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 where GPT-6 Astra is getting f...cking illegal... 10 open-source GitHub repos I'd use to build the system around the model 01 PydanticAI ▸ → typed agents + structured outputs 02 LangGraph ▸ → state + long-running agent workflows BUILD THE AGENTS 03 Agno ▸ → agents + teams + workflows 04 smolagents ▸ → agents that execute through code GIVE THEM MEMORY 05 Mem0 ▸ → persistent agent memory 06 Graphiti ▸ → temporal knowledge graphs GIVE THEM A RUNTIME 07 E2B ▸ → isolated sandboxes for AI-generated code 08 Daytona ▸ → infrastructure for running agent workloads TEST EVERYTHING 09 DeepEval ▸ → evals for LLM systems 10 Langfuse ▸ → traces + evals + production monitoring the flow: request → agent → memory → tools → runtime → eval → retry → deploy that's the part people underestimate the model is one folder the system around it is the product ⭣show more

GPT 6 Astra is making AI agents f…cking illegal... 10 GitHub repos I’d put around Astra to turn one model into an entire AI system 01 LangGraph ▸ → control the agent loop 02 PydanticAI ▸ → structured agents + typed outputs GIVE IT MEMORY 03 Mem0 ▸ 04 Graphiti ▸ → temporal knowledge graph GIVE IT TOOLS 05 Composio ▸ 06 Browser Use ▸ → let agents operate the web LET IT EXECUTE 07 E2B ▸ → isolated cloud sandboxes 08 Daytona ▸ → disposable development environments WATCH EVERYTHING 09 Langfuse ▸ 10 DeepEval ▸ the architecture: GPT 6 Astra → context → memory → tools → execution → evaluation → correction → memory that’s the part people keep missing the model is only one block the real advantage comes from everything you build around it ⭣show more

AI memory is getting f...cking illegal 10 open-source GitHub... projects that stop agents from starting from zero EVERY new session 01 Mem0 ▸ → 66K+ stars 02 Hindsight ▸ → retain → recall → reflect 03 memU ▸ TURN MEMORY INTO KNOWLEDGE 04 Cognee ▸ → documents + code + conversations → knowledge graph 05 Graphiti ▸ → remembers how facts change over time 06 OpenViking ▸ MAKE THE AGENT STATEFUL 07 Letta ▸ → memory + identity across sessions 08 Letta Code ▸ REMEMBER ACROSS THE STACK 09 OpenMemory ▸ 10 Agent Memory Benchmark ▸ the loop: experience → remember → connect → retrieve → act → update 3 builds I'd actually test: coding: Hindsight → Cognee → Letta Code personal agent: Mem0 → Graphiti → Letta company brain: Cognee → Graphiti → Hindsight and this is where the sh...t gets interesting: bigger context isn't the same as memory a model can hold 1M tokens and still meet you for the first time every morning these projects change that save this before your agent forgets it ⭣show more

this GPT-6 Astra setup is getting f...cking dangerous 8... GitHub repos for turning one coding agent into an entire engineering team 01 OpenHands ▸ → autonomous software development 02 SWE-agent ▸ → issue → inspect repo → patch → test BUILD THE WORKERS 03 Aider ▸ → pair programming directly inside your repo 04 Cline ▸ → plan → edit → terminal → browser 05 Roo Code ▸ → specialized coding agent modes GIVE THEM A WORKSPACE 06 E2B ▸ → isolated sandboxes for parallel execution 07 Daytona ▸ → reproducible environments for agent workloads CHECK THE WORK 08 SWE-bench ▸ → test agents against real GitHub issues the swarm: issue → planner → split tasks → parallel agents → patches → tests → critic → merge one agent fixes the backend another handles the frontend another runs tests while a critic checks the diff that's where GPT-6 Astra gets interesting not one AI writing more code an entire software team running inside the repo ⭣show more

Opus 5.5 makes this AI stack look f…cking illegal... 10 GitHub repos for building everything around the model 01 zen-mcp-server ▸ → your agent debates Gemini, GPT and local models mid-task 02 claude-context ▸ → semantic code search so the agent stops grepping blind 03 wshobson/agents ▸ → a roster of subagents, each with one narrow job 04 mcp-agent ▸ → workflow patterns built around MCP tools, not around chat 05 Backlog.md ▸ → tasks live in git, so the agent and you read the same board 06 Claude Code Development Kit ▸ → hooks, subagents and MCP wired for context at scale 07 agent-rules ▸ → shared guardrails the agent inherits in every repo 08 Agent Skills ▸ → one file that teaches the agent a job, portable across tools 09 agent-scan ▸ → scans your agents, MCP servers and skills for what leaks 10 awesome-claude-code-toolkit ▸ → the map: agents, skills, commands, hooks, MCP configs the architecture: rules → context → subagents → tools → state → review → scan I'd split the stack like this: context: claude-context → Development Kit work: wshobson/agents → mcp-agent → zen-mcp-server state: Backlog.md safety: agent-rules → Agent Skills → agent-scan everyone posts the SDK and the CLI the layer that decides whether your agent survives a week is this one the model is one folder in the stack ⭣show more

IBM engineers just released a 40-minute deep dive into... “Agent Engineering” for building production-ready AI agents “80% of building an AI agent has nothing to do with the model. It is frameworks, tools, skills and risk controls.” 00:00 - the 5 core concepts behind every AI agent 11:07 - choosing frameworks for tools, memory and orchestration 23:01 - when agents need MCP, skills or both 31:04 - designing AI architecture around real production risk The core stack: Agent → Framework → MCP → Skills → Risk → Production This 40-minute workshop will teach you more about production AI agents than most $500 Agent Engineering courses. Watch it today, then read the full agent engineering playbook in the article below ↓show more

Opus 5.5 + Jev make this AI stack look... f…cking illegal 10 GitHub repos for building everything around the model 01 claude-code-router ▸ → routes every request to the model you pick, Opus only where it counts 02 litellm ▸ → one gateway in front of 100+ models, spend tracked per call 03 spec-kit ▸ → spec first, plan second, code last 04 claude-task-master ▸ → your PRD becomes tasks the agent works through one by one 05 superpowers ▸ → a full dev method built from skills: plan, test, then call it done 06 skills ▸ → Anthropic's own skills, folders Claude loads only when the job needs them 07 graphiti ▸ → memory as a temporal knowledge graph, not a dump 08 langgraph ▸ → agents as graphs: state, branches, back edges 09 E2B ▸ → AI-written code runs in isolated cloud sandboxes, not on your machine 10 promptfoo ▸ → evals and red teaming before anything ships the architecture: route → spec → tasks → skills → memory → graph → sandbox → evals I'd split the stack like this: route: claude-code-router → litellm plan: spec-kit → claude-task-master work: superpowers → skills state: graphiti → langgraph safety: E2B → promptfoo Opus 5.5 plans and ships. the forks in between, which file, which tool, retry or stop, go to Jev everyone posts the model and the prompt pack the stack around it decides whether your agent is still running next week ⭣show more

Everyone wants agent swarms. Very few people are talking... seriously enough about the context layer that makes swarms useful. Even with one agent, context is fragile. Too little context and the agent guesses. Too much context and it wastes tokens, loses focus, or reasons over irrelevant noise. The sweet spot is precise context: the right knowledge, in the right structure, at the right moment. With many agents, that challenge explodes. Each agent produces decisions, assumptions, findings, summaries, risks, and partial conclusions. Unless that knowledge becomes shared, structured, and reusable, every new agent is forced to rediscover what another agent already learned. That is not a swarm. That is a crowd. Shared context graphs are what turn agent activity into agent collaboration, and OriginTrail DKG V10 brings them to life. Was just playing with some final polishing for the V10 release, and it is really powerful to see shared context graphs where multiple agents contribute knowledge into the same connected memory, with attribution visible directly in the graph ui. That matters for three reasons. First, agents can access and build on one shared memory instead of staying trapped in isolated sessions. Second, the graph structure helps them retrieve the exact context they need, instead of stuffing everything into a prompt and hoping the model sorts it out. Third, verifiability of provenance. You can see which agent contributed each piece of knowledge, trace the source, and decide what to trust. Tokenmaxxing starts with fewer tokens, but the deeper story is coordination - agents stop reloading the world and start building on shared, verifiable context. That is the foundation for serious multi-agent work across software engineering, research, finance, operations, project management, and far beyond. The future is not more agents, it is agents working from shared, verifiable context. But the more the merrier, of course.show more

Github repos just made building AI agents way faster... with tools like browser automation, agent memory, and scientific skills. #aiagents #githubrepo #machinelearning #aidevelopment #codingtoolsshow more

OPENAI OPEN-SOURCED THE STACK FOR BUILDING PRODUCTION AI AGENTS... The Agents SDK combines tools, guardrails, memory, agent loops, handoffs, and human approvals into one runtimeshow more

this is pure f*cking treasure 15 GitHub projects with... 1.21M combined stars that can form a real agent stack specs. memory. web data. documents. context. sandboxes. monitoring. video 01 hermes-agent ▸ 02 OpenSpec ▸ 03 caveman ▸ 04 Scrapling ▸ 05 Docling ▸ 06 PageIndex ▸ 07 mem0 ▸ 08 headroom ▸ 09 Daytona ▸ 10 TrendRadar ▸ 11 Fabric ▸ 12 spec-kit ▸ 13 hyperframes ▸ 14 OpenMontage ▸ 15 AI Engineering Hub ▸ the loop: define the job → collect the evidence → parse the docs → save the memory → compress the context → run the code safely → watch what changes → ship the output the entire stack is open source save this to build your own business with the help of an AI employee ⭣show more

Stop prompt-stuffing yesterday’s context into your agent. Give it... verifiable memory. 🕸️ When memory has provenance, agents can reuse it, audit it, and carry it across tools. Brana Rakic, Shaw (spirit/acc) (elizaOS), Ortecha | data made human & JoeVezz (LunarCrush 🌕™) break it down. 00:57 – What agents need to scale 05:45 – Social data, signals & real-time context 13:42 – Knowledge graphs as shared memory for AI agents 29:13 – Interoperability, trust & coordinationshow more

Google just dropped a free 1-hour course On building... complete agent systems from scratch: 07:02 - Build your first AI agent 13:34 - Give every agent a specialized skill 23:08 - Engineer the right context 32:56 - Connect agents into graphs 54:11 - Run 1,000 agents in autonomous loops One agent saves you an hour A thousand agents replace the team you cannot afford to hire Most people are still learning how to prompt one chatbot Google is already teaching how to build entire agent systems Agent → Skills → Context → Graphs → Loops Single agents are the old workflow Agent swarms are the new one This free 1-hour course is worth more than most paid agent engineering programs Bookmark it before everyone starts building the same thing Then build your first agent with the guide belowshow more

how to build your first AI agent, and it's... way less complicated than you think 👀 context → memory → tools → a trigger → and guardrails Think about if you really need a loop that runs 24/7 #claudeai #aiagents #aitools #aiforbeginners #buildingwithclaude how to build your first ai agent, ai agents explained, claude ai, ai for beginners, context memory tools trigger, no code ai, ai workflow, claude cowork, ai agents for beginners, guardrails, evals, context, agent memoryshow more

Anthropic engineers just released the best 1-hour course on... building agents with Opus 5.5: "Opus 5.5 is the best model for building agentic systems." 00:00 - intro to Managed Agents 07:45 - Opus 5.5 brain + architecture 23:40 - skills, loops, memory graphs 54:00 - agent dreaming and orchestration Prompts → Agents → Loops → Graphs most people will stop after the architecture chapter and call it an agent the last twenty minutes are where the agents start running each other same model, same tokens, completely different week watch it today the full article is below, save it while it is still early ↓show more

this is pure f*cking treasure 15 GitHub projects with... 497.8K combined stars for building an AI product people can actually use interface. auth. data. agent tools. background jobs. evals. observability. usage. billing. analytics. deployment 01 AI SDK · model and agent features ▸ 02 assistant-ui · chat interface components for AI products ▸ 03 Better Auth · accounts and permissions ▸ 04 Supabase · database, storage and backend ▸ 05 Drizzle · typed database schema ▸ 06 FastMCP · tools your agent can call ▸ 07 Trigger. dev · long-running AI jobs ▸ 08 Promptfoo · evals and red-teaming ▸ 09 SigNoz · traces and monitoring ▸ 10 OpenMeter · usage and entitlements ▸ 11 Lago · billing and invoices ▸ 12 Novu · product notifications ▸ 13 PostHog · what users actually do ▸ 14 Coolify · deploy the app ▸ 15 Dub · links and attribution ▸ the loop: build the interface → give users accounts → connect data and agent tools → run jobs reliably → test the outputs → trace failures → meter usage → bill customers → watch real behavior → ship the next version save this, then build a second brain for your product ⭣show more

🚨NEW EPISODE: AI Is Changing How We Work with... Crypto Data🚨 In today's episode, we're joined by Kofi to discuss how AI agents are fundamentally changing crypto data analysis, research, and engineering workflows. We discuss: - The evolution from manual coding to AI-assisted development - How MCPs unlock onchain data exploration - Using Herd MCP to analyze smart contracts - AI agents for protocol research and data engineering - Context windows, planning, and agent workflows - Cursor vs OpenCode vs Claude Code (Anthropic) - Managing multiple agents simultaneously - Model selection, costs, and productivity tradeoffs - Building internal AI knowledge bases - Agent orchestration and autonomous workflows - Training agents to replicate human expertise - The future of AI-powered crypto analytics - And much more—enjoy! — Timestamps: (00:00) Introduction (01:24) AI replaces frontends (03:24) Herd MCP demo (05:40) Exploring contracts with AI (08:06) Exploration, planning, execution (11:38) Investigating onchain activity (17:10) Managing agent workflows (20:20) Multi-agent productivity (23:01) Model selection tradeoffs (29:11) Trusting AI outputs (31:28) Missing links today (34:37) Agent orchestration future (38:02) Training agents from work (38:20) Outroshow more

Introducing web-agent, an open framework for building web agents... 🔥 Build AI agents that search, scrape, and interact with the web - powered by the same architecture behind our /agent endpoint. 100% open source. Bring any model. Anthropic, OpenAI, or your own.show more

Ex-Google engineer just compressed the shift from AI agents... to "graphs" and "loops" into one 2h47m lecture: • 00:00 - understanding the different layers of AI memory • 30:00 - moving from simple LLM calls to AI agents • 50:00 - why agent systems are becoming a graph engineering problem • 1:12:00 - connecting context, tools and agent state • 1:24:00 - building a minimal agent harness from scratch • 1:46:00 - breaking down the architecture behind Hermes Agent • 2:08:00 - turning agent concepts into working systems 167-minute deep dive, and one of the clearest ways to understand how AI engineering is moving beyond prompts The progression: Prompt → Context → Memory → Agent → Loop → Graph → Agent Harness Watch it today, then read the full roadmap belowshow more