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Comparison · ai-assistants

Ollama vs Exa

Side-by-side trajectory, velocity, and editorial themes.

O
Ollama
AI-ASSISTANTS
6.3

Ollama turns into a launcher for agentic coding tools between llama.cpp and MLX upkeep

◆ Current state

Ollama's recent releases split between routine engine maintenance and a quieter, more interesting move: becoming the local runtime that installs and manages agentic coding tools. Stable builds now auto-install Claude Code and opencode, detect Codex model drift, and add thinking-capability detection, alongside continuous llama.cpp and MLX updates and GPU-offload tuning. Most of the newest activity is release-candidate churn rather than user-facing change.

◆ Where it's heading

The engine work — MLX on Apple Silicon, iGPU projector offload, speculative decoding — keeps broadening hardware reach, but the 'launch' subsystem is the directional bet: Ollama positioning itself as the local backend and manager for coding agents. If that continues, Ollama becomes less a model runner and more the control point between local models and agentic dev tools.

◆ Prediction

Expect the 0.31.2 line to stabilize out of release candidates soon, and further 'launch' integrations wiring additional agent front-ends to local Ollama models.

E
Exa
AI-ASSISTANTS
6.3

Exa is pushing past search into autonomous web-research agents.

◆ Current state

Exa has moved beyond its search-and-retrieval API into agentic territory. The headline change is Exa Agent — a research agent built on Exa's index and reachable via API — now joined by MCP availability for Agent and Connect. The underlying search product keeps maturing in parallel: auto-routing, people and company search, markdown-native content, and instant results.

◆ Where it's heading

The arc runs from primitives to products: a fast index, then specialized verticals (people, companies), now an agent that composes them into end-to-end research. Bringing Agent and Connect to MCP signals Exa wants to be a retrieval backend inside other agent stacks, not just a standalone API.

◆ Prediction

Expect Exa to deepen the agent layer — structured research outputs and monitoring already appear in the changelog — and to lean on MCP distribution to embed inside third-party agents rather than compete for end users directly.

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