mini-swe-agent vs prime-agent
The 100-line agent from the SWE-bench team: >74% on SWE-bench Verified with no tools but bash, no config sprawl — the reference minimal harness, adopted by Meta, NVIDIA and Ramp. — 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.
The two poles of coding-agent design: mini-swe-agent proves 100 lines and bash suffice; Prime Agent bets on a persistent REPL, subagents and self-refining harness state.
| mini-swe-agent | prime-agent | |
|---|---|---|
| Stars | 6.3k | 6.4k |
| Forks | 866 | 515 |
| Language | Python | TypeScript |
| License | MIT | MIT |
| Last activity | 6 days ago | 2 days ago |
| Topics | coding, agents | coding, agents |
| Curated connections | 6 | 3 |
mini-swe-agent — the curator's take
The existence proof that most harness complexity is optional: the team that built SWE-bench and SWE-agent asked what a 100x simpler agent loses — the answer is almost nothing (>74% Verified), which is why it became the standard baseline harness for benchmarking models (Ramp's SWE-bench, DeepSWE — where it beats Claude Code and Codex as a harness). Read it to understand agents; use it to evaluate models fairly. NOT a daily driver: no MCP, no skills, no IDE plumbing — by design. If you're choosing a tool to ship features with, this is the control group, not the product.
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.