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Ashpreet Bedi

@ashpreetbedi • 19,160 subscribers

founder @agnoagi • prev @airbnb @facebook

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Anya Rossi

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Agents with gpt-4o from scratch 🔥 in 11 minutes we'll build: 🌎 Web Search Agent (2:40) 📈 Finance Agent (3:30) 🫡 Hackernews Agent (5:50) 📊 Data Analysis Agent (8:10) 🗒️ Research Agent (9:35) code:

Agents with gpt-4o from scratch 🔥 in 11 minutes we'll build: 🌎 Web Search Agent (2:40) 📈 Finance Agent (3:30) 🫡 Hackernews Agent (5:50) 📊 Data Analysis Agent (8:10) 🗒️ Research Agent (9:35) code:

697,070 次观看 • 2 年前

Generate dashboards on-demand using agents Agents can now generate beautiful, just-in-time dashboards. Dynamic software is the future

Generate dashboards on-demand using agents Agents can now generate beautiful, just-in-time dashboards. Dynamic software is the future

28,188 次观看 • 1 个月前

Phidata is now Agno. Announcing our new brand identity and the fastest way to build multi-modal Agents.

202,494 次观看 • 1 年前

Building the LLM OS by Andrej Karpathy with gpt-4o The speed + quality is 🔥🤯 code:

Building the LLM OS by Andrej Karpathy with gpt-4o The speed + quality is 🔥🤯 code:

251,546 次观看 • 2 年前

Prompt In → Video Out Introducing Vibe-Video: a multi-agent team that turns curiosity into motion-graphics videos. Built with HeyGen Hyperframes and Opus 4.7 Researches topics via Parallel Web Systems, explains GitHub repos, renders the result. Free and open-source ↓

Prompt In → Video Out Introducing Vibe-Video: a multi-agent team that turns curiosity into motion-graphics videos. Built with HeyGen Hyperframes and Opus 4.7 Researches topics via Parallel Web Systems, explains GitHub repos, renders the result. Free and open-source ↓

24,696 次观看 • 1 个月前

This was a great read. So I started building an open-source data agent with 6 layers of context: 1. Table usage 2. Human annotations 3. Query patterns 4. Institutional knowledge 5. Memory 6. Runtime context Early version if you want to follow along:

This was a great read. So I started building an open-source data agent with 6 layers of context: 1. Table usage 2. Human annotations 3. Query patterns 4. Institutional knowledge 5. Memory 6. Runtime context Early version if you want to follow along:

51,251 次观看 • 4 个月前

Lets build `Auto-RAG` where we let the LLM pull the data it needs from different sources. 🔎 The user asks a question. 🤔 LLM decides whether to search its knowledge, memory, internet or make an API call. ✍️ LLM answers with the context. Code:

Lets build `Auto-RAG` where we let the LLM pull the data it needs from different sources. 🔎 The user asks a question. 🤔 LLM decides whether to search its knowledge, memory, internet or make an API call. ✍️ LLM answers with the context. Code:

174,562 次观看 • 2 年前

🌶️ Hot take: The only way Autonomous Multi-Agent Systems work is by adding Reasoning & Agentic Context. I've tried it all, and here are my learnings👇

🌶️ Hot take: The only way Autonomous Multi-Agent Systems work is by adding Reasoning & Agentic Context. I've tried it all, and here are my learnings👇

91,445 次观看 • 1 年前

🚀 Say hello to the new & improved phidata 🚀 Build, ship, and monitor Agents with blazing-fast memory, knowledge, tools & reasoning 🔥 ⚡️ 70% faster memory & knowledge 🛠 100+ tools 🧠 Reasoning Agents 🤝 Multi-agent collaboration 📊 Built-in monitoring and Agent UI

🚀 Say hello to the new & improved phidata 🚀 Build, ship, and monitor Agents with blazing-fast memory, knowledge, tools & reasoning 🔥 ⚡️ 70% faster memory & knowledge 🛠 100+ tools 🧠 Reasoning Agents 🤝 Multi-agent collaboration 📊 Built-in monitoring and Agent UI

114,527 次观看 • 1 年前

🚀 Introducing the first-ever Agent UI 🚀 This is hands-down my favorite product! Chat with local Agents tailored to my needs. Local memory, storage, knowledge and tools 🔥 ⚡️ Your data, your control 🧠 Compatible with any LLM 🤝 Run multiple agents or a team of agents

🚀 Introducing the first-ever Agent UI 🚀 This is hands-down my favorite product! Chat with local Agents tailored to my needs. Local memory, storage, knowledge and tools 🔥 ⚡️ Your data, your control 🧠 Compatible with any LLM 🤝 Run multiple agents or a team of agents

68,970 次观看 • 1 年前

Build Multi Agent Systems with Reasoning and Context Thanks to sonnet-4, we have level 4 autonomous multi-agent systems working. Learn how to add: -> Reasoning Tools (think -> analyze) -> Shared Agentic Context -> Agentic Memory Code below 👇

Build Multi Agent Systems with Reasoning and Context Thanks to sonnet-4, we have level 4 autonomous multi-agent systems working. Learn how to add: -> Reasoning Tools (think -> analyze) -> Shared Agentic Context -> Agentic Memory Code below 👇

38,576 次观看 • 1 年前

Super impressed with the new Ollama updates! Adjusting my rating from 30-40% to a solid 80-90% -- thats how good it is now for building Agents 🙌 Exceptional work by the ollama team, not sure how they got this perf from 8b models 🔥

Super impressed with the new Ollama updates! Adjusting my rating from 30-40% to a solid 80-90% -- thats how good it is now for building Agents 🙌 Exceptional work by the ollama team, not sure how they got this perf from 8b models 🔥

39,270 次观看 • 1 年前

Introducing `Personalized Agentic RAG` - where the LLM remembers key details about the user and automatically chooses the tool for RAG. We'll build: 👏 ChatGPT like memory ⚙️ Function calling 🧙 Multi-Agent orchestration code:

Introducing `Personalized Agentic RAG` - where the LLM remembers key details about the user and automatically chooses the tool for RAG. We'll build: 👏 ChatGPT like memory ⚙️ Function calling 🧙 Multi-Agent orchestration code:

45,035 次观看 • 2 年前

While others fight, we ship 🚀 Agno’s ReasoningTools are now live on the Playground — giving non-reasoning models the ability to "think", "analyze", and outperform Reasoning Models on Agentic workloads. If you haven't tried `ReasoningTools` yet - you definitely should 👇

While others fight, we ship 🚀 Agno’s ReasoningTools are now live on the Playground — giving non-reasoning models the ability to "think", "analyze", and outperform Reasoning Models on Agentic workloads. If you haven't tried `ReasoningTools` yet - you definitely should 👇

27,864 次观看 • 1 年前

Introducing SQrL - a Text2SQL Reasoning Agent built with GPT4.1 It “thinks” before running SQL and "analyzes" the results of its queries to deliver the best possible answer to your questions. The video is 1x speed. Give it a try and let me know if you like it. Code below 👇

Introducing SQrL - a Text2SQL Reasoning Agent built with GPT4.1 It “thinks” before running SQL and "analyzes" the results of its queries to deliver the best possible answer to your questions. The video is 1x speed. Give it a try and let me know if you like it. Code below 👇

22,913 次观看 • 1 年前

Today we’re launching monitoring and observability support for Agno via OpenInference (compatible with OpenTelemetry). You can now use your favorite tracing and eval tools like arize phoenix, langsmith, langfuse, langtrace and weave directly with Agno Here are some examples with code 👇

Today we’re launching monitoring and observability support for Agno via OpenInference (compatible with OpenTelemetry). You can now use your favorite tracing and eval tools like arize phoenix, langsmith, langfuse, langtrace and weave directly with Agno Here are some examples with code 👇

13,804 次观看 • 1 年前

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