codex-agent-mem

Discoverability Metadata

This document captures the metadata and phrasing that best describes the current repository for GitHub, search, and agent-driven discovery.

Recommended GitHub description

Persistent local MCP memory for AI agents: scoped continuity packs, writable notes, closure checks, and token-aware context reuse for Codex, Claude Code, Gemini CLI, Qwen Code, and other MCP-compatible clients.

Recommended GitHub topics

• codex
• claude-code
• gemini-cli
• mcp
• model-context-protocol
• sqlite
• agent-memory
• mcp-memory
• ai-agents
• ai-agent-memory
• local-first
• token-efficiency
• context-compression
• agent-workflows
• deepseek
• ollama
• kimi
• fastapi
• developer-tools
• python

Recommended release framing

1.0.2 is the current public maintenance release for the stable 1.0.x local-first MCP core. The 1.0.x line is a stable local-first MCP continuity layer for MCP-compatible AI agents built on SQLite, scoped continuity packs, deterministic closure control, recent-change deltas, scope-guard retrieval, operational-state carry-forward, provenance-aware auditing, health diagnostics, project snapshots, read-only runtime mode, compact MCP responses, pack-hash reuse, lazy SQLite initialization, runtime heartbeat diagnostics, optional telemetry, an optional local daemon/stdio bridge, MCP retrieval, and a local inspection UI.

v1.0.2 also keeps the identity/scope hardening current, while v1.0.1 hardens daemon/stdio transport behavior, documents client response-mode differences, keeps object-root MCP structuredContent for list results, and adds release hygiene gates. 1.0.0 remains the reproducible verification baseline for the original token-reduction fixtures.

codex-agent-mem can also be documented as an optional companion to clean-process-ended (GitHub) v0.7.2. The projects are independent: codex-agent-mem preserves continuity and closure state, while clean-process-ended provides dry-run process-hygiene evidence and compact janitor receipts.

Search phrases this repo should naturally support

• Codex memory
• Codex CLI memory
• Codex Desktop memory
• Claude Code memory
• Google Gemini CLI memory
• Claude Code MCP memory
• Google Gemini CLI MCP memory
• Qwen Code MCP memory
• local MCP memory for MCP-compatible AI agents
• MCP memory
• MCP memory layer
• MCP context pack
• structuredContent MCP memory
• local SQLite MCP memory
• multi-runtime MCP memory
• local-first MCP continuity
• agent continuity memory
• local-first agent memory
• SQLite memory for Codex
• Codex notify capture
• Codex MCP retrieval
• auditable agent memory
• memory provenance for Codex
• Codex memory health
• Codex memory snapshots
• governed memory for Codex
• policy-based agent memory
• selective memory inheritance
• repairable agent memory
• context compression for agents
• token-saving context reuse for MCP-compatible AI agents
• token efficiency for AI agents
• token economy for agent workflows
• local memory layer for agent workflows
• persistent local memory for MCP clients
• MCP memory for Codex
• Claude Code memory MCP
• Gemini CLI memory MCP
• DeepSeek model workflow through MCP memory
• Qwen Code MCP memory
• Ollama model workflow with MCP memory
• Kimi Code MCP memory
• codex-agent-mem clean-process-ended
• continuity plus process hygiene for AI agents

Rules for future docs

• Prefer explicit, real keywords over abstract marketing language
• Repeat important terms naturally:
  • MCP
  • SQLite
  • local-first
  • agent memory
  • continuity packs
  • token efficiency
  • token-saving context reuse
  • validated MCP clients such as Codex, Claude Code, and Google Gemini CLI
• Do not document deferred areas as implemented
• Keep the first section of public docs specific and concrete
• When using model-route keywords such as DeepSeek or Ollama, state the validated MCP client path and do not imply a native Ollama or DeepSeek MCP adapter in v1.0.x.

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