DAGWorks Inc.
Winter 2023AcquiredOpen source tools & services for reliable AI Agents & AI Applications
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Turn this teardown into a decision-ready prompt for ChatGPT, Claude, or your agent.
Open source tools & services for reliable AI Agents & AI Applications
Turn this teardown into a decision-ready prompt for ChatGPT, Claude, or your agent.
If you only have a few minutes to spare, here’s what investors, operators, and founders should know about DAGWorks Inc. (W23).
DAGWorks was a two-person YC W23 company built around a sharp engineering idea: ordinary Python functions could declare a dataflow graph, making machine-learning and data pipelines easier to test, inspect, and move between execution systems. Hamilton supplied that open-source foundation; Burr later applied explicit state machines, persistence, and telemetry to AI agents. The commercial layer sold hosted observability and collaboration around both projects.[1][2]
This is an acquisition story, not a conventional failure. Salesforce acquired DAGWorks in early 2025, and co-founder Elijah ben Izzy moved into Agentforce work on Agent Script and determinism.[3] The more instructive tension is that DAGWorks created credible open-source primitives, but had to commercialize a broad platform while its underlying categories were still shifting. Salesforce could capture the team’s expertise; Apache could steward the code. Public evidence does not establish that the hosted business achieved independent scale.
Stefan Krawczyk and Elijah ben Izzy founded DAGWorks in 2022 and joined YC’s Winter 2023 batch. The available primary sources identify them and the product, but do not establish how they met, their precise division of responsibilities, or a detailed financing history. No reliable founder interview surfaced with the two verbatim origin quotes requested by the canonical format, so this report does not manufacture them.
Their initial thesis came from a familiar problem in production machine learning: teams wrote pipelines as procedural scripts, then accumulated hidden dependencies, brittle orchestration code, and debugging work. Hamilton inverted the model. Developers wrote small Python functions whose names, inputs, and outputs described a graph. That graph could be visualized, tested, and executed without binding the business logic to Airflow, Dagster, or another orchestrator.[4]
The company paired the open-source library with a proprietary control plane. The platform tracked executions, catalogued artifacts, exposed lineage, and helped teams debug failures. In its YC launch, DAGWorks positioned Hamilton as the expression layer rather than another scheduler, and invited teams into a private beta for the commercial product.[2]
Burr expanded the ambition. Instead of data assets, Burr modeled an application as actions and transitions over explicit state. Persistence, replay, human intervention, and telemetry made it relevant to chatbots, RAG systems, simulations, and agents.[5] The move was technically coherent: both products turned implicit Python behavior into inspectable graphs. Commercially, however, it widened the market story from data and ML workflows into a fast-changing agent framework category.
Hamilton treated a Python function as a node and its parameters as upstream dependencies. A developer could write transformation logic as plain functions, ask Hamilton to assemble the graph, and run only the outputs needed for a job. This made lineage visible and unit testing natural. It also separated computation from orchestration: teams could keep their Python model while changing where or how it ran.[4]
The commercial platform added execution history, a data catalog, observability, debugging, and integrations. The pitch resembled “dbt for Python,” but covered ML features and application logic that lived outside SQL. A browser playground lowered the trial cost, while the open-source package made adoption possible without a platform purchase.[2]
Read the complete post-mortem, the rebuild playbook, and the exact reasons DAGWorks Inc. is still worth studying now.