12 frameworks analyzed. Hundreds of features extracted. Choose the ingredients that matter to YOUR business.
The grocery store doesn’t install anything: it creates a shopping list in Markdown (meu-assistente.md) for a coding agent to read and build your assistant on top of INTELECTO v2, which is already running.
meu-assistente.md. Each item says whether it already exists in v2, is partial, or which piece to add it to.cd ~/intelecto-v2 && cp ~/Downloads/meu-assistente.md . # in Claude Code or Codex, open in this folder, paste: Read meu-assistente.md and implement the ingredients marked as "to add" and "partial", one at a time, following the "Rules for the agent" at the end of the file. Run python3 -m pytest -q after each one and show me the output.
Safer: run the agent inside caixa-do-agente and apply only the patch you reviewed (patch -p1).
Click the diagram to enlarge it. "Load into cart" marks the ingredients in the aisles (you can remove or add more); "Download the .md" gives you the file.
For anyone who wants an assistant on their phone that remembers things, gives timely reminders, and takes care of email and their calendar.
12 ingredients
For a shop or clinic that gets questions on WhatsApp and wants to answer based on its own documents, without falling for malicious messages.
12 ingredients
For anyone who lets an agent work on code: it works in a sandbox, asks for approval before risky actions, and only says it’s done when the tests pass.
12 ingredients
For anyone who doesn’t want anything leaving their computer: local AI, local voice, private search, and your notes.
10 ingredients
For anyone who wants to talk to their home: wake word, lights, sound, cameras, and routines, with approval required before any sensitive action.
12 ingredientsEach card captures the essence of a framework. Think of them as the "brands" in your grocery store.
The complete "operating system" for personal AI. Think of it as the iPhone of AI assistants: polished, integrated, everything works together. The Gateway architecture routes all your messages, voice, and visual canvas through a single control center.
OpenClaw reimagined in Rust for extreme efficiency. Runs on a $10 board with less than 5MB of RAM. Starts up in 10ms. Every component is an interchangeable "trait" (contract)—swap your AI model, messaging app, or storage without touching the agent’s code.
A masterpiece of simplicity with 5,200 lines. Your AI lives in WhatsApp. Each conversation group has its own brain, isolated in a secure container. Small enough to read all at once, powerful enough for everyday business use. A "no setup complexity" philosophy.
Ultra-lightweight Python framework with 4,000 core lines that supports 9+ chat platforms and 13+ AI providers. Uses a message bus architecture to keep channels, agents, and memory clearly separate. Ready for research and easy to extend.
Runs your AI assistant on a $10 circuit board. Written in Go for extreme portability. Single 10MB binary, no dependencies. It can even control hardware pins for IoT devices. Perfect for always-on AI, deployed anywhere, with almost no operating cost.
The "Fort Knox" of AI assistants. Tools run in WASM sandboxes that can’t leak your secrets. Built-in defense against prompt injection. Supports two databases (PostgreSQL + SQLite). If you handle sensitive data, IronClaw’s zero-trust architecture is the gold standard.
Build teams of AI agents that collaborate autonomously. @coder fixes bugs, hands off to @reviewer, who hands off to @writer. Agents communicate through a file-based queue. Mix Claude and OpenAI agents on one team. Runs 24/7 in the background.
An AI that gets smarter the more you use it. Every solution it finds is saved for next time. 100% transparent: every instruction is visible and editable. Docker-native with project isolation. Creates its own tools on the fly by writing code.
A personal assistant "operating system" delivered to your phone through Telegram. It runs Claude Code on your own machine and sends the results back in chat. Its standout feature is coordinating a TEAM of specialist agents that talk to each other (text and voice War Room), share a visual 3D "collective brain" (Hive Mind), and can even join real video meetings as avatars. All local: SQLite, no cloud.
Open-source agent runtime (MIT) from Nous Research: an agent that DOES, not just talks. It combines a reasoning loop (perceive → act → reflect → save), native local persistent memory (SQLite + FTS5), and a dynamic skills system where the agent writes new abilities when it detects a repeated pattern. Local-first, multi-channel, and model-agnostic via OpenRouter (200+ models). The more you use it, the better it knows you.
The AGI Research (Rutgers) project that embeds LLMs inside an operating system layer for agents. It is not a chat wrapper or chain framework: it is a persistent kernel where multiple LLM agents coexist, share models, memory, and tools, and are scheduled like OS processes. The agent talks to the kernel through syscalls (LLMCall, MemoryRead/Write, ToolCall, ContextSwitch) via the Cerebrum SDK. Scientific foundation in COLM/NAACL/ICLR papers.
It is not a framework—it is the definitive reference library. Cost calculators, 5,700+ community plugins, security advisories, setup guides for 30+ platforms, and real-world use cases. Your research companion when deciding what to build.
These are the ideas that drive every framework. Understanding them helps you choose what matters.
Every framework lets you define who your AI is using simple text files. It is like writing a job description for your digital employee.
Who uses it: OpenClaw, NanoBot, ZeroClaw, PicoClaw, NanoClaw, Agent Zero (each with minor variations)
A central hub that receives messages from all your apps (WhatsApp, Slack, Telegram) and routes them to the right AI agent. Think of it as a receptionist who knows which department handles each question.
Who uses it: OpenClaw (main), ZeroClaw, PicoClaw, NanoBot, IronClaw
When your AI runs code or uses tools, sandboxing keeps it contained. Like giving an employee access to only their office, not the whole building. Different frameworks use different “wall materials.”
Tools are your AI's hands and arms. Each framework gives your AI different abilities.
“Claw” family frameworks usually have one agent that talks with you. ClaudeClaw flips the switch: instead of one, you get a team of specialist agents, each its own Telegram bot, who talk to each other in a live room (the “War Room,” by text or voice) and share a collective brain. Think of a company where each employee logs what they did on a central board—that board is the “Hive Mind,” and ClaudeClaw even renders it as a 3D brain so you can see what the swarm has been up to.
Who uses it: ClaudeClaw (War Room in text and voice, 3D Hive Mind). TinyClaw and Agent Zero have teams of agents, but no live meeting or cross-agent visualization.
A regular chat responds and forgets. Hermes runs a closed loop: it perceives (perceive), acts (act), reflects (reflect), and saves (save). The reflection step uses a cheap model just to decide what is worth remembering—so memory curates itself. And there is more: when Hermes notices that you have asked for the same sequence of things three times, it writes a new skill (a draft SKILL.md file) and asks for your approval. In other words, the AI learns a new “trick” through use, without you programming anything.
Who uses it: Hermes (autonomous skill generation + memory curation during reflect). Agent Zero also learns from past solutions, but does not generate SKILL.md on its own.
All the other frameworks run the agent as a “session” or a “loop.” AIOS takes a different approach: it treats each agent as an operating system process, just like the programs running on your computer. There is a kernel (the core) that embeds the LLM, and a Scheduler that decides which agent talks to the model now—so one slow agent does not block all the others. The agent talks to the kernel through syscalls (explicit requests like “call the LLM,” “read memory,” “use a tool”). And the Context Manager can pause an agent, save its state, and resume it later—exactly as an OS does with processes.
Who uses it: AIOS (the only one with a fairness scheduler, context switch, and syscalls). Frameworks like LangChain, AutoGen, and CrewAI can run on top of it.
Terms that appear in the comparison table, in plain language:
Click items to add them to your cart. Build the ingredient list for your ideal framework.
Feature matrix across all frameworks. Green = supported, yellow = partial, gray = unavailable.
| Feature | OpenClaw | ZeroClaw | NanoClaw | NanoBot | PicoClaw | IronClaw | TinyClaw | Agent Zero | ClaudeClaw | Hermes | AIOS |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Language | TypeScript | Rust | TypeScript | Python | Go | Rust | TS/Bash | Python | TypeScript | Python | Python |
| Yes | Yes | Primary | Yes | Yes | Planned | Yes | - | Experimental | Yes | - | |
| Telegram | Yes | Yes | Via skill | Yes | Yes | Yes | Yes | - | Primary | Yes | - |
| Discord | Yes | Yes | - | Yes | Yes | Yes | Yes | - | - | Yes | - |
| Slack | Yes | Yes | Via skill | Yes | Yes | Yes | - | - | Yes | Yes | - |
| Web Interface | Yes | - | - | - | - | Yes | - | Yes | Dashboard | TUI | Web UI |
| Voice Control | Wake Word | - | - | - | Via Groq | - | - | - | War Room voice | TTS/STT | - |
| Canvas / Visualization | Canvas | - | - | - | - | - | - | - | 3D Brain | - | - |
| Multi-Agent | Yes | Config | Swarm | Spawn | Spawn | Parallel | Teams | Hierarchy | Team (War Room) | Sub-agents | Processes |
| Cron Scheduling | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Via code | Yes | APScheduler | Via code |
| Heartbeat | Yes | Yes | - | Yes | Yes | Yes | Yes | - | Yes | Zombie detection | - |
| Browser Control | CDP | WebDriver | Chromium | - | - | Vision | CLI tool | Vision | - | Chrome DevTools | LiteCUA/VM |
| MCP Support | Yes | - | - | Yes | - | Yes | - | Yes | Yes | First-class client | Via LiteCUA |
| Skills/Plugins | SDK + 5.7K | TOML | Claude Code | Markdown | Markdown | WASM | Folder | SKILL.md | Auto-/commands | Auto-generated | Agent Hub |
| Sandbox Type | Approval | Landlock | VM Container | Workspace | Workspace | WASM | Directory per agent | Docker | Tool policy | firejail/bwrap | KVM/Firecracker VM |
| Memory System | Embeddings | Hybrid FTS+Vec | CLAUDE.md | 2 Layers | 2 Layers | Hybrid FTS+Vec | Per agent | Vector+Solutions | 5 Layers+Decay | FTS5/BM25 | A-MEM+LSFS |
| Local LLM | Ollama | Ollama+vLLM | - | Ollama+vLLM | Ollama+vLLM | Ollama | - | Ollama+LM Studio | - | Ollama+vLLM+llama.cpp | HuggingFace+vLLM |
| AI Providers | 6+ | 22+ | 1 (Claude) | 13+ | 7+ | NEAR AI | 2 | 20+ | 8 engines | 14+ (200+ models) | 7+ |
| Encrypted Secrets | Keychain | ChaCha20 | - | chmod 600 | - | Keychain | - | secrets.env | AES-256-GCM | env vars | config.yaml |
| RAM Usage | ~1GB | <5MB | ~200MB | ~100MB | <10MB | ~50MB | ~300MB | ~500MB | - | - | - |
| IoT/Hardware | - | - | - | - | GPIO/I2C/SPI | - | - | - | - | - | - |
| Mobile Apps | iOS+Android | - | - | - | - | - | - | - | Via Telegram | - | - |
| Live War Room (text/voice) | - | - | - | - | - | - | - | - | Text + Voice | Sub-agents | - |
| Cross-Agent Visualization (brain) | - | - | - | - | - | - | - | - | 3D Hive Mind | - | - |
| Autonomous Skill Generation | - | - | - | - | - | - | - | Creates tools | - | SKILL.md draft | - |
| Self-Curating Memory / Decay | - | - | - | - | - | - | - | Solutions | 5 layers+decay | Reflect-step | A-MEM |
| Agent as OS Process / Scheduler | - | - | - | - | - | - | - | - | - | - | FIFO/Round-Robin |
| Context Switch / Preemption | - | - | - | - | - | - | - | - | - | - | Snapshot/Restore |
| Declarative MCP Client | Yes | - | - | Yes | - | Yes | - | Yes | Yes | stdio + SSE | LiteCUA |
| Output Exfiltration Guard | - | - | - | - | - | Leak detection | - | - | Outbound regex | Egress allowlist | - |
How each framework is structured internally. These are the blueprints you would follow.
Quick guide: which framework to choose based on what matters most to you.
Complete, polished, with voice, canvas, mobile apps, and 5,700+ plugins. The "iPhone" approach.
Smallest memory footprint, fastest startup, runs on $10 hardware. The "sports car" approach.
Small enough to read all at once. No hidden complexity. The "open book" approach.
WASM sandboxing, prompt injection defense, leak detection, zero-trust. The "fortress" approach.
Multiple specialized agents handing work off to one another. The "small business" approach.
Remembers solutions, builds knowledge, gets smarter. The "growing brain" approach.
WhatsApp-first design with group isolation. Simple, secure, purpose-built.
Direct control of hardware pins (GPIO, I2C, SPI). AI meets the physical world.
Supports 20+ AI providers, easy switching between them, use local models for free.
Python code with clean interfaces for adding channels, tools, and providers.
Comprehensive knowledge base with cost calculators, security audits, and 5,700+ plugin examples.
Multiple specialist agents that meet in real time (text and voice), share a collective brain you can visualize in 3D, and can even join video meetings. The "headquarters" approach.
Creates its own skills when it detects repeated patterns and heals its own memory during the reflect step. Local-first, model-agnostic, proactive 24/7. The "agent that grows with you" approach.
Schedules agents as processes, with a fairness Scheduler, context switching, and auditable syscalls. Existing frameworks run underneath. The "agent operating system" approach.
Choose the best ideas from all the repositories. Use this page as your shopping list!