Hamilton (DAGWorks)
Python micro-framework for writing reusable, modular dataflow functions. Hamilton turns Python functions into nodes in a directed acyclic graph (DAG) — functions declare their dependencies via argument names, and Hamilton automatically builds the execution graph. Used for feature engineering, LLM prompt chains, data transformations, and agent pipelines. Extremely lightweight: no YAML configs, no custom DSL — just plain Python functions with type hints.
Score Breakdown
⚙ Agent Friendliness
🔒 Security
Local framework with no network exposure. Apache 2.0 with active security-conscious development. Pure function design avoids hidden state and side effects that cause security issues. DAGWorks Platform uses standard web security for cloud features.
⚡ Reliability
Best When
You want to write LLM pipelines, feature engineering, or agent data flows as clean, testable Python functions with automatic lineage tracking and minimal framework overhead.
Avoid When
You need managed scheduling, distributed execution across machines, or a visual workflow UI — Hamilton handles the 'what to execute' not 'when and where'.
Use Cases
- • Build modular LLM agent pipelines where each step (retrieval, reranking, generation, validation) is a typed Python function automatically composed into a DAG
- • Create reusable feature engineering pipelines where features depend on other features — Hamilton auto-resolves execution order
- • Track data lineage automatically through the function DAG for debugging agent pipelines and reproducing outputs
- • A/B test different implementations of pipeline steps by swapping function overrides without changing calling code
- • Define agent prompt construction logic as composable functions with Hamilton managing context assembly and caching
Not For
- • Complex scheduling or event triggers — Hamilton is a execution framework, not an orchestrator; pair with Prefect/Dagster for scheduling
- • Teams wanting a visual workflow designer — Hamilton is code-first; use Kestra or n8n for no-code workflow design
- • Long-running distributed compute — Hamilton is for in-process dataflows; use Spark or Ray for large-scale distributed execution
Interface
Authentication
Hamilton open source is a local Python library with no auth. DAGWorks Platform (cloud UI for lineage visualization) requires account. Core framework runs locally with zero auth requirements.
Pricing
Core Hamilton library is completely free. DAGWorks Platform cloud add-on provides lineage UI, observability, and team features — free for individuals, paid for teams.
Agent Metadata
Known Gotchas
- ⚠ No REST API — agents interact via Python SDK only; hamilton.driver.Driver executes the graph in-process
- ⚠ Function argument names ARE the dependency declarations — agents generating Hamilton code must use exact output node names as argument names
- ⚠ Hamilton validates the full DAG at Driver init time — missing or misconfigured nodes cause startup failures, not runtime failures
- ⚠ Overrides and config are passed at Driver construction, not at execution time — agents must rebuild the Driver to change configuration
- ⚠ Hamilton's @config.when decorator enables conditional logic but requires understanding of Hamilton's config resolution system
- ⚠ The hamilton.plugins module is needed for dataframe integrations (pandas, polars) — not all integrations are in the core package
Alternatives
Full Evaluation Report
Detailed scoring breakdown, competitive positioning, security analysis, and improvement recommendations for Hamilton (DAGWorks).
Scores are editorial opinions as of 2026-03-06.