OpenManus vs prime-agent
Open-source general autonomous agent from the MetaGPT team — the 'Manus without an invite code': browsing, tool use and multi-step task execution from a simple Python core. — versus — Self-improving coding/research agent around a Recursive Language Model: persistent IPython as the core tool, programmatic subagents, durable harness state it refines via evidence-backed /refine.
Both are open autonomous agents for long tasks; OpenManus goes general-purpose browsing-and-tools, Prime Agent specializes in coding/research with durable programmatic state.
| OpenManus | prime-agent | |
|---|---|---|
| Stars | 58k | 6.4k |
| Forks | 10k | 515 |
| Language | Python | TypeScript |
| License | MIT | MIT |
| Last activity | 5 months ago | 2 days ago |
| Topics | agents | coding, agents |
| Curated connections | 4 | 3 |
OpenManus — the curator's take
The fastest way to feel what a general autonomous agent does: clone, add a model key, hand it a task — the 3-hour-prototype energy that earned 57k stars keeps the codebase small enough to actually read, which makes it a great learning skeleton. But look at the commit graph before betting on it: activity has been quiet for months while the team's focus moved on (OpenManus-RL and beyond), so treat it as a reference implementation, NOT a maintained production framework — for durable agent infrastructure reach for an actively developed harness instead.
prime-agent — the curator's take
The two abstractions are genuinely different: context as variables in a persistent REPL (not a transcript), and a harness that rewrites its own supplemental state with rollback — the closest thing to a shipping continual-learning agent. Daemon sessions and agent-to-agent messaging make it a long-horizon tool, not a chat CLI. When NOT: it's a full worldview — you adopt the RLM way or fight it; young codebase moving fast, and self-improvement means your harness drifts from everyone else's.