Agent Network Guides

Updated

Not just another AI tool — an entire team of specialists working for you

Claude Code is the real 2025–2026 breakthrough in software development: instead of treating AI as a side assistant that writes snippets for you in ChatGPT, Claude Code brings the most advanced AI in the world directly into your terminal and working environment — with the ability to edit files, run commands, manage git, test sites in a browser, and execute complex tasks completely on its own. It's built by Anthropic (creators of the Claude model — the direct competitor to OpenAI's ChatGPT, and widely considered the most advanced model in the world for coding today). The Claude Code CLI integrates with every leading development environment (VS Code, Cursor, JetBrains, plus a Mac/Windows desktop app), offers access to the three current 2026 models — Opus 4.7 (the strongest, with a one-million-token context window), Sonnet 4.6 (the balanced default — 400K tokens, with 1M in beta for some users), and Haiku 4.5 (fast and cost-efficient) — and supports a massive open ecosystem of extensions: Skills (ready-made capabilities that teach Claude to perform specific tasks), MCP Servers (connectors to external services), Sub-Agents and Managed Agents (a virtual team of specialists working in parallel, including background tasks), Hooks (automations that fire before or after any action), and more. What I've put together and share in this guide — my full working environment with 350+ professional Skills, 32 specialized agents, and 17 MCP servers — represents hundreds of hours of research, experimentation, and expensive mistakes I've already made on your behalf. Everything is open source, completely free, continuously updated, and ready to install on your machine with a single command. Whether you're a seasoned developer looking for a dramatic productivity jump, an entrepreneur who wants to build an MVP in a single night, or simply curious about the technology reshaping the world of work — Claude Code is the entry point, and this guide (together with the repositories linked below) is the shortest route in.

Hebrew-native WhatsApp agent

A personal AI assistant that lives inside WhatsApp — it listens, speaks, and remembers your conversations

Kami is a personal AI agent that lives inside WhatsApp — the messaging app we already use every day. Instead of opening yet another app like ChatGPT or Claude, you simply send a message to a WhatsApp number and get a reply, either in text or in voice. Under the hood, Kami is a service I built in TypeScript (the modern, type-checked version of JavaScript) that runs 24/7 on a small private server. It connects to WhatsApp through Green API — a lightweight, secure gateway that shuttles messages between WhatsApp and my code. When a message arrives, it is processed by [Claude Sonnet](/en/claude-code) — one of the most capable large language models (LLMs, AI systems trained to understand and generate text) on the market today. If the message is a voice note, OpenAI Whisper transcribes it into precise Hebrew; when Kami replies, it can answer with a spoken voice generated by Google Gemini TTS (free and natural sounding). Kami's signature ability is memory: it remembers older conversations using a specialized database ([Qdrant](/en/guide/qdrant), explained in its own guide), so you can pick up on an idea you started a week ago and continue from exactly where you left off. For me (Elad), Kami has become something like a chief of staff: it sends a morning briefing, reminds me of tasks, and handles my voice messages while I'm on the road. For you, the exact same architecture can power smart 24/7 customer support, a personal tutor that walks a student through their work, a digital family companion, or any other use case you can imagine for a conversational agent that lives inside WhatsApp.

Monitoring + Self-Healing

Who watches your server at 3 AM? An AI agent that never sleeps

Kaylee is the autonomous AI agent that keeps my entire agent network alive — around the clock, every day, without a break. Under the hood she runs on OpenClaw, a framework that has recently taken the agent world by storm: an AI agent that can perform almost any action you ask of it on a Linux server (start services, inspect logs, fix configuration, even edit source code) entirely on its own. OpenClaw pairs with Gemini Flash (Google's free tier) for reasoning, and ships with full access to the critical pieces of a host: containers (via [Docker](/en/guide/docker)), system services (systemd), and the file system. That power is also its risk — it fires a lot of parallel requests at the model, so unconstrained use can get expensive fast. The fix is to set boundaries up front: an allowlist of permitted actions, protected paths, and budget guards. In my setup Kaylee speaks over Telegram (the @kylie_elad_bot), watches ten services at once, and only wakes me when she genuinely doesn't know what to do. For you she can replace on-call rotation, tidy up log noise automatically, or act as a general maintenance agent for any server environment that needs to stay stable.

Multi-Agent Orchestration

Instead of a single agent, a team of specialists working together

CrewAI is an open-source Python orchestration framework by João Moura / CrewAI Inc., not just a library — a full multi-agent platform with Crews, Tasks, Agents, Flows (event-driven workflows added in 2024), and built-in tools (SerperDev, WebsiteSearchTool, ScrapeTool and more). It supports 100+ LLM providers through LiteLLM: Anthropic ([Claude](/en/claude-code) Sonnet 4.6, Opus 4.7, Haiku 4.5), Google Gemini 2.5 Pro/Flash, OpenAI, Groq, DeepSeek V3, Mistral, and local models via [Ollama](/en/guide/ollama). Each Agent is defined with a role, a goal, a set of tools, and its own LLM; workflows run as `sequential` or `hierarchical` Processes, or as event-driven Flows. A typical deployment runs behind FastAPI + [Docker](/en/guide/docker). I currently run 10 crews on my VPS (blog-he, marketing-team, yt-to-blog-he, research-crew and more) — but for you, CrewAI can power content automation, research ops, distributed code review, data analysis, customer research, or anything that needs more than a single prompt to a single LLM.

Health Coach AI

A health app people actually use — because it lives in WhatsApp

A personal coaching agent built on [Claude Code](/en/claude-code) + [CrewAI](/en/guide/crewai), with long-term memory in [Qdrant](/en/guide/qdrant), image OCR through Google Cloud Vision API, and an auto-generated ICS calendar feed. Interface: WhatsApp via the [Delegator](/en/guide/delegator). State is stored as a single JSON file plus an encrypted SQLite database. In my own setup it accompanies a personal weight-loss and strength program, but this is a coach-agent pattern — not a diet plan. You can adapt it to sleep, running, habit change, learning an instrument, money management, or any measurable personal goal the user sets for themselves.

Self-Healing Infrastructure

Self-healing — failures shouldn't wake you up

Hermes is a self-healing infrastructure CLI written in Go (v0.8.0 in my stack). The philosophy: a whitelist of permitted actions + verification-after-fix + learning from recurring failures. A five-stage architecture: detect → diagnose → fix → verify → learn. It runs as a cron job or a webhook responder and persists history to SQLite/JSON. In my setup it performs autoheal for [Kami](/en/guide/kami) and for OpenClaw (the engine behind [Kaylee](/en/guide/kaylee)) — but for you, it's a pattern you can adopt with any CLI (or even bash scripts): the five stages fit any production system, not just AI agents.

Vector Memory

The foundation for remembering by meaning, not by keywords

Qdrant is an open-source vector database written in Rust (v1.14+ as of late 2025), running inside a Docker container with both HTTP (6333) and gRPC (6334) APIs. Its capabilities: storing embeddings (up to 4096 dimensions per dense vector, plus sparse vectors for hybrid search), HNSW indexing, scalar and product quantization, semantic search, rich filters, arbitrary JSON payloads, sharding and replication. In my setup there are 10 collections ([kami_memory](/en/guide/kami), [box_coach](/en/guide/box), [network_memory](/en/guide/adopter) and more) holding thousands of vectors. In your product, Qdrant can serve as memory for a chatbot, power semantic search over a document corpus, drive a recommendation engine, or deduplicate content by meaning — anywhere you need to 'remember meaning' rather than just keywords.

Central API Router

One gateway, 100+ endpoints, the whole network behind it

Delegator is a plain-Python HTTP router (stdlib, no FastAPI) that runs on port 3900 on my VPS. It centralizes 100+ endpoints: email (Resend — free tier 100/day, then pay-per-use), SMS (Twilio — pay-per-message), calendar (Hebcal free + Google Calendar), Drive, research (Perplexity Pro API + Gemini), content-studio, landing-pages, campaigns, pipeline orchestration, and auto-routing. Auth is handled today with a simple API key (JWT-ready in middleware), and everything is logged to [Qdrant](/en/guide/qdrant). For me it fronts all 10 agents behind a single gateway — for you it can replace Zapier (free tier 100 tasks/month, Starter ~$29/mo, Professional ~$73/mo in 2026) or Make, and serve as an API gateway for any multi-agent architecture, without scattering credentials across five different .env files.

Autonomous Content Adoption

Instead of reading 500 posts a day — an agent that filters

Adopter is a Python service plus cron that listens to Telegram channels through Telethon (a Python client that speaks MTProto — the full Telegram user protocol, not the restricted Bot API), sends each post to Gemini 2.5 Flash with a classification schema (novelty × signal × actionability × risk), and stores only the top-K items in a [Qdrant](/en/guide/qdrant) collection called `network_memory`. A circuit breaker caps it at 5 adoptions per day. For me it filters 500 posts a day down to 3-5 findings — for you it can point at RSS feeds, Discord channels, Reddit or Twitter forums, mailing lists, or any content firehose that needs a smart filter.

Self-hosted Mission Control

One UI, 12 tabs, every agent at a glance

The Dashboard is a local self-hosted Node.js app (plain HTTP server, no framework) running on port 3456, with a dedicated WebSocket server on 3457 backed by the `ws` library. Its data sources: the hub.eladjak.com REST API (via the [Delegator](/en/guide/delegator)), [Qdrant](/en/guide/qdrant), local bridge files, PowerShell status scripts, and file-system watchers. It exposes 12 tabs — Mission Control, Agents, Projects, CrewAI, Costs, Health, Logs, and more. For me it's the main screen of my AI CEO setup; for you it can be the control panel for a home lab, a small SaaS ops team, an agent network, or a DevTooling squad. You just swap the data sources for your own adapters.

Containers & Compose

containers, docker-compose, and the architecture that lets an entire agent network live on a single VPS

Docker is one of the most important technologies to emerge from the software world in the last decade, and it is what allows most of today's cloud services and AI agents to run the way they do. At its core, Docker solves a simple but painful problem: every software service needs a specific environment to run (a particular language version, specific libraries, network settings), and when you try to install several services on the same machine they collide — and what worked yesterday stops working tomorrow. Docker solves this by packaging each service into its own isolated 'box' (a container), which holds everything the service needs — so it runs exactly the same on every machine, in every environment. Docker's extension called docker-compose lets you define many boxes together in a single file, spin them all up with one command, and manage the network between them — much like a conductor with an orchestra. For me (Elad), the entire agent network featured on this site (ten different services such as [Kami](/en/guide/kami), [Kaylee](/en/guide/kaylee), [Qdrant](/en/guide/qdrant), and [Delegator](/en/guide/delegator)) runs on a single docker-compose deployment on a Hetzner CPX11 (~€4.75/month, 2 vCPU · 2GB RAM). For you, Docker can be the foundation of any project: from a local dev environment, through a CI/CD pipeline, all the way to a full production service in the cloud. Once you understand docker-compose, most of what the other guides show becomes something you can build yourself.

Local LLM Runtime

Smart language models (like ChatGPT) running directly on your own machine — no cloud required

Ollama is an open-source platform that lets you run powerful AI language models — LLMs (Large Language Models, the engines behind ChatGPT, Claude, and friends) — directly on your own machine. No internet connection required, no data shipped off to OpenAI or Google, everything stays with you in full privacy. The platform is written in Go and knows how to run dozens of well-known models including Google's Gemma, Meta's Llama, Alibaba's Qwen, and DeepSeek — all completely free. For me (Elad), Ollama mostly serves as a safety net: when cloud models get too expensive or hit rate limits, my agents (like [Kami](/en/guide/kami), [Kaylee](/en/guide/kaylee), and [CrewAI](/en/guide/crewai)) automatically fall back to a local model — saving a lot of money on routine tasks. For you it can be much more than that: a full AI environment that works offline, a solution for organizations with strict privacy requirements (healthcare, legal, security), or simply a way to explore the world of open language models without spending a dollar.

Workflow Automation

Open-source Zapier — 500+ built-in integrations, self-hosted, unlimited executions

n8n is an open-source workflow automation platform (TypeScript/Node) built by n8n GmbH — a mature German company that raised a Series B — with a visual drag-and-drop interface for building pipelines from 500+ built-in integrations (Slack, Gmail, Postgres, Webhooks, HTTP, OpenAI, AI Agent, Vector Store nodes like [Qdrant](/en/guide/qdrant)/Pinecone/Supabase Vector, LangChain, and more). It runs on [Docker](/en/guide/docker) with PostgreSQL behind it. On my stack, n8n handles business cron jobs (invoicing, content publishing, agent heartbeats) and replaces three separate SaaS products that used to cost ~$80/month. On yours, n8n can be the glue of the entire stack — CRM automation, marketing ops, internal system integrations, or a full replacement for Zapier (Starter $29/mo for 750 tasks, Professional $73/mo for 2,000, Team $103/user/mo) and Make (Core $10.59/mo, Pro $18.82/mo, Teams $34.12/mo).

AI Pair-Programming CLI

Aider in any editor. Claude Code is my primary — Aider is the free backup

Aider is an AI pair-programming CLI written in Python. It supports 200+ models via LiteLLM (Anthropic Claude Sonnet 4.6 / Haiku 4.5 / Opus 4.5, OpenAI GPT-5 / GPT-4.1 / o4-mini, Google Gemini 2.5 Pro / Flash, xAI Grok, 300+ models via OpenRouter, plus local models through [Ollama](/en/guide/ollama)), edits files directly on disk, creates automatic git commits, and includes a smart repo-map powered by tree-sitter that understands your project. On my machine Aider is installed in an isolated environment with separate credentials (not [Claude Max](/en/claude-code)) and free models (Qwen 2.5 Coder:free and DeepSeek V3:free via OpenRouter) — a backup for when Claude Max hits its quota or when I want an extra-private session. For you, Aider can be the primary tool: if you don't have Claude Pro/Max, or if you're a privacy-focused developer who needs local-only inference (with [Ollama](/en/guide/ollama)), Aider delivers about 80% of [Claude Code](/en/claude-code)'s capability for absolutely zero cost.