One honest page: what agents actually are, a live star board of the 20 open-source frameworks that matter, runbooks to deploy your own on your own hardware — and the truthful, early-access status of ABUZ8's own agent stack.
// what an agent actually is
Strip the marketing away and every "AI agent" is the same three parts. Understanding them is the difference between deploying something useful and paying for a demo.
The reasoning engine. It can be a local open-weight model running on your own machine (via Ollama or llama.cpp) or a hosted API. Local means your data never leaves your hardware.
What the model is allowed to touch: a browser, your files, a search API, a CRM. Tools are where agents become useful — and where all of the real risk lives. Scope them tightly.
Plan → act → observe → repeat, until the goal is met or a limit is hit. Good loops have budgets, step caps, and human approval gates on anything irreversible.
ABUZ8's mission is sovereign local AI for everyone — models you run on your own hardware, no cloud lock-in. Everything we build and recommend follows that bias: local model first, hosted API only when you choose it, and your data staying on your machine by default. We build and test on a real RTX 5090 rig — one machine, not a data center — because that is the hardware reality most builders actually live in.
Our own agent stack is real and in active development on our own hardware — but there is no public download today, and we will not pretend otherwise. If you want in when a slot is genuinely ready, use the request form. That is the whole pitch: no fake "buy now" on something that can't ship to a stranger yet.
The 20 curated agent frameworks with live GitHub star counts, plus a neutral comparison table: language, category, best-for.
Tab 2 ↑Three runbooks: a local-model quickstart with Ollama, how to pick a framework, and safe agent practices before you hand one the keys.
Tab 3 ↑Related deep pages: Qadir OS architecture · agentic OS roadmap · models guide · open source.
// GET /api/github-agents · refreshed every 30 min
20 curated open-source agent projects, ranked by the community that actually uses them. Star counts are pulled live from the public GitHub API through our own endpoint. Every project belongs to its authors — we aggregate, credit, and link back. Want the monetization angle on each one? See the Agent Board.
Loading live star counts…
Visual workflow automation with native AI-agent nodes — self-hostable, connect anything to anything.
View on GitHub →One of the original autonomous-agent projects — give it a goal and it plans and executes the steps.
View on GitHub →Open-source LLM app platform — a visual builder for chatbots, agents, and RAG apps with an API.
View on GitHub →One of the most widely used frameworks for chaining LLM calls, tools, memory, and agents.
View on GitHub →Node-based engine for local image, video, and audio generation — the backbone of serious local AI media.
View on GitHub →Lets an LLM agent drive a real browser — click, type, extract, and complete web tasks.
View on GitHub →Polished open-source chat UI supporting many models, plugins, and agent personas.
View on GitHub →Autonomous software-engineering agent — reads a repo, writes code, runs it, and fixes itself.
View on GitHub →Multi-agent framework that simulates a software company — PM, architect, and engineer roles.
View on GitHub →A memory layer for AI agents — long-term, personalized recall across sessions.
View on GitHub →Microsoft's framework for multi-agent conversations that solve tasks collaboratively.
View on GitHub →Drag-and-drop builder for LLM flows and agents — low-code and self-hostable.
View on GitHub →Framework for orchestrating role-based agent "crews" that divide and conquer a task.
View on GitHub →The go-to framework for connecting LLMs to your own data — retrieval-augmented generation done right.
View on GitHub →Self-hosted, multi-model chat app with plugins, agents, and authentication.
View on GitHub →Programs and auto-optimizes LLM prompts instead of hand-tuning them — from Stanford NLP.
View on GitHub →Ready-made tool integrations (Gmail, Slack, GitHub, CRMs) for agents through one layer.
View on GitHub →Microsoft's SDK for embedding AI agents into applications — C#, Python, and Java.
View on GitHub →Autonomous agent that researches a topic and writes a cited report.
View on GitHub →OpenAI's official lightweight framework for building agents with handoffs and tools.
View on GitHub →Factual and neutral: primary implementation language, what category the project sits in, and what it is best suited for. Star counts join live when the feed is up.
| Framework | Language | Category | Best for | Stars |
|---|---|---|---|---|
| n8n | TypeScript | Workflow automation | Visual automations with AI-agent nodes, self-hosted | — |
| AutoGPT | Python | Autonomous agent | Goal-driven autonomous runs | — |
| Dify | TypeScript / Python | LLM app platform | Visual chatbot, agent, and RAG app building | — |
| LangChain | Python (JS port too) | Agent framework | Code-first chains of LLM calls, tools, and memory | — |
| ComfyUI | Python | Media generation | Node-based local image/video/audio workflows | — |
| Browser Use | Python | Browser automation | Agents that operate a real web browser | — |
| Lobe Chat | TypeScript | Chat UI | Self-hosted multi-model chat with personas | — |
| OpenHands | Python | Coding agent | Autonomous software engineering on real repos | — |
| MetaGPT | Python | Multi-agent | Simulated software-company role pipelines | — |
| Mem0 | Python | Memory layer | Long-term memory added to existing agents | — |
| AutoGen | Python | Multi-agent | Collaborative agent conversations (Microsoft) | — |
| Flowise | TypeScript | Low-code builder | Drag-and-drop LLM flows without much code | — |
| CrewAI | Python | Multi-agent | Role-based agent crews for multi-step work | — |
| LlamaIndex | Python | RAG / data framework | Connecting LLMs to your own documents and data | — |
| LibreChat | TypeScript | Chat UI | Private, self-hosted multi-model chat with auth | — |
| DSPy | Python | Prompt optimization | Programmatic prompt tuning for better/cheaper output | — |
| Composio | Python | Tool integrations | Giving agents ready-made app integrations | — |
| Semantic Kernel | C# | Agent SDK | Embedding agents in .NET/Java/Python apps | — |
| GPT Researcher | Python | Research agent | Autonomous, cited research reports | — |
| OpenAI Agents SDK | Python | Agent SDK | Lightweight agents with handoffs on OpenAI models | — |
// three runbooks · all public knowledge · all credited
Everything below uses public, open-source software you install yourself. No account with us needed, nothing to buy. This is the sovereign path: your model, your machine, your data.
Download the installer for Windows, macOS, or Linux from ollama.com (free, open source). It runs a local model server on port 11434.
Start small — a 3B-parameter quantized model runs on most modern laptops. Bigger models need more RAM or GPU VRAM.
ollama pull llama3.2:3b
Confirm it works before wiring anything else to it.
ollama run llama3.2:3b
Ollama exposes a local HTTP API — this is what agent frameworks talk to.
curl http://localhost:11434/api/generate -d '{
"model": "llama3.2:3b",
"prompt": "Explain what an AI agent is in one sentence.",
"stream": false
}'Ollama also serves an OpenAI-compatible endpoint at http://localhost:11434/v1. Most frameworks in the catalog accept a custom base URL, so you can swap a cloud API for your local model with two settings:
# In most Python frameworks (LangChain, CrewAI, etc.): # base_url = "http://localhost:11434/v1" # api_key = "ollama" # any non-empty string works locally # model = "llama3.2:3b"
Write one sentence: "I want an agent that ____ every ____." If you can't fill the blanks, no framework will save you. Narrow beats general — every time.
Look at n8n, Flowise, or Dify — drag-and-drop flows, self-hostable, fastest path from idea to something running.
Look at LangChain, CrewAI, or the OpenAI Agents SDK. Maximum control over tools, memory, and the loop. On the Microsoft stack, Semantic Kernel is built for embedding agents into existing apps.
That's retrieval, not agents — start with LlamaIndex for the data layer and put LibreChat or Lobe Chat in front as the UI.
Browser Use drives a real browser; OpenHands works a real repo. Both are specialized — use them for what they're for.
Ship a single narrow agent, run it for a week, measure whether it saved real time. Only then scale up. Frameworks are free; the month you lose picking wrong isn't.
Give the agent only the access the job needs: read-only where possible, one folder instead of the whole disk, one mailbox label instead of the inbox.
Sending email, spending money, deleting data, posting publicly — the agent drafts, a human clicks. Keep this gate until the agent has earned trust on a long boring record.
If the agent runs code or a browser, contain it: a container, a VM, or a separate user account. Assume it will eventually do something you didn't expect.
API keys and passwords go in environment variables or a secrets manager — never pasted into prompts, code blocks, or agent memory.
Every tool call, every action, timestamped. When something goes wrong (it will), the log is the difference between a five-minute fix and a mystery.
Cap steps per run, tokens per day, and dollars per month at the API level. A loop with no cap is an open tab you didn't agree to.
Prompt injection is real: a web page or email can contain text that tries to command your agent. Never let content the agent reads override the rules you gave it, and keep gates on anything that acts on the outside world.
// truth-status: EARLY ACCESS — exists, gated, no public download
These are our own systems — real, running on our own hardware, in active development. None are publicly downloadable today, and we won't pretend otherwise. Early access is by request only.
The coordinating agent of the ABUZ8 stack: it routes work across local models, tools, and memory on our own hardware. Qadir is the "brain that delegates" — the layer that turns a pile of models into a system. Public access is gated behind the request queue while we harden it.
Read the architecture →Our conversational agent — the front door to the stack. Zait handles the talking; Qadir handles the doing. Like everything here, it is an internal system today: it works for us, and we're not shipping it to strangers until it works for strangers.
Request access →A local-first Electron agentic OS in active development — internal builds are at v2.55 with a self-hosted update channel. It is the product all of this rolls up into: your models, your agents, your machine. No public download yet; the roadmap says what comes next.
See the roadmap →Our public honesty contract is the Truth-Status system: LIVE means working today and verifiable, Early Access means it exists but is gated, Roadmap means planned, not built. Nothing gets sold that doesn't exist. The agents above are Early Access: real systems, not yet stranger-installable. When that changes, this page changes.
Meanwhile, everything in the Deploy Your Own tab works today with public open-source software — you don't have to wait for us to start running local agents.
// straight answers
The same answers appear in this page's structured data — what we tell search engines is what we tell you.