| Ecosystem |
| 1 |
| 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Paid |
| Capabilities | 13 decomposed | 14 decomposed |
| Times Matched | 0 | 0 |
Dagu parses YAML files into directed acyclic graphs (DAGs) where each step is a node with dependencies explicitly declared. The engine validates the DAG structure at parse time, detects cycles, and builds an execution plan that respects task dependencies. This file-based approach eliminates the need for a UI or database schema — workflows are version-controllable text artifacts that can be audited, diffed, and reviewed like code.
Unique: File-based YAML DAG definition with zero external dependencies — workflows are plain text artifacts that can be version-controlled, diffed, and audited like code, with cycle detection at parse time rather than runtime
vs alternatives: Simpler and more portable than Airflow (no Python/database required) and more transparent than cloud-native orchestrators (Temporal, Prefect) because the entire workflow definition is a single readable YAML file
Dagu compiles to a single Go binary that can run standalone on a laptop or scale to a distributed cluster by spawning worker processes or connecting to remote nodes. The engine uses a local scheduler for single-machine execution and supports remote task execution via SSH or custom executors. This architecture eliminates the need for separate control planes, message brokers, or container orchestration — the same binary handles both local cron-like scheduling and distributed task dispatch.
Unique: Single statically-compiled Go binary that scales from laptop to distributed cluster without external dependencies (no database, message broker, or control plane) — same binary handles local scheduling and remote task dispatch via SSH or custom executors
vs alternatives: More portable and self-contained than Airflow (no Python/database) and simpler to deploy than Kubernetes-native orchestrators (Argo, Temporal) because it's a single binary with optional remote execution rather than a distributed system requiring infrastructure setup
Dagu enforces task ordering through explicit dependency declarations in YAML — each task specifies which tasks it depends on, creating a directed acyclic graph (DAG) of execution order. The engine validates dependencies at parse time, detects cycles, and builds an execution plan that respects the DAG. This ensures tasks run in the correct order without race conditions, and enables parallel execution of independent tasks.
Unique: Explicit dependency declaration with DAG validation and cycle detection at parse time — tasks specify their dependencies in YAML, and the engine builds an execution plan that respects the DAG and enables parallel execution of independent tasks
vs alternatives: More transparent than Airflow's implicit task ordering (dependencies are explicit in YAML, not inferred from code) and simpler than Temporal's workflow code because dependencies are declarative
Dagu supports defining reusable step templates that can be instantiated multiple times in a workflow with different parameters. Templates encapsulate common task patterns (e.g., 'run a Docker container', 'call an API', 'execute a script') and can be parameterized to avoid duplication. This enables DRY (Don't Repeat Yourself) workflow definitions where common patterns are defined once and reused across multiple workflows.
Unique: Built-in workflow templating with parameter substitution — reusable step templates can be defined once and instantiated multiple times with different parameters, reducing YAML duplication
vs alternatives: Simpler than Airflow's BaseOperator inheritance model (no Python code required) and more flexible than static YAML includes because templates support parameter substitution
Dagu implements signal handling (SIGTERM, SIGINT) to gracefully shut down running workflows and tasks. When a shutdown signal is received, the engine attempts to stop currently executing tasks cleanly (allowing them to finish or respond to signals) rather than forcefully killing them. This enables safe workflow interruption without data corruption or orphaned processes, and supports deployment scenarios where the Dagu daemon needs to be restarted or updated.
Unique: Built-in signal handling for graceful shutdown of running workflows and tasks — the engine responds to SIGTERM/SIGINT by cleanly stopping tasks rather than forcefully killing them, enabling safe restarts and updates
vs alternatives: More robust than shell scripts (which don't handle signals) and simpler than Kubernetes-native orchestrators (which require liveness/readiness probes) because signal handling is built into the Dagu binary
Dagu tracks task execution state (pending, running, success, failure) and persists this state to enable automatic retries, resume-on-failure, and idempotent re-execution. When a task fails, the engine can automatically retry with exponential backoff or skip to the next step based on configured policies. Failed workflows can be resumed from the point of failure without re-executing completed steps, enabling long-running pipelines to recover from transient failures without manual intervention.
Unique: Automatic retry and resume-on-failure with state persistence — failed workflows can be resumed from the last failed step without re-executing completed tasks, using local filesystem or external storage for durability
vs alternatives: Simpler than Temporal or Durable Task Framework (no distributed consensus required) but more robust than shell scripts with manual retry logic because state is tracked and persisted automatically
Dagu embeds a cron scheduler that interprets standard cron expressions (minute, hour, day, month, day-of-week) to trigger workflows on a schedule. The scheduler runs as part of the Dagu daemon and can trigger workflows based on wall-clock time or custom events. This eliminates the need for external cron daemons or scheduling services — the workflow engine itself handles scheduling, making it suitable for air-gapped environments where external services are unavailable.
Unique: Embedded cron scheduler in the Dagu binary — no external cron daemon or scheduling service required, making it suitable for air-gapped environments and simplifying deployment
vs alternatives: More portable than system cron (works on Windows with WSL, Docker, cloud VMs) and more observable than traditional cron because execution history and failures are tracked in the workflow engine
Dagu exposes a web dashboard and REST API that provide real-time visibility into workflow execution, task status, logs, and history. The UI displays DAG visualizations, execution timelines, and task output; the API enables programmatic workflow triggering, status queries, and log retrieval. This allows operators to monitor and control workflows without SSH access or command-line tools, and enables integration with external systems (Slack notifications, custom dashboards, alerting systems).
Unique: Built-in web dashboard and REST API in the single Dagu binary — no separate monitoring service or UI deployment required, with real-time execution visibility and programmatic workflow control
vs alternatives: More integrated than Airflow (UI is part of the same binary, not a separate Flask app) and simpler than Temporal (no separate UI service) because monitoring and control are embedded in the workflow engine
+5 more capabilities
Automatically loads and parses documents from diverse sources (PDFs, Word docs, HTML, Markdown, code files, databases) into a unified in-memory representation using format-specific loaders and node-based document abstractions. Each document is decomposed into Document objects containing metadata, content, and relationships, enabling downstream processing without format-specific handling in application code.
Unique: Provides a unified loader abstraction (BaseReader interface) that normalizes 100+ data source connectors into a single Document/Node API, eliminating format-specific branching logic in application code. Loaders are composable and chainable, allowing sequential transformations (e.g., load → split → extract metadata → embed).
vs alternatives: Broader out-of-the-box loader coverage than LangChain's document loaders and more structured node-based decomposition than raw text splitting, reducing boilerplate for multi-source RAG pipelines.
Splits documents into semantically coherent chunks using multiple strategies (character-based, token-aware, recursive, semantic) with configurable overlap and chunk size. Preserves document hierarchy and metadata through a node tree structure, enabling retrieval systems to maintain context relationships and enable hierarchical re-ranking or parent-document retrieval patterns.
Unique: Implements a node-tree abstraction that preserves document hierarchy and enables parent-document retrieval patterns. Supports multiple splitting strategies (recursive, semantic, code-aware) with pluggable custom splitters, and automatically propagates metadata through the node tree.
vs alternatives: More sophisticated than LangChain's text splitters because it preserves hierarchical relationships and supports semantic splitting; better for complex document structures than simple character-based splitting.
LlamaIndex scores higher at 40/100 vs dagu at 36/100. However, dagu offers a free tier which may be better for getting started.
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Processes documents containing mixed content (text, images, tables, code) by extracting and understanding each modality separately, then synthesizing information across modalities. Uses vision models for image understanding, specialized parsers for tables and code, and integrates results into a unified document representation for retrieval and generation.
Unique: Integrates vision models, table parsers, and code extractors into a unified multi-modal document processing pipeline that synthesizes information across modalities. Preserves modality-specific structure (table schemas, code formatting) while enabling cross-modal retrieval and generation.
vs alternatives: More comprehensive multi-modal support than text-only RAG; built-in vision integration reduces boilerplate for document understanding compared to manual vision API calls.
Enables streaming of LLM responses token-by-token and real-time retrieval updates, allowing applications to display partial results as they become available. Supports streaming from retrieval (progressive document discovery) and generation (token-by-token output) with backpressure handling and cancellation support for responsive user experiences.
Unique: Provides first-class streaming support for both retrieval and generation with automatic backpressure handling and cancellation. Enables progressive result display without custom async/streaming code in application layer.
vs alternatives: More integrated streaming support than manual LLM API streaming; built-in retrieval streaming and backpressure handling reduce complexity compared to custom streaming implementations.
Tracks API costs for LLM calls, embeddings, and other operations with per-query and per-session cost attribution. Provides cost optimization recommendations (e.g., batch processing, model selection, caching) and enables cost-aware query planning to balance quality and expense. Integrates with multiple LLM providers to normalize cost tracking across models.
Unique: Provides automatic cost tracking across multiple LLM providers with per-query attribution and cost optimization recommendations. Integrates with query execution to enable cost-aware planning without manual cost calculation.
vs alternatives: More integrated cost tracking than manual API billing review; built-in optimization recommendations reduce guesswork for cost reduction.
Enables building custom RAG pipelines by composing modular components (retrievers, synthesizers, agents, tools) through a declarative or programmatic API. Supports complex workflows with branching, loops, and conditional logic, with automatic dependency resolution and execution optimization. Pipelines are reusable, testable, and can be deployed as APIs or batch jobs.
Unique: Provides a flexible pipeline composition API supporting both declarative and programmatic definitions, with automatic dependency resolution and execution optimization. Enables complex workflows with branching and conditional logic without custom orchestration code.
vs alternatives: More flexible pipeline composition than fixed RAG architectures; better workflow support than manual component chaining.
Generates embeddings for documents/nodes using pluggable embedding providers (OpenAI, Hugging Face, local models) and stores them in a unified vector store interface that abstracts over multiple backends (Pinecone, Weaviate, Milvus, FAISS, Chroma, etc.). The abstraction layer enables switching vector stores without changing application code, and handles batching, retry logic, and metadata indexing.
Unique: Provides a unified VectorStore interface that abstracts 10+ vector database backends, enabling zero-code switching between providers. Handles embedding batching, retry logic, and metadata propagation automatically. Supports both cloud and local embedding models through a pluggable EmbedModel interface.
vs alternatives: Broader vector store coverage and more seamless provider switching than LangChain's vectorstore integrations; better abstraction consistency across backends than using raw vector store SDKs directly.
Retrieves semantically similar documents from vector stores using embedding-based similarity search, with optional re-ranking, filtering, and fusion strategies (hybrid search combining dense and sparse retrieval). Supports multiple retrieval modes (similarity, MMR, fusion) and enables custom retrieval logic through a pluggable Retriever interface that can combine multiple strategies.
Unique: Implements a pluggable Retriever abstraction supporting multiple retrieval strategies (similarity, MMR, fusion, custom) that can be composed and chained. Built-in support for re-ranking via LLM or cross-encoder, and hybrid search combining dense and sparse retrieval without custom integration code.
vs alternatives: More flexible retrieval composition than LangChain's retrievers; built-in re-ranking and fusion strategies reduce boilerplate for advanced retrieval pipelines.
+6 more capabilities