Signetai
Self-hosted memory and identity layer for AI agents.
A must-try for developers running multiple AI agents who want portable, local-first memory. It's open-source, respects privacy, and fills a real gap. But it's not for non-technical users or those who prefer managed cloud solutions.
- AI agent developers building multi-agent systems
- Power users running multiple coding agents (Claude Code, OpenCode, etc.)
- Privacy-conscious teams needing self-hosted agent memory
- Developers seeking persistent context across model switches
- Users who prefer fully managed cloud memory services
- Teams needing simple out-of-the-box setup without technical overhead
- Non-technical users uncomfortable with CLI and daemon configuration
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In short
Signetai — Self-hosted memory and identity layer for AI agents. Best for AI agent developers building multi-agent systems, Power users running multiple coding agents (Claude Code, OpenCode, etc.), Privacy-conscious teams needing self-hosted agent memory. Free to use.
What's new in Signetai
Checked 14 days agoAcross the latest 10 updates: 6 feature updates and 4 community discussions.
Self-Hosted AI Memory for Hermes Agent and OpenClaw
Signet is built as a portable local context layer for Hermes Agent and OpenClaw runtimes.
The Source and the Synthesis
A good memory system keeps source and synthesis separate to maintain auditability.
The Obsidian of AI Memory Systems
Users should own the shape of their thinking, similar to Obsidian's approach for personal knowledge management.
Bring Both Kinds of Memory
Signet supports both curated knowledge and structured data, letting agents use either form as needed.
Search Can't Find What It Doesn't Know to Look For
MemAware benchmark shows RAG fails on implicit cross-domain context; better indexing and graph traversal needed.
Retrieval Is Not Memory
Critique of Supermemory's benchmark stunt; architecture matters more than a single accuracy number.
The OS Moment
Jensen Huang called OpenClaw the OS for personal AI; persistent storage is missing from the stack.
You Think Signet Is a Memory System
Signet is a persistent cognition layer, not a vector database.
The Database Knows What You Did Last Summer
Deep dive into Signet's taxonomy, entity architecture, and extraction pipeline for understanding knowledge.
How to Migrate Your ChatGPT Memory to Claude
Step-by-step guide to switching from ChatGPT to Claude without losing preferences or context.
Viability Score
How likely is Signetai to still be operational in 12 months? Based on 4 signals — momentum (how recently it shipped), wrapper dependency, revenue model, and web presence.
Last calculated: July 2026
How we score →Key Features
- Portable agent memory across models (Claude Code, OpenCode, Codex, Pi, Hermes Agent)
- Automatic session distillation into structured memory (no manual saves)
- Knowledge graph with entity and relationship extraction
- Inspectable memory via web dashboard with full provenance
- Encrypted secrets management for credentials
- Multi-agent scoping (isolated, shared, or group memory)
- Context ranking with provenance and dampening
- Cross-platform agent state layer (unified identity, memory, secrets)
- Local-first storage (data never leaves your machine)
- CLI, HTTP API, and SDK for integration
- Git provider sync
- Scheduled tasks via cron-based daemon
- Document ingest and retrieval system
- Remote harness connector support
- Support for Hermes Agent and OpenClaw (as of May 2026)
About Signetai
Signet is an open-source, local-first persistence layer for AI agents that stores identity, memory, and secrets outside any single model or harness. It solves the problem of losing context when switching between models (e.g., Claude to GPT-4o) or agent frameworks (e.g., Claude Code to OpenClaw). By keeping agent state portable and inspectable, Signet ensures your agents retain what they learned across sessions. Designed for developers and power users running multiple AI coding agents, Signet runs as a local daemon with a CLI, web dashboard, and HTTP API. It automatically distills every session into structured memory—entities, relationships, timeline—without manual tool calls. Retrieval and ranking happen entirely on-device with no LLM at search time. Key differentiators include local-first architecture (data never leaves your machine), encrypted secrets management, multi-agent scoping (isolated, shared, or group memory), and a knowledge graph for deep context retrieval. Signet also ranks context with inspectable provenance and dampening to avoid flooding the model window. Apache 2.0 licensed, it supports integrations with Claude Code, OpenCode, OpenClaw, Codex, Oh My Pi, Pi Agent, and Hermes Agent. Latest news (May 2026) confirms support for Hermes Agent and OpenClaw, plus a unified memory system for both curated knowledge and structured data. Signet positions itself as a privacy-first alternative to cloud memory services like MemGPT or ChatGPT's memory, but requires technical setup.
Behind the Verdict
Signet nails the problem of agent memory stickiness. If you switch between Claude Code, OpenCode, and Codex regularly, you know the pain of lost context. Signet's automatic session distillation and knowledge graph are genuinely useful. We'd reach for this when building multi-agent systems where each agent needs to share or inherit memory. The local-first design means no data leaks to third parties—privacy-conscious teams will appreciate that. However, setup requires comfort with the CLI and daemon management. There's no one-click cloud deployment. Compared to MemGPT, which is cloud-hosted and simpler, Signet offers more control and portability at the cost of operational overhead. In practice, the auto-distillation works well, but you still need to peek at the dashboard to verify what's being stored. The benchmark results (87.5% accuracy on LoCoMo) are promising but early. One caveat: the tool is in active development; breaking changes may occur. For developers who want a self-hosted, open-source memory layer, Signet is currently the best option. For teams that want managed memory without DevOps, look elsewhere.
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Use Cases
- Run multiple AI coding agents with a unified memory that persists across sessions and model changes.
- Automatically extract and store structured knowledge from conversations without manual intervention.
- Manage credentials and secrets so agents use them without exposing plaintext in chat.
- Build multi-agent swarms with isolated or shared memory scopes for collaborative workflows.
- Self-host a portable identity and memory layer that works across Claude, OpenCode, and Codex.
Limitations
- Signet is a local-first tool; it does not provide a cloud sync or backup service.
- The retrieval is on-device, so performance scales with local hardware.
- Multi-agent features require careful configuration of memory scopes.
- It is best suited for technical users who are comfortable with CLI setup and daemon management.
Integrations
Resources & Guides
Official links
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