locus vs context-mode
locus: Locus provides persistent project-aware memory for AI coding tools, using three memory layers: structural, semantic, and episodic. It works with any MCP-compatible client and offers 14 tools and 3 resources for exploring, searching, and managing memory. Carbon Copy enables passive conversation capture for later recall.; context-mode: Every tool call in an MCP (Model-Controller-Program) environment dumps raw data into the context window, quickly consuming space and causing the agent to lose track of ongoing tasks. Context Mode is an MCP server that tackles this by sandboxing tool outputs to significantly reduce context usage, tracking session events in SQLite for continuity, and promoting 'think in code' to minimize data processing within the LLM.
TL;DR
Maintain project context across AI coding sessions to avoid losing architectural decisions and file structure awareness
- Your sessions fill up with tool output such as Playwright snapshots, GitHub issue lists or logs.
- You want work state (edits, git operations, tasks, decisions) to survive context compaction without being dumped back into the window.
- You use a hook-capable agent, where tool routing happens automatically.
- You need an OSI open-source licence: it uses the Elastic License 2.0, which restricts uses such as offering it as a managed service.
- Your sessions are short and rarely hit the context limit.
- You do not want hooks changing how your agent's tools behave.
Side-by-Side Comparison
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