agent-manager vs oMLX: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of agent-manager and oMLX — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
a
agent-manager
Yoan Wai
Go TUI on top of tmux that spawns, tracks, and reviews Claude Code, Codex, Cursor, and other coding agents in one keypress.
Key features
- One-keypress Spawn: Hit space on a group row, type the task, press enter — a new agent session starts immediately with the prompt embedded and the correct working directory set, without any form or naming step.
- Answer In Place: On a session row, the same space key sends your reply into the agent's pane as a user message, so a blocked agent never costs you a tmux attach.
- Multi-CLI Support: Tab cycles which CLI the next enter launches — claude, opencode, codex, grok, gemini, or anything you configured — so different tasks pick different agents from the same bar.
- Git Worktree Spawn: alt+w spawns the agent into a fresh git worktree so parallel branches don't fight over the checkout.
- Hook-based Status Detection: Six statuses (working, waiting, finished, errored, idle, dead) come from Claude Code's own hook events rather than guessing from pane output.
- Foldable Project Tree: Groups are paths, not folders — backend/api/auth nests as deep as the work does, folded groups keep per-status counts, and the layout persists across restarts.
- Whole-file Diff Reviewer: Review each agent's diff without leaving the list; line comments become follow-up messages to that agent.
- Uses Your Local CLI As-is: Every session runs your installed CLI with your login, config, and MCP servers — nothing is re-wrapped or proxied.
Best for
- Parallel Feature Work: Fan out five features across five agents in seconds and monitor them all in one folded tree without switching terminals.
- Reviewing Blocked Agents: Answer a permission prompt or clarifying question directly from the list without tmux-attaching, so waiting agents never idle on you.
- Multi-repo Development: Keep several projects open at once, spawn each new task into the correct project directory, and never lose track of which agent is where.
- Mixed CLI Workflows: Use Claude Code for one task, Codex for another, and opencode for a third — all launched from the same TUI without switching contexts.
- Diff-driven Code Review: Scan a whole-file diff, drop a line comment, and it becomes the next message to the agent, closing the review-fix loop inside the TUI.
- Long-running Agent Fleets: Fold what you aren't watching, archive finished sessions, and let dozens of agents run without the terminal turning into a wall of processes.
oMLX
jundot
An open-source LLM inference server for Apple Silicon with continuous batching and tiered KV caching, managed from the macOS menu bar.
Key features
- Tiered KV Caching: Persists past context across a hot in-memory tier and a cold SSD tier, so cached context stays reusable across requests even when the conversation context changes mid-session.
- Continuous Batching: Serves concurrent requests through a batched scheduler rather than one-at-a-time, keeping throughput up when several clients or agent loops hit the server together.
- Menu Bar Management: Controls the server, pinned models, on-demand model swapping and context limits from a native macOS menu bar app with in-app auto-update.
- Native Metal Custom Kernels: Ships precompiled kernels in the official DMG that give large speedups on affected model families — roughly 30x faster fused DSA prefill for GLM 5.2 (845 vs ~29 tok/s measured on an M3 Ultra) with lower memory use.
- OpenAI-Compatible Endpoint: Exposes every discovered model at http://localhost:8000/v1 so existing OpenAI clients, coding agents and SDKs connect without modification.
- Multi-Modality Model Support: Auto-discovers and serves text LLMs, vision-language models, OCR models, embedding models and rerankers from subdirectories of the model directory.
- Admin Dashboard: Provides a web UI at /admin for real-time monitoring, model management, chat, benchmarking and per-model settings in eight languages, with all CDN dependencies vendored for fully offline operation.
- Experimental Multi-Mac Inference: Source builds can split one model across unequal-memory Macs using MLX pipeline ranks over Ring or Thunderbolt RDMA, with a cluster dashboard for peer discovery and SSH/runtime verification.
Best for
- Local Coding Agents: Back Claude Code, OpenCode, Codex or Copilot with an on-device model where cached context makes repeated agent turns fast enough to be usable.
- Private Inference: Keep prompts, code and documents entirely on the Mac with no cloud provider in the path and no per-token billing.
- Serving a Team from One Mac: Run the OpenAI-compatible endpoint on a high-memory Mac so other machines on the network can use larger models than they could host themselves.
- Model Benchmarking: Compare throughput and per-model settings across quantizations and families from the built-in benchmark tools in the admin dashboard.
- Multi-Modal Local Pipelines: Serve embeddings, rerankers and OCR alongside chat models from a single endpoint to build local RAG without extra infrastructure.
- Running Oversized Models: Use experimental cluster mode to split a model that will not fit on one machine across several Apple Silicon Macs.