fix: bump pi deps and fix lint
This commit is contained in:
Peter Steinberger
413
src/agents/pi-embedded-runner.ts
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413
src/agents/pi-embedded-runner.ts
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@@ -0,0 +1,413 @@
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import fs from "node:fs/promises";
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import os from "node:os";
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import path from "node:path";
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import type { AppMessage, ThinkingLevel } from "@mariozechner/pi-agent-core";
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import type { Api, AssistantMessage, Model } from "@mariozechner/pi-ai";
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import {
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buildSystemPrompt,
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createAgentSession,
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defaultGetApiKey,
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findModelByProviderAndId,
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SessionManager,
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SettingsManager,
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type Skill,
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} from "@mariozechner/pi-coding-agent";
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import type { ThinkLevel, VerboseLevel } from "../auto-reply/thinking.js";
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import { formatToolAggregate } from "../auto-reply/tool-meta.js";
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import type { ClawdisConfig } from "../config/config.js";
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import { splitMediaFromOutput } from "../media/parse.js";
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import { enqueueCommand } from "../process/command-queue.js";
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import { resolveUserPath } from "../utils.js";
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import { DEFAULT_MODEL, DEFAULT_PROVIDER } from "./defaults.js";
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import {
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buildBootstrapContextFiles,
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ensureSessionHeader,
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formatAssistantErrorText,
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sanitizeSessionMessagesImages,
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} from "./pi-embedded-helpers.js";
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import { subscribeEmbeddedPiSession } from "./pi-embedded-subscribe.js";
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import { extractAssistantText } from "./pi-embedded-utils.js";
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import { createClawdisCodingTools } from "./pi-tools.js";
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import {
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applySkillEnvOverrides,
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applySkillEnvOverridesFromSnapshot,
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buildWorkspaceSkillSnapshot,
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loadWorkspaceSkillEntries,
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type SkillEntry,
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type SkillSnapshot,
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} from "./skills.js";
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import { buildAgentSystemPromptAppend } from "./system-prompt.js";
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import { loadWorkspaceBootstrapFiles } from "./workspace.js";
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export type EmbeddedPiAgentMeta = {
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sessionId: string;
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provider: string;
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model: string;
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usage?: {
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input?: number;
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output?: number;
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cacheRead?: number;
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cacheWrite?: number;
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total?: number;
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};
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};
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export type EmbeddedPiRunMeta = {
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durationMs: number;
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agentMeta?: EmbeddedPiAgentMeta;
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aborted?: boolean;
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};
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export type EmbeddedPiRunResult = {
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payloads?: Array<{
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text?: string;
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mediaUrl?: string;
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mediaUrls?: string[];
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}>;
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meta: EmbeddedPiRunMeta;
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};
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type EmbeddedPiQueueHandle = {
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queueMessage: (text: string) => Promise<void>;
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isStreaming: () => boolean;
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};
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const ACTIVE_EMBEDDED_RUNS = new Map<string, EmbeddedPiQueueHandle>();
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export function queueEmbeddedPiMessage(
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sessionId: string,
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text: string,
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): boolean {
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const handle = ACTIVE_EMBEDDED_RUNS.get(sessionId);
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if (!handle) return false;
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if (!handle.isStreaming()) return false;
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void handle.queueMessage(text);
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return true;
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}
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function mapThinkingLevel(level?: ThinkLevel): ThinkingLevel {
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// pi-agent-core supports "xhigh" too; Clawdis doesn't surface it for now.
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if (!level) return "off";
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return level;
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}
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function resolveModel(
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provider: string,
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modelId: string,
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agentDir?: string,
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): { model?: Model<Api>; error?: string } {
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const model = findModelByProviderAndId(
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provider,
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modelId,
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agentDir,
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) as Model<Api> | null;
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if (!model) return { error: `Unknown model: ${provider}/${modelId}` };
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return { model };
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}
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const defaultApiKey = defaultGetApiKey();
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async function getApiKeyForModel(model: Model<Api>): Promise<string> {
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if (model.provider === "anthropic") {
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const oauthEnv = process.env.ANTHROPIC_OAUTH_TOKEN;
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if (oauthEnv?.trim()) return oauthEnv.trim();
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}
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const key = await defaultApiKey(model);
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if (key) return key;
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throw new Error(`No API key found for provider "${model.provider}"`);
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}
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function resolvePromptSkills(
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snapshot: SkillSnapshot,
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entries: SkillEntry[],
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): Skill[] {
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if (snapshot.resolvedSkills?.length) {
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return snapshot.resolvedSkills;
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}
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const snapshotNames = snapshot.skills.map((entry) => entry.name);
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if (snapshotNames.length === 0) return [];
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const entryByName = new Map(
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entries.map((entry) => [entry.skill.name, entry.skill]),
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);
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return snapshotNames
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.map((name) => entryByName.get(name))
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.filter((skill): skill is Skill => Boolean(skill));
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}
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export async function runEmbeddedPiAgent(params: {
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sessionId: string;
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sessionFile: string;
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workspaceDir: string;
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config?: ClawdisConfig;
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skillsSnapshot?: SkillSnapshot;
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prompt: string;
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provider?: string;
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model?: string;
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thinkLevel?: ThinkLevel;
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verboseLevel?: VerboseLevel;
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timeoutMs: number;
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runId: string;
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abortSignal?: AbortSignal;
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shouldEmitToolResult?: () => boolean;
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onPartialReply?: (payload: {
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text?: string;
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mediaUrls?: string[];
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}) => void | Promise<void>;
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onToolResult?: (payload: {
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text?: string;
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mediaUrls?: string[];
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}) => void | Promise<void>;
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onAgentEvent?: (evt: {
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stream: string;
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data: Record<string, unknown>;
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}) => void;
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enqueue?: typeof enqueueCommand;
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}): Promise<EmbeddedPiRunResult> {
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const enqueue = params.enqueue ?? enqueueCommand;
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return enqueue(async () => {
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const started = Date.now();
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const resolvedWorkspace = resolveUserPath(params.workspaceDir);
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const prevCwd = process.cwd();
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const provider =
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(params.provider ?? DEFAULT_PROVIDER).trim() || DEFAULT_PROVIDER;
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const modelId = (params.model ?? DEFAULT_MODEL).trim() || DEFAULT_MODEL;
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const agentDir =
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process.env.PI_CODING_AGENT_DIR ??
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path.join(os.homedir(), ".pi", "agent");
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const { model, error } = resolveModel(provider, modelId, agentDir);
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if (!model) {
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throw new Error(error ?? `Unknown model: ${provider}/${modelId}`);
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}
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const thinkingLevel = mapThinkingLevel(params.thinkLevel);
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await fs.mkdir(resolvedWorkspace, { recursive: true });
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await ensureSessionHeader({
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sessionFile: params.sessionFile,
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sessionId: params.sessionId,
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cwd: resolvedWorkspace,
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});
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let restoreSkillEnv: (() => void) | undefined;
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process.chdir(resolvedWorkspace);
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try {
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const shouldLoadSkillEntries =
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!params.skillsSnapshot || !params.skillsSnapshot.resolvedSkills;
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const skillEntries = shouldLoadSkillEntries
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? loadWorkspaceSkillEntries(resolvedWorkspace)
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: [];
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const skillsSnapshot =
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params.skillsSnapshot ??
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buildWorkspaceSkillSnapshot(resolvedWorkspace, {
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config: params.config,
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entries: skillEntries,
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});
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restoreSkillEnv = params.skillsSnapshot
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? applySkillEnvOverridesFromSnapshot({
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snapshot: params.skillsSnapshot,
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config: params.config,
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})
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: applySkillEnvOverrides({
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skills: skillEntries ?? [],
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config: params.config,
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});
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const bootstrapFiles =
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await loadWorkspaceBootstrapFiles(resolvedWorkspace);
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const contextFiles = buildBootstrapContextFiles(bootstrapFiles);
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const promptSkills = resolvePromptSkills(skillsSnapshot, skillEntries);
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const tools = createClawdisCodingTools();
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const systemPrompt = buildSystemPrompt({
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appendPrompt: buildAgentSystemPromptAppend({
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workspaceDir: resolvedWorkspace,
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defaultThinkLevel: params.thinkLevel,
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}),
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contextFiles,
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skills: promptSkills,
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cwd: resolvedWorkspace,
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tools,
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});
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const sessionManager = SessionManager.open(params.sessionFile, agentDir);
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const settingsManager = SettingsManager.create(
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resolvedWorkspace,
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agentDir,
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);
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const { session } = await createAgentSession({
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cwd: resolvedWorkspace,
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agentDir,
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model,
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thinkingLevel,
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systemPrompt,
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// TODO(steipete): Once pi-mono publishes file-magic MIME detection in `read` image payloads,
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// remove `createClawdisCodingTools()` and use upstream `codingTools` again.
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tools,
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sessionManager,
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settingsManager,
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getApiKey: async (m) => {
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return await getApiKeyForModel(m as Model<Api>);
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},
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skills: promptSkills,
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contextFiles,
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});
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const prior = await sanitizeSessionMessagesImages(
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session.messages,
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"session:history",
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);
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if (prior.length > 0) {
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session.agent.replaceMessages(prior);
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}
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const queueHandle: EmbeddedPiQueueHandle = {
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queueMessage: async (text: string) => {
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await session.queueMessage(text);
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},
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isStreaming: () => session.isStreaming,
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};
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ACTIVE_EMBEDDED_RUNS.set(params.sessionId, queueHandle);
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let aborted = Boolean(params.abortSignal?.aborted);
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const {
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assistantTexts,
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toolMetas,
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unsubscribe,
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flush: flushToolDebouncer,
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} = subscribeEmbeddedPiSession({
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session,
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runId: params.runId,
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verboseLevel: params.verboseLevel,
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shouldEmitToolResult: params.shouldEmitToolResult,
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onToolResult: params.onToolResult,
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onPartialReply: params.onPartialReply,
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onAgentEvent: params.onAgentEvent,
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});
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const abortTimer = setTimeout(
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() => {
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aborted = true;
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void session.abort();
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},
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Math.max(1, params.timeoutMs),
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);
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let messagesSnapshot: AppMessage[] = [];
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let sessionIdUsed = session.sessionId;
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const onAbort = () => {
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aborted = true;
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void session.abort();
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};
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if (params.abortSignal) {
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if (params.abortSignal.aborted) {
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onAbort();
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} else {
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params.abortSignal.addEventListener("abort", onAbort, { once: true });
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}
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}
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let promptError: unknown | null = null;
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try {
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try {
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await session.prompt(params.prompt);
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} catch (err) {
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promptError = err;
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} finally {
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messagesSnapshot = session.messages.slice();
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sessionIdUsed = session.sessionId;
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}
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} finally {
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clearTimeout(abortTimer);
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unsubscribe();
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flushToolDebouncer();
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if (ACTIVE_EMBEDDED_RUNS.get(params.sessionId) === queueHandle) {
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ACTIVE_EMBEDDED_RUNS.delete(params.sessionId);
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}
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session.dispose();
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params.abortSignal?.removeEventListener?.("abort", onAbort);
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}
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if (promptError && !aborted) {
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throw promptError;
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}
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const lastAssistant = messagesSnapshot
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.slice()
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.reverse()
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.find((m) => (m as AppMessage)?.role === "assistant") as
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| AssistantMessage
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| undefined;
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const usage = lastAssistant?.usage;
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const agentMeta: EmbeddedPiAgentMeta = {
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sessionId: sessionIdUsed,
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provider: lastAssistant?.provider ?? provider,
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model: lastAssistant?.model ?? model.id,
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usage: usage
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? {
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input: usage.input,
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output: usage.output,
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cacheRead: usage.cacheRead,
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cacheWrite: usage.cacheWrite,
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total: usage.totalTokens,
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}
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: undefined,
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};
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const replyItems: Array<{ text: string; media?: string[] }> = [];
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const errorText = lastAssistant
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? formatAssistantErrorText(lastAssistant)
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: undefined;
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if (errorText) replyItems.push({ text: errorText });
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const inlineToolResults =
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params.verboseLevel === "on" &&
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!params.onPartialReply &&
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!params.onToolResult &&
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toolMetas.length > 0;
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if (inlineToolResults) {
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for (const { toolName, meta } of toolMetas) {
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const agg = formatToolAggregate(toolName, meta ? [meta] : []);
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const { text: cleanedText, mediaUrls } = splitMediaFromOutput(agg);
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if (cleanedText)
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replyItems.push({ text: cleanedText, media: mediaUrls });
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}
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}
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for (const text of assistantTexts.length
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? assistantTexts
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: lastAssistant
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? [extractAssistantText(lastAssistant)]
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: []) {
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const { text: cleanedText, mediaUrls } = splitMediaFromOutput(text);
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if (!cleanedText && (!mediaUrls || mediaUrls.length === 0)) continue;
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replyItems.push({ text: cleanedText, media: mediaUrls });
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}
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const payloads = replyItems
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.map((item) => ({
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text: item.text?.trim() ? item.text.trim() : undefined,
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mediaUrls: item.media?.length ? item.media : undefined,
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mediaUrl: item.media?.[0],
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}))
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.filter(
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(p) =>
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p.text || p.mediaUrl || (p.mediaUrls && p.mediaUrls.length > 0),
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);
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return {
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payloads: payloads.length ? payloads : undefined,
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meta: {
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durationMs: Date.now() - started,
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agentMeta,
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aborted,
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},
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};
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} finally {
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restoreSkillEnv?.();
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process.chdir(prevCwd);
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}
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});
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}
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Block a user