TrustGraph – Do More with AI with Less (Open Source AI Infrastructure)
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…
In plain words
TrustGraph is an open-source AI infrastructure that automates knowledge graph construction and querying while integrating modular agents. It is designed for developers and organizations working with large unstructured datasets who need to convert disconnected information into structured, queryable knowledge. The platform's distinguishing feature is its one-time graph-building process that creates reusable knowledge cores capable of being stored, shared, and reloaded, reducing redundant processing when working with extensive documents and facts.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
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 with huge datasets of unstructured knowledge - thousands of pages, tens of thousands of pages of documents with facts disconnected by thousands of pages. Knowledge graphs and AI unlock a solution to convert this unstructured knowledge to an enriched knowledge structure enabling AI to extract accurate intelligence. Unlike AI frameworks, TrustGraph is your infrastructure. All the services, stores, observability, and backbone gets deployed in a single package. Once deployed, TrustGraph enables you to: - Ingest PDF, TXT, and MD files in batches - Chunk ingested docs with multiple chunking options and parameters - Structure the knowledge in each chunk as RDF - Convert each chunk to mapped vector embeddings - Store RDF triples in either Cassandra or Neo4j - Store vector embeddings in Qdrant - Monitor system performance, like CPU/memory resources and token throughput, with a Grafana dashboard - Store and load knowledge cores - Query the graph store and VectorDB for AI generation - Easy agent integration with Apache Pulsar backbone TrustGraph is model agnostic and currently supports: - Anthropic API - AWS Bedrock API - AzureAI API - OpenAI API in Azure - Cohere API - Llamafile API - Ollama API - OpenAI API - VertexAI API TrustGraph is deployed with either Docker or Kubernetes. For knowledge extraction, we’ve seen Claude 3 Haiku and Gemini 1.5 Flash provide the best value. However, with a quality knowledge core extraction, you can use locally deployed SLMs to get big LLM performance on your dataset. Our goal is to be able to use TLMs (models 3B parameters and smaller) for AI generation on knowledge cores. Trust is the foundation of TrustGraph’s goals. TrustGraph aims to enable “accuracy first” AI generation with our fully transparent and open source approach. Realtime observability will enable doing more with AI with less - less compute, less memory, and less power. We have a vision for “open knowledge cores”, that will contain common terms, definitions, and information to aid the generation for niche use cases driven by community. We hope you join us on this journey of doing more with less. Daniel and Mark Give us a try: https://github.com/trustgraph-ai/trustgraph Full Documentation: https://trustgraph.ai/docs/getstarted Blog: https://blog.trustgraph.ai Join the Community: https://discord.gg/sQMwkRz5GX
More ai this month
the category →
I trained a 125M-parameter transformer to autocomplete piano performances in real time (~108 notes/sec on an iPhone 15). The idea is basically GitHub Copilot or Tabnine, except instead of prompting it with code, you prompt it by playing a few notes on a MIDI piano. The model then continues what you played, entirely on-device. The app is free if anyone wants to try it. Happy to answer questions about the model, training, Core ML, or the many things that didn't work.
AI · 17d ago · simedw.com
Astute▲585Automate your B2B brand going viral, with new media creators
AI · 18d ago · company-app.joinastute.com
Hey HN, Henry from Cactus here! We previously released Cactus Needle, a 14MB agentic LLM for tool call, device use, and structured extraction for phones, wearables, smart homes, small robots and microcontrollers. We got really great feedback here, and have now incorporated the suggestions to release Needle 2. The whole model is a single 14MB binary that runs a full session in 28MB of RAM; 45m parameters at 2bit compression. Needle hits 500 tokens/sec decode speed on a Raspberry Pi 5, sits between 400-1,500 tokens/sec on VR devices like Meta Quest 3S and Apple Vision Pro, and ranges…
AI · 27d ago · cactuscompute.com
Launched alongside, October 2024
the whole month →
Softr for Notion▲925Turn Notion databases into portals & apps with no code
Dev tools · 2024 · softr.io
One inbox for all your work discussions
Work · 2024 · generalcollaboration.com