ai-memory vs gentle-ai
Persistent memory and workflows for coding agents compared
ai-memory and gentle-ai both help coding agents retain context across sessions. ai-memory captures activity into a self-hosted, git-backed Markdown wiki and supports handoffs, while gentle-ai configures existing agents with persistent memory, development workflows, and review evidence.
ai-memory: Share Long-Term Memory Across Coding Agents
A self-hosted Rust service that captures coding-agent activity into a searchable, git-backed Markdown wiki and hands work between agents. It suits developers and teams who want portable project memory without requiring LLM calls.
gentle-ai: Add Memory and Workflows to Coding Agents
Gentle-AI configures existing AI coding agents with persistent memory, development workflows and evidence-based review, without locking you into one agent. It supports 17 agents across macOS, Linux and Windows.
| ai-memory | gentle-ai | |
|---|---|---|
| Language | Rust | Go |
| License | MIT | MIT |
| Stars | 9.1k | 7.9k |
| Forks | 632 | 845 |
| Last analyzed | Oct 10, 2026 | Oct 19, 2026 |
Key differences
- ai-memory is a Rust service that stores its source of truth in git-backed Markdown; gentle-ai is a Go tool that configures agents through their native configuration.
- ai-memory focuses on capturing activity, searchable project knowledge, and claim-once handoffs; gentle-ai adds proportional development workflows and risk-adjusted review evidence.
- ai-memory can run locally or as a shared server with multi-user authentication; gentle-ai configures agents across macOS, Linux, and Windows rather than supplying them.
- ai-memory's default capture, search, and handoff paths do not require LLM API keys; gentle-ai's listed features include Engram memory, curated skills, optional personas, and MCP tools.
- Both projects use the MIT license and are not archived. ai-memory has 9.1k stars and 632 forks; gentle-ai has 7.9k stars and 845 forks.
Choose ai-memory if you…
- want a self-hosted, searchable project wiki whose source files are ordinary Markdown.
- need to hand off unfinished work between supported agents or share project knowledge across machines and teammates.
- prefer default capture, search, and handoff paths that do not require an LLM API key.
Choose gentle-ai if you…
- want to configure existing coding agents with a shared development and review workflow.
- need workflows that keep small tasks lightweight while preserving substantial work for later sessions.
- want review evidence tied to a specific candidate, with review depth adjusted to assessed risk.
Compare the numbers side by side, or add more projects
This comparison is generated with AI from the OSRepos analyses of both projects. Always check each project's repository and documentation before choosing.