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We’re kicking off 2025 with everything we've got! 🤩 Introducing CodeGPT's Knowledge Graphs, navigating through the entire Anthropic SDK repository. In this example, we loaded Anthropic Python SDK repository and successfully provided the LLM with all the knowledge it needs to fully understand the codebase. You can leverage these... show more

Daniel San

32,692 subscribers

35,682 次观看 • 1 年前 •via X (Twitter)

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10 条评论

Daniel San 的头像

Daniel San1 年前

Hey @alexalbert__ , we’d love to teach the Anthropic developer community how to use the SDK with our repository knowledge graphs 🌐 Happy to collaborate on something together!

Cris 的头像

Cris1 年前

@AnthropicAI I dont think people realize what can be achieved with this 🍰

Arjun 的头像

Arjun1 年前

@AnthropicAI Looks amazing... I bet this would be more useful than the repo search that Cursor does. @cursor_ai should open up the ecosystem for us to integrate with such tools!

Daniel San 的头像

Daniel San1 年前

@aiguy_arjun @AnthropicAI @cursor_ai We would love to connect our Knowledge Graphs with Cursor... happy to collaborate! 🙌 @cursor_ai @amanrsanger

Rethynk AI 的头像

Rethynk AI1 年前

@AnthropicAI This is an incredible start to 2025! CodeGPT’s Knowledge Graphs are a game-changer for navigating complex repositories like Anthropic’s SDK. Simplifying codebase understanding for developers will boost productivity and collaboration.

David Olivencia 的头像

David Olivencia1 年前

@AnthropicAI Let's GOOOOOOO @codegptAI 🚀 #AI #Copilot #AgenticAI

stijn sagaert 的头像

stijn sagaert1 年前

@AnthropicAI @VictorTaelin is this something you are looking for to use with your codebase?

🐧 lalo adrian morales 𝕏 的头像

🐧 lalo adrian morales 𝕏1 年前

@AnthropicAI and it looks cool too!

Saïd Aitmbarek 的头像

Saïd Aitmbarek1 年前

@AnthropicAI looks amazing, big fan of ontologies brings a lot of explainability to datasets!

Kekius Optimus 的头像

Kekius Optimus1 年前

@AnthropicAI "In this example, we loaded @AnthropicAI Python SDK repository" Nothing like scratching his own back first, right?

相关视频

Introducing Codebase Knowledge Graphs in Cursor 🤩 In this video, I’ll walk you through how we went from using a knowledge graph of a repository on the CodeGPT platform to leveraging it directly within the Cursor editor This tool is specifically designed to navigate massive codebases, identify nodes and relationships, and deliver richer context to models specialized in code development, such as Anthropic Claude 3.5 Sonnet Stay tuned for an upcoming tutorial where I'll show you step-by-step how to create knowledge graphs and integrate them seamlessly into your entire software development workflow, from Commits and Pull Requests to even Trello tickets!

Introducing Codebase Knowledge Graphs in Cursor 🤩 In this video, I’ll walk you through how we went from using a knowledge graph of a repository on the CodeGPT platform to leveraging it directly within the Cursor editor This tool is specifically designed to navigate massive codebases, identify nodes and relationships, and deliver richer context to models specialized in code development, such as Anthropic Claude 3.5 Sonnet Stay tuned for an upcoming tutorial where I'll show you step-by-step how to create knowledge graphs and integrate them seamlessly into your entire software development workflow, from Commits and Pull Requests to even Trello tickets!

Daniel San

230,303 次观看 • 1 年前

You can now use Knowledge Graphs with Claude 3.5 Sonnet in Cursor! 🤩 CodeGPT’s Knowledge Graphs (KG) let you explore your entire codebase. In this case, we're navigating through Anthropic Python SDK. We asked how to send messages in batches and the tool identified the nodes and relationships in all relevant functions and classes. If you're curious about how LLMs will start handling large code repositories, you'll want to start programming CodeGPT’s KGs... I bet Andrej Karpathy will find this fascinating! I hope you get to try them out, because for me, reading documentation feels so 2024... now I just turn repos into KGs and get to work! 🧑🏽‍💻

You can now use Knowledge Graphs with Claude 3.5 Sonnet in Cursor! 🤩 CodeGPT’s Knowledge Graphs (KG) let you explore your entire codebase. In this case, we're navigating through Anthropic Python SDK. We asked how to send messages in batches and the tool identified the nodes and relationships in all relevant functions and classes. If you're curious about how LLMs will start handling large code repositories, you'll want to start programming CodeGPT’s KGs... I bet Andrej Karpathy will find this fascinating! I hope you get to try them out, because for me, reading documentation feels so 2024... now I just turn repos into KGs and get to work! 🧑🏽‍💻

Daniel San

42,046 次观看 • 1 年前

Claude 3.5 Sonnet V2 + Code Interpreter is now available in VSCode, and it's amazing! 🤩 Install the CodeGPT extension in VSCode, and you’ll find this incredible model in the "LLM" tab, released TODAY! 😱 When you pair it with tools like Code Interpreter or Code Builder from CodeGPT, the results are simply outstanding! Congrats to Alex Albert and the entire Anthropic team! 👏

Claude 3.5 Sonnet V2 + Code Interpreter is now available in VSCode, and it's amazing! 🤩 Install the CodeGPT extension in VSCode, and you’ll find this incredible model in the "LLM" tab, released TODAY! 😱 When you pair it with tools like Code Interpreter or Code Builder from CodeGPT, the results are simply outstanding! Congrats to Alex Albert and the entire Anthropic team! 👏

Daniel San

50,681 次观看 • 1 年前

Our new short course, Knowledge Graphs for RAG, is now available! Knowledge graphs are a data structure that is great at capturing complex relationships between data of multiple types. By enabling more sophisticated retrieval of text than similarity search alone, knowledge graphs can improve the context you pass to the LLM and the performance of your RAG applications. In this course, taught by Andreas Kollegger of Neo4j, you’ll - Explore how knowledge graphs work by building a graph of public financial documents from scratch - Learn to write queries that retrieve text and data from the graph and use it to enhance the context you pass to an LLM chatbot - Combine a knowledge graph with a question-answer chain to build better RAG-powered chat systems Sign up here!

Our new short course, Knowledge Graphs for RAG, is now available! Knowledge graphs are a data structure that is great at capturing complex relationships between data of multiple types. By enabling more sophisticated retrieval of text than similarity search alone, knowledge graphs can improve the context you pass to the LLM and the performance of your RAG applications. In this course, taught by Andreas Kollegger of Neo4j, you’ll - Explore how knowledge graphs work by building a graph of public financial documents from scratch - Learn to write queries that retrieve text and data from the graph and use it to enhance the context you pass to an LLM chatbot - Combine a knowledge graph with a question-answer chain to build better RAG-powered chat systems Sign up here!

Andrew Ng

244,435 次观看 • 2 年前

Knowledge graphs for representing information are unbeatable. After this, you will never build a RAG system without knowledge graphs. It will take you five lines of code to build a knowledge graph with your data. I recorded a video to show you how you can do this. I used Cognee, an open-source library that outperforms any basic vector search approach in terms of retrieval relevance. They are collaborating with me on this post. Cognee is: • Easy to use • Reduces hallucinations • Open-source Here is a link to the repository: They also offer a comprehensive platform and UI with Python notebooks you can utilize to manage your data. Here is the link:

Knowledge graphs for representing information are unbeatable. After this, you will never build a RAG system without knowledge graphs. It will take you five lines of code to build a knowledge graph with your data. I recorded a video to show you how you can do this. I used Cognee, an open-source library that outperforms any basic vector search approach in terms of retrieval relevance. They are collaborating with me on this post. Cognee is: • Easy to use • Reduces hallucinations • Open-source Here is a link to the repository: They also offer a comprehensive platform and UI with Python notebooks you can utilize to manage your data. Here is the link:

Santiago

126,434 次观看 • 11 个月前

introducing knowledge bases. greptile maintains detailed docs about how your codebase works, and uses them during review. for each part of the codebase, it maintains a running list of past bugs. this way, the next time it reviews that part of the code, it knows what to look for. greptile catches 12.2% more bugs with knowledge bases enabled! now available to all users :)

introducing knowledge bases. greptile maintains detailed docs about how your codebase works, and uses them during review. for each part of the codebase, it maintains a running list of past bugs. this way, the next time it reviews that part of the code, it knows what to look for. greptile catches 12.2% more bugs with knowledge bases enabled! now available to all users :)

Daksh Gupta

30,701 次观看 • 19 天前

Exploring Andrej Karpathy Nanochat through a Knowledge Graph Love how this project combines simplicity with power. Want to navigate and understand the entire repo? Check it out here: Plus, you can connect it via MCP to any AI coding assistant and work directly on the repository. Powered by Deep Graph MCP AI at Meta and GroqInc

Exploring Andrej Karpathy Nanochat through a Knowledge Graph Love how this project combines simplicity with power. Want to navigate and understand the entire repo? Check it out here: Plus, you can connect it via MCP to any AI coding assistant and work directly on the repository. Powered by Deep Graph MCP AI at Meta and GroqInc

Daniel San

126,715 次观看 • 11 个月前

I added a Knowledge Graph to Cursor using MCP. You gotta see this working! Knowledge graphs are a game-changer for AI Agents, and this is one example of how you can take advantage of them. How this works: 1. Cursor connects to Graphiti's MCP Server. Graphiti is a very popular open-source Knowledge Graph library for AI agents. 2. Graphiti connects to Neo4j running locally. Now, every time I interact with Cursor, the information is synthesized and stored in the knowledge graph. In short, Cursor now "remembers" everything about our project. Huge! Here is the video I recorded.

I added a Knowledge Graph to Cursor using MCP. You gotta see this working! Knowledge graphs are a game-changer for AI Agents, and this is one example of how you can take advantage of them. How this works: 1. Cursor connects to Graphiti's MCP Server. Graphiti is a very popular open-source Knowledge Graph library for AI agents. 2. Graphiti connects to Neo4j running locally. Now, every time I interact with Cursor, the information is synthesized and stored in the knowledge graph. In short, Cursor now "remembers" everything about our project. Huge! Here is the video I recorded.

Santiago

155,647 次观看 • 1 年前

Introducing Deep Graph Codebase to MCP with One Click... Now Available for Gemini CLI too! 🤩 Transform any public GitHub repository into an AI-ready Code Graph by simply changing the URL from to This instantly prepares the repository for seamless integration with AI assistants. It's that easy! You can now use repositories like ollama as a code graph within any project using Gemini CLI... just add the MCP Server JSON configuration to your .gemini/settings.json file

Introducing Deep Graph Codebase to MCP with One Click... Now Available for Gemini CLI too! 🤩 Transform any public GitHub repository into an AI-ready Code Graph by simply changing the URL from to This instantly prepares the repository for seamless integration with AI assistants. It's that easy! You can now use repositories like ollama as a code graph within any project using Gemini CLI... just add the MCP Server JSON configuration to your .gemini/settings.json file

Daniel San

53,516 次观看 • 1 年前

Turn any GitHub repository into LLM-ready text! Simply replace "hub" with "ingest" in a GitHub URL and receive a prompt-friendly text ingest for LLMs. Gitingest is 100% open-source and provides: - Directory structure - A brief summary of the project - The entire content as LLM-ready text Plus, it comes with a nice python package and you can run the UI locally! Stay tuned, I'm working on something really cool with this!✨ Link to the GitHub repo in the next tweet! _____ Find me → Akshay 🚀 ✔️ For more insights and tutorials on ML and AI Engineering!

Turn any GitHub repository into LLM-ready text! Simply replace "hub" with "ingest" in a GitHub URL and receive a prompt-friendly text ingest for LLMs. Gitingest is 100% open-source and provides: - Directory structure - A brief summary of the project - The entire content as LLM-ready text Plus, it comes with a nice python package and you can run the UI locally! Stay tuned, I'm working on something really cool with this!✨ Link to the GitHub repo in the next tweet! _____ Find me → Akshay 🚀 ✔️ For more insights and tutorials on ML and AI Engineering!

Akshay 🚀

191,449 次观看 • 1 年前

Use Copilot for free and completely privately with the DeepSeek models in VSCode Follow these 5 steps: - Download the CodeGPT extension for VSCode: - Download @Ollama_ai: - Once installed, run the following command in the terminal: "ollama run deepseek-coder" - In CodeGPT settings in VSCode, select Ollama as the provider and the model as deepseek-coder. - All set! ✅ Start coding with these powerful expert models in mathematics and programming. In this new version of CodeGPT, the interface has been improved, and the following models have been added: ✨ codellama:13b ✨ zephyr ✨ deepseek-coder ✨ deepseek-coder:33b Open-source models are gradually taking their rightful place, and the best part is... they run on CPUs 🤩

Use Copilot for free and completely privately with the DeepSeek models in VSCode Follow these 5 steps: - Download the CodeGPT extension for VSCode: - Download @Ollama_ai: - Once installed, run the following command in the terminal: "ollama run deepseek-coder" - In CodeGPT settings in VSCode, select Ollama as the provider and the model as deepseek-coder. - All set! ✅ Start coding with these powerful expert models in mathematics and programming. In this new version of CodeGPT, the interface has been improved, and the following models have been added: ✨ codellama:13b ✨ zephyr ✨ deepseek-coder ✨ deepseek-coder:33b Open-source models are gradually taking their rightful place, and the best part is... they run on CPUs 🤩

Daniel San

190,584 次观看 • 2 年前

$Did these Orthodox monks know something we don't? Religious symbology or fractal antennas? Domed resonators, the use of MERCURY and knowledge of the AETHER... that provided clean ENERGY for this realm. Who built these structures with this knowledge in ancient times? Were these MONKS the original CUSTODIANS? The buildings 'change of use' and the symbols coming to represent something else?$

Did these Orthodox monks know something we don't? Religious symbology or fractal antennas? Domed resonators, the use of MERCURY and knowledge of the AETHER... that provided clean ENERGY for this realm. Who built these structures with this knowledge in ancient times? Were these MONKS the original CUSTODIANS? The buildings 'change of use' and the symbols coming to represent something else?

WhatdoIknow

109,394 次观看 • 2 年前

This is one of the coolest open-source AI agent projects I've seen in a while: 'Understand Anything' It's a plugin for Claude Code, Codex, OpenCode etc. that analyzes your codebase and turns it into a knowledge base that you can interact with. It explains the codebase to you, rather than showing you the structure. It seems like it's designed for code but I opened my Obsidian vault of podcast highlights in Claude Code, then ran /understand. The result is a knowledge graph that I can search of highlights from 888 podcast episodes and 144K lines of markdown text.

This is one of the coolest open-source AI agent projects I've seen in a while: 'Understand Anything' It's a plugin for Claude Code, Codex, OpenCode etc. that analyzes your codebase and turns it into a knowledge base that you can interact with. It explains the codebase to you, rather than showing you the structure. It seems like it's designed for code but I opened my Obsidian vault of podcast highlights in Claude Code, then ran /understand. The result is a knowledge graph that I can search of highlights from 888 podcast episodes and 144K lines of markdown text.

Dan McAteer

174,887 次观看 • 2 个月前

Important new course: Agent Skills with Anthropic, built with Anthropic and taught by Elie Schoppik! Skills are constructed as folders of instructions that equip agents with on-demand knowledge and workflows. This short course teaches you how to create them following best practices. Because skills follow an open standard format, you can build them once and deploy across any skills-compatible agent, like Claude Code. What you'll learn: - Create custom skills for code generation and review, data analysis, and research - Build complex workflows using Anthropic's pre-built skills (Excel, PowerPoint, skill creation) and custom skills - Combine skills with MCP and subagents to create agentic systems with specialized knowledge - Deploy the same skills across Claude Code, the Claude API, and the Claude Agent SDK Join and learn to equip agents with the specialized knowledge they need for reliable, repeatable workflows.

Important new course: Agent Skills with Anthropic, built with Anthropic and taught by Elie Schoppik! Skills are constructed as folders of instructions that equip agents with on-demand knowledge and workflows. This short course teaches you how to create them following best practices. Because skills follow an open standard format, you can build them once and deploy across any skills-compatible agent, like Claude Code. What you'll learn: - Create custom skills for code generation and review, data analysis, and research - Build complex workflows using Anthropic's pre-built skills (Excel, PowerPoint, skill creation) and custom skills - Combine skills with MCP and subagents to create agentic systems with specialized knowledge - Deploy the same skills across Claude Code, the Claude API, and the Claude Agent SDK Join and learn to equip agents with the specialized knowledge they need for reliable, repeatable workflows.

Andrew Ng

889,989 次观看 • 7 个月前

ELON: WE’RE BUILDING A MODERN LIBRARY OF ALEXANDRIA ACROSS EARTH, THE MOON, AND MARS “The idea behind Galactica is to create an open source repository of all knowledge, a distillation of all knowledge, meaning anyone can access it, anyone can use it, and if other people want to train on it, they can do so. We want to create copies of this and distribute them throughout Earth, and even put them on the Moon, Mars, and out into deep space. In a way, it’s a modern day Library of Alexandria. It was a great tragedy that the Library of Alexandria burned down, and so to preserve this knowledge, we want to literally etch it in stone and distribute it widely." Source: Source: Baron Capital

ELON: WE’RE BUILDING A MODERN LIBRARY OF ALEXANDRIA ACROSS EARTH, THE MOON, AND MARS “The idea behind Galactica is to create an open source repository of all knowledge, a distillation of all knowledge, meaning anyone can access it, anyone can use it, and if other people want to train on it, they can do so. We want to create copies of this and distribute them throughout Earth, and even put them on the Moon, Mars, and out into deep space. In a way, it’s a modern day Library of Alexandria. It was a great tragedy that the Library of Alexandria burned down, and so to preserve this knowledge, we want to literally etch it in stone and distribute it widely." Source: Source: Baron Capital

Mario Nawfal

90,633 次观看 • 10 个月前

Build better RAG by letting a team of agents extract and connect your reference materials into a knowledge graph. Our new short course, “Agentic Knowledge Graph Construction,” taught by Neo4j Innovation Lead Andreas Kollegger, shows you how. Knowledge graphs are an important way to store information accurately but they are a lot of work to build manually. In this course you’ll learn how to build a team of agents that turn data– in this case product reviews and invoices from suppliers–into structured graphs of entities and relationships for RAG. Learn how agents can automatically handle the time-consuming work of building graphs — extracting entities and relationships (e.g., Product "contains" Assembly, Part "supplied_by" Supplier, Customer review "mentions" Product), deduplicating them, fact-checking them, and committing them to a graph database — so your retrieval system can find right information to generate accurate output. For example, you can use agents to help trace customer complaints directly to specific suppliers, manufacturing processes, and product hierarchies, thus turning fragmented information into queryable business intelligence. Skills you’ll gain: - Build, store, and access knowledge graphs using the Neo4j graph database - Build multi-agent systems using Google’s Agent Development Kit (ADK) - Set up a loop of agentic workflows to propose and refine a graph schema through fact-checking - Connect agent-generated graphs of unstructured and structured data into a unified knowledge graph This course gets into the practicum of why knowledge graphs give more accurate information retrieval than vector search alone, especially for high-stakes applications where precision matters more than fuzzy similarity matching. Sign up here:

Build better RAG by letting a team of agents extract and connect your reference materials into a knowledge graph. Our new short course, “Agentic Knowledge Graph Construction,” taught by Neo4j Innovation Lead Andreas Kollegger, shows you how. Knowledge graphs are an important way to store information accurately but they are a lot of work to build manually. In this course you’ll learn how to build a team of agents that turn data– in this case product reviews and invoices from suppliers–into structured graphs of entities and relationships for RAG. Learn how agents can automatically handle the time-consuming work of building graphs — extracting entities and relationships (e.g., Product "contains" Assembly, Part "supplied_by" Supplier, Customer review "mentions" Product), deduplicating them, fact-checking them, and committing them to a graph database — so your retrieval system can find right information to generate accurate output. For example, you can use agents to help trace customer complaints directly to specific suppliers, manufacturing processes, and product hierarchies, thus turning fragmented information into queryable business intelligence. Skills you’ll gain: - Build, store, and access knowledge graphs using the Neo4j graph database - Build multi-agent systems using Google’s Agent Development Kit (ADK) - Set up a loop of agentic workflows to propose and refine a graph schema through fact-checking - Connect agent-generated graphs of unstructured and structured data into a unified knowledge graph This course gets into the practicum of why knowledge graphs give more accurate information retrieval than vector search alone, especially for high-stakes applications where precision matters more than fuzzy similarity matching. Sign up here:

Andrew Ng

168,153 次观看 • 1 年前

Use this FREE tool to analyze data in 10 sec. This tool is used by more than 500,000 researchers. 1. Go to 2. Click on Data Analysis and upload your data file. 3. Write your prompt for data analysis. 4. For example, generate graphs from data. 5. AnswerThis will generate variety of graphs 6. It also generates an insightful data analysis report. You can download the graphs and the report. Try AnswerThis today. It’s FREE.

Use this FREE tool to analyze data in 10 sec. This tool is used by more than 500,000 researchers. 1. Go to 2. Click on Data Analysis and upload your data file. 3. Write your prompt for data analysis. 4. For example, generate graphs from data. 5. AnswerThis will generate variety of graphs 6. It also generates an insightful data analysis report. You can download the graphs and the report. Try AnswerThis today. It’s FREE.

Faheem Ullah

26,483 次观看 • 8 个月前