Suna

Manus queues autonomous agent runs inside a closed cloud sandbox where engineers wait on shared compute pools and rigid concurrency tiers. Suna moves that execution loop onto self-hosted Linux infrastructure, allowing engineering teams to run agent workflows directly on their own hardware with custom model routing. By backing every agent workspace with isolated Docker containers and Git branches, it turns autonomous web browsing, terminal tasks, and code changes into auditable internal pull requests.

Why Suna instead of Manus

Manus isolates agent operations within proprietary cloud environments where task execution depends on remote scheduling queues and provider-enforced rate limits. While managed infrastructure simplifies initial setup, it throttles turnaround times for multi-step engineering jobs and prevents teams from inspecting runtime execution internals. Suna moves the operational runtime onto self-managed infrastructure. Deploying the platform locally or across private cloud instances gives DevOps engineers complete control over compute allocation, memory limits, and disk throughput.

Performance diverges sharply across throughput and inference latency. Manus restricts command speeds inside shared containers and routes requests through closed upstream proxies. Suna connects directly to local inference engines like vLLM or Ollama alongside private API keys via LiteLLM, eliminating external queue delays and network hops. Autonomous scripts running compiler builds, heavy file transformations, or containerized test suites execute at bare-metal speed without cloud throttling. In exchange, teams take on infrastructure upkeep: running Suna requires maintaining host machines, Docker socket access, and database persistence.

What it does well

• Isolated Docker sandboxing creates disposable execution runtimes for every session, protecting host environments during unattended shell commands and script evaluations.
• Git-native session tracking commits code and configuration changes to dedicated branches, converting agent deliverables into standard pull requests for review.
• Dynamic inference routing through LiteLLM enables switching between commercial models and local LLM endpoints to balance token throughput against latency.
• Direct protocol integration connects Model Context Protocol servers, OpenAPI definitions, and custom shell utilities without external bridge daemons.