mirror of
https://github.com/rowboatlabs/rowboat.git
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Enhance Rowboat Copilot functionality:
- Expanded the system prompt to support a wider range of actions, including general chat and agent management. - Implemented conversational memory, allowing the assistant to maintain context and respond in natural language. - Updated the interpret function to accept conversation history, improving command parsing. - Enhanced the rendering of assistant responses to include command outcomes and maintain conversational flow. - Updated documentation to reflect new features, including conversational context and debug mode for inspecting raw commands.
This commit is contained in:
tusharmagar
Ramnique Singh
parent
4310b1d45d
commit
0eda81b33c
4 changed files with 661 additions and 64 deletions
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@ -3,4 +3,5 @@ Rowboat Copilot (demo)
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- Entry point: `npm run copilot` (runs `src/x.ts` after building)
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- Natural language interface to list/create/update/delete workflow JSON under `.rowboat/workflows`
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- Uses existing zod schemas for validation; errors bubble up plainly for easy debugging
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- Maintains conversational memory within a session and replies in natural language (append `--debug` or set `COPILOT_DEBUG=1` to view raw JSON commands)
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- Data folders ensured automatically: `.rowboat/workflows`, `.rowboat/agents`, `.rowboat/mcp`
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@ -10,3 +10,7 @@ Example prompts once running:
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- `create a workflow demo that calls function get_date`
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- `add an agent step default_assistant to demo`
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- `delete the demo workflow`
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While the session is open the copilot keeps conversational context, so you can ask follow-ups such as “what was the first thing I asked?” or “add that step again”. Responses are natural language summaries of the structured actions it performs.
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Need to inspect the underlying JSON command/results? Run in debug mode with `npm run copilot -- --debug` (or set `COPILOT_DEBUG=1`) to keep the raw interpreter output visible.
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@ -1,80 +1,149 @@
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import readline from "readline";
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import { z } from "zod";
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import { openai } from "@ai-sdk/openai";
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import { generateObject } from "ai";
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import { Workflow } from "../../application/entities/workflow.js";
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import { listWorkflows, getWorkflow, upsertWorkflow, deleteWorkflow } from "./workflows/service.js";
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import { generateObject, streamText } from "ai";
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import type { CoreMessage } from "ai";
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import {
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ChatCommand,
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ChatCommandT,
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CommandOutcome,
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executeCommand,
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} from "./commands.js";
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const ChatCommand = z.object({
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action: z.enum([
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"help",
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"list_workflows",
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"get_workflow",
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"create_workflow",
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"update_workflow",
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"delete_workflow",
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"unknown",
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]),
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id: z.string().optional(),
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updates: Workflow.partial().optional(),
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});
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type ChatCommandT = z.infer<typeof ChatCommand>;
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type ConversationMessage = {
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role: "user" | "assistant";
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content: string;
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};
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const systemPrompt = `
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You are a CLI assistant that converts the user's natural language into a JSON command for managing workflows.
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You are a general-purpose CLI copilot that converts the user's natural language into structured commands the Rowboat assistant runtime can execute, and you can also hold a regular conversation when no command fits.
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Rules:
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- Only output JSON matching the provided schema. No extra commentary.
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- Choose the most appropriate action from: help, list_workflows, get_workflow, create_workflow, update_workflow, delete_workflow, unknown.
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- For actions that need an id (get/update/delete/create), set "id" to the workflow identifier (e.g. "example_workflow").
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- For create/update, include only provided fields in "updates". If not provided, omit.
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- Select the most appropriate action from: help, general_chat, list_workflows, get_workflow, describe_workflows, create_workflow, update_workflow, delete_workflow, list_agents, get_agent, create_agent, update_agent, delete_agent, list_mcp_servers, add_mcp_server, remove_mcp_server, run_workflow, unknown.
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- Use describe_workflows with { scope: "all" } to show every workflow, or provide specific ids when the user names particular workflows (including pronouns like "them" or "those" referring to previously listed workflows).
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- For actions that need an id (workflow/agent), set "id" to the identifier (e.g. "example_workflow").
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- For create/update actions, only include provided fields in "updates".
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- Workflow shape reminder: { name: string, description: string, steps: Step[] } where Step is either { type: "function", id: string } or { type: "agent", id: string }.
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- Agent shape reminder: { name: string, model: string, description: string, instructions: string }.
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- MCP server shape reminder: { name: string, url: string }.
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- If the request is ambiguous, set action to "unknown".
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- If the user is just chatting or asking for general help or explanations, use action "general_chat" with their full prompt in "query".
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`;
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async function interpret(input: string): Promise<ChatCommandT> {
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const { object } = await generateObject({
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model: openai("gpt-4.1"),
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system: systemPrompt,
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prompt: input,
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schema: ChatCommand,
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});
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return object;
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const responseSystemPrompt = `
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You are Skipper, the Rowboat CLI copilot. You maintain an ongoing conversation, remember prior questions, run commands when requested, and give helpful free-form answers when a general reply is appropriate.
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Guidelines:
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- Respond in natural language with short, helpful paragraphs or bullet lists when useful.
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- Summarise command results plainly (lists, confirmations, errors) and mention next steps when appropriate.
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- If a command could not be inferred (action "unknown"), clarify what additional detail is needed or answer the query directly using the conversation history when possible.
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- Use the conversation history to answer memory questions (for example "what was the first question I asked?").
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- Avoid repeating the raw JSON command or result unless explicitly asked; focus on what the outcome means.
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- Deliver everything requested in one response. Do not say you'll follow up later—include all available details right away.
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- For general_chat actions, respond directly to the user's query with the best answer you can provide.
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`;
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function buildMessageHistory(history: ConversationMessage[]): CoreMessage[] {
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return history.map((message) => ({
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role: message.role,
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content: message.content,
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}));
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}
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async function execute(cmd: ChatCommandT): Promise<unknown> {
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switch (cmd.action) {
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case "help":
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return {
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usage: [
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"Examples:",
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"- list workflows",
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"- show workflow example_workflow",
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"- create workflow demo with one step calling function get_date",
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"- update workflow demo: add agent step default_assistant",
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"- delete workflow demo",
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],
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};
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case "list_workflows":
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return { items: listWorkflows() };
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case "get_workflow":
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if (!cmd.id) return { error: "id required" };
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return getWorkflow(cmd.id) ?? null;
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case "create_workflow":
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if (!cmd.id) return { error: "id required" };
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return upsertWorkflow(cmd.id, { ...(cmd.updates ?? {}) });
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case "update_workflow":
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if (!cmd.id) return { error: "id required" };
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return upsertWorkflow(cmd.id, { ...(cmd.updates ?? {}) });
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case "delete_workflow":
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if (!cmd.id) return { error: "id required" };
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return { deleted: deleteWorkflow(cmd.id) };
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case "unknown":
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return { error: "Could not determine intent. Try again or ask for help." };
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async function interpret(input: string, history: ConversationMessage[]): Promise<ChatCommandT> {
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const stopSpinner = startSpinner("Analyzing…", { persist: false });
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const conversation: CoreMessage[] = [
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{ role: "system", content: systemPrompt },
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...buildMessageHistory(history),
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{ role: "user", content: input },
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];
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try {
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const { object } = await generateObject({
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model: openai("gpt-4.1"),
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messages: conversation,
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schema: ChatCommand,
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});
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return object;
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} finally {
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stopSpinner();
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}
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}
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function startSpinner(
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label: string,
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options: { persist?: boolean } = {}
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): (finalMessage?: string) => void {
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const { persist = true } = options;
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const frames = ["⠋", "⠙", "⠹", "⠸", "⠼", "⠴"];
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let index = 0;
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const render = () => {
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const frame = frames[index];
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index = (index + 1) % frames.length;
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process.stdout.write(`\r${frame} ${label}`);
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};
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render();
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const timer = setInterval(render, 80);
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return (finalMessage?: string) => {
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clearInterval(timer);
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const doneFrame = frames[(index + frames.length - 1) % frames.length];
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const message = finalMessage ?? "done";
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const clearWidth = doneFrame.length + label.length + (persist ? message.length + 3 : 2);
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const clear = " ".repeat(clearWidth);
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process.stdout.write(`\r${clear}`);
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if (persist) {
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process.stdout.write(`\r${doneFrame} ${label} ${message}\n`);
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} else {
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process.stdout.write("\r");
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}
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};
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}
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async function renderAssistantResponse(
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input: string,
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cmd: ChatCommandT,
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outcome: CommandOutcome,
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history: ConversationMessage[]
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): Promise<string> {
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const condensedCommand = JSON.stringify(cmd, null, 2);
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const condensedResult = JSON.stringify(outcome, null, 2);
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const { textStream } = await streamText({
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model: openai("gpt-4.1"),
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messages: [
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{ role: "system", content: responseSystemPrompt },
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...buildMessageHistory(history),
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{
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role: "user",
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content: [
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`Most recent request: ${input}`,
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`Interpreter output:\n${condensedCommand}`,
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`Command result:\n${condensedResult}`,
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].join("\n\n"),
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},
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],
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});
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let final = "";
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for await (const textChunk of textStream as AsyncIterable<unknown>) {
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const chunk =
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typeof textChunk === "string"
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? textChunk
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: typeof (textChunk as { value?: string }).value === "string"
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? (textChunk as { value?: string }).value ?? ""
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: "";
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if (!chunk) continue;
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process.stdout.write(chunk);
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final += chunk;
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}
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if (!final.endsWith("\n")) {
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process.stdout.write("\n");
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}
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return final.trim();
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}
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export async function startCopilot(): Promise<void> {
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if (!process.env.OPENAI_API_KEY) {
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console.error("OPENAI_API_KEY is not set. Please export it to use chat.");
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@ -85,16 +154,38 @@ export async function startCopilot(): Promise<void> {
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const rl = readline.createInterface({ input: process.stdin, output: process.stdout });
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console.log("Rowboat Copilot (type 'exit' to quit)");
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const debugMode = process.argv.includes("--debug") || process.env.COPILOT_DEBUG === "1";
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const conversationHistory: ConversationMessage[] = [];
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const ask = () => rl.question("> ", async (line) => {
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if (!line || line.trim().toLowerCase() === "exit") {
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rl.close();
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return;
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}
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try {
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const cmd = await interpret(line);
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console.log("\n=== Parsed Command ===\n" + JSON.stringify(cmd, null, 2));
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const result = await execute(cmd);
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console.log("\n=== Result ===\n" + JSON.stringify(result, null, 2) + "\n");
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const trimmed = line.trim();
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const cmd = await interpret(trimmed, conversationHistory);
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let outcome: CommandOutcome;
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try {
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outcome = await executeCommand(cmd);
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} finally {
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// no-op
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}
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const historyWithLatestUser: ConversationMessage[] = [
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...conversationHistory,
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{ role: "user", content: trimmed },
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];
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const assistantReply = await renderAssistantResponse(trimmed, cmd, outcome, historyWithLatestUser);
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console.log("");
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if (debugMode) {
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console.log("=== Parsed Command ===\n" + JSON.stringify(cmd, null, 2));
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console.log("\n=== Outcome ===\n" + JSON.stringify(outcome, null, 2) + "\n");
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}
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conversationHistory.push({ role: "user", content: trimmed });
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conversationHistory.push({ role: "assistant", content: assistantReply });
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} catch (err) {
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console.error("Error:", (err as Error).message);
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}
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501
apps/cli/src/application/assistant/commands.ts
Normal file
501
apps/cli/src/application/assistant/commands.ts
Normal file
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@ -0,0 +1,501 @@
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import { z } from "zod";
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import {
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listWorkflows,
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getWorkflow,
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upsertWorkflow,
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deleteWorkflow,
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} from "./workflows/service.js";
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import {
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listAgents,
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getAgent,
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upsertAgent,
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deleteAgent,
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} from "./agents/service.js";
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import {
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readMcpConfig,
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writeMcpConfig,
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} from "./mcp/service.js";
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import { Agent } from "../entities/agent.js";
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import { Workflow } from "../entities/workflow.js";
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export const ChatCommand = z.object({
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action: z.enum([
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"help",
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"general_chat",
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"list_workflows",
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"get_workflow",
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"describe_workflows",
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"create_workflow",
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"update_workflow",
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"delete_workflow",
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"list_agents",
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"get_agent",
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"create_agent",
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"update_agent",
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"delete_agent",
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"list_mcp_servers",
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"add_mcp_server",
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"remove_mcp_server",
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"run_workflow",
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"unknown",
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]),
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id: z.string().optional(),
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query: z.string().optional(),
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updates: Workflow.partial().optional(),
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server: z
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.object({
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name: z.string(),
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url: z.string(),
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})
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.optional(),
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name: z.string().optional(),
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clarification: z.string().optional(),
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ids: z.array(z.string()).optional(),
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scope: z.enum(["all"]).optional(),
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});
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export type ChatCommandT = z.infer<typeof ChatCommand>;
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export type CommandStatus = "ok" | "error";
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export interface CommandOutcome {
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status: CommandStatus;
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headline: string;
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details?: string;
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list?: string[];
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data?: unknown;
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}
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function asCommandOutcome(
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outcome: Omit<CommandOutcome, "status"> & { status?: CommandStatus }
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): CommandOutcome {
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return {
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status: outcome.status ?? "ok",
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headline: outcome.headline,
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details: outcome.details,
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list: outcome.list,
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data: outcome.data,
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};
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}
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function normalizeKey(value: string): string {
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return value.toLowerCase().replace(/[^a-z0-9]/g, "");
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}
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function levenshtein(a: string, b: string): number {
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if (a === b) return 0;
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if (a.length === 0) return b.length;
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if (b.length === 0) return a.length;
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const matrix: number[][] = Array.from({ length: a.length + 1 }, (_, i) =>
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Array.from({ length: b.length + 1 }, (_, j) => (i === 0 ? j : j === 0 ? i : 0))
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);
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for (let i = 1; i <= a.length; i++) {
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for (let j = 1; j <= b.length; j++) {
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const cost = a[i - 1] === b[j - 1] ? 0 : 1;
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matrix[i][j] = Math.min(
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matrix[i - 1][j] + 1, // deletion
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matrix[i][j - 1] + 1, // insertion
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matrix[i - 1][j - 1] + cost // substitution
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);
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}
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}
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return matrix[a.length][b.length];
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}
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function resolveWorkflowId(
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input: string,
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existing: string[]
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): { id?: string; suggestion?: string } {
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const exact = existing.find((candidate) => candidate === input);
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if (exact) return { id: exact };
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const normalizedInput = normalizeKey(input);
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const normalizedMap = new Map<string, string>();
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for (const candidate of existing) {
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const key = normalizeKey(candidate);
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if (!normalizedMap.has(key)) normalizedMap.set(key, candidate);
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}
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const normalizedMatch = normalizedMap.get(normalizedInput);
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if (normalizedMatch) return { id: normalizedMatch };
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const ranked = existing
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.map((candidate) => ({
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id: candidate,
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distance: levenshtein(normalizeKey(candidate), normalizedInput),
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}))
|
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.sort((a, b) => a.distance - b.distance);
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const best = ranked[0];
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if (best && best.distance <= 2) {
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return { id: best.id };
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}
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return { suggestion: best?.id };
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}
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export async function executeCommand(cmd: ChatCommandT): Promise<CommandOutcome> {
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switch (cmd.action) {
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case "help":
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return asCommandOutcome({
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headline: "Try asking for workflows, agents, or MCP servers.",
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list: [
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"list workflows",
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"show workflow example_workflow",
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"show all workflows in detail",
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"create workflow demo that calls function get_date",
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"list agents",
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"add mcp server staging at http://localhost:8800",
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],
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});
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case "list_workflows": {
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const items = listWorkflows();
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return asCommandOutcome({
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headline:
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items.length === 0
|
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? "No workflows saved yet."
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: `Found ${items.length} workflow${items.length === 1 ? "" : "s"}.`,
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list: items,
|
||||
data: { items },
|
||||
});
|
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}
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case "get_workflow": {
|
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if (!cmd.id) {
|
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return asCommandOutcome({
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status: "error",
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||||
headline: "Workflow id required.",
|
||||
details: "Provide the workflow name you want to inspect.",
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||||
});
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}
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const allWorkflows = listWorkflows();
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const { id: resolvedId, suggestion } = resolveWorkflowId(cmd.id, allWorkflows);
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if (!resolvedId) {
|
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return asCommandOutcome({
|
||||
status: "error",
|
||||
headline: `Workflow "${cmd.id}" was not found.`,
|
||||
details: suggestion ? `Did you mean "${suggestion}"?` : undefined,
|
||||
});
|
||||
}
|
||||
const workflow = getWorkflow(resolvedId);
|
||||
if (!workflow) {
|
||||
return asCommandOutcome({
|
||||
status: "error",
|
||||
headline: `Workflow "${resolvedId}" could not be loaded.`,
|
||||
});
|
||||
}
|
||||
return asCommandOutcome({
|
||||
headline: `Loaded workflow "${resolvedId}".`,
|
||||
details: workflow.description || "No description set.",
|
||||
data: workflow,
|
||||
list: workflow.steps.map((step, index) => `${index + 1}. ${step.type} → ${step.id}`),
|
||||
});
|
||||
}
|
||||
case "describe_workflows": {
|
||||
const allWorkflows = listWorkflows();
|
||||
const explicitIds = cmd.ids?.map((value) => value.trim()).filter((value) => value.length > 0) ?? [];
|
||||
const targetIds =
|
||||
explicitIds.length > 0 ? Array.from(new Set(explicitIds)) : cmd.scope === "all" ? [...allWorkflows] : [];
|
||||
|
||||
if (targetIds.length === 0) {
|
||||
return asCommandOutcome({
|
||||
status: "error",
|
||||
headline: "No workflows specified.",
|
||||
details:
|
||||
explicitIds.length === 0 && cmd.scope !== "all"
|
||||
? "Provide workflow ids or set scope to \"all\"."
|
||||
: "No workflows found to describe.",
|
||||
});
|
||||
}
|
||||
|
||||
const described: Array<{ id: string; workflow: z.infer<typeof Workflow> }> = [];
|
||||
const missing: string[] = [];
|
||||
const suggestions: string[] = [];
|
||||
const seen = new Set<string>();
|
||||
|
||||
if (explicitIds.length === 0 && cmd.scope === "all") {
|
||||
for (const id of allWorkflows) {
|
||||
const workflow = getWorkflow(id);
|
||||
if (workflow && !seen.has(id)) {
|
||||
seen.add(id);
|
||||
described.push({ id, workflow });
|
||||
}
|
||||
}
|
||||
} else {
|
||||
for (const requestedId of targetIds) {
|
||||
const { id: resolvedId, suggestion } = resolveWorkflowId(requestedId, allWorkflows);
|
||||
if (!resolvedId) {
|
||||
missing.push(requestedId);
|
||||
if (suggestion) suggestions.push(`${requestedId} → ${suggestion}`);
|
||||
continue;
|
||||
}
|
||||
if (seen.has(resolvedId)) continue;
|
||||
seen.add(resolvedId);
|
||||
const workflow = getWorkflow(resolvedId);
|
||||
if (workflow) {
|
||||
described.push({ id: resolvedId, workflow });
|
||||
} else {
|
||||
missing.push(requestedId);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
if (described.length === 0) {
|
||||
return asCommandOutcome({
|
||||
status: "error",
|
||||
headline: "No workflows found.",
|
||||
details: `Checked: ${targetIds.join(", ")}`,
|
||||
});
|
||||
}
|
||||
|
||||
const list = described.map(({ workflow }) => {
|
||||
const description = workflow.description ? workflow.description : "No description set.";
|
||||
const steps = workflow.steps.map((step, index) => `${index + 1}. ${step.type} → ${step.id}`).join("; ");
|
||||
return `${workflow.name}: ${description} Steps: ${steps || "None"}.`;
|
||||
});
|
||||
|
||||
const details =
|
||||
missing.length > 0
|
||||
? `Missing workflows: ${missing.join(", ")}.${suggestions.length > 0 ? ` Closest matches: ${suggestions.join(", ")}.` : ""}`
|
||||
: suggestions.length > 0
|
||||
? `Closest matches: ${suggestions.join(", ")}.`
|
||||
: undefined;
|
||||
|
||||
return asCommandOutcome({
|
||||
headline: `Showing ${described.length} workflow${described.length === 1 ? "" : "s"}.`,
|
||||
details,
|
||||
list,
|
||||
data: {
|
||||
workflows: described.map(({ workflow }) => workflow),
|
||||
missing,
|
||||
},
|
||||
});
|
||||
}
|
||||
case "general_chat":
|
||||
if (!cmd.query) {
|
||||
return asCommandOutcome({
|
||||
status: "error",
|
||||
headline: "Need the question to answer.",
|
||||
details: "Repeat your request so I can help.",
|
||||
});
|
||||
}
|
||||
return asCommandOutcome({
|
||||
headline: "General assistance requested.",
|
||||
details: cmd.query,
|
||||
data: { query: cmd.query },
|
||||
});
|
||||
case "create_workflow": {
|
||||
if (!cmd.id) {
|
||||
return asCommandOutcome({
|
||||
status: "error",
|
||||
headline: "Workflow id required.",
|
||||
details: "Name the workflow you want to create.",
|
||||
});
|
||||
}
|
||||
const created = upsertWorkflow(cmd.id, { ...(cmd.updates ?? {}) });
|
||||
return asCommandOutcome({
|
||||
headline: `Workflow "${cmd.id}" saved.`,
|
||||
data: created,
|
||||
});
|
||||
}
|
||||
case "update_workflow": {
|
||||
if (!cmd.id) {
|
||||
return asCommandOutcome({
|
||||
status: "error",
|
||||
headline: "Workflow id required.",
|
||||
details: "Name the workflow you want to update.",
|
||||
});
|
||||
}
|
||||
const updated = upsertWorkflow(cmd.id, { ...(cmd.updates ?? {}) });
|
||||
return asCommandOutcome({
|
||||
headline: `Workflow "${cmd.id}" updated.`,
|
||||
data: updated,
|
||||
});
|
||||
}
|
||||
case "delete_workflow": {
|
||||
if (!cmd.id) {
|
||||
return asCommandOutcome({
|
||||
status: "error",
|
||||
headline: "Workflow id required.",
|
||||
details: "Name the workflow you want to delete.",
|
||||
});
|
||||
}
|
||||
const deleted = deleteWorkflow(cmd.id);
|
||||
return asCommandOutcome({
|
||||
headline: deleted
|
||||
? `Workflow "${cmd.id}" deleted.`
|
||||
: `Workflow "${cmd.id}" did not exist.`,
|
||||
data: { deleted },
|
||||
});
|
||||
}
|
||||
case "list_agents": {
|
||||
const items = listAgents();
|
||||
return asCommandOutcome({
|
||||
headline:
|
||||
items.length === 0
|
||||
? "No agents saved yet."
|
||||
: `Found ${items.length} agent${items.length === 1 ? "" : "s"}.`,
|
||||
list: items,
|
||||
data: { items },
|
||||
});
|
||||
}
|
||||
case "get_agent": {
|
||||
if (!cmd.id) {
|
||||
return asCommandOutcome({
|
||||
status: "error",
|
||||
headline: "Agent id required.",
|
||||
details: "Provide the agent name you want to inspect.",
|
||||
});
|
||||
}
|
||||
const agent = getAgent(cmd.id);
|
||||
if (!agent) {
|
||||
return asCommandOutcome({
|
||||
status: "error",
|
||||
headline: `Agent "${cmd.id}" was not found.`,
|
||||
});
|
||||
}
|
||||
return asCommandOutcome({
|
||||
headline: `Loaded agent "${cmd.id}".`,
|
||||
details: agent.description || "No description set.",
|
||||
data: agent,
|
||||
});
|
||||
}
|
||||
case "create_agent": {
|
||||
if (!cmd.id) {
|
||||
return asCommandOutcome({
|
||||
status: "error",
|
||||
headline: "Agent id required.",
|
||||
details: "Name the agent you want to create.",
|
||||
});
|
||||
}
|
||||
const created = upsertAgent(cmd.id, { ...(cmd.updates ?? {}) });
|
||||
return asCommandOutcome({
|
||||
headline: `Agent "${cmd.id}" saved.`,
|
||||
data: created,
|
||||
});
|
||||
}
|
||||
case "update_agent": {
|
||||
if (!cmd.id) {
|
||||
return asCommandOutcome({
|
||||
status: "error",
|
||||
headline: "Agent id required.",
|
||||
details: "Name the agent you want to update.",
|
||||
});
|
||||
}
|
||||
const updated = upsertAgent(cmd.id, { ...(cmd.updates ?? {}) });
|
||||
return asCommandOutcome({
|
||||
headline: `Agent "${cmd.id}" updated.`,
|
||||
data: updated,
|
||||
});
|
||||
}
|
||||
case "delete_agent": {
|
||||
if (!cmd.id) {
|
||||
return asCommandOutcome({
|
||||
status: "error",
|
||||
headline: "Agent id required.",
|
||||
details: "Name the agent you want to delete.",
|
||||
});
|
||||
}
|
||||
const deleted = deleteAgent(cmd.id);
|
||||
return asCommandOutcome({
|
||||
headline: deleted
|
||||
? `Agent "${cmd.id}" deleted.`
|
||||
: `Agent "${cmd.id}" did not exist.`,
|
||||
data: { deleted },
|
||||
});
|
||||
}
|
||||
case "list_mcp_servers": {
|
||||
const config = readMcpConfig();
|
||||
const servers = config.mcpServers;
|
||||
return asCommandOutcome({
|
||||
headline:
|
||||
servers.length === 0
|
||||
? "No MCP servers configured."
|
||||
: `Found ${servers.length} MCP server${servers.length === 1 ? "" : "s"}.`,
|
||||
list: servers.map((server) => `${server.name} → ${server.url}`),
|
||||
data: servers,
|
||||
});
|
||||
}
|
||||
case "add_mcp_server": {
|
||||
const serverConfig = cmd.server;
|
||||
if (!serverConfig) {
|
||||
return asCommandOutcome({
|
||||
status: "error",
|
||||
headline: "Server details required.",
|
||||
details: "Provide a name and url for the MCP server.",
|
||||
});
|
||||
}
|
||||
const config = readMcpConfig();
|
||||
const withoutExisting = config.mcpServers.filter(
|
||||
(server) => server.name !== serverConfig.name
|
||||
);
|
||||
const updated = {
|
||||
mcpServers: [...withoutExisting, { ...serverConfig }],
|
||||
};
|
||||
writeMcpConfig(updated);
|
||||
return asCommandOutcome({
|
||||
headline: `MCP server "${serverConfig.name}" saved.`,
|
||||
data: updated.mcpServers,
|
||||
});
|
||||
}
|
||||
case "remove_mcp_server": {
|
||||
const name = cmd.name;
|
||||
if (!name) {
|
||||
return asCommandOutcome({
|
||||
status: "error",
|
||||
headline: "Server name required.",
|
||||
details: "Tell me which MCP server to remove.",
|
||||
});
|
||||
}
|
||||
const config = readMcpConfig();
|
||||
const remaining = config.mcpServers.filter(
|
||||
(server) => server.name !== name
|
||||
);
|
||||
const removed = remaining.length !== config.mcpServers.length;
|
||||
writeMcpConfig({ mcpServers: remaining });
|
||||
return asCommandOutcome({
|
||||
headline: removed
|
||||
? `MCP server "${name}" removed.`
|
||||
: `MCP server "${name}" was not registered.`,
|
||||
data: remaining,
|
||||
});
|
||||
}
|
||||
case "run_workflow": {
|
||||
if (!cmd.id) {
|
||||
return asCommandOutcome({
|
||||
status: "error",
|
||||
headline: "Workflow id required.",
|
||||
details: "Name the workflow you want to run.",
|
||||
});
|
||||
}
|
||||
const workflow = getWorkflow(cmd.id);
|
||||
if (!workflow) {
|
||||
return asCommandOutcome({
|
||||
status: "error",
|
||||
headline: `Workflow "${cmd.id}" was not found.`,
|
||||
});
|
||||
}
|
||||
if (workflow.steps.length === 0) {
|
||||
return asCommandOutcome({
|
||||
headline: `Workflow "${cmd.id}" is empty.`,
|
||||
details: "Add function or agent steps before running.",
|
||||
data: workflow,
|
||||
});
|
||||
}
|
||||
return asCommandOutcome({
|
||||
headline: `Workflow "${cmd.id}" is ready.`,
|
||||
details:
|
||||
"Running from the copilot will be available once the runtime bridge is connected.",
|
||||
list: workflow.steps.map((step, index) => `${index + 1}. ${step.type} → ${step.id}`),
|
||||
data: workflow,
|
||||
});
|
||||
}
|
||||
case "unknown":
|
||||
return asCommandOutcome({
|
||||
status: "error",
|
||||
headline: "I need more detail before taking action.",
|
||||
details: cmd.clarification ?? "Try rephrasing or be more specific about the workflow, agent, or MCP server.",
|
||||
});
|
||||
}
|
||||
}
|
||||
Loading…
Add table
Add a link
Reference in a new issue