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Mahoney"},"license":"MIT","homepage":"https://github.com/ryan-mahoney/prompt-orchestration-pipeline#readme","keywords":["llm","prompt-engineering","pipeline","orchestration","chatgpt","ai","workflow","automation"],"repository":{"type":"git","url":"git+https://github.com/ryan-mahoney/prompt-orchestration-pipeline.git"},"description":"A Prompt-orchestration pipeline (POP) is a framework for building, running, and experimenting with complex chains of LLM tasks.","maintainers":[{"name":"ryan-fw","email":"rmahoney@firstwho.co"}],"readme":"# Prompt Orchestration Pipeline (POP)\n\n**A local-first, process-isolated framework for building reliable, autonomous AI agents.**\n\nThis system allows engineers to compose complex chains of LLM tasks into resilient pipelines. Instead of relying on fragile \"mega-prompts\" or black-box scripts, POP decomposes work into standardized stages, enforcing quality through autonomous critique loops and robust architecture.\n\n---\n\n## Documentation\n\n- **[Architecture Deep-Dive](docs/current-architecture.md)**: Detailed breakdown of the orchestrator, runner, and state management.\n- **[Task Development Guide](docs/pop-task-guide.md)**: How to build valid pipeline tasks using the 11-stage lifecycle.\n- **[LLM Provider Requirements](docs/provider-requirements.md)**: Standards for implementing new model providers.\n- **[Feature Audit](docs/feature-audit.md)**: Comprehensive list of current system capabilities.\n- **[Jobs to be Done (JTBD)](docs/jtbd.md)**: User motivations and desired outcomes for the framework.\n\n---\n\n## What It Does (and Why It Matters)\n\nThis framework is designed for AI Engineers and Systems Integrators who need to move beyond simple chatbots to build reliable, multi-step workflows.\n\n### 1. Orchestrate Reliable, Multi-Stage Workflows\nRunning long-duration AI tasks locally can be fragile. A single script crash or API timeout can waste hours of execution and dollars in token costs.\n*   **Process Isolation**: Every pipeline runs in its own dedicated child process. If one agent crashes, your orchestrator stays alive.\n*   **Resumability**: Pause, stop, and resume jobs from any specific task. Fix a bug in step 5 and restart exactly where you left off.\n*   **Run Control**: Tasks can pause for human approval, skip downstream work, or add follow-up tasks to the current run.\n*   **Atomic State**: Every stage transition is saved to disk instantly. You never lose progress.\n\n### 2. Gain Radical Observability\n\"Black box\" agents are impossible to debug. POP provides deep visibility into the \"thought process\" of your pipelines.\n*   **Real-Time Dashboard**: Watch jobs progress stage-by-stage via a built-in UI (Server-Sent Events).\n*   **Gate Decisions**: Approve or reject waiting jobs directly from the job detail view.\n*   **Granular Logging**: Every input, output, and internal thought is captured in dedicated log files.\n*   **Cost Tracking**: See exact token usage and cost breakdown for every task and model call.\n\n### 3. Enforce Quality Autonomously\nLLMs are probabilistic and prone to errors. POP enforces a rigid **11-stage lifecycle** for every task to catch mistakes before they propagate.\n*   **Standardized Flow**: `Ingestion` → `Inference` → `Validation` → `Critique` → `Refinement`.\n*   **Self-Correction**: Configurable loops allow the system to detect invalid outputs, critique itself, and refine the answer automatically.\n*   **Structured Output**: Built-in validators ensuring JSON schema compliance.\n\n### 4. Avoid Vendor Lock-In\nSwitch models globally or per-task without rewriting your logic.\n*   **Supported Providers**:\n    *   **OpenAI** (GPT-4, GPT-4o)\n    *   **Anthropic** (Claude 3.5 Sonnet, Haiku, Opus)\n    *   **DeepSeek** (V3, R1)\n    *   **Google Gemini** (Pro, Flash)\n    *   **Moonshot** (Kimi)\n    *   **Zhipu** (GLM-4)\n    *   **Claude Code** (CLI integration)\n*   **CLI Agents**: Tasks can also drive tool-using CLI coding agents (Claude, Codex, OpenCode) via the injected `runAgent()` helper — for file-aware, multi-turn work alongside single LLM calls. See the [Task Development Guide](docs/pop-task-guide.md#agent-api).\n\n---\n\n## System Architecture\n\nThe system follows a \"Watch Folder\" architectural pattern, designed for simplicity and ease of integration.\n\n### Level 1: System Context\nThe **Prompt Orchestration Pipeline** sits between your external triggers (users, scripts, cron jobs) and the LLM Providers.\n\n*   **Input**: A JSON \"seed\" file dropped into a watched folder.\n*   **Processing**: The system orchestrates the flow of data through defined tasks.\n*   **Output**: Completed artifacts and logs written to disk.\n\n### Level 2: Containers\nThe system comprises three main runtime containers:\n\n1.  **Orchestrator (Daemon)**\n    *   **Role**: The long-running supervisor.\n    *   **Responsibility**: Watches the `pipeline-data/pending/` directory. When a file appears, it initializes the job and spawns a Runner.\n2.  **Pipeline Runner (Process)**\n    *   **Role**: An ephemeral, isolated worker.\n    *   **Responsibility**: Loads the specific pipeline configuration (e.g., \"Content Gen\") and executes the sequence of tasks.\n3.  **UI Server (Optional)**\n    *   **Role**: Observability layer.\n    *   **Responsibility**: Reads state files and pushes real-time updates to the web dashboard via SSE.\n\n### Level 3: Components\nInside the **Pipeline Runner**, the logic is structured into:\n\n*   **Task Runner**: The engine that drives a specific task (e.g., \"Research\") through the standardized lifecycle.\n*   **Run Control Contract**: A task can write `tasks/{taskName}/control.json` to append tasks, skip pending tasks, or create an approval gate after it succeeds.\n*   **LLM Layer**: A unified abstraction for all model providers, handling retries, cost calculation, and normalization.\n*   **Symlink Bridge**: A specialized component that ensures every task has deterministic access to `node_modules` and shared utilities, regardless of where it is defined on disk.\n*   **Status Writer**: An atomic file-writer that updates `tasks-status.json` safely, preventing data corruption.\n\n### Level 4: Code (The Task Lifecycle)\nEvery task implementation enforces a strict 11-stage flow to ensure consistency:\n\n```mermaid\nflowchart LR\n    Ingest[1. Ingestion] --> Templ[3. Templating]\n    Templ --> Infer[4. Inference]\n    Infer --> Parse[5. Parsing]\n    Parse --> Val[6. Validate]\n    Val -->|Pass| Integ[11. Integration]\n    Val -->|Fail| Crit[8. Critique]\n    Crit --> Refine[9. Refine]\n    Refine --> Templ\n```\n\n*(Note: PreProcessing, ValidateQuality, and FinalValidation stages are also available but optional)*\n\n---\n\n## Getting Started\n\nChoose one runtime path:\n- **Bun CLI path**: for Bun-based projects (`bunx pipeline-orchestrator ...`).\n- **Standalone binary path**: download a prebuilt release binary, no Bun runtime required.\n\n### 1A. Install for Bun projects\nAdd the orchestrator as a dependency:\n```bash\nbun add @ryanfw/prompt-orchestration-pipeline\n```\n\n### 1B. Install standalone binary (no Bun required)\nDownload a release asset from:\n`https://github.com/ryan-mahoney/prompt-orchestration-pipeline/releases`\n\nAvailable assets:\n- `pipeline-orchestrator-linux-x64`\n- `pipeline-orchestrator-macos-x64`\n- `pipeline-orchestrator-macos-arm64`\n- `pipeline-orchestrator-windows-x64.exe`\n\nLinux/macOS example:\n```bash\ncurl -L -o pipeline-orchestrator \\\n  https://github.com/ryan-mahoney/prompt-orchestration-pipeline/releases/latest/download/pipeline-orchestrator-macos-arm64\nchmod +x pipeline-orchestrator\n```\n\n### 2. Initialize Structure\nUse one of:\n```bash\n# Bun path\nbunx pipeline-orchestrator init --root ./pipelines\n\n# Standalone binary path\n./pipeline-orchestrator init --root ./pipelines\n```\n\n### 3. Configure Scripts (Bun path)\nFor Bun projects, add:\n```json\n{\n  \"scripts\": {\n    \"pipeline\": \"bunx pipeline-orchestrator start --root pipelines --port 3010\"\n  }\n}\n```\n\n### 4. Start the System\nUse one of:\n```bash\n# Bun path\nbun run pipeline\n\n# Standalone binary path\n./pipeline-orchestrator start --root pipelines --port 3010\n```\nThis starts the file watcher and the web dashboard at `http://localhost:3010`.\n\n> Note: `npx pipeline-orchestrator ...` requires Bun to be installed because the npm bin entry runs through a Bun shebang.\n\n### 5. Run a Job\nDrop a JSON file into `pipelines/pipeline-data/pending/`:\n```json\n{\n  \"name\": \"my-first-run\",\n  \"pipeline\": \"default\",\n  \"data\": { \"topic\": \"AI Architecture\" }\n}\n```\n\nThe Orchestrator will pick it up, move it to `current/`, and start processing.\n\n---\n\n## Building a Custom Frontend\n\nThe orchestrator exposes a documented HTTP/SSE API at `http://localhost:<port>/api/*` that any frontend can consume. See **[HTTP/SSE Protocol Reference](docs/http-api.md)** for the full contract.\n\nCustom frontends should run against a configured fixed port, usually the default `4000` or an explicit `--port` value. Treat bind failures or port conflicts as startup failures; the protocol does not provide dynamic port discovery.\n\n### Integration Modes\n\n**Subprocess mode** (recommended): Run the orchestrator as a child process via `pipeline-orchestrator start`. The orchestrator runs in its own process — if it crashes, your application stays alive. This is the default and recommended approach.\n\n**Programmatic mode**: Import `startServer` or `startOrchestrator` directly to embed the server in your process. This is simpler but forfeits process isolation — a crash in the orchestrator will take down your host process.\n\n### Cross-Origin Access\n\nBy default, the server only accepts same-origin requests. To allow a custom frontend on a different origin:\n\n```bash\npipeline-orchestrator start --cors-origins \"https://my-app.example,views://mainview\"\n```\n\nFor desktop webviews that report `Origin: null`, add `--cors-allow-null-origin`. Prefer allowlisting the concrete origin (e.g. `views://mainview`) when possible.\n\n### Protocol Versioning\n\nThe API follows a client-robustness contract: clients must ignore unknown JSON fields and unknown SSE event types. This means additive changes (new routes, new event types, new fields) are backward-compatible. Breaking changes (removing or renaming fields, changing types) require a `protocolVersion` bump and are documented in `docs/http-api.md`.\n\nCapability negotiation is not built. Custom frontends should use the documented protocol and `GET /api/meta` instead of expecting negotiated feature flags or per-client capability exchange.\n","readmeFilename":"README.md"}