Using Docker Compose for AI Agent Development
A modern agent project depends on at least four services. There is the LLM proxy that fronts your model providers. There is the vector store that holds your embeddings. There is the observability backend that captures traces of every model call and tool invocation. There is at least one MCP server exposing tools to the agent. Once these services are built, your local machine is running a small distributed system, and the next immediate priority should be how to keep your work reproducible and close to production.
I have done my fair share of work developing locally and deploying with Docker, so I will try to explain it in this article. The stack I used covers the LLM proxy, the vector store, the observability backend, an MCP tool server, and the agent application itself. The example agent is a docs research agent: it indexes a local documents folder, answers questions...
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