AI · alternatives · 2026
24 alternatives to Ontology-driven knowledge graph extraction from text
TrustGraph now supports automatic knowledge graph construction guided by OWL ontologies. You provide an ontology (OWL/Turtle format or build one in the Workbench editor), point it at your documents, and it extracts entities and relationships that conform to your schema. The problem this solves: generic GraphRAG approaches extract whatever relationships an LLM thinks are relevant, which often misses domain-specific semantics. If you're working in healthcare, finance, or intelligence analysis, you likely already have ontologies (or can adapt standards like SOSA, FIBO, etc.) that define…
TrustGraph is an AI tool that automatically builds knowledge graphs from text using OWL ontologies as a guide. Users provide an ontology in OWL or Turtle format, or create one in the built-in editor, and point it at… Below are 24 products that do a similar job, ranked by how close each is in meaning and then by launch-day votes.
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Hi HN, We’re Daniel and Mark, the creators of TrustGraph (https://github.com/trustgraph-ai/trustgraph). TrustGraph is an open source, full end-to-end AI infrastructure that automates knowledge graph building and querying along with modular agent integration. A unique aspect of TrustGraph is that the graph building is a one-time process that builds reusable knowledge cores that can be stored, shared, and reloaded. You can read more about TrustGraph knowledge cores here (https://trustgraph.ai/docs/cores/). Throughout our careers, we’ve been faced…
2024 · github.com · its alternatives →
- 2KG
Hi HN! My latest side project is knowledge graph that maps the French culinary network using data extracted from restaurant reviews from LeFooding.com. The project uses LLMs to extract structured information from unstructured text. Some technical aspects you may be interested in: - Used structured generation to reliably parse unstructured text into a consistent schema - Tested multiple models (Mistral-7B-v0.3, Llama3.2-3B, gpt4o-mini) for information extraction - Created an interactive visualization using gephi-lite and Retina (WebGL) - Built (with Claude) a simple Flask web app to clean and…
2025 · theophilecantelob.re · its alternatives →
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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 · its alternatives →
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Graphiti▲307Build personalized AI agents that learn from dynamic data
2025 · github.com · its alternatives →
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The network graph that holds all the wonderful reads I've ever found on the internet.
2022 · prashantbarahi.com.np · its alternatives →
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Hey HN! We're Paul, Preston, and Daniel from Zep. We've just open-sourced Graphiti, a Python library for building temporal Knowledge Graphs using LLMs. Graphiti helps you create and query graphs that evolve over time. Knowledge Graphs have been explored extensively for information retrieval. What makes Graphiti unique is its ability to build a knowledge graph while handling changing relationships and maintaining historical context. At Zep, we build a memory layer for LLM applications. Developers use Zep to recall relevant user information from past conversations without including the entire…
2024 · github.com · its alternatives →
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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…
2025 · github.com · its alternatives →
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Graphify▲195Turn your Notion notes into an interactive knowledge map
2025 · getgraphify.com · its alternatives →
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GitBook AI Lens▲229Semantic search for your technical documentation & knowledge
2023 · gitbook.com · its alternatives →
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InstaGraph▲102Text2Graph AI Agent: A text prompt into a knowledge graph
2023 · instagraph.ai · its alternatives →
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Build the semantic layer that makes AI analytics trustworthy
Mar 2026 · metabase.com · its alternatives →
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A great way to enhance chatbots is to allow them to look up information for context, typically using a vectordb. If you have writings you would like to share with others, you can offer a server that allows others to do semantic lookup, and that way anyone can have a chatbot which can pull from your writing. The goal of this project is to have a protocol that makes that easy. Strictly speaking, the protocol is for semantic retrieval and doesn't require using LLMs although LLMs are the motivating application. For far more details and how to get a demo up and running, see the readme.
2024 · github.com · its alternatives →
- 13GT
Hey HN - Paul, Preston, and Daniel from Zep here. We’re excited to show you graphiti, a library for building and searching dynamic, temporally aware knowledge graphs. https://git.new/graphiti With graphiti, you can model complex, evolving relationships between entities over time. graphiti ingests both unstructured and structured data and the resulting graph may be queried using a fusion of time, full-text, semantic, and graph algorithm approaches. With graphiti, you can build LLM applications such as: - Assistants that learn from user interactions, fusing personal knowledge…
2024 · its alternatives →
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Hey HN, This is Kanon 2 Enricher, the first hierarchical graphitization model. It represents an entirely new class of AI models designed to transform document corpora into rich, highly structured knowledge graphs. In brief, our model is capable of: - Entity extraction, classification, and linking: identifying key entities like individuals, companies, governments, locations, dates, documents, and more, and classifying and linking them together. - Hierarchical segmentation: breaking a document up into its full hierarchy, including divisions, sections, subsections, paragraphs, and so on. - Text…
Mar 2026 · isaacus.com · its alternatives →
- 15
A graph-based functional API for building complex scikit-learn pipelines. - alegonz/baikal
2020 · github.com · its alternatives →
- 16GO
Small experiment of visualization of wikipedia articles as a graph using d3.js.<p>Articles with more traffic are bigger. I computed the semantic similarity using LSI with python (gensim) You have to scroll down/right a bit!<p>http://similarityapi.appspot.com/graph/?title=blade%20runner<p>There is also a JSON api: http://similarityapi.appspot.com/api/v1/?limit=100&title=blade%20runner<p>All feedback is appreciated:<p>@lucamartinetti [email protected]
2012 · similarityapi.appspot.com · its alternatives →
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Build AI with contextual memory using knowledge graphs.
Dec 2025 · github.com · its alternatives →
- 19
It’s written in Python and I call it GoalChain. It lets you build a conversation flow graph that the user traverses. When there’s enough input it spits out a dictionary with the defined fields. Otherwise it will jump state to state as led by the user. It was fun to write, and it’s surprisingly effective if you keep in mind you’re prompt-engineering every string and field name. README.md has a mini-tutorial. Would be cool to get some ideas for how to build it further and what improvements I could make.
2024 · github.com · its alternatives →
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Property graphs that fit your LLM's context window
Jul 2026 · github.com · its alternatives →
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DigGraph▲2Turn AI conversations into a visual knowledge graph.
Jul 2026 · diggraph.com · its alternatives →
- 24
The graph is free to explore and requires no registration. SynapTale builds a model of a story as a temporal graph made up of nodes (entities) and edges (their actions and relationships). The graph is not a visualization of the wiki. The wiki, timelines, relationship histories, and analytics are projections of the graph. The current demo contains 232 entities, 1,852 edges, and a snapshot of the story’s state at every chapter. By chapter 100, it still remembers a promise made in chapter 8 and turns the story into a set of source-verifiable facts. The most interesting things can be found in…
Jul 2026 · synaptale.com · its alternatives →
Also compare
- TrustGraph – Do More with AI with Less (Open Source AI Infrastructure) alternatives
- Knowledge graph of restaurants and chefs, built using LLMs alternatives
- A Karpathy-style LLM wiki your agents maintain (Markdown and Git) alternatives
- Graphiti alternatives
- Every great read I've come across, compiled into a knowledge graph alternatives
- Graphiti – LLM-Powered Temporal Knowledge Graphs alternatives
Ranked by how close each launch is in meaning, then by votes. Prices were read from each product’s own site when checked and can change. Refine with your own description →