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Think of it as\nwhat Django is for web developers, but for building intelligent agents.\n\n## Documentation\n\nThe project documentation uses [MkDocs Material](https://squidfunk.github.io/mkdocs-material/).\nFirst install the development extras:\n\n```bash\npip install -e .[dev]\n```\n\nThis will install `mkdocs-material`, `mkdocstrings[python]` and\n`pymdown-extensions`. The docs use `pymdownx.highlight` with the\nGitHub Pygments style so Python snippets are rendered\nwith proper syntax highlighting in both light and dark mode.\n\nThen start the preview server:\n\n```bash\nmkdocs serve\n```\n\nGitHub Pages builds the docs from the `gh-pages` branch automatically.\nThe Material theme provides a search box, workspace tabs for\n**Getting Started**, **Concepts \u0026 Features**, and **API Reference**.\nCode blocks are highlighted using Pygments in the GitHub style and a\nlight/dark palette toggle.\n## Features\n\n| Feature | Description |\n|---------|-------------|\n| **Steps and Tasks** | `Step` provides basic lifecycle hooks while `Task` adds typed dependencies and retry support |\n| **Workflow orchestration** | Chain steps using `\u003e\u003e` or dependency injection and run them with `Workflow` |\n| **Typed dependencies** | Pass values between tasks using `Depends` and `Computed` |\n| **Asynchronous tasks** | Use `AsyncTask` and `AsyncWorkflow` for async execution |\n| **Batch workflows** | `BatchTask` and `BatchWorkflow` run tasks for multiple parameter sets |\n| **Parallel execution** | `PoolEngine` executes independent branches concurrently |\n| **Conditional dependencies** | Attach `Condition` functions to `Depends` for branch logic |\n| **Retries with backoff** | Attach `RetryPolicy` to retry failed steps |\n| **Policies** | Attach `Policy` objects to customise retries, timeouts and other behaviour |\n| **Workflow export** | Serialize graphs to YAML with `Workflow.export` and `YamlExporter` |\n| **Tracing** | Record execution events using `FileTracer` |\n| **Runtime storage** | Persist workflow state so multiple workers can resume runs |\n| **Function workflows** | Wrap callables with `FunctionTask` or use `workflow_from_functions` |\n| **Branching actions** | Steps can return action names to select successor steps |\n| **Fail-fast policy** | Downstream steps are cancelled when a dependency fails |\n| **AND/OR joins** | Support joining branches after all or any parent steps finish |\n\n## Roadmap\n\n| Missing Feature | Importance | Description |\n|-----------------|------------|-------------|\n| **Web UI and monitoring** | High | Visual dashboard to inspect runs and logs, similar to Airflow or Prefect |\n| **Persistent storage** | High | Database-backed state to resume workflows after interruption |\n| **Distributed executors** | High | Native support for Kubernetes, Dask or Celery clusters |\n| **Scheduling** | High | Cron-like triggers and recurring workflow runs |\n| **Step caching / resume** | High | Avoid re-executing completed steps across runs |\n| **Artifact management** | Medium | Built‑in integration with object stores and artifact tracking |\n| **Dynamic parameter mapping** | Medium | Generate task instances from lists or grids of values |\n| **Command line tools** | Medium | CLI for creating, running and inspecting workflows |\n| **Data lineage and metadata** | Medium | Track dataset versions and dependency history |\n| **Visual DAG editing** | Low | Drag-and-drop interface for authoring workflows |\n| **Multi-language tasks** | Low | Ability to implement steps in languages other than Python |\n| **Security \u0026 access control** | Low | Authentication and role-based permissions for multi-user deployments |\n\n\n","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjsam%2Ffuseline","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fjsam%2Ffuseline","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjsam%2Ffuseline/lists"}