Quick start (about 5 minutes)

bun add -g signetai        # or: npm install -g signetai
signet setup               # interactive setup wizard
signet status              # confirm daemon + pipeline health
signet dashboard           # open memory + retrieval inspector

If you already use Claude Code, OpenCode, OpenClaw, Codex, or Hermes Agent, keep your existing harness. Signet installs under it.

Docker self-hosting

Run Signet as a containerized daemon with first-party Compose assets:

cd deploy/docker
cp .env.example .env
docker compose up -d --build

See docs/SELF-HOSTING.md for token bootstrap, backup, and upgrade runbook details.

First proof of value (2-session test)

Run this once:

signet remember "my primary stack is bun + typescript + sqlite"

Then in your next session, ask your agent:

what stack am i using for this project?

You should see continuity without manually reconstructing context. If not, inspect recall and provenance in the dashboard or run:

signet recall "primary stack"

Want the deeper architecture view? Jump to How it works or Architecture.

Core capabilities

These are the product surface areas Signet is optimized around:

| Core | What it does | |---|---| | 🧠 Ambient memory extraction | Sessions are distilled automatically, no memory tool calls required | | 🎯 Structured context selection | Graph traversal, hybrid search, provenance, and scoped ranking surface useful context without flooding the window | | 💾 Session continuity | Checkpoint and transcript-backed context carried across sessions | | 🏠 Local-first storage | Data lives on your machine in SQLite and markdown, portable by default | | 🤝 Cross-harness runtime | Claude Code, OpenCode, OpenClaw, Codex, Pi, one shared memory substrate |

Is Signet right for you?

Use Signet if you want:

• memory continuity across sessions without manual prompt bootstrapping
• local ownership of agent state and history
• one memory layer across multiple agent harnesses

Signet may be overkill if you only need short-lived chat memory inside a single hosted assistant.

Why you can trust this

• runs local-first by default
• memory is stored in SQLite + markdown
• recall is inspectable with provenance and scopes
• memory can be repaired (edit, supersede, delete, reclassify)
• no vendor lock-in, your data stays portable

What keeps it reliable

These systems improve quality and reliability of the core memory loop:

| Supporting | What it does | |---|---| | 📜 Lossless transcripts | Raw session history preserved alongside extracted memories | | 🕸️ Structured retrieval substrate | Graph traversal + FTS5 + vector search produce bounded candidate context | | 🎯 Feedback-aware ranking | Recency, provenance, importance, and dampening signals help separate useful context from repeated noise | | 🔬 Noise filtering | Hub and similarity controls reduce low-signal memory surfacing | | 📄 Document ingestion | Pull PDFs, markdown, and URLs into the same retrieval pipeline | | 🖥️ CLI + Dashboard | Operate and inspect the system from terminal or web UI |

Advanced capabilities (optional)

These extend Signet for larger deployments and custom integrations:

| Advanced | What it does | |---|---| | 🔐 Agent-blind secrets | Encrypted secret storage, injected at execution time, not exposed to agent text | | 👯 Multi-agent policies | Isolated/shared/group memory visibility for multiple named agents | | 🔄 Git sync | Identity and memory can be versioned in your own remote | | 📦 SDK + middleware | Typed client, React hooks, and Vercel AI SDK middleware | | 🔌 MCP aggregation | Register MCP servers once, expose across connected harnesses | | 👥 Team controls | RBAC, token policy, and rate limits for shared deployments | | 🏪 Ecosystem installs | Install skills and MCP servers from skills.sh and ClawHub | | ⚖️ Apache 2.0 | Fully open source, forkable, and self-hostable |

When memory is wrong

Memory quality is not just recall quality. It is governance quality.

Signet is built to support:

• provenance inspection (where a memory came from)
• scoped visibility controls (who can see what)
• memory repair (edit, supersede, delete, or reclassify)
• transcript fallback (verify extracted memory against raw source)
• lifecycle controls (retention, decay, and conflict handling)

Harness support

Signet is not a harness. It doesn't replace Claude Code, OpenClaw, OpenCode, Pi, or Hermes Agent — it runs alongside them as an enhancement. Bring the harness you already use. Signet handles the memory layer underneath it.

| Harness | Status | Integration | |---|---|---| | Claude Code | Supported | Hooks | | Forge | First-party | Native runtime / reference harness | | OpenCode | Supported | Plugin + Hooks | | OpenClaw | Supported | Runtime plugin + NemoClaw compatible | | Codex | Supported | Hooks + MCP server | | Hermes Agent | Supported | Memory provider plugin | | Pi | Supported | Extension + Hooks | | Gemini CLI | Planned | — |

Don't see your favorite harness? file an issue and request that it be added!

LoCoMo Benchmark

LoCoMo is the standard benchmark for conversational memory systems. No standardized leaderboard exists — each system uses different judge models, question subsets, and evaluation prompts. These numbers are collected from published papers and repos.

| Rank | System | Score | Metric | Open Source | Local? | LLM at Search? | |------|--------|-------|--------|-------------|--------|----------------| | 1 | Kumiho | 97.5% adv, 0.565 F1 | Official F1 + adv subset | SDK open | No | Yes | | 2 | EverMemOS | 93.05% | Judge (self-reported) | No | No | Yes | | 3 | MemU | 92.09% | Judge | Yes | No | Yes | | 4 | MemMachine | 91.7% | Judge | No | No | Yes | | 5 | Hindsight | 89.6% | Judge | Yes (MIT) | No | Yes | | 6 | SLM V3 Mode C | 87.7% | Judge | Yes (MIT) | Partial | Yes | | 7 | Signet | 87.5% | Judge (GPT-4o) | Yes (Apache) | Yes | No | | 8 | Zep/Graphiti | ~85% | Judge (third-party est) | Partial | No | Yes | | 9 | Letta/MemGPT | ~83% | Judge | Yes (Apache) | No | Yes | | 10 | Engram | 80% | Judge | Yes | No | Yes | | 11 | SLM V3 Mode A | 74.8% | Judge | Yes (MIT) | Yes | No | | 12 | [Mem0+Graph](https://arxiv.org/abs/25