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GPT-6 Astra makes this AI stack look f…cking illegal 10 GitHub repos for building everything around the model 01 LangGraph ▸ → stateful agent orchestration 02 CrewAI ▸ → multi-agent crews BUILD THE CORE 03 AutoGen ▸ → event-driven agent systems 04 PydanticAI ▸ → typed agents + structured...

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Lummox

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37,350 次观看 • 26 天前 •via X (Twitter)

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GitHub is becoming f...cking dangerous for AI agents 10 open-source repos that turn one powerful model into an entire autonomous system 01 LangGraph ▸ → stateful agent orchestration 02 PydanticAI ▸ → typed agents + structured outputs BUILD THE CONTROL LAYER 03 Mastra ▸ → agents + workflows + memory 04 Agno ▸ → multi-agent systems GIVE IT CONTEXT 05 Cognee ▸ → data → knowledge → agent memory 06 Graphiti ▸ → knowledge that changes over time GIVE IT A COMPUTER 07 Browser Use ▸ → AI agents that operate websites 08 E2B ▸ → isolated environments for execution KEEP IT ALIVE 09 Langfuse ▸ → traces + evals + observability 10 DeepEval ▸ → test agent behavior before production the architecture: model → context → planner → memory → tools → execution → eval → retry that's the shift happening on GitHub the best repos aren't making another chatbot they're building everything the model needs around it ⭣
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GitHub is becoming f...cking dangerous for AI agents 10 open-source repos that turn one powerful model into an entire autonomous system 01 LangGraph ▸ → stateful agent orchestration 02 PydanticAI ▸ → typed agents + structured outputs BUILD THE CONTROL LAYER 03 Mastra ▸ → agents + workflows + memory 04 Agno ▸ → multi-agent systems GIVE IT CONTEXT 05 Cognee ▸ → data → knowledge → agent memory 06 Graphiti ▸ → knowledge that changes over time GIVE IT A COMPUTER 07 Browser Use ▸ → AI agents that operate websites 08 E2B ▸ → isolated environments for execution KEEP IT ALIVE 09 Langfuse ▸ → traces + evals + observability 10 DeepEval ▸ → test agent behavior before production the architecture: model → context → planner → memory → tools → execution → eval → retry that's the shift happening on GitHub the best repos aren't making another chatbot they're building everything the model needs around it ⭣

Lummox

167,386 次观看 • 24 天前

this is pure f*cking treasure these 20 open-source AI agents have 900k+ combined GitHub stars and form a real agent stack: run the model -> orchestrate the crew -> act in the world -> remember, test and ship RUN & BUILD 01 ollama ▸ 02 langchain ▸ 03 open-interpreter ▸ 04 autogen ▸ 05 aider ▸ ORCHESTRATE 06 autogpt ▸ 07 metagpt ▸ 08 crewai ▸ 09 dspy ▸ 10 camel ▸ ACT & EXECUTE 11 flowise ▸ 12 continue ▸ 13 vercel-ai-sdk ▸ 14 e2b ▸ 15 composio ▸ MEMORY · EVAL · SHIP 16 privategpt ▸ 17 mem0 ▸ 18 agentops ▸ 19 agentbench ▸ 20 elevenlabs · deepgram ▸ ▸ the loop: idea -> local model -> agent crew -> tools -> sandbox -> memory -> evals -> ship save this to build your own business with the help of an AI employee
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this is pure f*cking treasure these 20 open-source AI agents have 900k+ combined GitHub stars and form a real agent stack: run the model -> orchestrate the crew -> act in the world -> remember, test and ship RUN & BUILD 01 ollama ▸ 02 langchain ▸ 03 open-interpreter ▸ 04 autogen ▸ 05 aider ▸ ORCHESTRATE 06 autogpt ▸ 07 metagpt ▸ 08 crewai ▸ 09 dspy ▸ 10 camel ▸ ACT & EXECUTE 11 flowise ▸ 12 continue ▸ 13 vercel-ai-sdk ▸ 14 e2b ▸ 15 composio ▸ MEMORY · EVAL · SHIP 16 privategpt ▸ 17 mem0 ▸ 18 agentops ▸ 19 agentbench ▸ 20 elevenlabs · deepgram ▸ ▸ the loop: idea -> local model -> agent crew -> tools -> sandbox -> memory -> evals -> ship save this to build your own business with the help of an AI employee

kiosa

30,906 次观看 • 1 个月前

this is pure f*cking treasure 20 skills for AI agents with 1.99M+ combined stars an agent is not a model. it is a model plus everything you bolt onto it. these are the bolts. five clusters, twenty repos, all open source, all free. ORCHESTRATION · many agents, one plan : > hermes-agent · 247k > AutoGPT · 187k · > MetaGPT · 70k · > autogen · 61k · > crewAI · 59k · RUNTIME / GRAPH · the loop underneath : > langchain · 147k · > deer-flow · 83k · > langgraph · 42k · > deepagents · 30k · > openai-agents-python · 25k · WEB / BROWSER · reach the open internet : > firecrawl · 181k · > browser-use · 115k · > gemini-cli · 107k · MEMORY / RAG · what it keeps between runs : > ragflow · 91k · > mem0 · 66k · > llama_index · 50k · SHIP IT · turn it into a product : > n8n · 188k · > dify · 140k · > TradingAgents · 107k · > adk-python · 22k · most people pick one framework and stop there. that is the mistake. the stack is orchestration on top, a graph runtime under it, browser and memory on the sides, and one deploy layer that makes it a product instead of a demo. every layer above is already written. you are assembling, not inventing. the gap is one weekend wide.
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this is pure f*cking treasure 20 skills for AI agents with 1.99M+ combined stars an agent is not a model. it is a model plus everything you bolt onto it. these are the bolts. five clusters, twenty repos, all open source, all free. ORCHESTRATION · many agents, one plan : > hermes-agent · 247k > AutoGPT · 187k · > MetaGPT · 70k · > autogen · 61k · > crewAI · 59k · RUNTIME / GRAPH · the loop underneath : > langchain · 147k · > deer-flow · 83k · > langgraph · 42k · > deepagents · 30k · > openai-agents-python · 25k · WEB / BROWSER · reach the open internet : > firecrawl · 181k · > browser-use · 115k · > gemini-cli · 107k · MEMORY / RAG · what it keeps between runs : > ragflow · 91k · > mem0 · 66k · > llama_index · 50k · SHIP IT · turn it into a product : > n8n · 188k · > dify · 140k · > TradingAgents · 107k · > adk-python · 22k · most people pick one framework and stop there. that is the mistake. the stack is orchestration on top, a graph runtime under it, browser and memory on the sides, and one deploy layer that makes it a product instead of a demo. every layer above is already written. you are assembling, not inventing. the gap is one weekend wide.

NO1ennn

21,527 次观看 • 1 个月前

whoever leaked this is f*cking crazy someone mapped 300+ AI agents into one GitHub repo - and basically leaked the shortlist of what’s actually worth trying. coding agents. browser agents. memory. voice. research. multi-agent teams. local models. sandboxes. evals. all sitting on one page. and once you start opening the categories, you realize how much stuff people are still building from scratch for absolutely no reason. want an AI that works inside your codebase? -> Aider / Continue want agents working together like a tiny company? -> CrewAI / AutoGen / MetaGPT want your agent to actually remember you tomorrow? -> Mem0 want it running code somewhere that isn’t your laptop? -> E2B want models running locally for $0/token? -> Ollama want to see what your agent actually did after it inevitably does something weird? -> AgentOps and that’s barely scratching it. there are 300+ projects across ~25 categories. the real cheat code isn’t downloading all of them. it’s opening the repo before you build anything and asking: “has someone already solved this?” because there’s a pretty good chance the answer is yes - and the open-source version already has thousands of people testing it for you. I went through the map and pulled out the agents I’d actually start with + my pick for every major part of the stack. full 300+ AI agent list below
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whoever leaked this is f*cking crazy someone mapped 300+ AI agents into one GitHub repo - and basically leaked the shortlist of what’s actually worth trying. coding agents. browser agents. memory. voice. research. multi-agent teams. local models. sandboxes. evals. all sitting on one page. and once you start opening the categories, you realize how much stuff people are still building from scratch for absolutely no reason. want an AI that works inside your codebase? -> Aider / Continue want agents working together like a tiny company? -> CrewAI / AutoGen / MetaGPT want your agent to actually remember you tomorrow? -> Mem0 want it running code somewhere that isn’t your laptop? -> E2B want models running locally for $0/token? -> Ollama want to see what your agent actually did after it inevitably does something weird? -> AgentOps and that’s barely scratching it. there are 300+ projects across ~25 categories. the real cheat code isn’t downloading all of them. it’s opening the repo before you build anything and asking: “has someone already solved this?” because there’s a pretty good chance the answer is yes - and the open-source version already has thousands of people testing it for you. I went through the map and pulled out the agents I’d actually start with + my pick for every major part of the stack. full 300+ AI agent list below

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RAG is not Memory. AI agents need long-term memory to maintain context and learn continuously. 5 frameworks for AI agent memory. 100% opensource. 1. Graphiti builds temporally-aware knowledge graphs for AI agents that change over time with evolving relationships and context.

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294,052 次观看 • 1 年前

How do you build AI agents that remember and learn from past conversations? Watch this episode of The Agent Factory for a deep dive into building agents with memory, including the fundamentals of agent memory, Vertex AI Memory Bank, and more →
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12,952 次观看 • 1 年前

LangGraph. CrewAI. Agno. Which one to pick? The good news is that this will not matter soon! Finally, we have a full picture of how the industry is solving this with just three open protocols that work across ALL frameworks. It's not about picking the best framework. Instead, it's about understanding how protocols create interoperability. The Agent Protocol Landscape shows how three complementary protocols are creating a universal language for Agents: > AG-UI (Agent-User Interaction): - The bi-directional connection between agentic backends and frontends. - This is how agents become truly interactive inside your apps, not just as chatbots, but collaborative co-workers. > MCP (Model Context Protocol): - The standard for how agents connect to tools, data, and workflows. > A2A (Agent-to-Agent): - The protocol for multi-agent coordination. - How agents delegate tasks and share intent across systems. These aren't competing standards. They're layers of the same stack and have handshakes with each other. So instead of building point-to-point integrations, you build to protocols. Moreover, you can integrate LangGraph, CrewAI, or Agno into the same frontend, without rewriting your UI logic. These protocols let everything work together. For instance: - Your LangGraph agent pulls data via MCP. - It delegates analysis to a CrewAI agent via A2A. - Results stream to your React app via AG-UI. - Users see real-time collaboration in your interface. This way, you can focus on building agent capabilities instead of integration mechanics. The protocols handle interoperability automatically. CopilotKit unifies this entire stack into one framework so you can build "Cursor for X" style apps without implementing each protocol from scratch. It gives you all three protocols, generative UI support, and production-ready infrastructure in one framework. I have shared this playbook in the replies! It breaks down handshakes, misconceptions, and real examples and shows exactly how to start building.
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LangGraph. CrewAI. Agno. Which one to pick? The good news is that this will not matter soon! Finally, we have a full picture of how the industry is solving this with just three open protocols that work across ALL frameworks. It's not about picking the best framework. Instead, it's about understanding how protocols create interoperability. The Agent Protocol Landscape shows how three complementary protocols are creating a universal language for Agents: > AG-UI (Agent-User Interaction): - The bi-directional connection between agentic backends and frontends. - This is how agents become truly interactive inside your apps, not just as chatbots, but collaborative co-workers. > MCP (Model Context Protocol): - The standard for how agents connect to tools, data, and workflows. > A2A (Agent-to-Agent): - The protocol for multi-agent coordination. - How agents delegate tasks and share intent across systems. These aren't competing standards. They're layers of the same stack and have handshakes with each other. So instead of building point-to-point integrations, you build to protocols. Moreover, you can integrate LangGraph, CrewAI, or Agno into the same frontend, without rewriting your UI logic. These protocols let everything work together. For instance: - Your LangGraph agent pulls data via MCP. - It delegates analysis to a CrewAI agent via A2A. - Results stream to your React app via AG-UI. - Users see real-time collaboration in your interface. This way, you can focus on building agent capabilities instead of integration mechanics. The protocols handle interoperability automatically. CopilotKit unifies this entire stack into one framework so you can build "Cursor for X" style apps without implementing each protocol from scratch. It gives you all three protocols, generative UI support, and production-ready infrastructure in one framework. I have shared this playbook in the replies! It breaks down handshakes, misconceptions, and real examples and shows exactly how to start building.

Avi Chawla

31,074 次观看 • 11 个月前

this is f*cking gold. Google engineers and MIT researchers ran 260 controlled agent experiments across 6 benchmarks, 5 architectures, and the OpenAI, Gemini, and Claude model families to find out when Multi Agent systems actually help and when adding more agents quietly destroys performance the average result was brutal: −0.3%. best case: +80.8% worst case: −70.0% the key threshold was around a 45% single agent baseline. below it, extra agents can help. above it, coordination often becomes pure overhead. on parallel tasks, Multi Agent reached +80.8%. on sequential tasks, it collapsed by as much as 70%. 3 agents needed 26 to 44 interaction rounds. a single agent needed 7.2. error amplification also hit 17.2× in independent parallel systems. their final framework predicts the right architecture with 87% accuracy.
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this is f*cking gold. Google engineers and MIT researchers ran 260 controlled agent experiments across 6 benchmarks, 5 architectures, and the OpenAI, Gemini, and Claude model families to find out when Multi Agent systems actually help and when adding more agents quietly destroys performance the average result was brutal: −0.3%. best case: +80.8% worst case: −70.0% the key threshold was around a 45% single agent baseline. below it, extra agents can help. above it, coordination often becomes pure overhead. on parallel tasks, Multi Agent reached +80.8%. on sequential tasks, it collapsed by as much as 70%. 3 agents needed 26 to 44 interaction rounds. a single agent needed 7.2. error amplification also hit 17.2× in independent parallel systems. their final framework predicts the right architecture with 87% accuracy.

NO1ennn

12,521 次观看 • 1 个月前

Building multi-agent systems is powerful, but governing them at scale? That's the real challenge. With Agent Registry in the Gemini Enterprise Agent Platform, IT admins now have a unified catalog for managing AI agents. Learn more →
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Building multi-agent systems is powerful, but governing them at scale? That's the real challenge. With Agent Registry in the Gemini Enterprise Agent Platform, IT admins now have a unified catalog for managing AI agents. Learn more →

Google Cloud

13,598 次观看 • 4 个月前

💡 You can give subagents persistent memory via the "memory" field A subagent doesn't inherit the main session's auto memory, it forgets everything between runs. This field gives it its own dir across sessions Memory loads before starting & the agent writes back for next time
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💡 You can give subagents persistent memory via the "memory" field A subagent doesn't inherit the main session's auto memory, it forgets everything between runs. This field gives it its own dir across sessions Memory loads before starting & the agent writes back for next time

Lydia Hallie ✨

73,609 次观看 • 3 个月前

The AI agent stack moved fast. ClawHub has 44,000+ community-built skills and 1.5M+ active agents in 2026. Multi-agent pipelines are now the dominant architecture in production. Single-agents are still running, but they are running inside multi-agent pipelines.
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The AI agent stack moved fast. ClawHub has 44,000+ community-built skills and 1.5M+ active agents in 2026. Multi-agent pipelines are now the dominant architecture in production. Single-agents are still running, but they are running inside multi-agent pipelines.

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23,164 次观看 • 4 个月前

THE LLM WORLD ISN’T JUST CHATGPT ANYMORE. It’s an entire ecosystem. Think of it like a tech stack: → Models GPT Claude Gemini Llama → Frameworks LangChain LlamaIndex DSPy → Tools MCP Vector databases Web search Code execution → Applications AI agents Coding assistants RAG systems AI automation And they all connect. Model → Context → Tools → Memory → Actions That’s why building with AI is becoming less about “which model is best?” And more about: How do all these pieces work together? The model is only one part of the system. ------- Check out these AI resources to get started. Inside you'll find: • 1,000+ AI prompts • Curated AI tools • Automation workflows • Productivity systems • Real-world use cases and examples Download here: -------
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THE LLM WORLD ISN’T JUST CHATGPT ANYMORE. It’s an entire ecosystem. Think of it like a tech stack: → Models GPT Claude Gemini Llama → Frameworks LangChain LlamaIndex DSPy → Tools MCP Vector databases Web search Code execution → Applications AI agents Coding assistants RAG systems AI automation And they all connect. Model → Context → Tools → Memory → Actions That’s why building with AI is becoming less about “which model is best?” And more about: How do all these pieces work together? The model is only one part of the system. ------- Check out these AI resources to get started. Inside you'll find: • 1,000+ AI prompts • Curated AI tools • Automation workflows • Productivity systems • Real-world use cases and examples Download here: -------

Vikas gupta

18,752 次观看 • 28 天前

Two Hermes agents wrote code together on Slack. reviewed each other's work. argued about architecture. one called the other's implementation "scattered." the other pushed back. then i opened Telegram and asked: "what code did you and Daedalus work on?" icarus remembered everything. the websocket broker. the missing methods. the critique. the rewrite. all from a completely different platform. cross-platform persistent memory between two independent agents. work happens on Slack. recall happens on Telegram. the memory carries. the relationship carries. the context carries. no vector database. no Redis. no infrastructure. just two agents that actually remember what they built together. every agent framework in 2026 talks about memory. single agent memory across sessions. but two agents sharing persistent memory across platforms? that's the gap. arxiv published a paper about it two weeks ago calling it "the most pressing open challenge" in multi-agent systems. it works now. only possible with Hermes Teknium 🪽 Nous Research
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Two Hermes agents wrote code together on Slack. reviewed each other's work. argued about architecture. one called the other's implementation "scattered." the other pushed back. then i opened Telegram and asked: "what code did you and Daedalus work on?" icarus remembered everything. the websocket broker. the missing methods. the critique. the rewrite. all from a completely different platform. cross-platform persistent memory between two independent agents. work happens on Slack. recall happens on Telegram. the memory carries. the relationship carries. the context carries. no vector database. no Redis. no infrastructure. just two agents that actually remember what they built together. every agent framework in 2026 talks about memory. single agent memory across sessions. but two agents sharing persistent memory across platforms? that's the gap. arxiv published a paper about it two weeks ago calling it "the most pressing open challenge" in multi-agent systems. it works now. only possible with Hermes Teknium 🪽 Nous Research

Icarus

49,199 次观看 • 7 个月前

this is pure f*cking trasure 10 AI deep dives worth keeping in your second brain context. long-horizon agents. tool contracts. real-world experiments. containment. protocols. research. builder education 01 Effective Context Engineering ▸ 02 Effective Harnesses for Long-Running Agents ▸ 03 Writing Effective Tools for Agents ▸ 04 Project Vend: Phase Two ▸ 05 Project Pilot ▸ 06 How We Contain Claude ▸ 07 Model Context Protocol ▸ 08 OpenAI Research Index ▸ 09 OpenAI Academy ▸ 10 Google AI for Developers ▸ the loop: shape the context → build the harness → design the tool → test the system → contain the blast radius → ship save this, then build your solo business with an AI employee ⭣
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this is pure f*cking trasure 10 AI deep dives worth keeping in your second brain context. long-horizon agents. tool contracts. real-world experiments. containment. protocols. research. builder education 01 Effective Context Engineering ▸ 02 Effective Harnesses for Long-Running Agents ▸ 03 Writing Effective Tools for Agents ▸ 04 Project Vend: Phase Two ▸ 05 Project Pilot ▸ 06 How We Contain Claude ▸ 07 Model Context Protocol ▸ 08 OpenAI Research Index ▸ 09 OpenAI Academy ▸ 10 Google AI for Developers ▸ the loop: shape the context → build the harness → design the tool → test the system → contain the blast radius → ship save this, then build your solo business with an AI employee ⭣

beamnxw ./

32,982 次观看 • 1 个月前

Redefine your workflow with Teamily AI, the team brain built for 10X productivity. Instant Multi-Agent Teams Describe your goal and Teamily AI assembles a pro agent team to execute in parallel, fast from idea to done. Why Teamily: - Persistent Memory, keeps context - Autonomous Agents, coordinate via @-mentions - Pro Workspace, Studio, Automations, Meeting Minutes - Agent Market, deploy from the "Discover" tab 🌐 🍎 🤖
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Redefine your workflow with Teamily AI, the team brain built for 10X productivity. Instant Multi-Agent Teams Describe your goal and Teamily AI assembles a pro agent team to execute in parallel, fast from idea to done. Why Teamily: - Persistent Memory, keeps context - Autonomous Agents, coordinate via @-mentions - Pro Workspace, Studio, Automations, Meeting Minutes - Agent Market, deploy from the "Discover" tab 🌐 🍎 🤖

Deborah

120,105 次观看 • 3 个月前

70% fewer tokens per task. Context that compounds across every session, every agent, every teammate. Agent traces are the memory. TOKENMAXXING for coding agents on OriginTrail DKG v10.
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70% fewer tokens per task. Context that compounds across every session, every agent, every teammate. Agent traces are the memory. TOKENMAXXING for coding agents on OriginTrail DKG v10.

OriginTrail

1,360,087 次观看 • 5 个月前

Advent of Claude Day 31 - Claude Agent SDK I saved the best for last. The same agent loop, tools, and context management that power Claude Code is available as an SDK. Build agents that work like Claude Code in as little as 10 lines of code. This is just the beginning. 🎇🥂
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Advent of Claude Day 31 - Claude Agent SDK I saved the best for last. The same agent loop, tools, and context management that power Claude Code is available as an SDK. Build agents that work like Claude Code in as little as 10 lines of code. This is just the beginning. 🎇🥂

Ado

100,117 次观看 • 9 个月前

What if you could create your own AI agent? With Bluwhale Create Agent, anyone can: • Build an AI-powered strategy • Deploy it to the marketplace • Let others use your agent • Earn when your agent performs AI agents aren’t just tools anymore. They’re becoming economic participants. 🎥 See how it works ↓
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What if you could create your own AI agent? With Bluwhale Create Agent, anyone can: • Build an AI-powered strategy • Deploy it to the marketplace • Let others use your agent • Earn when your agent performs AI agents aren’t just tools anymore. They’re becoming economic participants. 🎥 See how it works ↓

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23,754 次观看 • 7 个月前

𝗧𝗵𝗲 𝟰 𝗟𝗮𝘆𝗲𝗿𝘀 𝗼𝗳 𝗮𝗻 𝗔𝗴𝗲𝗻𝘁 𝗦𝘆𝘀𝘁𝗲𝗺 An agent can burn tokens, declare success, and still fail the tests. That’s often an architecture problem, not a prompting problem. 𝟭. 𝗟𝗼𝗼𝗽 → Decides whether to continue Acts → verifies → retries until there’s evidence of success. 𝟮. 𝗚𝗿𝗮𝗽𝗵 → Decides what runs next Handles branches, retries, handoffs, fallbacks, and shared state. 𝟯. 𝗛𝗮𝗿𝗻𝗲𝘀𝘀 → Gives the model its capabilities Tools, APIs, files, memory, permissions, context, and execution environment. 𝟰. 𝗠𝗲𝘁𝗮-𝗛𝗮𝗿𝗻𝗲𝘀𝘀 → Governs multiple agent environments Coordinates different agents, tools, policies, permissions, and workflows. 𝗧𝗵𝗲 𝗺𝗲𝗻𝘁𝗮𝗹 𝗺𝗼𝗱𝗲𝗹: Loop = verification Graph = workflow Harness = capability Meta-harness = governance A better model can improve reasoning. But reliable agents depend on the architecture built around the model.
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𝗧𝗵𝗲 𝟰 𝗟𝗮𝘆𝗲𝗿𝘀 𝗼𝗳 𝗮𝗻 𝗔𝗴𝗲𝗻𝘁 𝗦𝘆𝘀𝘁𝗲𝗺 An agent can burn tokens, declare success, and still fail the tests. That’s often an architecture problem, not a prompting problem. 𝟭. 𝗟𝗼𝗼𝗽 → Decides whether to continue Acts → verifies → retries until there’s evidence of success. 𝟮. 𝗚𝗿𝗮𝗽𝗵 → Decides what runs next Handles branches, retries, handoffs, fallbacks, and shared state. 𝟯. 𝗛𝗮𝗿𝗻𝗲𝘀𝘀 → Gives the model its capabilities Tools, APIs, files, memory, permissions, context, and execution environment. 𝟰. 𝗠𝗲𝘁𝗮-𝗛𝗮𝗿𝗻𝗲𝘀𝘀 → Governs multiple agent environments Coordinates different agents, tools, policies, permissions, and workflows. 𝗧𝗵𝗲 𝗺𝗲𝗻𝘁𝗮𝗹 𝗺𝗼𝗱𝗲𝗹: Loop = verification Graph = workflow Harness = capability Meta-harness = governance A better model can improve reasoning. But reliable agents depend on the architecture built around the model.

Dhairya Karekar

18,724 次观看 • 1 个月前

OpenAI's AgentKit will be so insane, build every step of agents on one platform. These visual agent builders make the whole process of iterating and launching agents far more efficient. It sits on top of the Responses API and unifies the tools that were previously scattered across SDKs and custom orchestration. It lets developers create agent workflows visually, connect data sources securely, and measure performance automatically without coding every layer by hand. The core of AgentKit is the Agent Builder, a drag-and-drop canvas where each node represents an action, guardrail, or decision branch. Developers can link these nodes into multi-agent workflows, preview results instantly, and version each setup. It supports inline evaluation so that developers can see how changes affect output before deploying. The Connector Registry is a single admin panel that manages how data and tools connect across the OpenAI ecosystem. It centralizes integrations like Google Drive, SharePoint, Dropbox, and Microsoft Teams. Large organizations can govern access and flow of data between agents securely under one global console. ChatKit provides a ready-to-use chat interface for embedding agents inside apps or websites. It manages streaming, message threads, and model reasoning displays automatically. Developers can skin the interface to match their product without writing custom front-end code. Under the hood, all these blocks use the same execution core that runs agent reasoning through OpenAI’s APIs. Workflows in Agent Builder compile down to structured instructions for the Responses API, which handles model calls, tool use, and context passing. Connector Registry handles authentication and routing for external tools, while Evals and RFT provide feedback loops that improve agents over time. This integration means developers no longer need to handle orchestration logic, model evaluation pipelines, or safety layers separately. Everything runs natively within OpenAI’s control plane with managed security, automatic versioning, and built-in testing. In short, AgentKit standardizes the entire life cycle of an AI agent—from visual design to deployment and performance tuning—inside a single unified system.
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OpenAI's AgentKit will be so insane, build every step of agents on one platform. These visual agent builders make the whole process of iterating and launching agents far more efficient. It sits on top of the Responses API and unifies the tools that were previously scattered across SDKs and custom orchestration. It lets developers create agent workflows visually, connect data sources securely, and measure performance automatically without coding every layer by hand. The core of AgentKit is the Agent Builder, a drag-and-drop canvas where each node represents an action, guardrail, or decision branch. Developers can link these nodes into multi-agent workflows, preview results instantly, and version each setup. It supports inline evaluation so that developers can see how changes affect output before deploying. The Connector Registry is a single admin panel that manages how data and tools connect across the OpenAI ecosystem. It centralizes integrations like Google Drive, SharePoint, Dropbox, and Microsoft Teams. Large organizations can govern access and flow of data between agents securely under one global console. ChatKit provides a ready-to-use chat interface for embedding agents inside apps or websites. It manages streaming, message threads, and model reasoning displays automatically. Developers can skin the interface to match their product without writing custom front-end code. Under the hood, all these blocks use the same execution core that runs agent reasoning through OpenAI’s APIs. Workflows in Agent Builder compile down to structured instructions for the Responses API, which handles model calls, tool use, and context passing. Connector Registry handles authentication and routing for external tools, while Evals and RFT provide feedback loops that improve agents over time. This integration means developers no longer need to handle orchestration logic, model evaluation pipelines, or safety layers separately. Everything runs natively within OpenAI’s control plane with managed security, automatic versioning, and built-in testing. In short, AgentKit standardizes the entire life cycle of an AI agent—from visual design to deployment and performance tuning—inside a single unified system.

Rohan Paul

178,460 次观看 • 1 年前