{"data":{"slug":"nolabs-ai-deepfabric","name":"deepfabric","tagline":"Generate, Train, Measure, and Evaluate Synthetic Data in One Pipeline","github_url":"https://github.com/nolabs-ai/deepfabric","owner":"nolabs-ai","repo":"deepfabric","owner_avatar_url":"https://avatars.githubusercontent.com/u/289819636?v=4","primary_language":"Python","stars":886,"forks":82,"topics":["agents","ai","data-science","dataset","distillation","evaluation","fine-tuning","huggingface","huggingface-datasets","machine-learning","open","open-source","python","source","synthetic","synthetic-data","unsloth"],"archived":false,"github_pushed_at":"2026-09-14T19:45:17+00:00","maintenance_label":"Very active","stars_delta_30d":9,"url":"https://www.graphcanon.com/tools/nolabs-ai-deepfabric","markdown_url":"https://www.graphcanon.com/tools/nolabs-ai-deepfabric.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/nolabs-ai-deepfabric","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=nolabs-ai-deepfabric","description":"Generate High-Quality Synthetics, Train, Measure, and Evaluate in a Single Pipeline","homepage_url":"http://docs.deepfabric.dev","license":"Apache-2.0","open_issues":20,"watchers":8,"ai_summary":"deepfabric provides tools for generating high-quality synthetic data and incorporating it seamlessly into machine-learning pipelines for training, evaluation, and fine-tuning.","readme_excerpt":"\n\n**DeepFabric** generates synthetic training data for language models and agent evaluations. By combining reasoning traces with tool-calling patterns, it creates high-quality, domain-specific datasets that teach models to think, plan, and act effectively, call tools correctly, and conform to strict schema structures.\n\nWhat sets DeepFabric apart from other dataset generation tools is its ability to ensure high diversity yet domain-anchored relevance through unique topic graph generation algorithms. This guides sample creation to cover all necessary subtopics while avoiding redundancy, which is where other tools often fall short, resulting in model overfit.\n\n![](\"/assets/df-demo.gif\")
\n\nConstrained decoding and response validation, along with real tool executions within isolated webassembly environments, ensure that generated samples strictly adhere to structured schema, variable constraints, and execution correctness, ensuring datasets have exact syntax and structure for use in model training pipelines. Tool definations can be either directly imported from MCP (Model Context Protocol) server schemas and automatically mocked, real life interfaces along with a standard set of common tools (`list_files()`, `'read_file()` etc)\n\nOnce your dataset is generated, it can be automatically uploaded to Hugging Face and directly imported into popular training frameworks like TRL, Unsloth, and Axolotl. \n\nPost-training, DeepFabric's built-in evaluation engine assesses model performance, whereby models prove their capabilities on unseen tasks derived from training splits—covering evaluation-only questions, answers, and tool traces.\n\n## Quickstart\n\nDeepFabric can be used in several ways, as a library, CLI tool, or via YAML configuration. Here's a quick example using the CLI:\n\n```bash\npip install deepfabric\n```\n\n```bash\nexport OPENAI_API_KEY=\"your-api-key\"\n\ndeepfabric generate \\\n --topic-prompt \"Python programming fundamentals\" \\\n --generation-system-prompt \"You are a Python expert\" \\\n --mode graph \\\n --depth 3 \\\n --degree 3 \\\n --num-samples 9 \\\n --batch-size 3 \\\n --provider openai \\","github_created_at":"2024-10-25T10:59:05+00:00","created_at":"2026-07-11T11:39:44.218485+00:00","updated_at":"2026-09-20T06:30:21.379619+00:00","categories":[{"slug":"evaluation-observability","name":"Evaluation & Observability","url":"https://www.graphcanon.com/categories/evaluation-observability","markdown_url":"https://www.graphcanon.com/categories/evaluation-observability.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/evaluation-observability"},{"slug":"model-training","name":"Model Training","url":"https://www.graphcanon.com/categories/model-training","markdown_url":"https://www.graphcanon.com/categories/model-training.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/model-training"}],"tags":[{"slug":"agents","name":"agents"},{"slug":"ai","name":"ai"},{"slug":"data-science","name":"data-science"},{"slug":"dataset","name":"dataset"},{"slug":"distillation","name":"distillation"},{"slug":"evaluation","name":"evaluation"},{"slug":"fine-tuning","name":"fine-tuning"},{"slug":"machine-learning","name":"machine-learning"}],"trust":{"provenance":{"is_fork":false,"github_id":878416567,"owner_type":"Organization","methodology":"github_public_v1","parent_repo":null,"near_duplicate_slugs":[]},"computed_at":"2026-09-19T18:25:51.871Z","maintenance":{"label":"Very active","score":96,"methodology":"github_public_v1","releases_90d":0,"days_since_push":4,"last_release_at":"2026-02-03T18:31:20Z","stars_delta_30d":9,"open_issues_delta_30d":-2},"security_summary":{"status":"no_lockfile","scanner":null,"low_count":0,"high_count":0,"last_scan_at":"2026-07-11T11:39:45.385Z","medium_count":0,"scan_profile":"none","critical_count":0}},"capability_facts":{"scan":{"source":"repo_scan","observed_at":"2026-09-19T18:25:52.886Z"},"has_cli":{"value":true,"source":"pyproject.toml:[project.scripts]","observed_at":"2026-09-19T18:25:52.886Z"},"languages":{"value":["python"],"source":"github.language+pyproject.toml","observed_at":"2026-09-19T18:25:52.886Z"},"license_spdx":{"value":"Apache-2.0","source":"github.license","observed_at":"2026-09-19T18:25:52.886Z"}},"decision_facts":{"hosting":null,"pricing":null,"requirements":null,"constraints":null,"when_to_use":["Use it when your project requires extensive training on augmented datasets without compromising the quality of the original data.","Ideal if you seek to enhance model robustness by incorporating diverse synthetic examples, particularly in scenarios with limited real-world data."],"when_not_to_use":["Avoid using DeepFabric for projects that strictly require real-world datasets for training and validation due to legal or domain-specific standards.","Not recommended for teams already heavily invested in proprietary synthetic data solutions that offer unique features unavailable in open-source alternatives like DeepFabric."],"source":"enrich:decision_facts","observed_at":"2026-07-17T13:31:56.131Z"},"constraint_facets":null,"decision_summary":[{"label":"Adopt for","value":"Consider DeepFabric for seamless synthetic data generation and integration into machine-learning projects where high-quality data augmentation is critical."}]}} ![\"DeepFabric](\"./assets/logo-light.png\"){kind=logo}
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