Forget Skills. Embrace Memory. Keep your model in the sweet spot.
npm install -g march-cliMarch is obsessively token-efficient. After each turn, context resets to ~8K — we discard all intermediate execution and keep only two things: the user's question and the AI's final response.
Most agents fight context bloat with compression, truncation, retrieval, summarization. March's answer: just throw away what you don't need.
The result:
- 91% cache hit rate, individual model calls rarely exceed 50K tokens
- Context never grows unbounded — no context rot
- Your model always operates in the sweet spot, not drowning in 100K of noise
March has a built-in memory system. Anything you tell March — preferences, project conventions, technical decisions — it remembers. When you need it again, March automatically recalls relevant memories during its thinking process. No manual search required.
The problem with Skill systems: Skill files inject context upfront. What happens when you have too many?
March takes a different approach: every memory is a "latent skill," recalled on-demand rather than always sitting in context. What you've discussed is the best prompt.
March stores memories as Markdown files under ~/.march/March Memories/. You can directly edit, delete, or add files — March auto-detects changes.
Image Generation: If you have access to ChatGPT Codex, March can generate images directly — no extra API keys or third-party services.
Web Search: Connect SuperGrok and all your configured models gain web search. March dispatches Grok to search and injects results into the current conversation.
More Search: Tavily Search and Brave Search are built in.
March uses ~/.march/config.json (global) or <project>/.march/config.json (project-level) for model and provider configuration. Compatible with any OpenAI-compatible API.
{
"provider": "openai",
"model": "gpt-5.1"
}See the configuration docs for network proxy setup, custom providers, multi-model setup, and more.
March is similarly capable but takes a fundamentally different approach to context. Instead of keeping everything in context and relying on compression, March resets context each turn — you get ~8K clean context every time, with 91% cache hit rate. March also replaces Skill files with a built-in memory system that recalls on demand.
Both are open source, terminal-native agents. March's key differentiators: extreme token efficiency via per-turn context reset, a built-in Markdown memory system with automatic recall, and the philosophy that memories should be recalled on-demand — not injected upfront like Skills.
- Full Documentation — configuration, context management, memory system
- Custom Provider — connect local models or third-party APIs
- Context Management — March's context architecture explained
- Memory System — storage and recall mechanism