Running A Multi-Agent Platform On One Docker Host

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A lot of the self-hosting write-ups here stop at the model and skip the part that actually costs a weekend. This one is the wiring side, written for anyone who has tried to stand up an agent stack and quit somewhere around the vector store.

The Five Services Problem

Standing up an agent platform yourself usually means running a vector store for embeddings, a queue for background jobs, a database for state, a tool server, and a web UI. Each piece is simple on its own. Wiring five of them together, keeping their versions compatible, and working out which one dropped the request is not, and that is where most attempts die.

Auto Learning Agents goes the other way. It ships as a single Docker image with the Elixir runtime, the Python services, the local database and the full tool layer already inside. The four step install is:

git clone https://github.com/AIAppsAPI/auto-learning-agents
cd auto-learning-agents
cp .env.example .env
docker compose up

First run prepares the local database and starts the supervision tree of agent nodes. The dashboard is on localhost.

Why Elixir For The Supervision Tree

Agents fail. A tool call times out, a model returns malformed JSON, a subagent wedges on a loop. The interesting question is not how to prevent that, it is what happens to the other twelve agents when one of them dies.

Elixir answers that at the runtime level instead of in application code. Each agent node is a supervised process, a crash restarts that node from a known state, and nothing above it in the tree notices. If you have built this by hand in Python with a process pool and a watchdog, you already know how much code you are not writing here.

Bring Your Own Model, Or None

Keys go in .env for whichever providers you want. Claude, OpenAI and Gemini can coexist in the same install, so different agents can run on different models. Or you point the whole thing at Ollama and run with no external keys at all.

That last option is the one worth trying first if you are just evaluating. The entire loop including the model runs on your machine, so nothing is billed and nothing leaves the box while you decide whether the thing is useful.

What You Actually Own

Storage and search run on one bundled database, so there is no cloud account to create and no external service to keep alive. Conversation history, embeddings, tool results and keys all sit on your own disk.

That is the real argument for self-hosting an agent platform, and it only holds if the platform does not quietly depend on something hosted. Worth checking before you commit a weekend to any of them.

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