{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hamilton","slug":"hamilton","name":"Hamilton","type":"framework","url":"https://github.com/dagworks-inc/hamilton","page_url":"https://unfragile.ai/hamilton","categories":["data-pipelines"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hamilton__cap_0","uri":"capability://data.processing.analysis.function.to.dag.compilation.with.automatic.lineage.tracking","name":"function-to-dag compilation with automatic lineage tracking","description":"Converts Python functions into directed acyclic graph nodes by introspecting function signatures and dependencies, automatically building a computation graph without explicit edge declarations. 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The framework provides a decorator and plugin interface that enables users to wrap transformation functions with custom logic while maintaining the same DAG semantics and lineage tracking.","intents":["I want to add retry logic to transformations that call external APIs","I need to implement custom caching strategies for expensive operations","I want to add monitoring or logging to specific transformations without modifying their code"],"best_for":["Advanced users building custom pipeline extensions","Teams implementing domain-specific transformation patterns","Organizations integrating Hamilton with custom infrastructure"],"limitations":["Custom decorators can break lineage tracking if not implemented carefully — incorrect implementations may produce incorrect results","No standardized interface for custom node types — different extensions may have incompatible APIs","Debugging custom decorators is harder than built-in functionality — errors in custom code are harder to trace"],"requires":["Python 3.8+","Understanding of Hamilton's internal APIs and decorator patterns"],"input_types":["Custom decorator functions","Custom node type implementations"],"output_types":["Extended transformation functions","Custom DAG behavior"],"categories":["code-generation-editing","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hamilton__cap_2","uri":"capability://data.processing.analysis.incremental.execution.with.selective.node.re.computation","name":"incremental execution with selective node re-computation","description":"Executes only the nodes in the DAG whose inputs have changed since the last run, skipping unchanged transformations to reduce computation time. The framework tracks input hashes or timestamps and compares them against cached results, re-running only downstream nodes affected by changed inputs while preserving cached outputs from unchanged branches.","intents":["I want to re-run only the transformations affected by new data, not the entire pipeline","I need to speed up iterative development by skipping expensive transformations that haven't changed","I want to understand which nodes were re-computed and why in each execution"],"best_for":["Data scientists iterating on feature engineering with expensive transformations","ML teams running daily pipelines where only recent data changes","Organizations optimizing compute costs by avoiding redundant calculations"],"limitations":["Caching strategy depends on input hashing — side effects (external API calls, database writes) are not tracked, potentially causing stale cache hits","No distributed caching across machines — cache is local to execution environment, limiting multi-node pipeline benefits","Cache invalidation requires manual intervention if transformation logic changes but function signature remains identical"],"requires":["Python 3.8+","Local filesystem or configured cache backend for storing intermediate results"],"input_types":["DAG with execution history","Input data with timestamps or hashes"],"output_types":["Execution results (cached or recomputed)","Execution report showing which nodes were skipped"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hamilton__cap_3","uri":"capability://data.processing.analysis.multi.backend.execution.with.pluggable.drivers","name":"multi-backend execution with pluggable drivers","description":"Abstracts execution logic behind a driver interface, allowing the same DAG to execute on different backends (local Python, Dask, Ray, Pandas, etc.) by swapping drivers without code changes. Each driver implements a common execution contract, translating Hamilton's node definitions into backend-specific operations while preserving lineage and parameter semantics.","intents":["I want to prototype my pipeline locally and scale it to distributed compute without rewriting code","I need to run the same transformations on different backends depending on data size","I want to test my pipeline with different execution engines to optimize performance"],"best_for":["ML teams scaling from laptop to cloud without code refactoring","Data engineers building portable pipelines across environments","Organizations evaluating different compute backends (Dask vs Ray vs Spark)"],"limitations":["Driver abstraction hides backend-specific optimizations — code optimized for one backend may be inefficient on another","Not all backends support all Python operations — some drivers may fail on complex transformations (e.g., custom classes, external libraries)","Debugging distributed execution is harder than local — errors in distributed drivers require backend-specific logging and tracing"],"requires":["Python 3.8+","Target backend installed (Dask, Ray, Pandas, etc.)","Driver implementation for target backend"],"input_types":["Hamilton DAG definition","Backend-specific configuration"],"output_types":["Execution results from target backend","Backend-specific performance metrics"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hamilton__cap_4","uri":"capability://data.processing.analysis.dataframe.aware.transformations.with.column.level.lineage","name":"dataframe-aware transformations with column-level lineage","description":"Tracks data lineage at the column level for dataframe transformations, enabling visibility into which input columns contribute to each output column. The framework infers column dependencies from function operations (e.g., selecting, joining, aggregating columns) and builds a fine-grained lineage graph that maps raw inputs to final features through intermediate transformations.","intents":["I want to understand which raw data columns feed into each feature","I need to trace a feature back to its source data to debug quality issues","I want to document feature dependencies for compliance or impact analysis"],"best_for":["Feature engineering teams building complex feature stores","Data quality teams tracing data issues to root causes","Organizations managing feature dependencies for model governance"],"limitations":["Column-level lineage inference relies on static analysis of dataframe operations — dynamic column selection or computed column names are not tracked","Lineage tracking adds overhead to execution — large pipelines with many columns may see performance degradation","Not all dataframe operations are supported — custom functions or external libraries may break lineage tracking"],"requires":["Python 3.8+","Pandas or compatible dataframe library","Functions operating on dataframes with explicit column references"],"input_types":["Pandas DataFrames or compatible structures","Column names and operations"],"output_types":["Dataframe results","Column-level lineage metadata","Lineage visualization or reports"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hamilton__cap_5","uri":"capability://code.generation.editing.unit.testing.with.isolated.node.execution","name":"unit testing with isolated node execution","description":"Enables testing individual transformation functions in isolation by executing single nodes with mocked or fixture-provided inputs, without running the entire DAG. The framework provides utilities to inject test data into specific nodes and verify outputs, supporting parameterized tests across multiple input scenarios while maintaining the same function definitions used in production.","intents":["I want to test individual transformations without running the full pipeline","I need to verify that my feature engineering logic is correct with different input scenarios","I want to catch bugs in transformations before they propagate through the pipeline"],"best_for":["ML engineers building robust feature engineering code","Data teams practicing test-driven development for transformations","Organizations enforcing code quality standards for data pipelines"],"limitations":["Isolated node testing doesn't catch integration issues between nodes — dependencies between functions are not validated","Test fixtures must be manually created or mocked — no automatic fixture generation from production data","Testing side effects (external API calls, database writes) requires manual mocking setup"],"requires":["Python 3.8+","Testing framework (pytest, unittest, etc.)","Test fixtures or mock data"],"input_types":["Test data (fixtures, mocks)","Function definitions"],"output_types":["Test results (pass/fail)","Assertion errors with context"],"categories":["code-generation-editing","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hamilton__cap_6","uri":"capability://data.processing.analysis.interactive.exploration.with.jupyter.notebook.integration","name":"interactive exploration with jupyter/notebook integration","description":"Integrates with Jupyter notebooks to enable interactive exploration of DAG execution, allowing users to inspect intermediate results, visualize the computation graph, and re-run subsets of the pipeline within notebook cells. The framework provides utilities to materialize node outputs as variables in the notebook namespace and visualize the DAG structure with execution status.","intents":["I want to explore intermediate transformation results in a notebook without re-running the entire pipeline","I need to visualize the DAG structure to understand data flow","I want to interactively debug transformations by inspecting node outputs"],"best_for":["Data scientists prototyping features in notebooks","ML engineers debugging pipeline issues interactively","Teams using notebooks for exploratory data analysis and feature development"],"limitations":["Notebook integration is stateful — restarting the kernel loses execution context and cached results","Visualizations are static or require additional libraries — no built-in interactive graph exploration","Notebook-based development doesn't enforce the same code organization as production pipelines"],"requires":["Python 3.8+","Jupyter notebook or compatible environment","Hamilton installed in notebook kernel"],"input_types":["Hamilton DAG definitions","Notebook cells with execution requests"],"output_types":["Intermediate results materialized as notebook variables","DAG visualizations","Execution logs and metadata"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hamilton__cap_7","uri":"capability://data.processing.analysis.validation.and.schema.enforcement.with.type.checking","name":"validation and schema enforcement with type checking","description":"Enforces data type and schema constraints on function inputs and outputs using Python type hints and optional schema validators, catching type mismatches and schema violations at execution time. The framework validates that function inputs match expected types and that outputs conform to declared schemas, providing detailed error messages when validation fails.","intents":["I want to catch type errors in my transformations early, before they propagate","I need to enforce schema constraints on dataframe columns to prevent data quality issues","I want to document expected input/output types for each transformation"],"best_for":["Teams building production feature pipelines with strict data quality requirements","Organizations enforcing type safety in data transformations","ML engineers preventing silent data corruption from type mismatches"],"limitations":["Type validation relies on Python type hints — untyped code or dynamic types bypass validation","Schema validation adds execution overhead — large pipelines with many columns may see performance impact","Custom validators require manual implementation — no built-in validators for domain-specific constraints"],"requires":["Python 3.8+","Type hints on function parameters and return types","Optional: schema validation library (Pydantic, Pandera, etc.)"],"input_types":["Python type hints","Schema definitions (optional)"],"output_types":["Validation results (pass/fail)","Type error messages with context"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hamilton__cap_8","uri":"capability://automation.workflow.execution.monitoring.and.observability.with.metrics.collection","name":"execution monitoring and observability with metrics collection","description":"Collects execution metrics (runtime, input/output sizes, memory usage) for each node and aggregates them into pipeline-level statistics, enabling performance analysis and bottleneck identification. The framework tracks execution time, data volumes, and resource consumption per node, exposing metrics through logging, callbacks, or external monitoring systems.","intents":["I want to identify slow transformations in my pipeline","I need to understand memory usage and data volumes for each step","I want to monitor pipeline performance over time to detect regressions"],"best_for":["ML teams optimizing pipeline performance","Data engineers monitoring production pipelines","Organizations tracking compute costs and resource utilization"],"limitations":["Metrics collection adds overhead to execution — fine-grained tracking may impact performance","No built-in integration with external monitoring systems — requires custom callbacks for Prometheus, DataDog, etc.","Memory profiling is approximate — accurate memory tracking requires additional profiling tools"],"requires":["Python 3.8+","Optional: external monitoring system (Prometheus, DataDog, etc.)"],"input_types":["DAG execution logs","Node execution events"],"output_types":["Execution metrics (runtime, data volumes, memory)","Pipeline-level statistics","Performance reports"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hamilton__cap_9","uri":"capability://text.generation.language.documentation.generation.from.transformation.code","name":"documentation generation from transformation code","description":"Automatically generates documentation for the data pipeline by extracting docstrings, type hints, and parameter information from transformation functions and organizing them into a structured format. The framework creates documentation that maps functions to DAG nodes, describes inputs/outputs, and visualizes the computation graph, enabling self-documenting pipelines.","intents":["I want to generate documentation for my feature pipeline without manual effort","I need to explain what each transformation does to stakeholders","I want to maintain documentation that stays in sync with code changes"],"best_for":["Teams building shared feature pipelines that need documentation","Organizations maintaining data governance and lineage documentation","ML teams onboarding new members to understand pipeline logic"],"limitations":["Documentation quality depends on docstring quality — poorly documented functions produce poor documentation","Complex transformation logic is hard to explain from function signatures alone — documentation may be incomplete","No built-in support for custom documentation formats — requires manual post-processing for specialized needs"],"requires":["Python 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