ai-memory v0.8.0

Windsurf (Codeium) — MCP server + windsurfrules

Category 2. Windsurf is MCP-capable; configure in Settings → Cascade → MCP Servers, or via ~/.codeium/windsurf/mcp_config.json.

Quick install

ai-memory install windsurf              # dry-run (default)
ai-memory install windsurf --apply      # write ~/.codeium/windsurf/mcp_config.json
ai-memory install windsurf --uninstall --apply

Handles Part 1 (MCP server registration). Part 2 (.windsurfrules) is project-scoped and still manual.

Part 1 — MCP server

{
  "mcpServers": {
    "ai-memory": {
      "command": "ai-memory",
      "args": ["mcp"],
      "env": { "AI_MEMORY_DB": "${HOME}/.claude/ai-memory.db" }
    }
  }
}

Using --tier smart or --tier autonomous with a non-default LLM backend? Post-#1146 (v0.7.0) the recommended path is a [llm] section in ~/.config/ai-memory/config.toml — single source of truth across MCP / HTTP daemon / CLI / boot banner / doctor probe. Example: backend = "xai", model = "grok-4.3", api_key_env = "XAI_API_KEY" (the env-var name, not the literal key — inline keys are rejected at parse time). Export the named env var in your shell rc; the MCP config can stay minimal. Override path: extend the env block above with AI_MEMORY_LLM_BACKEND, AI_MEMORY_LLM_API_KEY, and AI_MEMORY_LLM_MODEL — shell exports don’t reach MCP-spawned subprocesses (#1144). Full schema + per-vendor recipes: ../CONFIG_SCHEMA.md + llm-backends.md.

Part 2 — .windsurfrules (best-effort)

At the project root:

At the start of every new conversation, call memory_session_start then
memory_recall against the project's namespace before responding. Reference
recalled titles in your first reply.

Limitation + better

Category-2 limitation. Cross-file upstream tracked in #487.