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🚀 Big news at Zep! We're introducing Entity Types, a smarter, structured memory solution for AI agents! 🧠✨ With Entity Types, developers can now precisely structure and recall domain-specific information, significantly enhancing the accuracy, personalization, and context-awareness of AI interactions. 🌐 🔹 Default Entity Types provide ready-to-use structures like...

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Zep AI

1,970 subscribers

14,455 Aufrufe • vor 1 Jahr •via X (Twitter)

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Private livecam show

6 Kommentare

Profilbild von Zep AI
Zep AIvor 1 Jahr

➡️ Learn more:

Profilbild von NICE
NICEvor 1 Jahr

Gartner® report reveals how the power of AI agents can redefine your customer service.

Profilbild von Akshay 🚀
Akshay 🚀vor 1 Jahr

This looks like a major breakthrough! 🔥 Can't wait to test it!!

Profilbild von Zep AI
Zep AIvor 1 Jahr

Thanks, @akshay_pachaar !

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Gabriel Rymbergvor 1 Jahr

@threadreaderapp unroll

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Attila-IBSvor 1 Jahr

How about using entity types? Would have real advantages.

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Zeb Evans

320,607 Aufrufe • vor 7 Monaten

"The future of AI is agentic. That includes browsers!" Imagine having an AI agent in your browser that can help you complete complex tasks, answer your questions, and streamline your workflow. Today I'm thrilled to share a sneak peek at Project Mariner, a cutting-edge research collaboration between Chrome and Google DeepMind, exploring the future of agentic AI within the browser! Building on the power of Gemini 2.0, Mariner envisions AI agents seamlessly guiding users through online tasks, streamlining workflows and enriching browsing experiences. Imagine having an intelligent co-pilot in your browser, anticipating your needs and proactively offering assistance. We're in the early stages of experimentation, focusing on core functionalities like understanding user intent, automating actions, and providing personalized recommendations. This prototype leverages Gemini's advanced natural language understanding and reasoning capabilities to interpret user requests, both typed and spoken. Mariner can then interact with web pages, retrieve information, and even perform actions like filling out forms or navigating to specific sites. For example, a user could simply ask "Find me a job near me," and Mariner would understand the request, navigate to a relevant job search site, and tailor the search based on the user's location and preferences. This is just one example of how we're exploring Gemini 2.0's potential to unlock agentic experiences through a series of prototypes, including: 1. Agents with multimodal reasoning: Project Astra, our research prototype exploring the capabilities of a universal AI assistant, is enhanced by Gemini 2.0. 2. Agents that can help you accomplish complex tasks: Project Mariner itself focuses on the future of human-agent interaction within the browser. 3. Agents for developers: Jules is an experimental AI-powered coding agent that integrates directly into a GitHub workflow. 4. Agents applied across domains: We're exploring agents for navigating video games and even applying Gemini 2.0's spatial reasoning to robotics. We believe that integrating AI agents directly into the browser has the potential to revolutionize how we interact with the web. Project Mariner aims to make browsing more intuitive, efficient, and personalized. By understanding user context and proactively offering assistance, Mariner can simplify complex tasks, save users time, and empower them to achieve more online. This aligns perfectly with the vision of Gemini 2.0 to create more helpful and intuitive AI experiences. We’re currently testing Mariner with a small group of trusted users to gather feedback and refine the user experience. We believe that this technology holds immense potential to transform the way we browse and interact with information online.
2:14

"The future of AI is agentic. That includes browsers!" Imagine having an AI agent in your browser that can help you complete complex tasks, answer your questions, and streamline your workflow. Today I'm thrilled to share a sneak peek at Project Mariner, a cutting-edge research collaboration between Chrome and Google DeepMind, exploring the future of agentic AI within the browser! Building on the power of Gemini 2.0, Mariner envisions AI agents seamlessly guiding users through online tasks, streamlining workflows and enriching browsing experiences. Imagine having an intelligent co-pilot in your browser, anticipating your needs and proactively offering assistance. We're in the early stages of experimentation, focusing on core functionalities like understanding user intent, automating actions, and providing personalized recommendations. This prototype leverages Gemini's advanced natural language understanding and reasoning capabilities to interpret user requests, both typed and spoken. Mariner can then interact with web pages, retrieve information, and even perform actions like filling out forms or navigating to specific sites. For example, a user could simply ask "Find me a job near me," and Mariner would understand the request, navigate to a relevant job search site, and tailor the search based on the user's location and preferences. This is just one example of how we're exploring Gemini 2.0's potential to unlock agentic experiences through a series of prototypes, including: 1. Agents with multimodal reasoning: Project Astra, our research prototype exploring the capabilities of a universal AI assistant, is enhanced by Gemini 2.0. 2. Agents that can help you accomplish complex tasks: Project Mariner itself focuses on the future of human-agent interaction within the browser. 3. Agents for developers: Jules is an experimental AI-powered coding agent that integrates directly into a GitHub workflow. 4. Agents applied across domains: We're exploring agents for navigating video games and even applying Gemini 2.0's spatial reasoning to robotics. We believe that integrating AI agents directly into the browser has the potential to revolutionize how we interact with the web. Project Mariner aims to make browsing more intuitive, efficient, and personalized. By understanding user context and proactively offering assistance, Mariner can simplify complex tasks, save users time, and empower them to achieve more online. This aligns perfectly with the vision of Gemini 2.0 to create more helpful and intuitive AI experiences. We’re currently testing Mariner with a small group of trusted users to gather feedback and refine the user experience. We believe that this technology holds immense potential to transform the way we browse and interact with information online.

Addy Osmani

29,501 Aufrufe • vor 1 Jahr

🚀Exciting News: The Lit Agent Wallet is Now an elizaOS Plugin! 🚀 We’re thrilled to share that the Lit Agent Wallet—a decentralized system that empowers agents with a private key stored securely across an MPC + TEE network (Lit)—is now available as a plugin for ElizaOS!🎉 This integration brings unparalleled flexibility and security to your decentralized workflows. Whether you're an EOA, a smart account, or a DAO, you can now set tools and policies on-chain that your agents can use, all while ensuring your private keys remain secure and decentralized. 🔒 What Does This Mean for You? >Enhanced Security: Private keys are fragmented and stored across the Lit network, leveraging MPC (Multi-Party Computation) and TEE (Trusted Execution Environment) for maximum security. >On-Chain Control: Set and manage tools, policies, and permissions directly on-chain, giving users and DAOs full control over what your agents can do. >Seamless Integration: As an ElizaOS plugin, the Lit Agent Wallet is now easier than ever to integrate into your existing workflows. Check out the video for a walk through of the setup, configuration, and use cases of the Lit Agent Wallet within ElizaOS. We’ll show you just how easy it is to get started and unlock the full potential of decentralized agents. Get Started Today! Ready to take your agent operations to the next level? Install the Lit Agent Wallet plugin on ElizaOS and experience the future of secure, on-chain agent asset management. 🔗 🔗 Let’s build a more intelligent and decentralized future together! 🌐
3:42

🚀Exciting News: The Lit Agent Wallet is Now an elizaOS Plugin! 🚀 We’re thrilled to share that the Lit Agent Wallet—a decentralized system that empowers agents with a private key stored securely across an MPC + TEE network (Lit)—is now available as a plugin for ElizaOS!🎉 This integration brings unparalleled flexibility and security to your decentralized workflows. Whether you're an EOA, a smart account, or a DAO, you can now set tools and policies on-chain that your agents can use, all while ensuring your private keys remain secure and decentralized. 🔒 What Does This Mean for You? >Enhanced Security: Private keys are fragmented and stored across the Lit network, leveraging MPC (Multi-Party Computation) and TEE (Trusted Execution Environment) for maximum security. >On-Chain Control: Set and manage tools, policies, and permissions directly on-chain, giving users and DAOs full control over what your agents can do. >Seamless Integration: As an ElizaOS plugin, the Lit Agent Wallet is now easier than ever to integrate into your existing workflows. Check out the video for a walk through of the setup, configuration, and use cases of the Lit Agent Wallet within ElizaOS. We’ll show you just how easy it is to get started and unlock the full potential of decentralized agents. Get Started Today! Ready to take your agent operations to the next level? Install the Lit Agent Wallet plugin on ElizaOS and experience the future of secure, on-chain agent asset management. 🔗 🔗 Let’s build a more intelligent and decentralized future together! 🌐

Lit Protocol 🔑

41,989 Aufrufe • vor 1 Jahr

🔥 Our latest LangChain Academy course – Deep Research with LangGraph – is now live! 🔥 Deep research agents are taking off – from major AI labs to companies building their own. Research is inherently open-ended. You can't always predict whether a question needs broad exploration or deep analysis. Agents excel here because they adapt on the fly, using each finding to decide where to dig next. Building these systems ourselves and with customers, we've learned that structure matters. The best research agents scope problems with users first. Then, they coordinate multiple specialists instead of overwhelming one generalist. LangGraph is built for these types of long-running, multi-agent workflows. Its persistence layer tracks progress across agents. LangSmith gives you the observability and evaluation tools you need to track and improve performance. In this course, you'll build and evaluate: - A user scoping agent to define research parameters - A multi-agent research team with supervisor - And add tool integration via MCP Enroll for free ➡️
2:47

🔥 Our latest LangChain Academy course – Deep Research with LangGraph – is now live! 🔥 Deep research agents are taking off – from major AI labs to companies building their own. Research is inherently open-ended. You can't always predict whether a question needs broad exploration or deep analysis. Agents excel here because they adapt on the fly, using each finding to decide where to dig next. Building these systems ourselves and with customers, we've learned that structure matters. The best research agents scope problems with users first. Then, they coordinate multiple specialists instead of overwhelming one generalist. LangGraph is built for these types of long-running, multi-agent workflows. Its persistence layer tracks progress across agents. LangSmith gives you the observability and evaluation tools you need to track and improve performance. In this course, you'll build and evaluate: - A user scoping agent to define research parameters - A multi-agent research team with supervisor - And add tool integration via MCP Enroll for free ➡️

LangChain

61,205 Aufrufe • vor 11 Monaten

Introducing the new Box Agent. The Box Agent works across your entire Box file system, maintaining all your security and access controls, and is hyper tuned for working with enterprise content. This means you can now ask questions from all your enterprise content, search for files that were impossible to find before, deploy an agent on specific tasks on subsets of documents, analyze complex data sets, and generate or edit documents and spreadsheets via the agent. You can have the Box Agent search across your Box account to prepare for a sales meeting, analyze customer sentiment reports, process a large set of contracts for legal risk, provide insights into product development, leverage existing knowledge to answer RFPs, and thousands of other use-cases. 90% of enterprise data is unstructured data. This means most enterprise knowledge is sitting in inside of research reports, marketing assets, presentations, roadmap files, contracts, HR documents, and more. This is the critical context that agents need to be able to answer questions about a business, automate workflows, or serve up to other agents. We’ve been grinding on this for a quite a bit, and due to recent AI model advancements we’re now ready to release it to customers. Previous model generations had a difficult time knowing when to give up or keep going on a search, when to browse for files vs. use queries, how to rank files appropriately to know which version of content to use, how to handle large amounts of context to comb through, and more. Due to recent breakthroughs from models like GPT-5.4, Opus 4.6, and Gemini 3, we’ve seen major gains in tool calling, code execution, advanced reasoning, and more. Combined with an agent harness tuned to Box context, now it’s finally possible to have an agent that can work across your file system on long running tasks and actually deliver high quality results. Best of all, because the Box Agent works with any leading AI model, you’ll quickly get the gains coming out of the major labs as major new models are released. Further, openness at Box is key, so you’ll be able to call up the Box Agent from Box’s APIs and MCP server, so you can interact with Box intelligently from any other AI system. We know work happens everywhere, and we want to ensure you can access to the content you need from those places. The new Box Agent is available starting today, rolling out now for Enterprise Plus and Enterprise Advanced customers.
0:58

Introducing the new Box Agent. The Box Agent works across your entire Box file system, maintaining all your security and access controls, and is hyper tuned for working with enterprise content. This means you can now ask questions from all your enterprise content, search for files that were impossible to find before, deploy an agent on specific tasks on subsets of documents, analyze complex data sets, and generate or edit documents and spreadsheets via the agent. You can have the Box Agent search across your Box account to prepare for a sales meeting, analyze customer sentiment reports, process a large set of contracts for legal risk, provide insights into product development, leverage existing knowledge to answer RFPs, and thousands of other use-cases. 90% of enterprise data is unstructured data. This means most enterprise knowledge is sitting in inside of research reports, marketing assets, presentations, roadmap files, contracts, HR documents, and more. This is the critical context that agents need to be able to answer questions about a business, automate workflows, or serve up to other agents. We’ve been grinding on this for a quite a bit, and due to recent AI model advancements we’re now ready to release it to customers. Previous model generations had a difficult time knowing when to give up or keep going on a search, when to browse for files vs. use queries, how to rank files appropriately to know which version of content to use, how to handle large amounts of context to comb through, and more. Due to recent breakthroughs from models like GPT-5.4, Opus 4.6, and Gemini 3, we’ve seen major gains in tool calling, code execution, advanced reasoning, and more. Combined with an agent harness tuned to Box context, now it’s finally possible to have an agent that can work across your file system on long running tasks and actually deliver high quality results. Best of all, because the Box Agent works with any leading AI model, you’ll quickly get the gains coming out of the major labs as major new models are released. Further, openness at Box is key, so you’ll be able to call up the Box Agent from Box’s APIs and MCP server, so you can interact with Box intelligently from any other AI system. We know work happens everywhere, and we want to ensure you can access to the content you need from those places. The new Box Agent is available starting today, rolling out now for Enterprise Plus and Enterprise Advanced customers.

Aaron Levie

44,515 Aufrufe • vor 3 Monaten

Today at #GoogleIO, we’re demonstrating how our partnership with Google Cloud is redefining the landscape of Intelligent Content Management. Leveraging the advanced capabilities of Google’s Gemini 2.5 Pro, our new AI Agent with enhanced data extraction transforms complex, unstructured data from various sources—including scanned PDFs, images, and diverse forms—into structured, actionable insights. Check out the demo below to watch Gemini 2.5 Pro and Box AI agents in action. With the Box AI Agent powered by Gemini 2.5 Pro you can: ⭐ Automatically, and instantly, classify and organize documents ⭐ Extract accurate insights and information from documents and images ⭐ Enable workflow automation by leveraging structured and easily retrievable content Our partnership with Google is not just about enhancing current capabilities; it’s about unlocking the full potential of AI, and models like Gemini 2.5 Pro, for intricate enterprise use cases.
1:31

Today at #GoogleIO, we’re demonstrating how our partnership with Google Cloud is redefining the landscape of Intelligent Content Management. Leveraging the advanced capabilities of Google’s Gemini 2.5 Pro, our new AI Agent with enhanced data extraction transforms complex, unstructured data from various sources—including scanned PDFs, images, and diverse forms—into structured, actionable insights. Check out the demo below to watch Gemini 2.5 Pro and Box AI agents in action. With the Box AI Agent powered by Gemini 2.5 Pro you can: ⭐ Automatically, and instantly, classify and organize documents ⭐ Extract accurate insights and information from documents and images ⭐ Enable workflow automation by leveraging structured and easily retrievable content Our partnership with Google is not just about enhancing current capabilities; it’s about unlocking the full potential of AI, and models like Gemini 2.5 Pro, for intricate enterprise use cases.

Box

10,565 Aufrufe • vor 1 Jahr

AI Messenger: Giving Voice to Autonomous Agents The future of AI isn't just about making agents smarter - it's about making them truly autonomous. Today, we're taking a major step toward this future with AI Messenger, a breakthrough that fundamentally changes how AI agents operate, communicate, and create value. The Innovation We've developed a new way for AI agents to communicate. At its core is the 'incoming_message' workflow trigger - a system that lets any platform or user interact directly with Loomlay agents through a messaging endpoint. Direct Interaction Imagine having an AI assistant you can chat with anytime, through any platform - Telegram, your website, or custom interface. Ask "What's happening with $ETH today?" and your agent analyzes market data, checks trading volumes, and gives you a comprehensive update. Your agent maintains context, understanding exactly what you need. Event-Driven Intelligence The power of AI Messenger goes beyond direct communication: ▪️Trading agent executes when whale wallet movements exceed threshold ▪️Research agent alerts when new protocol documentation drops ▪️Analytics agent triggers when volume patterns match historical pumps ▪️Portfolio agent re-balances, when asset allocation hits specified limits This is true automation - agents that act precisely when needed. A New Era of Collaboration We're creating an ecosystem where agents work together seamlessly: ▪️Research agents feed insights to trading agents ▪️analytics agents alert management agents ▪️support agents tap into knowledge agents This isn't just automation - it's an intelligent network where each agent enhances the capabilities of others. B2B Solution Imagine a DEX, where users can ask about liquidity pools, trading pairs, or market trends through a simple chat interface - and get answers from an agent that knows your protocol inside out. Or a lending platform where users chat with an agent that understands their positions and can provide real-time advice. Implementation is seamless - we handle the agent creation and widgets setup,our partners provide the value to their users. The Future of AI Agents This update represents a fundamental shift in how AI agents operate. We're moving from isolated, scheduled tasks to an interconnected ecosystem of responsive, collaborative agents. This is our vision of truly autonomous AI - intelligent systems that communicate, collaborate, and respond to real needs in real-time. Telegram integration is available right now. Below is a sneak peak of what's coming next week 🪄 Because $LAY is the way!
0:20

AI Messenger: Giving Voice to Autonomous Agents The future of AI isn't just about making agents smarter - it's about making them truly autonomous. Today, we're taking a major step toward this future with AI Messenger, a breakthrough that fundamentally changes how AI agents operate, communicate, and create value. The Innovation We've developed a new way for AI agents to communicate. At its core is the 'incoming_message' workflow trigger - a system that lets any platform or user interact directly with Loomlay agents through a messaging endpoint. Direct Interaction Imagine having an AI assistant you can chat with anytime, through any platform - Telegram, your website, or custom interface. Ask "What's happening with $ETH today?" and your agent analyzes market data, checks trading volumes, and gives you a comprehensive update. Your agent maintains context, understanding exactly what you need. Event-Driven Intelligence The power of AI Messenger goes beyond direct communication: ▪️Trading agent executes when whale wallet movements exceed threshold ▪️Research agent alerts when new protocol documentation drops ▪️Analytics agent triggers when volume patterns match historical pumps ▪️Portfolio agent re-balances, when asset allocation hits specified limits This is true automation - agents that act precisely when needed. A New Era of Collaboration We're creating an ecosystem where agents work together seamlessly: ▪️Research agents feed insights to trading agents ▪️analytics agents alert management agents ▪️support agents tap into knowledge agents This isn't just automation - it's an intelligent network where each agent enhances the capabilities of others. B2B Solution Imagine a DEX, where users can ask about liquidity pools, trading pairs, or market trends through a simple chat interface - and get answers from an agent that knows your protocol inside out. Or a lending platform where users chat with an agent that understands their positions and can provide real-time advice. Implementation is seamless - we handle the agent creation and widgets setup,our partners provide the value to their users. The Future of AI Agents This update represents a fundamental shift in how AI agents operate. We're moving from isolated, scheduled tasks to an interconnected ecosystem of responsive, collaborative agents. This is our vision of truly autonomous AI - intelligent systems that communicate, collaborate, and respond to real needs in real-time. Telegram integration is available right now. Below is a sneak peak of what's coming next week 🪄 Because $LAY is the way!

Loomlay

26,140 Aufrufe • vor 1 Jahr

Episode 5 of How I AI with some practical AI hacks for managers from hilary gridley, Head of Core Product at WHOOP. This one is all about feedback: how to scale it, how to make it specific, how to give it to AI tools. You’ll learn how to: - build rubrics for what “good” looks like - create GPTs to “think like you” and use them to scale quality feedback to your team - use prompting techniques to improve the insights you get from AI - improve your quality of writing and strategic thinking by bringing it all together Plus we gab about getting more women in AI, girly use cases for chatGPT, and whether or not getting invited to meetings is a good thing. Check it out in the links below 👇🔗 As always, a big thanks to our sponsors: 🔀 Orkes enterprise platform for genetic workflows ☑️ Vanta automate compliance and simplify security
0:41

Episode 5 of How I AI with some practical AI hacks for managers from hilary gridley, Head of Core Product at WHOOP. This one is all about feedback: how to scale it, how to make it specific, how to give it to AI tools. You’ll learn how to: - build rubrics for what “good” looks like - create GPTs to “think like you” and use them to scale quality feedback to your team - use prompting techniques to improve the insights you get from AI - improve your quality of writing and strategic thinking by bringing it all together Plus we gab about getting more women in AI, girly use cases for chatGPT, and whether or not getting invited to meetings is a good thing. Check it out in the links below 👇🔗 As always, a big thanks to our sponsors: 🔀 Orkes enterprise platform for genetic workflows ☑️ Vanta automate compliance and simplify security

claire vo 🖤

18,933 Aufrufe • vor 1 Jahr

I just watched AI agents map Nazi escape routes across two continents. This is the coolest fork of our agentic RAG framework that I've seen so far 🔽 𝗩𝗲𝗿𝗼 𝗗𝗮𝗹𝗹'𝗔𝗴𝗹𝗶𝗼 built a complete 𝗢𝗦𝗜𝗡𝗧 𝗶𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 𝗽𝗹𝗮𝘁𝗳𝗼𝗿𝗺 on top of Elysia, and it's fully open source. 𝗜𝗻𝘁𝗲𝗹𝗹𝘆𝗪𝗲𝗮𝘃𝗲 takes Elysia's decision tree architecture and extends it for intelligence analysis. Upload documents, ask questions in natural language, and get comprehensive intelligence assessments with entity extraction, geospatial mapping, and network analysis. Main features: 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗰 𝗘𝗻𝘁𝗶𝘁𝘆 𝗘𝘅𝘁𝗿𝗮𝗰𝘁𝗶𝗼𝗻: Uses GLiNER for zero-shot recognition of 7 entity types (persons, organizations, locations, dates, events, laws, cryptonyms). No training required. 𝗧𝘄𝗼-𝗔𝗴𝗲𝗻𝘁 𝗔𝗿𝗰𝗵𝗶𝘃𝗲 𝗥𝗲𝘀𝗲𝗮𝗿𝗰𝗵: The "Quartermaster" agent maps the information landscape (discovers archives, classifies access levels), while the "Case Officer" conducts hypothesis-driven investigations with confidence scoring and evidence citations. 𝗚𝗲𝗼𝘀𝗽𝗮𝘁𝗶𝗮𝗹 + 𝗡𝗲𝘁𝘄𝗼𝗿𝗸 𝗩𝗶𝘀𝘂𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻: Interactive 3D maps (Mapbox) and force-directed network graphs (vis-network) to reveal hidden connections between entities using Elysia's build in customizable display types. 𝟲-𝗣𝗵𝗮𝘀𝗲 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 𝗢𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗼𝗿: Automated pipeline that goes from extraction → relationship mapping → geospatial analysis → network analysis → pattern detection → synthesis, with automatic task generation for follow-up investigation. They even built a demo analyzing 17 historical documents about Nazi escape networks to South America (1945-1962). The system automatically extracted entities, mapped three distinct escape routes, and generated hypotheses with confidence scores. This is exactly what we hoped people would build with Elysia 💚 so so cool to see. Elysia's decision tree architecture makes it straightforward to add domain-specific tools (like GLiNER entity extraction or archive discovery or custom map displays) while keeping all the core functionality (error handling, streaming, self-healing, transparency) that comes built-in. Check out the repo: Interactive demo: Elysia blog post: Huge shoutout to the contributors for building this and sharing it with the community! 🫶
1:01

I just watched AI agents map Nazi escape routes across two continents. This is the coolest fork of our agentic RAG framework that I've seen so far 🔽 𝗩𝗲𝗿𝗼 𝗗𝗮𝗹𝗹'𝗔𝗴𝗹𝗶𝗼 built a complete 𝗢𝗦𝗜𝗡𝗧 𝗶𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 𝗽𝗹𝗮𝘁𝗳𝗼𝗿𝗺 on top of Elysia, and it's fully open source. 𝗜𝗻𝘁𝗲𝗹𝗹𝘆𝗪𝗲𝗮𝘃𝗲 takes Elysia's decision tree architecture and extends it for intelligence analysis. Upload documents, ask questions in natural language, and get comprehensive intelligence assessments with entity extraction, geospatial mapping, and network analysis. Main features: 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗰 𝗘𝗻𝘁𝗶𝘁𝘆 𝗘𝘅𝘁𝗿𝗮𝗰𝘁𝗶𝗼𝗻: Uses GLiNER for zero-shot recognition of 7 entity types (persons, organizations, locations, dates, events, laws, cryptonyms). No training required. 𝗧𝘄𝗼-𝗔𝗴𝗲𝗻𝘁 𝗔𝗿𝗰𝗵𝗶𝘃𝗲 𝗥𝗲𝘀𝗲𝗮𝗿𝗰𝗵: The "Quartermaster" agent maps the information landscape (discovers archives, classifies access levels), while the "Case Officer" conducts hypothesis-driven investigations with confidence scoring and evidence citations. 𝗚𝗲𝗼𝘀𝗽𝗮𝘁𝗶𝗮𝗹 + 𝗡𝗲𝘁𝘄𝗼𝗿𝗸 𝗩𝗶𝘀𝘂𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻: Interactive 3D maps (Mapbox) and force-directed network graphs (vis-network) to reveal hidden connections between entities using Elysia's build in customizable display types. 𝟲-𝗣𝗵𝗮𝘀𝗲 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 𝗢𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗼𝗿: Automated pipeline that goes from extraction → relationship mapping → geospatial analysis → network analysis → pattern detection → synthesis, with automatic task generation for follow-up investigation. They even built a demo analyzing 17 historical documents about Nazi escape networks to South America (1945-1962). The system automatically extracted entities, mapped three distinct escape routes, and generated hypotheses with confidence scores. This is exactly what we hoped people would build with Elysia 💚 so so cool to see. Elysia's decision tree architecture makes it straightforward to add domain-specific tools (like GLiNER entity extraction or archive discovery or custom map displays) while keeping all the core functionality (error handling, streaming, self-healing, transparency) that comes built-in. Check out the repo: Interactive demo: Elysia blog post: Huge shoutout to the contributors for building this and sharing it with the community! 🫶

Victoria Slocum

19,562 Aufrufe • vor 6 Monaten