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 outputs GIVE IT MEMORY 05 Mem0 ▸ → persistent agent memory 06 Graphiti ▸ → temporal knowledge graphs GIVE IT TOOLS 07 Composio ▸ → connect agents to real tools 08 E2B ▸ → isolated sandboxes for agent code MAKE IT SURVIVE 09 OpenHands ▸ → agents that execute real software tasks 10 AgentOps ▸ → trace + debug agent runs the architecture: context → router → agent → memory → tools → execution → eval → retry I’d split the stack like this: brain: LangGraph → PydanticAI → CrewAI memory: Mem0 → Graphiti execution: Composio → E2B → OpenHands observability: AgentOps the f…cking interesting part isn’t adding another model it’s everything around the model that keeps it working the model is becoming one folder in the stack ⭣show more

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 ⭣show more

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 employeeshow more

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.show more

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 belowshow more

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.show more

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 →show more

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.show more

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.show more

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 →show more

💡 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 timeshow more

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.show more

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: -------show more

Memory in AI Agent Harness. LLMs are stateless, so... every bit of memory in an AI agent is something the harness saves to disk and loads back in when it needs it. most good agent harnesses split that memory into a few layers, let's look at each one of them: • context: what the model can see in the current session. when it gets close to full, the harness summarizes the older messages to make room. • notes: a progress file the agent keeps updating while it works, so if a session ends halfway the next one can read it and continue from there. • semantic: facts about you, your team and your project, like your preferences or which repo you work in. Hermes keeps these in MEMORY.md and USER.md. • procedural: how to do a specific job. when the agent figures out something hard, it writes it down as a skill and uses that skill the next time. • episodic: a log of every past session that the agent can search when it needs to remember what happened before. First two only last as long as the session or the task. Other three are persistent, so they're still there the next time you start the agent. save this for the next time someone asks how agents remember. follow Shubham Saboo for more on Agent Harness.show more

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 ⭣show more

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 Researchshow more

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 🌐 🍎 🤖show more

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.show more

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. 🎇🥂show more

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 ↓show more

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.show more