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sgx-labs/statelessagent

by Various

Your AI forgets everything between sessions. SAME fixes that. Local-first, no API keys, single binary.

S

MCP

sgx-labs/statelessagent

Added 1 June 2026

#ai-agent #ai-memory #claude-code #context-surfacing #cursor #golang #llm-memory #local-first

Overview

A local-first AI tool that gives agents persistent memory across sessions, packaged as a single binary with no API keys required. It solves the problem of AI forgetting context between interactions by maintaining state locally.

Best for

Best for
Developers seeking a simple, self-hosted memory solution for AI agents without cloud dependencies

Use cases

  • Provide persistent context to AI chat assistants across multiple sessions
  • Run offline AI agents without external network dependencies
  • Deploy a lightweight, self-contained memory layer for custom tools

Notes

A local-first AI tool that gives agents persistent memory across sessions, packaged as a single binary with no API keys required. It solves the problem of AI forgetting context between interactions by maintaining state locally.

19 stars on GitHub. Last updated 2026-04-08.

Use cases

  • Provide persistent context to AI chat assistants across multiple sessions
  • Run offline AI agents without external network dependencies
  • Deploy a lightweight, self-contained memory layer for custom tools

Pros

  • Local-first approach ensures privacy and no ongoing API costs
  • Single binary simplifies installation and deployment
  • No API keys eliminates external service dependencies

Cons

  • Very early stage (19 GitHub stars) with limited community support and polish
  • Scalability and performance under heavy use are unproven
  • May lack integration with popular AI frameworks or providers

Indexed from awesome-mcp-servers-punkpeye and enriched against its public facts.

Pros

  • Local-first approach ensures privacy and no ongoing API costs
  • Single binary simplifies installation and deployment
  • No API keys eliminates external service dependencies

Cons

  • Very early stage (19 GitHub stars) with limited community support and polish
  • Scalability and performance under heavy use are unproven
  • May lack integration with popular AI frameworks or providers