Deepfabric is live · free · last verified 30 Oct 2026 · viability monitor
Deepfabric
Generate LLM training datasets with DAG topic graphs and real sandboxed tool-calling traces
For agentic fine-tunes, Deepfabric's sandboxed execution traces (Spin, VFS, MCP tools) are the reason to pick it — simulated tool calls teach a model the shape of a call, not how to handle a real return. DAG topic expansion with explicit depth and degree goes after redundancy structurally instead of with post-hoc dedup. Pass if you want a dashboard; this is a pip-installed CLI that assumes developer habits.
Verdict generatedVerified 9h ago · liveness 68/100 · cite: rightaichoice.com/tools/deepfabric
- AI researchers building custom LLM training datasets with controlled DAG-based topic coverage
- Data engineers who need synthetic agent data grounded in real sandboxed execution traces
- Python teams that want MIT-licensed tooling and full control of the generation pipeline
- Developers producing reproducible datasets from YAML configs for reasoning or tool-calling fine-tunes
- Non-technical users who need a no-code GUI or dashboard
- Buyers who want to purchase a catalogue of ready-made domain datasets
- Projects that cannot absorb sandbox and tool-wiring setup work before generating data
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Skip Deepfabric if you want a dashboard and a dataset delivered to you rather than a pip-installed CLI you run yourself against a provider key or local model.
Every generation run burns tokens on the provider you pick — OpenAI, Anthropic or Gemini — so a large depth-and-degree graph multiplies your bill per run, not per month.
Deepfabric itself is an MIT-licensed package you install with pip, so the recurring cost is whatever your provider charges per generated token — your effective spend scales with graph depth and degree, not with seats. That makes a local Ollama run the cheapest way to prototype, and a hosted provider the sane choice once you scale up. Managed synthetic-data vendors bundle the compute into a subscription; Deepfabric trades that predictability for control.
In short
Deepfabric — Generate LLM training datasets with DAG topic graphs and real sandboxed tool-calling traces. Best for AI researchers building custom LLM training datasets with controlled DAG-based topic coverage, Data engineers who need synthetic agent data grounded in real sandboxed execution traces, Python teams that want MIT-licensed tooling and full control of the generation pipeline. Free to use.
What is Deepfabric?
Deepfabric — Generate LLM training datasets with DAG topic graphs and real sandboxed tool-calling traces. Best for AI researchers building custom LLM training datasets with controlled DAG-based topic coverage, Data engineers who need synthetic agent data grounded in real sandboxed execution traces, Python teams that want MIT-licensed tooling and full control of the generation pipeline. Free to use.
Deepfabric is an MIT-licensed Python package for building synthetic LLM training datasets where each sample is anchored to a topic you actually control. You give it a root prompt, it expands subtopics into a DAG (or a tree in tree mode), then generates a sample per node. Depth and degree are explicit flags — a depth of 2 and degree of 2, for example, gives you a root plus two levels of two subtopics each — so coverage breadth is configured rather than left to chance. The pitch is aimed squarely at the repetitive flat Q&A that naive synthetic pipelines churn out. The agent dataset type is the part that separates it from prompt-only generators. Instead of asking a model to pretend it called a tool, Deepfabric runs tools in real sandboxes — the Spin framework, a virtual filesystem (VFS) component, MCP and mock tools — and writes actual execution traces into the JSONL output. Basic Q&A and chain-of-thought reasoning datasets round out the three types, with freetext as a reasoning style for step-by-step traces. Generation runs two ways: CLI flags for fast experiments, or a YAML config for reproducible pipelines you can commit and rerun. The command surface covers generate, validate, visualize, evaluate, topic inspect, topic prune, import-tools, and upload to Hugging Face or Kaggle. Documented providers are OpenAI, Anthropic, Google Gemini and Ollama — the last needs no API key, which makes a local run the cheapest way to test the waters. Output passes constrained decoding and strict syntax validation before landing as JSONL. Install is pip install deepfabric, or pip install "deepfabric[training]" if you want the training and evaluation utilities. Against managed synthetic-data vendors, Deepfabric sells control rather than a catalogue: you supply the Python environment, the provider key or local model, and the judgement about what a good dataset looks like.
Key features
- Topic graph (DAG) generation with configurable depth and degree
- Tree mode as an alternative to graph mode for topic expansion
- Three dataset types: basic Q&A, chain-of-thought reasoning, agent tool-calling
- Chain-of-thought traces with freetext reasoning style
- Sandboxed tool execution via the Spin framework
- Virtual filesystem (VFS) component for tool runs
- MCP server tools plus mock tools for agent datasets
- Custom tool plugins via the import-tools command
- Constrained decoding and strict output syntax validation
- Multi-provider LLM support: OpenAI, Anthropic, Google Gemini, Ollama
- Local Ollama generation with no API key required
- CLI commands: generate, validate, visualize, evaluate, info, import-tools
- Topic management via topic inspect and topic prune
- YAML config files for reproducible generation pipelines
- num-samples auto mode to generate one sample per topic path
Behind the verdict
We'd reach for Deepfabric when the fine-tune target is tool use. Recording what Spin, VFS or an MCP server actually returned is a different category of data from asking a model to narrate a fake call, and it's the distinction most synthetic-data tools gloss over. If your agent needs to recover from an error payload or a malformed file listing, traces of real execution are the only ones worth training on. The second reason to pick it is coverage control. Depth and degree are flags, not vibes. You can reason about the generated topic graph before spending tokens, and topic inspect plus topic prune let you trim branches rather than regenerate everything. That's a more honest approach to dataset breadth than dedup runs after the fact. Where it bites: you absorb real setup work. Tool wiring, sandbox configuration, provider keys and the YAML plumbing all land on you before a single useful sample appears. Non-technical teams will bounce off the CLI immediately, and anyone hoping to buy a ready-made domain dataset is in the wrong shop — this is a generator, not a marketplace. Reproducibility is the quiet win. Commit the YAML config and the whole pipeline — topics, conversation type, provider, sample count — reruns identically. For teams maintaining training sets across model releases, that beats a notebook somebody edited in place. Compared to managed synthetic-data vendors, you're trading a catalogue and a UI for control and an MIT license. Compared to rolling your own generator, you're trading a weekend of scaffolding for something with validation, constrained decoding and upload commands already wired. For small teams weighing that trade, the local Ollama path means you can evaluate the output quality before committing to API spend. Caveats worth knowing: the tool is
Models under the hood
as of 2026-10-22
Limitations
- Deepfabric is a CLI- and config-driven tool with no GUI; comfort with Python, pip and a terminal is assumed.
- Generation needs an API key for the chosen provider (OpenAI, Anthropic or Gemini) or a local model served through Ollama, which needs no key.
- Agent datasets depend on sandbox tooling — Spin, VFS, MCP and mock tools — being wired up before traces are meaningful, and the training and evaluation utilities require the separate "deepfabric[training]" install rather than the default package.
- Coverage quality is a function of the depth, degree and system prompts you choose, so the tool makes breadth configurable but does not supply the judgement about whether a dataset is good.
as of 2026-10-19
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Is Deepfabric still active?
Deepfabric is live · free · last verified 30 Oct 2026 · viability monitor.
Viability score
How well maintained and how widely used is Deepfabric? 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 Deepfabric?
What people actually say about Deepfabric — is it worth it?
We scanned public community sources for Deepfabric on Sep 9, 2026 and could not establish that the discussion we found is about this tool rather than something else sharing its name. Only 1 of the posts we fetched could be positively tied to Deepfabric. Rather than publish a sentiment score built on the wrong subject, we publish nothing here and re-run the scan.
How we verified this
Every listed fact about Deepfabric— 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 30 Oct 2026. Pages mix model-assisted synthesis with human review; see how we verify.
Verification history
We have re-verified Deepfabric 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 features, our verdict, pricing tiers, description, summary
- — changed website status
- — changed SSL certificate, domain expiry
- — changed pricing tiers, description, our verdict, summary
- — changed website status, platforms
- — changed who should skip it, integrations, who it suits, switching in, limitations, description, our verdict, setup time, summary, pricing fit, switching out, hidden costs
Showing the 6 most recent of 10 verification passes.
Free to cite with attribution — this page re-verifies continuously.
How much does Deepfabric cost in 2026?
Deepfabric: Free to use. Pricing last verified 30 Oct 2026.
Plans compared
Every published Deepfabric tier, its price as the vendor publishes it, who it fits, and what it adds over the tier below. The 12-month cost projection follows.
| Plan | Price | Billing | Who it's for |
|---|---|---|---|
| Open Source (MIT) | Free | — | Python developers and research teams who want full control of the generation pipeline and are happy to bring a provider key or run Ollama locallyAdds: The only published tier: MIT-licensed package via pip install deepfabric, with the training extra for dataset preparation and evaluation |
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 Deepfabric's pricing actually pencils out — and where peers do it cheaper.
Deepfabric itself is an MIT-licensed package you install with pip, so the recurring cost is whatever your provider charges per generated token — your effective spend scales with graph depth and degree, not with seats. That makes a local Ollama run the cheapest way to prototype, and a hosted provider the sane choice once you scale up. Managed synthetic-data vendors bundle the compute into a subscription; Deepfabric trades that predictability for control.
Has Deepfabric's pricing changed?
No plan price for Deepfabric has moved since we began logging in Aug 2026; the pricing page is re-read on every verification pass.
Who should pick Deepfabric?
Pick Deepfabric if you are AI researchers building custom LLM training datasets with controlled DAG-based topic coverage, Data engineers who need synthetic agent data grounded in real sandboxed execution traces, Python teams that want MIT-licensed tooling and full control of the generation pipeline, Developers producing reproducible datasets from YAML configs for reasoning or tool-calling fine-tunes.
Look elsewhere if you are Non-technical users who need a no-code GUI or dashboard, Buyers who want to purchase a catalogue of ready-made domain datasets, Projects that cannot absorb sandbox and tool-wiring setup work before generating data, Python teams that want a hosted pipeline rather than a pip-installed library.
Skip it if Skip Deepfabric if you want a dashboard and a dataset delivered to you rather than a pip-installed CLI you run yourself against a provider key or local model.
GeneratedReal-world workflow fit
Concrete scenarios for the personas Deepfabric actually fits — and what changes day-one when you adopt it.
You set an OPENAI_API_KEY, write a config.yaml with mode graph, depth 2 and degree 3 around your domain topic, and switch conversation type to the agent dataset type with MCP and mock tools registered.
Outcome: You get JSONL where each sample carries a real sandboxed tool execution trace rather than a hallucinated call, and deepfabric validate confirms syntax before the data reaches training.
You install Ollama, pull a local model and run deepfabric generate with --conversation-type cot --reasoning-style freetext and --num-samples auto so every node in the topic graph yields one sample.
Outcome: A chain-of-thought dataset with reasoning traces that maps one sample to each topic path, produced without any provider API key.
You commit a YAML config to the repo, run deepfabric topic inspect and topic prune to trim the graph before generating, then push the result with upload-hf or upload-kaggle.
Outcome: The same config reproduces the same graph structure on any machine, and finished datasets land in Hugging Face or Kaggle for the rest of the team.
Use cases
- Generate diverse chain-of-thought reasoning datasets for LLM fine-tuning
- Create agent interaction datasets from real sandboxed tool execution traces
- Build domain-specific synthetic data for DevOps and platform engineering
- Evaluate model performance on custom reasoning tasks using built-in metrics
- Prepare fine-tuning data with chat templates through the training extra
- Validate generated samples with constrained decoding before training
- Produce reproducible datasets from YAML config files for repeatable experiments
- Push finished datasets to Hugging Face or Kaggle with the upload commands
Setup time & first value
How long it actually takes to get something useful out of Deepfabric — broken out by persona, not the marketing-page minute.
Python developer trying Ollama: minutes to a first JSONL, since there is no API key to obtain. With a hosted provider: one exported key, then a single generate command. Agent datasets take longer — Spin, VFS, MCP or mock tools have to be wired before the traces are worth generating from.
Switching to or from Deepfabric
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 synthetic data scripts: replace the ad-hoc prompt loop with a YAML config that declares mode, depth, degree and conversation type.
- →From a managed synthetic-data vendor: keep your prompt engineering, move generation to the CLI and upload the output to Hugging Face with upload-hf.
- →From Gretel or Mostly AI dataset purchases: rebuild the coverage map as a topic graph with explicit depth and degree instead of buying a fixed catalogue.
- ↗To a managed synthetic-data platform: export your YAML topic and system prompts as the coverage spec and hand them to the vendor's generator.
- ↗To a plain provider SDK loop: the JSONL output is standard, so downstream training code carries over unchanged.
Integrations
Resources & guides
Tutorials & Learning
YouTube returned 6 videos for “Deepfabric”, and we withheld 6: 6 could not be judged, because “Deepfabric” 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 Deepfabric.
Official links
What are the best Deepfabric alternatives?
The closest alternatives to Deepfabric we list are AfterQuery, Snorkel AI and Potato. Ranked by direct product-type match, not category overlap.
| Alternative | Pricing | Starts at | Last verified |
|---|---|---|---|
| AfterQuery | Contact Sales | Custom pricing | — |
| Snorkel AI | Contact Sales | Custom pricing | — |
| Potato | Free | Free | — |
Alternatives to Deepfabric
View all
AfterQuery
AfterQuery builds expert reasoning training data — SFT, RL rubrics, agent environments, and computer-use trajectories for frontier models.
Snorkel AI
Snorkel AI builds expert-curated training data and runnable eval environments that fix where frontier models and agents break.
Tools that pair well with Deepfabric
Common stack mates teams adopt alongside Deepfabric, with the specific reason each pairing earns its keep.
AfterQuery
AfterQuery builds expert reasoning training data — SFT, RL rubrics, agent environments, and computer-use trajectories for frontier models.
Snorkel AI
Snorkel AI builds expert-curated training data and runnable eval environments that fix where frontier models and agents break.
Potato
Open-source data annotation for text, image, audio, video, 3D, and AI agent traces — built in YAML, self-hosted free.
Deepfabric vs its closest rival
Deepfabric's closest head-to-head is with Geologicai — Choose GeologicAI if you're in mining and need rapid, AI-driven core scanning from sample to resource model. Choose Deepfabric if you're building LLM training sets and need grounded, diverse synthetic data with real execution traces. They serve entirely different industries. Verdict generated
Head-to-head comparisons
Deepfabric vs Geologicai
Choose GeologicAI if you're in mining and need rapid, AI-driven core scanning from sample to resource model. Choose Deepfabric if you're building LLM training sets and need grounded, diverse synthetic data with real execution traces. They serve entirely different industries.
GeneratedDeepfabric vs Versatile
Choose Versatile if you manage steel erection projects and need real-time crane data without disrupting workflows. Choose Deepfabric if you're an AI developer needing a free, open-source tool to generate diverse synthetic datasets with real execution traces. They serve entirely different domains — the only common ground is AI, but applied to construction vs. data generation.
GeneratedDeepfabric vs Screenplayiq
ScreenplayIQ is for film professionals who need data-driven script analysis and market predictions; Deepfabric is for AI developers who need to generate custom synthetic datasets. Choose based on your domain: screenwriting vs. machine learning. Neither tool overlaps.
GeneratedFrequently asked questions
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