Quickstart
Get Signet running in about five minutes.
Why Signet
Section titled “Why Signet”Your agent starts every session from zero. It doesn’t know what you worked on yesterday. It doesn’t know your preferences, your projects, or the decisions you’ve already made together. Every session is a first date.
The industry’s answer to this has often been to give agents memory tools — “remember this,” “recall that.” That’s not memory. That’s a filing cabinet the agent sometimes opens. It puts the LLM in charge of micromanaging what to store and when to retrieve it.
Signet takes a different approach. The goal is ambient context selection: turn interactions into durable memory substrate, preserve the record of what actually happened, and surface the right pieces when the next session begins.
The distillation layer
Section titled “The distillation layer”At the end of every conversation, Signet reviews the session and distills it. A local LLM breaks the conversation into atomic facts, checks them against what’s already known, and decides whether to add new facts, skip duplicates, or record proposals for more complex changes. Your agent won’t store “prefers dark mode” fourteen times.
The knowledge graph
Section titled “The knowledge graph”Named entities — people, projects, tools, concepts — are extracted and linked. When you ask about a project, Signet traverses the graph: the project’s architecture, the people involved, the tools it depends on, the constraints that apply. This structure improves the quality of candidate context instead of treating memory as a flat pile of fragments.
Context selection
Section titled “Context selection”The structured candidate pool gives Signet something better than a flat list of snippets. Retrieval can combine graph traversal, keyword search, semantic similarity, provenance, scope, recency, and feedback without hiding the result behind an opaque ranking model.
The aim is practical precision: surface the context that helps the agent work now, and keep noisy or repeatedly unhelpful memories from haunting the context window forever.
Retrieval
Section titled “Retrieval”Retrieval blends graph traversal, keyword search, and semantic similarity into a bounded candidate set, then reranks and filters it. The constellation view in the dashboard lets you inspect the agent’s knowledge topology.
Document ingest
Section titled “Document ingest”Feed any document into the distillation layer. PDFs, specs, reference pages, URLs. They’re chunked, embedded, and indexed alongside your agent’s insights.
Safety guarantees
Section titled “Safety guarantees”- Raw-first: content is persisted before any LLM processing begins
- Pinned insights are sacred: the distillation layer cannot modify them. Only you can.
- Everything is recoverable: deletions are soft, with a recovery window and full audit trail
Automatic destructive memory mutations remain conservative and gated in the current implementation. Explicit user/operator repair flows are the reliable path today.
The same agent follows you across Claude Code, OpenCode, and OpenClaw. Same personality, same knowledge, same secrets. Switch tools without starting over.
For deeper technical details, see Architecture. For the long-term vision, see VISION.md.
In this section
Section titled “In this section”- Install Install Signet and choose the right distribution path.
- Set up Signet Run the setup wizard and understand the workspace it creates.
- Your first session Use memory, secrets, skills, and the dashboard in a first Signet session.
- Operate your installation Run Signet as a service, edit the agent, secure access, and troubleshoot it.