Mitii AI Agent

I ran a small log-based comparison of Mitii vs Cline on the same repo, same workspace, and same practical tasks:

1. Explain how to run the project and configure Ollama.
2. Determine whether the project currently supports a vector database.
3. Plan how vector DB support should be implemented.

This is not a universal benchmark. It is a failure-analysis style case study from one real workspace.

The interesting result: both tools handled the easy setup question. The decisive difference appeared on the vector DB support question. Cline ran a broad search for vector/embedding-related terms, got a large result set, pushed the prompt to 32,767 tokens, hit an invalid provider response, retried, and then drifted away from the original question. Mitii searched, filtered the evidence, and correctly answered that the project had vector-related references but did not currently implement vector database support.

For the implementation-planning task, Mitii produced a structured four-step plan: vector service + embedding interface, ingestion sync, semantic search mode, and RAG integration. Cline returned some useful ideas, but the output included visible corruption and protocol artifacts.

My conclusion: in this run, Mitii did not win because it was magically smarter. It won because its context orchestration was safer under pressure. Retrieval alone is not enough; the agent has to filter, summarize, preserve intent, and avoid driving the local model into the context cliff.