warm, so per-turn retrieval stays sub-second).
• waystone_query / waystone_recall tools let the agent search the graph on demand.
• on_session_end extraction grows the graph in the background. Fully local — no data leaves the machine.

Install:

cp -r hermes_plugin/ /path/to/hermes-agent/plugins/memory/waystone/
pip install waystone
hermes memory setup        # pick "waystone", set the project

Key CLI commands

waystone init <project>              # create a project
waystone extract <project> <file>    # extract facts from a transcript
waystone query <project> "<query>"   # retrieve relevant context
waystone onboard <project>           # import existing session history
waystone show <project>              # view project stats

How it works

At session end — waystone extract reads the conversation transcript and pulls structured facts: decisions, constraints, implementations, lessons learned, open questions. These are stored as nodes in a local SQLite knowledge graph (~/.waystone/). Superseded facts are retired automatically — if a decision changes, the graph reflects the current state.

At session start — waystone_query (or a hook) runs BFS traversal from the most relevant entry points and surfaces the top 10–25 facts. Only what's relevant to the current context, not everything ever stored.

Benchmarks

Tested on 23 questions across 3 domains (API design, auth systems, data pipelines):

	Recall
Baseline	82%
With retrieval improvements	89%

Token usage vs. naive MEMORY.md on a mature project: typically 60–80% fewer context tokens per session (exact savings depend on project age and query specificity).

Full results: BENCHMARK_RESULTS.md

Hosted API

The default store is local SQLite — no cloud dependency, no infra to manage. For cross-machine sync and team access, a hosted API is available:

• Pro ($20/mo) — unlimited projects, hosted API, 1 user
• Team ($80/mo) — unlimited projects, hosted API, up to 10 users

unbidden.ai/pricing/

Docs

• Quickstart
• MCP Server setup
• CLI Reference
• REST API
• Hermes Agent integration
• Advanced Configuration — API embeddings (no PyTorch), strategy tuning, attachment extraction

License

MIT — see LICENSE