5 Docker containers for AI agent developers: spin up and get to work

Running an AI agent requires several services at once: a language model, a vector database for memory, a task orchestrator, observability tools. Each of them used to take hours to set up. Docker changes the equation — the right set of containers can be brought up in minutes and is ready to use immediately.

Ollama: local LLMs without the cloud

Ollama is a Docker container that runs open language models on local hardware. Inside is a REST server with an OpenAI-compatible API: change one endpoint in the agent code, and Llama 3.1 runs on your GPU instead of GPT-4. No changes to the rest of the code.

Supported out of the box:

• Llama 3.1, Mistral 0.3, Gemma 2, Qwen2.5, Phi-3, and more than 50 models
• GPU acceleration via nvidia-container-toolkit (CUDA)
• Automatic download and caching of model weights
• Parallel requests with an internal queue

For development, this means zero API costs, zero rate limits, and full control over the model — with no risk of data leaking to the cloud.

Qdrant: vectors for agent memory

Agents need long-term memory: store tool results, index documents, find semantically similar content. Qdrant is one of the fastest vector databases, with REST and gRPC API, built-in metadata filtering, and a ready-made web interface.

When the agent cannot find the right document, visual inspection of points saves hours of debugging — in the interface, you can see both the vectors and the payload next to them. Qdrant scales well: starting from a prototype on localhost, you can move to a replicated cluster without changing client code.

Alternatives are ChromaDB (simpler to start with) and Weaviate (richer in features). Qdrant usually wins on speed once collections reach several million vectors.

n8n: visual-first orchestration

n8n is a self-hosted automation platform that agent developers use as a workflow orchestrator. More than 400 built-in integrations, nodes for OpenAI and Anthropic, HTTP triggers, webhooks, and built-in error handling with retries.

“The visual diagram of the data flow is understandable not only to

developers — product and QA teams can immediately see what the agent is doing,” is a typical argument in favor of n8n in the developer community.

It is convenient for multi-agent systems where several agents exchange results: every step is logged, visible in the interface, and can be restarted from the required point without recalculating the entire pipeline.

Flowise: drag-and-drop for agent chains

Flowise is built on top of LangChain and LlamaIndex and offers a visual builder for agent chains, RAG pipelines, and multi-agent systems. Each flow automatically gets a REST API endpoint — it is enough to add a single HTTP call to the application.

Feature set:

• AgentExecutor, Tool Use, Memory, and ReAct nodes
• Connection to Ollama, OpenAI, Anthropic, Hugging Face, Bedrock
• Support for custom JavaScript functions inside nodes
• Export of flow configuration to JSON for reproducibility

Flowise is especially valuable at the prototyping stage, when you need to test a hypothesis quickly without getting buried in boilerplate code and manual LangChain configuration.

Open WebUI: prompt testing without scripts

Open WebUI is a full-featured chat interface that connects to Ollama or any OpenAI-compatible backend. It works together with Ollama via docker-compose and supports uploading documents directly into the chat for quick RAG testing.

For a developer, this is a convenient testing tool without extra code: change the system prompt, connect a different model, upload a document, and compare the results — in a few clicks. It supports multiple users and saves conversation history.

What it means

Five containers — Ollama, Qdrant, n8n, Flowise, Open WebUI — cover the basic stack of agent development: LLM, vector memory, orchestration, visual builder, and a testing UI. Docker has lowered the entry barrier to the point where going from an idea to a working prototype is one `docker-compose up` away.