[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-72597":3},{"id":4,"name":5,"fullName":6,"owner":7,"repo":5,"description":8,"homepage":9,"htmlUrl":10,"language":11,"languages":10,"totalLinesOfCode":10,"stars":12,"forks":13,"watchers":14,"openIssues":15,"contributorsCount":16,"subscribersCount":16,"size":16,"stars1d":17,"stars7d":18,"stars30d":19,"stars90d":16,"forks30d":16,"starsTrendScore":20,"compositeScore":21,"rankGlobal":10,"rankLanguage":10,"license":22,"archived":23,"fork":23,"defaultBranch":24,"hasWiki":25,"hasPages":23,"topics":26,"createdAt":10,"pushedAt":10,"updatedAt":46,"readmeContent":47,"aiSummary":48,"trendingCount":16,"starSnapshotCount":16,"syncStatus":49,"lastSyncTime":50,"discoverSource":51},72597,"trustgraph","trustgraph-ai\u002Ftrustgraph","trustgraph-ai","The semantic deployment platform.","https:\u002F\u002Ftrustgraph.ai",null,"Python",2155,248,26,30,0,13,31,89,39,103.59,"Apache License 2.0",false,"master",true,[27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,5],"agent","agent-memory","agent-runtime","ai-infra","ai-tools","context","context-engineering","context-graph","context-os","developer-tools","graph","graph-database","knowledge-graph","neuro-symbolic-ai","ontology","ontology-engineering","open-source","rdf","sparql","2026-06-12 04:01:06","\n\u003Cdiv align=\"center\">\n\n\u003Cimg src=\"TG-fullname-logo.svg\" width=100% \u002F>\n\n[![PyPI version](https:\u002F\u002Fimg.shields.io\u002Fpypi\u002Fv\u002Ftrustgraph.svg)](https:\u002F\u002Fpypi.org\u002Fproject\u002Ftrustgraph\u002F) [![License](https:\u002F\u002Fimg.shields.io\u002Fgithub\u002Flicense\u002Ftrustgraph-ai\u002Ftrustgraph?color=blue)](LICENSE) ![E2E Tests](https:\u002F\u002Fgithub.com\u002Ftrustgraph-ai\u002Ftrustgraph\u002Factions\u002Fworkflows\u002Frelease.yaml\u002Fbadge.svg)\n[![Discord](https:\u002F\u002Fimg.shields.io\u002Fdiscord\u002F1251652173201149994\n)](https:\u002F\u002Fdiscord.gg\u002FsQMwkRz5GX) [![Ask DeepWiki](https:\u002F\u002Fdeepwiki.com\u002Fbadge.svg)](https:\u002F\u002Fdeepwiki.com\u002Ftrustgraph-ai\u002Ftrustgraph)\n\n[**Website**](https:\u002F\u002Ftrustgraph.ai) | [**Docs**](https:\u002F\u002Fdocs.trustgraph.ai) | [**YouTube**](https:\u002F\u002Fwww.youtube.com\u002F@TrustGraphAI?sub_confirmation=1) | [**Configuration Terminal**](https:\u002F\u002Fconfig-ui.demo.trustgraph.ai\u002F) | [**Discord**](https:\u002F\u002Fdiscord.gg\u002FsQMwkRz5GX) | [**Blog**](https:\u002F\u002Fblog.trustgraph.ai\u002Fsubscribe)\n\n\u003Ca href=\"https:\u002F\u002Ftrendshift.io\u002Frepositories\u002F17291\" target=\"_blank\">\u003Cimg src=\"https:\u002F\u002Ftrendshift.io\u002Fapi\u002Fbadge\u002Frepositories\u002F17291\" alt=\"trustgraph-ai%2Ftrustgraph | Trendshift\" style=\"width: 250px; height: 55px;\" width=\"250\" height=\"55\"\u002F>\u003C\u002Fa>\n\n# The agent runtime platform\n\n\u003C\u002Fdiv>\n\nTrustGraph is an agent runtime platform built around context graphs — structured, queryable representations of your domain knowledge that ground every agent query in verified, explainable facts in private deployments with sovereign control. The platform is the full stack for agentic systems: context graphs, memory, retrieval, orchestration, and inference for precision-critical agent workloads.\n\nThe platform:\n- [x] Multi-model and multimodal database system\n  - [x] Tabular\u002Frelational, key-value\n  - [x] Document, graph, and vectors\n  - [x] Images, video, and audio\n- [x] Context Graph engine\n  - [x] Automated entity and relationship extraction\n  - [x] Ontology-driven graph construction\n  - [x] Graph-grounded retrieval for explainable outputs\n- [x] Automated data ingest and loading\n  - [x] Quick ingest with semantic similarity retrieval\n  - [x] Ontology structuring for precision retrieval\n- [x] Out-of-the-box RAG pipelines\n  - [x] DocumentRAG\n  - [x] GraphRAG\n  - [x] OntologyRAG     \n- [x] 3D GraphViz for exploring context\n- [x] Fully Agentic System\n  - [x] Single or Multi Agent\n  - [x] ReAct, Plan-then-Execute, and Supervisor patterns\n  - [x] MCP integration \n- [x] Run anywhere\n  - [x] Deploy locally with Docker\n  - [x] Deploy in cloud with Kubernetes\n- [x] Support for all major LLMs\n  - [x] API support for Anthropic, Cohere, Gemini, Mistral, OpenAI, and others\n  - [x] Model inferencing with vLLM, Ollama, TGI, LM Studio, and Llamafiles\n- [x] Developer friendly\n  - [x] REST API [Docs](https:\u002F\u002Fdocs.trustgraph.ai\u002Freference\u002Fapis\u002Frest.html)\n  - [x] Websocket API [Docs](https:\u002F\u002Fdocs.trustgraph.ai\u002Freference\u002Fapis\u002Fwebsocket.html)\n  - [x] Python API [Docs](https:\u002F\u002Fdocs.trustgraph.ai\u002Freference\u002Fapis\u002Fpython)\n  - [x] CLI [Docs](https:\u002F\u002Fdocs.trustgraph.ai\u002Freference\u002Fcli\u002F)\n     \n## No API Keys Required\n\nHow many times have you cloned a repo and opened the `.env.example` to see the dozens of API keys for 3rd party dependencies needed to make the services work? There are only 3 things in TrustGraph that might need an API key:\n\n- 3rd party LLM services like Anthropic, Cohere, Gemini, Mistral, OpenAI, etc.\n- 3rd party OCR like Mistral OCR\n- The API key *you set* for the TrustGraph API gateway\n\nEverything else is included.\n- [x] Managed Multi-model storage in [Cassandra](https:\u002F\u002Fcassandra.apache.org\u002F_\u002Findex.html)\n- [x] Managed Vector embedding storage in [Qdrant](https:\u002F\u002Fgithub.com\u002Fqdrant\u002Fqdrant)\n- [x] Managed File and Object storage in [Garage](https:\u002F\u002Fgithub.com\u002Fdeuxfleurs-org\u002Fgarage) (S3 compatible)\n- [x] Managed High-speed Pub\u002FSub messaging fabric with [Pulsar](https:\u002F\u002Fgithub.com\u002Fapache\u002Fpulsar)\n- [x] Complete LLM inferencing stack for open LLMs with [vLLM](https:\u002F\u002Fgithub.com\u002Fvllm-project\u002Fvllm), [TGI](https:\u002F\u002Fgithub.com\u002Fhuggingface\u002Ftext-generation-inference), [Ollama](https:\u002F\u002Fgithub.com\u002Follama\u002Follama), [LM Studio](https:\u002F\u002Fgithub.com\u002Flmstudio-ai), and [Llamafiles](https:\u002F\u002Fgithub.com\u002Fmozilla-ai\u002Fllamafile) \n\n## Quickstart\n\nThere's no need to clone this repo, unless you want to build from source. TrustGraph is a fully containerized app that deploys as a set of Docker containers. To configure TrustGraph on the command line:\n\n```\nnpx @trustgraph\u002Fconfig\n```\n\nThe config process will generate an app config that can be run locally with Docker, Podman, or Minikube. The process will output:\n- `deploy.zip` with either a `docker-compose.yaml` file for a Docker\u002FPodman or `resources.yaml` for Kubernetes\n- Deployment instructions as `INSTALLATION.md`\n\n\u003Cp align=\"center\">\n  \u003Cvideo src=\"https:\u002F\u002Fgithub.com\u002Fuser-attachments\u002Fassets\u002F2978a6aa-4c9c-4d7c-ad02-8f3d01a1c602\"\nwidth=\"80%\" controls>\u003C\u002Fvideo>\n\u003C\u002Fp>\n\nFor a browser based configuration, try the [Configuration Terminal](https:\u002F\u002Fconfig-ui.demo.trustgraph.ai\u002F). \n\n## Watch What is a Context Graph?\n\n[![What is a Context Graph?](https:\u002F\u002Fimg.youtube.com\u002Fvi\u002FgZjlt5WcWB4\u002Fmaxresdefault.jpg)](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=gZjlt5WcWB4) \n\n## Watch Context Graphs in Action\n\n[![Context Graphs in Action with TrustGraph](https:\u002F\u002Fimg.youtube.com\u002Fvi\u002FsWc7mkhITIo\u002Fmaxresdefault.jpg)](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=sWc7mkhITIo)\n\n## Getting Started with TrustGraph\n\n- [**Getting Started Guides**](https:\u002F\u002Fdocs.trustgraph.ai\u002Fgetting-started)\n- [**Using the Workbench**](#workbench)\n- [**Developer APIs and CLI**](https:\u002F\u002Fdocs.trustgraph.ai\u002Freference)\n- [**Deployment Guides**](https:\u002F\u002Fdocs.trustgraph.ai\u002Fdeployment)\n\n## Workbench\n\nThe **Workbench** provides tools for all major features of TrustGraph. The **Workbench** is on port `8888` by default.\n\n- **Vector Search**: Search the installed knowledge bases\n- **Agentic, GraphRAG and LLM Chat**: Chat interface for agents, GraphRAG queries, or direct to LLMs\n- **Relationships**: Analyze deep relationships in the installed knowledge bases\n- **Graph Visualizer**: 3D GraphViz of the installed knowledge bases\n- **Library**: Staging area for installing knowledge bases\n- **Flow Classes**: Workflow preset configurations\n- **Flows**: Create custom workflows and adjust LLM parameters during runtime\n- **Knowledge Cores**: Manage resuable knowledge bases\n- **Prompts**: Manage and adjust prompts during runtime\n- **Schemas**: Define custom schemas for structured data knowledge bases\n- **Ontologies**: Define custom ontologies for unstructured data knowledge bases\n- **Agent Tools**: Define tools with collections, knowledge cores, MCP connections, and tool groups\n- **MCP Tools**: Connect to MCP servers\n\n## TypeScript Library for UIs\n\nThere are 3 libraries for quick UI integration of TrustGraph services.\n\n- [@trustgraph\u002Fclient](https:\u002F\u002Fwww.npmjs.com\u002Fpackage\u002F@trustgraph\u002Fclient)\n- [@trustgraph\u002Freact-state](https:\u002F\u002Fwww.npmjs.com\u002Fpackage\u002F@trustgraph\u002Freact-state)\n- [@trustgraph\u002Freact-provider](https:\u002F\u002Fwww.npmjs.com\u002Fpackage\u002F@trustgraph\u002Freact-provider)\n\n## Context Cores\n\nContext Cores are how TrustGraph treats context like code. A Context Core is a **portable, versioned bundle of context** that you can ship between projects and environments, pin in production, and reuse across agents. It packages the “stuff agents need to know” (structured knowledge + embeddings + evidence + policies) into a single artifact, so you can treat context like code: build it, test it, version it, promote it, and roll it back. TrustGraph is built to support this kind of end-to-end context engineering and orchestration workflow.\n\n### What’s inside a Context Core\nA Context Core typically includes:\n- Ontology (your domain schema) and mappings\n- Context Graph (entities, relationships, supporting evidence)\n- Embeddings \u002F vector indexes for fast semantic entry-point lookup\n- Source manifests + provenance (where facts came from, when, and how they were derived)\n- Retrieval policies (traversal rules, freshness, authority ranking)\n\n## Tech Stack\nTrustGraph provides component flexibility to optimize agent workflows.\n\n\u003Cdetails>\n\u003Csummary>LLM APIs\u003C\u002Fsummary>\n\u003Cbr>\n\n- Anthropic\u003Cbr>\n- AWS Bedrock\u003Cbr>\n- AzureAI\u003Cbr>\n- AzureOpenAI\u003Cbr>\n- Cohere\u003Cbr>\n- Google AI Studio\u003Cbr>\n- Google VertexAI\u003Cbr>\n- Mistral\u003Cbr>\n- OpenAI\u003Cbr>\n\n\u003C\u002Fdetails>\n\u003Cdetails>\n\u003Csummary>LLM Orchestration\u003C\u002Fsummary>\n\u003Cbr>\n\n- LM Studio\u003Cbr>\n- Llamafiles\u003Cbr>\n- Ollama\u003Cbr>\n- TGI\u003Cbr>\n- vLLM\u003Cbr>\n\n\u003C\u002Fdetails>\n\u003Cdetails>\n\u003Csummary>Multi-model storage\u003C\u002Fsummary>\n\u003Cbr>\n\n- Apache Cassandra\u003Cbr>\n\n\u003C\u002Fdetails>\n\u003Cdetails>\n\u003Csummary>VectorDB\u003C\u002Fsummary>\n\u003Cbr>\n\n- Qdrant\u003Cbr>\n\n\u003C\u002Fdetails>\n\u003Cdetails>\n\u003Csummary>File and Object Storage\u003C\u002Fsummary>\n\u003Cbr>\n\n- Garage\u003Cbr>\n\n\u003C\u002Fdetails>\n\u003Cdetails>\n\u003Csummary>Observability\u003C\u002Fsummary>\n\u003Cbr>  \n\n- Prometheus\u003Cbr>\n- Grafana\u003Cbr>\n- Loki\u003Cbr>\n\n\u003C\u002Fdetails>\n\u003Cdetails>\n\u003Csummary>Data Streaming\u003C\u002Fsummary>\n\u003Cbr>\n\n- Apache Pulsar\u003Cbr>\n- RabbitMQ\u003Cbr>\n- Apache Kafka\u003Cbr>\n\n\u003C\u002Fdetails>\n\u003Cdetails>\n\u003Csummary>Clouds\u003C\u002Fsummary>\n\u003Cbr>\n\n- AWS\u003Cbr>\n- Azure\u003Cbr>\n- Google Cloud\u003Cbr>\n- OVHcloud\u003Cbr>\n- Scaleway\u003Cbr>\n\n\u003C\u002Fdetails>\n\n## Observability & Telemetry\n\nOnce the platform is running, access the Grafana dashboard at:\n\n```\nhttp:\u002F\u002Flocalhost:3000\n```\n\nDefault credentials are:\n\n```\nuser: admin\npassword: admin\n```\n\nThe default Grafana dashboard tracks the following:\n\n\u003Cdetails>\n\u003Csummary>Telemetry\u003C\u002Fsummary>\n\u003Cbr>\n\n- LLM Latency\u003Cbr>\n- Error Rate\u003Cbr>\n- Service Request Rates\u003Cbr>\n- Queue Backlogs\u003Cbr>\n- Chunking Histogram\u003Cbr>\n- Error Source by Service\u003Cbr>\n- Rate Limit Events\u003Cbr>\n- CPU usage by Service\u003Cbr>\n- Memory usage by Service\u003Cbr>\n- Models Deployed\u003Cbr>\n- Token Throughput (Tokens\u002Fsecond)\u003Cbr>\n- Cost Throughput (Cost\u002Fsecond)\u003Cbr>\n   \n\u003C\u002Fdetails>\n\n## Contributing\n\n[Developer's Guide](https:\u002F\u002Fdocs.trustgraph.ai\u002Fguides\u002Fbuilding\u002Fintroduction.html)\n\n## License\n\n**TrustGraph** is licensed under [Apache 2.0](https:\u002F\u002Fwww.apache.org\u002Flicenses\u002FLICENSE-2.0).\n\n   Copyright 2024-2025 TrustGraph\n\n   Licensed under the Apache License, Version 2.0 (the \"License\");\n   you may not use this file except in compliance with the License.\n   You may obtain a copy of the License at\n\n       http:\u002F\u002Fwww.apache.org\u002Flicenses\u002FLICENSE-2.0\n\n   Unless required by applicable law or agreed to in writing, software\n   distributed under the License is distributed on an \"AS IS\" BASIS,\n   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n   See the License for the specific language governing permissions and\n   limitations under the License.\n\n## Support & Community\n- Bug Reports & Feature Requests: [Discord](https:\u002F\u002Fdiscord.gg\u002FsQMwkRz5GX)\n- Discussions & Questions: [Discord](https:\u002F\u002Fdiscord.gg\u002FsQMwkRz5GX)\n- Documentation: [Docs](https:\u002F\u002Fdocs.trustgraph.ai\u002F)\n","TrustGraph 是一个基于上下文图的代理运行时平台，它通过结构化、可查询的知识表示来支持每个代理查询，确保在私有部署中以验证过的、可解释的事实为基础。其核心功能包括多模型和多模态数据库系统、自动化实体与关系提取、本体驱动的图构建以及图支撑的检索等，能够处理从表格\u002F关系型数据到图像、视频和音频等多种类型的数据。此外，TrustGraph 提供了开箱即用的RAG管道（如DocumentRAG, GraphRAG, OntologyRAG）、3D GraphViz可视化工具，并支持多种主流的大语言模型及其API。该平台适用于需要高精度、可解释性的代理工作负载场景，比如企业级知识管理、智能客服或复杂的决策支持系统。",2,"2026-06-11 03:42:44","high_star"]