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workflow engine for AI agent orchestration — code as config, no DSL","maintainers":[{"name":"anyasky","email":"anyasky91@yandex.ru"}],"readme":"# Weft\n\n**TypeScript-native workflow engine for orchestrating AI agent calls.**\n\nNo YAML. No DSL. Just typed TypeScript with a fluent builder API.\n\n```ts\nimport { z } from 'zod';\nimport { weave } from '@human-horizon/weft';\n\nconst pipeline = weave('review')\n  .prompt('analyze', (ctx) => `Analyze ${ctx.files}`, { schema: BugSchema })\n  .step('report', (ctx) => `Found ${ctx.analyze.bugs.length} bugs`)\n  .build();\n\nconst result = await pipeline.run({ files: ['src/main.ts'] });\n// result = { files: ['src/main.ts'], analyze: { bugs: [...] }, report: '...' }\n```\n\n## Installation\n\n```bash\npnpm add @human-horizon/weft\n```\n\nRequires Node.js 20+.\n\n---\n\n## Quick Start\n\n### 1. Setup a new project\n\n```bash\nmkdir my-project && cd my-project\nweft install\n```\n\nThis creates `.lore/weft/package.json` and `.lore/weft/pipelines/`, then runs `pnpm install` inside `.lore/weft/`. It does not create `.gitignore` or `pnpm-workspace.yaml`.\n\n### 2. Create a pipeline\n\n```bash\nweft init my-pipeline\n```\n\nEdit `.lore/weft/pipelines/my-pipeline.ts`:\n\n```typescript\nexport const meta = {\n    description: \"Analyzes code for bugs\",\n    args: [\n        { name: \"path\", type: \"string\", description: \"Path to file\" },\n    ],\n};\n\nexport async function main(args: string[]) {\n    const path = args[0];\n    if (!path) {\n        console.error(\"Usage: weft run my-pipeline.ts <path>\");\n        process.exit(1);\n    }\n    // ... your pipeline code\n}\n```\n\n### 3. Run it\n\n```bash\nweft run my-pipeline.ts ./src/main.ts\n```\n\nOr interactively:\n\n```bash\nweft\n# → Shows list of pipelines\n# → Asks for arguments based on meta\n# → Runs the selected one\n```\n\n### 4. Pipeline plan preview\n\nBefore executing, weft shows the pipeline tree:\n\n```\n⚡ Pipeline plan:\n├── ◆ analyze\n│   model: free → home-pc/qwen-3.5-9b thinking: high [reviewer]\n└── ◆ report\n    model: medium → ollama-cloud/deepseek-v4-flash\n```\n\nEach prompt step shows: model tag → resolved model name, thinking level, and session name (if any).\n\n---\n\n## CLI Reference\n\n| Command | Description |\n|---------|-------------|\n| `weft run <file> [args...]` | Run a pipeline |\n| `weft list [dir]` | List available pipelines (recursively) |\n| `weft init <name>` | Create a new pipeline template |\n| `weft install` | Setup `.lore/weft/` in the project |\n| `weft` (no args) | Interactive mode — select and run |\n| `weft help [command]` | Show help |\n\nSee [specs/cli.spec.md](./specs/cli.spec.md) for details.\n\n---\n\n## Core concept: context accumulation\n\nEach step **adds** a named field to the context object. Everything accumulates.\n\n```ts\nconst pipeline = weave('example')\n  .prompt('analyze', (ctx) => `Work with ${ctx.lang}`, { schema: BugSchema })\n  // ctx after: { lang: 'ts', analyze: { bugs: [...] } }\n\n  .step('count', (ctx) => ctx.analyze.bugs.length)\n  // ctx after: { lang: 'ts', analyze: { bugs: [...] }, count: 3 }\n\n  .build();\n\nconst result = await pipeline.run({ lang: 'ts' });\n// result contains all fields: lang, analyze, count\n```\n\n## API Reference\n\n### `weave(name?: string): Workflow<{}>`\n\nCreate a new pipeline.\n\n```ts\nconst pipeline = weave('my-pipeline');\n```\n\n---\n\n### `.prompt(name, fn, opts): Workflow<Ctx & { name: AgentResult | T }>`\n\nCall the agent. Result is stored under `name` in the context.\n\n```ts\n.prompt('analyze', (ctx) => `Analyze ${ctx.lang} code`, {\n  session: 'my-session',  // optional: multi-turn\n  model: 'sonnet',         // optional: model level (free, simple, medium, high, expert)\n  schema: MySchema,        // optional: zod schema for structured response\n  retry: 3,                // optional: retry on failure\n  timeout: '30s',          // optional: per-step timeout\n  continueOnError: true,   // optional: don't fail on this step\n})\n```\n\n**Context access:** the prompt function receives the full accumulated context:\n\n```ts\n.prompt('fix', (ctx) => `Fix these bugs: ${JSON.stringify(ctx.analyze)}`, { session: 'fixer' })\n```\n\n**Return type:**\n- Without `schema` → `Workflow<Ctx & { name: AgentResult }>`\n- With `schema` → `Workflow<Ctx & { name: z.infer<typeof schema> }>`\n\n```ts\ninterface AgentResult {\n  stdout: string;\n  stderr: string;\n  exitCode: number;\n  duration: number;\n  ok: boolean;\n}\n```\n\n**Model levels** (resolved from `.lore/weft/.env`):\n\nCreate a `.env` file in your project's weft folder (`.lore/weft/.env`) mapping tags to models, one per line:\n\n```\n# .lore/weft/.env\nsimple=ollama-cloud/deepseek-v4-flash\nmedium=ollama-cloud/deepseek-v4-flash\nhigh=openai-codex/gpt-5.4\nexpert=openai-codex/gpt-5.5\n```\n\nThis file is project-local and should not be committed. Each pipeline/project defines its own tags.\n\n---\n\n### `.step(name, fn, opts?): Workflow<Ctx & { name: Awaited<Out> }>`\n\nTransform the context with a JS function.\n\n```ts\n.step('count', (ctx) => ctx.analyze.bugs.length)\n.step('summary', async (ctx) => {\n  await db.save(ctx.analyze);\n  return `Processed ${ctx.count} bugs`;\n})\n```\n\n---\n\n### `.clearSession(sessionId): Workflow<Ctx>`\n\nDelete a session folder (clears conversation history). Context passes through.\n\n---\n\n### `.when(predicate)`\n\nConditional branching. Both branches add fields to the context.\n\n```ts\n.when(ctx => ctx.analyze.critical > 0)\n  .then(w => w.prompt('alert', () => 'Alert!', { session: 'alerts' }))\n  .else(w => w.prompt('log', () => 'All good', { session: 'logs' }))\n.end()\n```\n\n---\n\n### `.parallel(tasks)`\n\nRun sub-pipelines concurrently. All results merge into the context.\n\n```ts\n.parallel({\n  security: weave()\n    .prompt('audit', () => 'Security audit', { schema: AuditSchema }),\n\n  quality: weave()\n    .prompt('lint', () => 'Run linter', { schema: LintSchema }),\n})\n// ctx now has both audit and lint results\n```\n\n---\n\n### `.use(pipeline)`\n\nEmbed a pre-built pipeline. Its context fields merge in.\n\n```ts\nconst shared = weave()\n  .prompt('setup', () => 'Initialize', { schema: SetupSchema })\n  .build();\n\nweave('job')\n  .use(shared)\n  .prompt('process', (ctx) => `Process: ${JSON.stringify(ctx.setup)}`)\n  .build();\n```\n\n---\n\n### `.build(): Pipeline<Ctx>`\n\nCompile the pipeline.\n\n---\n\n### `Pipeline.run(ctx, opts?): Promise<Ctx>`\n\nExecute the pipeline with initial context.\n\n```ts\nawait pipeline.run({ files: ['src/'], lang: 'ts' });\nawait pipeline.run({}, { dryRun: true });\nawait pipeline.run({}, { failFast: false });\nawait pipeline.run({}, { signal: controller.signal });\ncontroller.abort();\n```\n\n---\n\n## Pipeline files — `main()` pattern\n\nEach pipeline is a `.ts` file with `export async function main(args: string[])`:\n\n```typescript\n// .lore/weft/pipelines/МойПайплайн.ts\nimport { weave } from '@human-horizon/weft';\n\nexport const meta = {\n    description: \"What this pipeline does\",\n    args: [\n        { name: \"input\", type: \"string\", description: \"Input value\" },\n    ],\n};\n\nexport async function main(args: string[]) {\n    const input = args[0];\n    // ... pipeline logic\n}\n\nawait main(process.argv.slice(2));\n```\n\nThis makes the file both:\n- Importable programmatically (call `main(args)`)\n- Runnable directly via `weft run` or `bun/tsx`\n\n---\n\n## Options Reference\n\n### Step Options (`StepOpts`)\n\n| Option | Type | Default | Description |\n|--------|------|---------|-------------|\n| `session` | `string` | none | Session ID for multi-turn |\n| `model` | `string` | inherited | Model level or full name |\n| `thinking` | `string` | inherited | Thinking level |\n| `schema` | `z.ZodType` | none | Zod schema for response |\n| `retry` | `number` | `0` | Retries on failure |\n| `retryDelay` | `number` | `1000` | Initial retry delay (ms) |\n| `retryBackoff` | `'linear' \\| 'exponential'` | none | Backoff strategy |\n| `timeout` | `string \\| number` | none | Per-step timeout (`'30s'`, `'1m'`) |\n| `continueOnError` | `boolean` | `false` | Don't fail on this step |\n\n### Run Options (`RunOpts`)\n\n| Option | Type | Default | Description |\n|--------|------|---------|-------------|\n| `dryRun` | `boolean` | `false` | Print steps without executing |\n| `failFast` | `boolean` | `true` | Stop pipeline on first error |\n| `signal` | `AbortSignal` | none | Cancel the pipeline mid-flight |\n\n## Architecture\n\n```\nweave()           WorkflowImpl<Ctx>     PipelineImpl<Ctx>\n   │                  │                    │\n   ├── .prompt()      ├── accumulate Step[] ├── spread ctx\n   ├── .step()        │     (IR)           ├── add field to ctx\n   ├── .when()        │                    ├── invoke agent\n   ├── .parallel()    │                    ├── retry / timeout\n   ├── .clearSession()│                    ├── validate zod schema\n   ├── .use()         │                    └── resolve model\n   └── .build()       ▼                    ▼\n              Pipeline<Ctx>            RunOpts\n```\n\n## Project Structure\n\n```\nproject/\n├── .lore/\n│   └── weft/                      # weft config (separate package)\n│       ├── package.json           # latest @human-horizon/weft dependency\n│       ├── pnpm-lock.yaml         # generated by pnpm\n│       ├── node_modules/          # generated by pnpm\n│       └── pipelines/             # pipeline .ts files\n├── src/                           # project source code\n├── code-specs/                    # per-file specs (generated)\n├── specs/                         # project specs (generated)\n├── docs/                          # documentation\n└── package.json                   # project manifest\n```\n\n## Environment\n\n| Variable | Default | Description |\n|----------|---------|-------------|\n| `WEFT_PIPELINES_DIR` | `.lore/weft/pipelines/` | Pipelines directory |\n| `WEFT_PI_HOME` | `~/.ai/weft/pi/` | Pi environment |\n| `PI_CODING_AGENT_DIR` | `~/.pi/agent/` | Pi agent directory |\n\n## License\n\nMIT","readmeFilename":"README.md"}