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Products that do what OntoCast – ontology-assisted KG generation does
Hey HN, I'm excited to announce a new release of OntoCast — an open-source framework for extracting semantic triples and building knowledge graphs (KG) from unstructured documents (PDF, JSON, Markdown, and more). Before extracting facts, OntoCast automatically selects or creates a relevant ontology and iteratively refines it, leading to much more accurate and context-aware fact extraction. This is especially valuable for cross-domain or complex documents where a static ontology falls short. - Agentic workflow: Uses LLMs (OpenAI/Ollama) to drive the extraction and ontology refinement…
- 1AK
I shipped a wiki layer for AI agents that uses markdown + git as the source of truth, with a bleve (BM25) + SQLite index on top. No vector or graph db yet. It runs locally in ~/.wuphf/wiki/ and you can git clone it out if you want to take your knowledge with you. The shape is the one Karpathy has been circling for a while: an LLM-native knowledge substrate that agents both read from and write into, so context compounds across sessions rather than getting re-pasted every morning. Most implementations of that idea land on Postgres, pgvector, Neo4j, Kafka, and a dashboard. I…
Apr 2026 · github.com
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- 3SD
2019 · github.com
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- 5AE
2021 · archivy.github.io
- 6SA
2017 · symatem.github.io
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- 8KE
2014 · quickanswers.io
- 9PM
2020 · medium.com
- 10
Not just nodes and edges, it reasons about the connections.
Jul 2026 · github.com
- 11SN
2014 · structr.org
- 12TL
2020 · muzejs.org
- 13
ExtractBench - A Benchmark for Schema-Guided Enterprise Document Extraction - run-llama/ExtractBench
26d ago · github.com
- 14KK
2017 · github.com
- 15OK
We release an open source version of Andrej Karparthy's open knowledge base, and we scale it to support long PDFs with Pageindex. Any feedback is welcome to help us improve this project! Github repo: https://github.com/VectifyAI/OpenKB
Apr 2026
- 16SP
I built Sculptor after repeatedly seeing founders try to hire data scientists for a task that ultimately boiled down to extracting structured data from unstructured text (customer records, social posts, websites, etc) using an LLM API. We ended up reinventing this pattern internally at least three times in the past year, so I published Sculptor as a streamlined, open-source solution: - Simple schema-based extraction, with parallelization and type validation. - Multi-step pipelines with filtering or transforms between steps. - Configure everything in YAML/JSON for easy reuse. It’s MIT…
2025 · github.com
- 17IP
To be specific, the content is generated by a GPT-2 based model. https://amzn.to/2TCc0v2 Let me know if you have any questions :-)
2020
- 18AO
Hi, We are building an open-source framework for loading and structuring LLM context to create accurate and explainable LLM answers using knowledge graphs and vector stores. We built the tool with four main concepts in mind: 1. Loader -> uses dlt in the backend to load and structure the data 2. Cognify step -> creates a graph with summaries, labels and factoids that are interconnected across the documents and stored as a representation in the vector store 3. Optimizer -> Uses DSPy to optimize LLM queries, and we plan to extend it to most of the knobs we can turn, like chunking etc. 4. Search…
2024 · github.com
- 19LW
Apr 2026 · llmwiki.app
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2020 · crates.io
- 22IO
Hey folks, I’m the creator of WFGY — a semantic reasoning framework for LLMs. After open-sourcing it, I did a full technical and value audit — and realized this engine might be worth $8M–$17M based on AI module licensing norms. If embedded as part of a platform core, the valuation could exceed $30M. Too late to pull it back. So here it is — fully free, open-sourced under MIT. --- ### What does it solve? Current LLMs (even GPT-4+) lack *self-consistent reasoning*. They struggle with: - Fragmented logic across turns - No internal loopback or self-calibration - No modular thought units - Weak…
2025 · github.com
- 23KA
Hey HN! I've spent the past year full-time building Knowing, a tool for interacting with LLMs directly inside hierarchical structures instead of the usual prompt-response format. The idea started because I realized how much more intuitive it felt to build concept hierarchies continuously—no more endless copy-pasting or wondering how everything connects. The journey’s been a struggle. While I see huge potential in structuring AI interactions this way (writing books fast, planning projects, or organizing ideas), it’s been hard to pin down clear use cases in the market. I’m also working in near…
2024
- 24LF
2023 · blogseo.ai
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