89da970455
Remove --tools "" restriction so the agent has native bash/read/write/edit access. Set vault path as working directory for the subprocess. Simplify MCP to 4 Laputa-specific tools (search_notes, get_vault_context, get_note, open_note) — everything else is handled by native tools. Add file operation detection from Write/Edit tool calls to auto-open created notes and refresh modified notes in the UI. Enhanced tool call labels show bash commands, file paths, and note names. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
108 lines
3.5 KiB
TypeScript
108 lines
3.5 KiB
TypeScript
/**
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* AI Agent utilities — Claude CLI agent mode with full shell access + MCP vault tools.
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*
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* The agent has full native tool access (bash, read, write, edit) plus
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* Laputa-specific MCP tools (search_notes, get_vault_context, get_note, open_note).
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* The frontend receives streaming events for text, tool calls, and completion.
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*/
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import { isTauri } from '../mock-tauri'
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// --- Agent system prompt ---
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const AGENT_SYSTEM_PREAMBLE = `You are working inside Laputa, a personal knowledge management app.
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Notes are markdown files with YAML frontmatter. Standard fields: title, type (aliased is_a), date, tags.
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You have full shell access. Use bash for file operations, search, bulk edits.
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Use the provided MCP tools for: full-text search (search_notes), vault orientation (get_vault_context), parsed note reading (get_note), and opening notes in the UI (open_note).
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When you create or edit a note, call open_note(path) so the user sees it in Laputa.
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Be concise and helpful. When you've completed a task, briefly summarize what you did.`
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export function buildAgentSystemPrompt(vaultContext?: string): string {
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if (!vaultContext) return AGENT_SYSTEM_PREAMBLE
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return `${AGENT_SYSTEM_PREAMBLE}\n\nVault context:\n${vaultContext}`
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}
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// --- Claude CLI agent streaming ---
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type ClaudeAgentStreamEvent =
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| { kind: 'Init'; session_id: string }
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| { kind: 'TextDelta'; text: string }
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| { kind: 'ThinkingDelta'; text: string }
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| { kind: 'ToolStart'; tool_name: string; tool_id: string; input?: string }
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| { kind: 'ToolDone'; tool_id: string; output?: string }
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| { kind: 'Result'; text: string; session_id: string }
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| { kind: 'Error'; message: string }
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| { kind: 'Done' }
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export interface AgentStreamCallbacks {
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onText: (text: string) => void
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onThinking: (text: string) => void
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onToolStart: (toolName: string, toolId: string, input?: string) => void
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onToolDone: (toolId: string, output?: string) => void
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onError: (message: string) => void
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onDone: () => void
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}
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/**
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* Stream an agent task through the Claude CLI subprocess with full tool access.
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* The CLI handles the tool-use loop; we receive events for UI updates.
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*/
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export async function streamClaudeAgent(
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message: string,
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systemPrompt: string | undefined,
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vaultPath: string,
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callbacks: AgentStreamCallbacks,
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): Promise<void> {
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if (!isTauri()) {
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setTimeout(() => {
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callbacks.onText('AI Agent requires the Claude CLI. Install it and run the native app.')
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callbacks.onDone()
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}, 300)
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return
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}
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const { invoke } = await import('@tauri-apps/api/core')
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const { listen } = await import('@tauri-apps/api/event')
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const unlisten = await listen<ClaudeAgentStreamEvent>('claude-agent-stream', (event) => {
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const data = event.payload
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switch (data.kind) {
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case 'TextDelta':
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callbacks.onText(data.text)
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break
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case 'ThinkingDelta':
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callbacks.onThinking(data.text)
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break
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case 'ToolStart':
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callbacks.onToolStart(data.tool_name, data.tool_id, data.input)
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break
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case 'ToolDone':
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callbacks.onToolDone(data.tool_id, data.output)
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break
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case 'Error':
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callbacks.onError(data.message)
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break
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case 'Done':
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callbacks.onDone()
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break
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}
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})
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try {
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await invoke<string>('stream_claude_agent', {
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request: {
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message,
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system_prompt: systemPrompt || null,
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vault_path: vaultPath,
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},
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})
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} catch (err) {
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callbacks.onError(err instanceof Error ? err.message : String(err))
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callbacks.onDone()
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} finally {
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unlisten()
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}
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}
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