gentle-ai vs cq
Memory and shared learning for coding agents compared
gentle-ai and cq help coding agents reuse knowledge across sessions. gentle-ai configures existing agents with persistent project memory, workflows and review evidence, while cq provides a store where agents can query and contribute reusable lessons.
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.
cq: Share Collective Learning Between AI Agents
cq is a knowledge store and workflow for coding agents to query, contribute, and validate reusable lessons. It helps teams and individual developers avoid repeating debugging work across agent sessions and machines.
| gentle-ai | cq | |
|---|---|---|
| Language | Go | Go |
| License | MIT | Apache-2.0 |
| Stars | 7.9k | 1.3k |
| Forks | 845 | 69 |
| Last analyzed | Oct 19, 2026 | Oct 3, 2026 |
Key differences
- gentle-ai adds memory, development workflows and review evidence to agents; cq focuses on querying, proposing and validating reusable knowledge units.
- gentle-ai configures 17 agent integrations through their native settings; cq offers installations for seven named agent hosts and provides MCP tools, SDKs and a server image.
- gentle-ai is designed for local agent configuration across macOS, Linux and Windows; cq supports local SQLite storage, a hosted service or a self-hosted server.
- gentle-ai is licensed under MIT; cq is licensed under Apache-2.0.
- cq is in 0.x and warns that breaking changes are expected; gentle-ai describes ongoing development and notes that integration capabilities vary.
Choose gentle-ai if you…
- want persistent project context alongside workflows and evidence-based review.
- need to configure existing agents across macOS, Linux or Windows without replacing them.
- prefer a shared workflow for a team using different coding agents.
Choose cq if you…
- want agents to query and contribute reusable lessons about recurring problems.
- need a knowledge store that can be local, hosted or self-hosted for team sharing.
- want MCP tools, Go and Python SDKs, or a server component for integrating knowledge workflows.
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This comparison is generated with AI from the OSRepos analyses of both projects. Always check each project's repository and documentation before choosing.