feat: Integrate GitHub Copilot SDK and LangChain for provider-independent orchestration
- Added @github/copilot-sdk and related dependencies for GitHub Copilot integration. - Implemented ChatCopilot adapter for LangChain compatibility. - Created LLM factory to return provider-specific models (OpenAI, Anthropic, Copilot). - Developed Orchestrator agent using LangGraph for intent routing and context assembly. - Enhanced IPC communication for streaming AI responses to the renderer. - Updated progress documentation with implementation details and learnings.
This commit is contained in:
Roberto Musso
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src/main/ai/orchestrator.ts
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401
src/main/ai/orchestrator.ts
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/**
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* @Orchestrator agent — LangGraph-based intent routing.
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*
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* The agent logic (routing, state) lives here and is fully LLM-agnostic.
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* The LLM is a swappable connector obtained via `getLLM()`.
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*/
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import { Annotation, StateGraph, START, END } from '@langchain/langgraph';
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import { SystemMessage, HumanMessage, type BaseMessage } from '@langchain/core/messages';
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import { z } from 'zod';
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import { eq, asc } from 'drizzle-orm';
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import { getDb } from '../db';
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import { projects, tasks, checkpoints, notes, clients } from '../db/schema';
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import { getLLM } from './llm';
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const AI_STREAM_CHANNEL = 'ai:stream';
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// ---------------------------------------------------------------------------
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// Types
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// ---------------------------------------------------------------------------
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export interface OrchestrateInput {
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message: string;
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context: { type: 'global' | 'project'; projectId?: string };
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sender?: Electron.WebContents;
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}
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export interface OrchestrateResult {
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response: string;
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error?: string;
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}
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// ---------------------------------------------------------------------------
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// Context assembly (DB queries — provider-independent)
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// ---------------------------------------------------------------------------
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function buildProjectContext(projectId: string): string {
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const db = getDb();
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const project = db.select().from(projects).where(eq(projects.id, projectId)).all()[0];
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if (!project) return 'Project not found.';
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let clientName = '';
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if (project.clientId) {
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const client = db.select().from(clients).where(eq(clients.id, project.clientId)).all()[0];
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if (client) clientName = client.name;
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}
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const projectTasks = db
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.select({ title: tasks.title, status: tasks.status, priority: tasks.priority, dueDate: tasks.dueDate })
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.from(tasks)
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.where(eq(tasks.projectId, projectId))
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.orderBy(asc(tasks.createdAt))
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.all();
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const projectCheckpoints = db
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.select({ title: checkpoints.title, date: checkpoints.date, isApproved: checkpoints.isApproved })
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.from(checkpoints)
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.where(eq(checkpoints.projectId, projectId))
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.orderBy(asc(checkpoints.date))
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.all();
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const projectNotes = db
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.select({ title: notes.title, content: notes.content })
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.from(notes)
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.where(eq(notes.projectId, projectId))
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.orderBy(asc(notes.createdAt))
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.all();
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const lines: string[] = [
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`## Project: ${project.name}`,
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clientName ? `Client: ${clientName}` : '',
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`Status: ${project.status ?? 'active'}`,
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project.aiSummary ? `AI Summary: ${project.aiSummary}` : '',
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'',
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`### Tasks (${projectTasks.length})`,
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...projectTasks.map((t) => {
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const due = t.dueDate ? new Date(t.dueDate).toLocaleDateString() : 'no due date';
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return `- [${t.status}] ${t.title} (${t.priority}, ${due})`;
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}),
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'',
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`### Checkpoints (${projectCheckpoints.length})`,
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...projectCheckpoints.map((c) => {
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const approved = c.isApproved ? 'approved' : 'pending';
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return `- ${c.title} — ${new Date(c.date).toLocaleDateString()} (${approved})`;
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}),
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'',
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`### Notes (${projectNotes.length})`,
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...projectNotes.map((n) => {
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const excerpt = n.content.length > 500 ? n.content.slice(0, 500) + '…' : n.content;
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return `#### ${n.title}\n${excerpt}`;
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}),
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];
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return lines.filter(Boolean).join('\n');
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}
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function buildGlobalContext(): string {
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const db = getDb();
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const allProjects = db
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.select({ id: projects.id, name: projects.name, status: projects.status })
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.from(projects)
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.where(eq(projects.status, 'active'))
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.orderBy(asc(projects.name))
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.all();
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const allTasks = db.select().from(tasks).all();
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const todoCount = allTasks.filter((t) => t.status === 'todo').length;
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const inProgressCount = allTasks.filter((t) => t.status === 'in_progress').length;
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const doneCount = allTasks.filter((t) => t.status === 'done').length;
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const now = Date.now();
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const weekFromNow = now + 7 * 24 * 60 * 60 * 1000;
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const upcomingTasks = allTasks
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.filter((t) => t.dueDate && t.dueDate >= now && t.dueDate <= weekFromNow && t.status !== 'done')
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.sort((a, b) => (a.dueDate ?? 0) - (b.dueDate ?? 0));
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const projectMap = new Map(allProjects.map((p) => [p.id, p.name]));
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const lines: string[] = [
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`## Workspace Overview`,
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`Active projects: ${allProjects.length}`,
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`Tasks: ${allTasks.length} total (${todoCount} todo, ${inProgressCount} in progress, ${doneCount} done)`,
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'',
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`### Active Projects`,
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...allProjects.map((p) => `- ${p.name}`),
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'',
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`### Tasks Due This Week (${upcomingTasks.length})`,
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...upcomingTasks.map((t) => {
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const projectName = t.projectId ? (projectMap.get(t.projectId) ?? 'Unknown') : 'No project';
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const due = t.dueDate ? new Date(t.dueDate).toLocaleDateString() : '';
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return `- ${t.title} [${projectName}] — due ${due}`;
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}),
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];
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return lines.join('\n');
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}
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// ---------------------------------------------------------------------------
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// System prompts
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// ---------------------------------------------------------------------------
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function makeProjectAgentPrompt(contextData: string): string {
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return `You are @ProjectAgent, an AI assistant specialized in a specific project within Adiuva.
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You have access to the following project data:
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${contextData}
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Answer the user's question based on this project context. Be concise and helpful.
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When referencing tasks, notes, or checkpoints, mention them by name.
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If you don't have enough information, say so.`;
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}
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function makeGeneralAgentPrompt(contextData: string): string {
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return `You are @GeneralAgent, an AI assistant for the Adiuva workspace.
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You have access to the following workspace data:
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${contextData}
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Help the user with their question based on this workspace context. Provide concise, actionable answers.
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When discussing tasks or projects, reference them by name.`;
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}
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function makeKnowledgeAgentPrompt(contextData: string): string {
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return `You are @KnowledgeAgent, an AI assistant that searches across all project knowledge in Adiuva.
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You have access to the following workspace data:
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${contextData}
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Note: Semantic vector search is not yet available. Answer based on the workspace summary data above.
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If the user asks about specific note contents that aren't included here, let them know that full cross-project search will be available soon.`;
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}
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// ---------------------------------------------------------------------------
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// LangGraph State
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// ---------------------------------------------------------------------------
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const RouteSchema = z.object({
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route: z.enum(['project', 'knowledge', 'general']).describe(
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'Which specialist agent should handle this request',
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),
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});
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const OrchestratorState = Annotation.Root({
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/** The user's original message */
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userMessage: Annotation<string>(),
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/** Chat context (global vs project-scoped) */
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chatContext: Annotation<{ type: 'global' | 'project'; projectId?: string }>(),
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/** The route chosen by the orchestrator */
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route: Annotation<'project' | 'knowledge' | 'general'>(),
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/** Messages for the specialist agent */
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messages: Annotation<BaseMessage[]>({
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reducer: (existing, incoming) =>
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Array.isArray(incoming) ? existing.concat(incoming) : existing.concat([incoming]),
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default: () => [],
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}),
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/** The final response text */
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response: Annotation<string>(),
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});
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type State = typeof OrchestratorState.State;
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// ---------------------------------------------------------------------------
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// Graph nodes
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// ---------------------------------------------------------------------------
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/** Node 1: Classify intent using structured output */
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async function classifyIntent(state: State): Promise<Partial<State>> {
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const llm = await getLLM();
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if (!llm) throw new Error('AI provider not configured. Please add your token in Settings.');
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const routerLLM = llm.withStructuredOutput(RouteSchema);
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const result = await routerLLM.invoke([
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new SystemMessage(
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`You are a routing classifier for Adiuva, a project management workspace.
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Classify the user's message into one of these categories:
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- "project": Question about a specific project (tasks, notes, checkpoints, progress, summaries)
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- "knowledge": Cross-project or historical question (e.g., "what did we decide about X?", "find notes about Y")
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- "general": Everything else (general help, scheduling, task overviews, workspace summaries)`,
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),
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new HumanMessage(state.userMessage),
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]);
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return { route: result.route };
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}
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/** Node 2a: Project agent — answer project-scoped questions */
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async function projectAgent(state: State): Promise<Partial<State>> {
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const llm = await getLLM();
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if (!llm) throw new Error('AI provider not configured.');
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const projectId = state.chatContext.projectId;
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const contextData = projectId ? buildProjectContext(projectId) : buildGlobalContext();
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const systemPrompt = projectId
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? makeProjectAgentPrompt(contextData)
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: makeGeneralAgentPrompt(contextData);
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const response = await llm.invoke([
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new SystemMessage(systemPrompt),
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new HumanMessage(state.userMessage),
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]);
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const content = typeof response.content === 'string' ? response.content : '';
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return {
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messages: [response],
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response: content,
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};
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}
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/** Node 2b: Knowledge agent — cross-project search */
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async function knowledgeAgent(state: State): Promise<Partial<State>> {
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const llm = await getLLM();
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if (!llm) throw new Error('AI provider not configured.');
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const contextData = buildGlobalContext();
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const systemPrompt = makeKnowledgeAgentPrompt(contextData);
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const response = await llm.invoke([
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new SystemMessage(systemPrompt),
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new HumanMessage(state.userMessage),
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]);
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const content = typeof response.content === 'string' ? response.content : '';
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return {
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messages: [response],
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response: content,
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};
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}
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/** Node 2c: General agent — workspace-wide questions */
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async function generalAgent(state: State): Promise<Partial<State>> {
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const llm = await getLLM();
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if (!llm) throw new Error('AI provider not configured.');
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const contextData = buildGlobalContext();
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const systemPrompt = makeGeneralAgentPrompt(contextData);
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const response = await llm.invoke([
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new SystemMessage(systemPrompt),
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new HumanMessage(state.userMessage),
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]);
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const content = typeof response.content === 'string' ? response.content : '';
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return {
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messages: [response],
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response: content,
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};
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}
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/** Routing function: reads state.route and returns the next node name */
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function routeDecision(state: State): string {
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switch (state.route) {
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case 'project': return 'projectAgent';
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case 'knowledge': return 'knowledgeAgent';
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case 'general': return 'generalAgent';
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default: return 'generalAgent';
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}
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}
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// ---------------------------------------------------------------------------
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// Compile the graph (singleton, reused across calls)
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// ---------------------------------------------------------------------------
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function buildGraph() {
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return new StateGraph(OrchestratorState)
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.addNode('classifyIntent', classifyIntent)
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.addNode('projectAgent', projectAgent)
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.addNode('knowledgeAgent', knowledgeAgent)
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.addNode('generalAgent', generalAgent)
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.addEdge(START, 'classifyIntent')
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.addConditionalEdges('classifyIntent', routeDecision, [
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'projectAgent', 'knowledgeAgent', 'generalAgent',
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])
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.addEdge('projectAgent', END)
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.addEdge('knowledgeAgent', END)
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.addEdge('generalAgent', END)
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.compile();
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}
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let compiledGraph: ReturnType<typeof buildGraph> | null = null;
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function getGraph() {
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if (!compiledGraph) {
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compiledGraph = buildGraph();
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}
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return compiledGraph;
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}
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// ---------------------------------------------------------------------------
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// Streaming helper
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// ---------------------------------------------------------------------------
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function sendStreamChunk(sender: Electron.WebContents | undefined, token: string, done: boolean): void {
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if (!sender || sender.isDestroyed()) return;
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sender.send(AI_STREAM_CHANNEL, { token, done });
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}
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// ---------------------------------------------------------------------------
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// Orchestrate (public entry point)
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// ---------------------------------------------------------------------------
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export async function orchestrate(input: OrchestrateInput): Promise<OrchestrateResult> {
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const { message, context, sender } = input;
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// Quick check: is an LLM available?
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const llm = await getLLM();
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if (!llm) {
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return { response: '', error: 'AI provider not configured. Please add your token in Settings.' };
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}
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try {
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const graph = getGraph();
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// Use streaming to push tokens to the renderer in real-time
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const stream = await graph.stream(
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{
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userMessage: message,
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chatContext: context,
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route: 'general' as const,
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response: '',
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},
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{ streamMode: 'messages' as const },
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);
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let fullResponse = '';
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for await (const [chunk, metadata] of stream) {
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// Only stream tokens from the specialist agent nodes (not the classifier)
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if (
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metadata.langgraph_node !== 'classifyIntent' &&
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chunk.content &&
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typeof chunk.content === 'string'
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) {
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fullResponse += chunk.content;
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sendStreamChunk(sender, chunk.content, false);
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}
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}
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// Signal stream completion
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sendStreamChunk(sender, '', true);
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return { response: fullResponse };
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} catch (err) {
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sendStreamChunk(sender, '', true);
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const errMsg = err instanceof Error ? err.message : String(err);
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if (errMsg.includes('401') || errMsg.includes('403') || errMsg.includes('auth') || errMsg.includes('Unauthorized')) {
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return { response: '', error: 'Authentication failed. Please check your token in Settings.' };
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}
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if (errMsg.includes('timeout') || errMsg.includes('Timeout')) {
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return { response: '', error: 'Request timed out. Please try again.' };
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}
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return { response: '', error: errMsg };
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}
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}
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Block a user