DAGWorks Inc. is live · free plan, paid from $99/user/mo · last verified 22 Oct 2026 · viability monitor
DAGWorks Inc.
Apache Hamilton and Apache Burr are DAGWorks' open-source Python frameworks for building, debugging, and observing AI pipelines and agents.
Pick DAGWorks if your problem is that your RAG or agent codebase has become a pile of imperative glue and you want execution order, caching, and state written down somewhere. Apache Hamilton gives Python teams a readable DAG with node-level testing, and Apache Burr gives multi-step agents persistence plus observability. That is a genuinely different trade from LangChain, which offers far more prebuilt connectors but hides orchestration behind abstractions that are harder to debug. The hosted UIs are optional — the libraries run self-hosted — so evaluate the open-source core first. Skip it if nobody on the team writes Python comfortably or if you want a no-code builder.
Verdict generatedVerified 8d ago · liveness 65/100 · cite: rightaichoice.com/tools/dagworks-inc
- Python-first AI/ML engineering teams
- Data scientists building RAG or ML pipelines
- Teams debugging complex multi-step agents
- Organizations standardizing pipeline observability
- Non-technical users wanting a no-code AI builder
- Teams looking for a fully managed LLM API provider
- Beginners unfamiliar with DAG or state-machine concepts
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Skip DAGWorks if nobody on your team writes Python comfortably or if you need a no-code builder — both Hamilton and Burr require you to author pipelines as code and to learn DAG and state-machine concepts before you see any payoff.
DAGWorks's structure means the libraries themselves cost nothing to adopt — Hamilton and Burr are Apache open-source — so the real budget decision is whether to pay for the hosted UIs or fund the engineering time to self-host and operate them. Compare against the cost of the glue code you are replacing, not against other AI SaaS subscriptions.
In short
DAGWorks Inc. — Apache Hamilton and Apache Burr are DAGWorks' open-source Python frameworks for building, debugging, and observing AI pipelines and agents. Best for Python-first AI/ML engineering teams, Data scientists building RAG or ML pipelines, Teams debugging complex multi-step agents. Free to start; paid plans from $99/user/mo.
What is DAGWorks Inc.?
DAGWorks Inc. — Apache Hamilton and Apache Burr are DAGWorks' open-source Python frameworks for building, debugging, and observing AI pipelines and agents. Best for Python-first AI/ML engineering teams, Data scientists building RAG or ML pipelines, Teams debugging complex multi-step agents. Free to start; paid plans from $99/user/mo.
DAGWorks maintains two open-source Python projects — Apache Hamilton and Apache Burr — plus self-hosted or SaaS UIs layered on top of each. Hamilton is the RAG/ML side: you declare transforms as plain Python functions, decorate them as nodes, and Hamilton compiles the result into a DAG that handles execution order and caching. Burr is the GenAI/agent side: it adds state management and persistence for multi-step agent runs, with hosted execution available through Apache Burr Cloud. The hosted Hamilton UI adds provenance and lineage tracking, an observability view, and a catalog of your pipeline assets; the site also advertises a single-line hook that pushes observability data into the external tooling you already run. That framing matters for the buyer: DAGWorks sells deterministic, testable Python structure rather than a prompt wrapper, and the libraries stay open-source and self-hostable if you want to run everything yourself. The audience is developers, AI/ML engineers, and data scientists who are already fluent in Python and would rather read a DAG than untangle hand-written glue code. Plan on learning dataflow and state-machine concepts before the payoff lands.
Key features
- Apache Hamilton: declarative Python-function pipeline nodes compiled into a DAG
- Decorator-based API for defining pipeline nodes
- Automatic execution ordering resolved from the DAG
- Pipeline caching and memoization
- Node-level testing of individual pipeline functions
- Apache Burr state management for agent applications
- State persistence across multi-step agent runs
- Hosted execution via Apache Burr Cloud
- Provenance and lineage tracking in the hosted Hamilton UI
- Observability dashboard for Hamilton pipelines
- Catalog of pipeline assets in the hosted Hamilton UI
- Single-line integration with external observability tools
- Self-hosted deployment for Hamilton and Burr
- SaaS-hosted UIs for Hamilton and Burr
- Command-line interface for local development
Our long-form take written Jul 17, 2026 is more than 90 days old, so it is being re-reviewed against current data and is not shown.
Limitations
- Both Hamilton and Burr assume you write Python and are comfortable with dataflow and state-machine ideas, so there is a real learning curve before you get value — the site itself divides the decision into "Hamilton for RAG/ML, Burr for GenAI," which means teams doing both must understand two mental models.
- The open-source libraries are self-hosted unless you adopt the hosted UIs, which puts operational work on your team.
as of 2026-10-17
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Is DAGWorks Inc. still active?
DAGWorks Inc. is live · free plan, paid from $99/user/mo · last verified 22 Oct 2026 · viability monitor.
Viability score
How well maintained and how widely used is DAGWorks Inc.? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: October 2026
How we score →What do users say about DAGWorks Inc.?
How we verified this
Every listed fact about DAGWorks Inc.— pricing, features, links, status — is re-read from the vendor's own pages by our verification pipeline and each change is logged with a date; the last pass ran on 22 Oct 2026. Pages mix model-assisted synthesis with human review; see how we verify.
Verification history
We have re-verified DAGWorks Inc. 10 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — changed switching in, our verdict, summary, switching out, hidden costs, who should skip it, description, setup time, pricing fit, use cases, limitations, features
- — changed who it suits, description, our verdict, features, hidden costs, limitations, switching in, switching out, who should skip it, pricing fit, setup time, summary, use cases
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- — changed platforms
- — changed pricing fit, switching in, switching out, setup time, hidden costs, who should skip it
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How much does DAGWorks Inc. cost in 2026?
DAGWorks Inc.: Free to start; paid plans from $99/user/mo. Pricing last verified 22 Oct 2026.
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Where the pricing makes sense
The company stage and team size where DAGWorks Inc.'s pricing actually pencils out — and where peers do it cheaper.
DAGWorks's structure means the libraries themselves cost nothing to adopt — Hamilton and Burr are Apache open-source — so the real budget decision is whether to pay for the hosted UIs or fund the engineering time to self-host and operate them. Compare against the cost of the glue code you are replacing, not against other AI SaaS subscriptions.
Has DAGWorks Inc.'s pricing changed?
No plan price for DAGWorks Inc. has moved since we began logging in Aug 2026; the pricing page is re-read on every verification pass.
Who should pick DAGWorks Inc.?
Pick DAGWorks Inc. if you are Python-first AI/ML engineering teams, Data scientists building RAG or ML pipelines, Teams debugging complex multi-step agents, Organizations standardizing pipeline observability.
Look elsewhere if you are Non-technical users wanting a no-code AI builder, Teams looking for a fully managed LLM API provider, Beginners unfamiliar with DAG or state-machine concepts.
Skip it if Skip DAGWorks if nobody on your team writes Python comfortably or if you need a no-code builder — both Hamilton and Burr require you to author pipelines as code and to learn DAG and state-machine concepts before you see any payoff.
GeneratedReal-world workflow fit
Concrete scenarios for the personas DAGWorks Inc. actually fits — and what changes day-one when you adopt it.
You take over a retrieval pipeline built from sequential scripts and want to know what runs after what. You rewrite the transforms as Hamilton nodes and let the DAG resolve ordering, then use node-level tests to validate each transform in isolation.
Outcome: A pipeline whose structure is readable in the code itself, with caching on the expensive transforms and individual nodes testable without a full re-run.
An agent loops or loses context mid-run. You move the flow onto Apache Burr, which adds explicit state management and persistence so each step's state is inspectable, and use the observability view to see what the run actually did.
Outcome: A reproducible trace of the run and persistent state you can resume from, instead of re-running the agent from scratch to reproduce a bug.
Your team already runs external observability tooling and does not want another dashboard. You wire DAGWorks' single-line observability emission into the tools you already use.
Outcome: Pipeline telemetry lands where your on-call already looks, without a migration of your monitoring setup.
Use cases
- Define a RAG pipeline as declarative Python functions so Hamilton resolves execution order and caching instead of hand-written glue.
- Build and debug a multi-step agent whose state persists across steps with Apache Burr.
- Track provenance and lineage for a production pipeline in the hosted Hamilton UI.
- Run evaluation and testing on individual pipeline nodes instead of re-running the whole flow.
- Cache expensive transform steps to avoid recomputing them on every run.
- Emit pipeline observability data to an existing monitoring tool with a single line of configuration.
- Deploy a Hamilton pipeline as a service for repeated batch inference.
Setup time & first value
How long it actually takes to get something useful out of DAGWorks Inc. — broken out by persona, not the marketing-page minute.
For a Python developer, a first Hamilton DAG from a single decorated function is a same-day exercise; getting productive with Burr's state model is a longer ramp measured in days. Add time if you self-host the UIs rather than adopting the SaaS versions, since someone has to run that infrastructure.
Switching to or from DAGWorks Inc.
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From hand-written pipeline scripts: rewrite each transform as a Hamilton node and let the compiler handle execution order and caching.
- →From an agent framework with implicit state: model the steps explicitly in Apache Burr so state and persistence become inspectable.
- ↗To LangChain: port Hamilton nodes back into LangChain chains or runnables if you need LangChain's broader connector ecosystem.
- ↗To plain scripts: the DAG is Python functions, so a Hamilton pipeline can be flattened back into imperative calls at the cost of caching and node-level testing.
Resources & guides
Tutorials & Learning
YouTube returned 6 videos for “DAGWorks Inc.”, and we withheld 6: 6 could not be judged, because “DAGWorks Inc.” is a single word that other videos use for other things. We are showing none, because we could not prove any of them are about DAGWorks Inc..
Official links
What are the best DAGWorks Inc. alternatives?
The closest alternatives to DAGWorks Inc. we list are LangChain, Haystack and AgentScope. Ranked by direct product-type match, not category overlap.
| Alternative | Pricing | Starts at | Last verified |
|---|---|---|---|
| LangChain | Freemium | Pricing not published | — |
| Haystack | Freemium | Pricing not published | — |
| AgentScope | Free | Free | — |
Alternatives to DAGWorks Inc.
View allLangChain
LangChain's open agent frameworks plus LangSmith — tracing, evals, deployment, sandboxes and an LLM gateway for agents in production.
Haystack
Haystack is deepset's open-source Python framework for building inspectable RAG pipelines and production-grade AI agents
AgentScope
AgentScope is an open-source Python framework from Alibaba for building distributed multi-agent AI systems.
Tools that pair well with DAGWorks Inc.
Common stack mates teams adopt alongside DAGWorks Inc., with the specific reason each pairing earns its keep.
LangChain
LangChain's open agent frameworks plus LangSmith — tracing, evals, deployment, sandboxes and an LLM gateway for agents in production.
Haystack
Haystack is deepset's open-source Python framework for building inspectable RAG pipelines and production-grade AI agents
AgentScope
AgentScope is an open-source Python framework from Alibaba for building distributed multi-agent AI systems.
DAGWorks Inc. vs its closest rival
DAGWorks Inc.'s closest head-to-head is with Spider Cloud — Choose Spider Cloud if your need is real-time web data extraction for AI agents, with low-cost, high-success scraping and AI-powered extraction. Choose DAGWorks if you are building complex LLM pipelines and need declarative orchestration, tracing, and evaluation to ensure reliability and performance. They solve different problems: Spider Cloud feeds data into AI, while DAGWorks orchestrates and monitors the AI itself. Verdict generated
Head-to-head comparisons
Dagworks Inc vs Spider Cloud
Choose Spider Cloud if your need is real-time web data extraction for AI agents, with low-cost, high-success scraping and AI-powered extraction. Choose DAGWorks if you are building complex LLM pipelines and need declarative orchestration, tracing, and evaluation to ensure reliability and performance. They solve different problems: Spider Cloud feeds data into AI, while DAGWorks orchestrates and monitors the AI itself.
GeneratedDagworks Inc vs Presto Voice
Choose Presto Voice if you operate QSR drive-thrus and need proven voice AI to boost revenue and efficiency—Dairy Queen and Taco John's are real customers. Choose DAGWorks if you build multi-step AI agents or LLM pipelines and need robust observability, testing, and cost optimization—it's open-source and developer-centric. Not comparable: one solves physical restaurant operations, the other solves software AI workflows.
GeneratedDagworks Inc vs Temporal Ai
Pick Temporal AI if you need a robust durable execution platform for fault-tolerant, long-running workflows (AI agents, microservices orchestration) with automatic retries and state recovery. Choose DAGWorks Inc. if you are building LLM-centric pipelines and need declarative DAGs, built-in tracing, and evaluation tools, especially in a Python-heavy, data-science environment.
GeneratedFrequently asked questions
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