Codebase Memory vs Superlocalmemory
Two MCP servers from our memory ranking, compared on live data - updated nightly, never sponsored.
Bottom line · 2026-08-25
Codebase Memory records significantly more monthly package installs (39,800 vs 7,909); both are actively maintained, with commits inside the last month.
How they differ in kind
Codebase Memory focuses on: The fastest and most efficient code intelligence engine for AI coding agents. Superlocalmemory focuses on: Rent the LLM. Own the memory. Rent an LLM - but own the memory, for your company and for your industry. Environment-variable extraction: Codebase Memory yielded no variables extracted; Superlocalmemory yielded 4 variables. Implementation languages differ (C vs Python).
What each one does
Codebase Memory
The fastest and most efficient code intelligence engine for AI coding agents. Full-indexes an average repository in milliseconds, the Linux kernel (28M LOC, 75K files) in 3 minutes. Answers structural queries in under 1ms. Ships as a native executable with a small verified runtime-asset set for macOS, Linux, and Windows - download, run install, done.
From the project's README.
Superlocalmemory
Rent the LLM. Own the memory. Rent an LLM - but own the memory, for your company and for your industry. It runs locally over stdio via the published package.
From the project's README.
14 signals, side by side
| Signal | Codebase Memory | Superlocalmemory |
|---|---|---|
| Monthly installs | 39,800 | 7,909 |
| GitHub stars | 40,198 | 221 |
| Last commit | 2026-08-23 | 2026-08-25 |
| Releases · last 90 days | 8 | 10+ |
| In the registry since | Feb 2026 | Feb 2026 |
| Registry versions | 13 | 1 |
| Documented tools | not extracted - see README | not extracted - see README |
| Env vars extracted | none extracted - see README | 4 |
| Runs | local (stdio) | local (stdio) |
| Endpoint auth | local only | local only |
| Maintenance | actively maintained | actively maintained |
| License | MIT | AGPL-3.0 |
| Language | C | Python |
| Category rank | #1 of 259 | #4 of 259 |
Environment variables found in the READMEs
These are extracted names, not a requiredness check. Project docs may mark them optional or require other setup.
Codebase Memory
No environment variables were extracted from the README setup. Check the project documentation for other authentication or configuration steps.
Superlocalmemory
- SLM_MESH_HOST
- SLM_MESH_SHARED_SECRET
- SLM_MESH_PEER_URL
- SLM_MCP_PROFILE
Which one, for what
Derived from the signals above, not hands-on testing.
Pick Codebase Memory if…
- → you want the more widely installed option - 39,800 monthly installs vs 7,909
- → you prefer a C codebase to extend or audit
Pick Superlocalmemory if…
- → you prefer a Python codebase to extend or audit
- → category standing - #4 of 259 maintained memory servers
Who's behind them
Codebase Memory - deusdata: 1 MCP server tracked, 1 maintained, 40,198 combined stars.
Superlocalmemory - varun369: 1 MCP server tracked, 1 maintained, 221 combined stars.
Not sold on either?
The next-ranked memory servers we track:
Quick answers
Is Codebase Memory better than Superlocalmemory?
Package installs favor Codebase Memory: 39,800 monthly installs to 7,909. Superlocalmemory still ranks #4 of 259 maintained memory servers. Data as of 2026-08-25; we haven't hands-on tested either.
Can I use Codebase Memory and Superlocalmemory together?
Yes - MCP clients accept multiple servers in one config, so you can enable both memory servers side by side. If their tools overlap, keep the one whose toolset fits to keep your agent's tool list lean.
What environment variables do their READMEs document?
Codebase Memory: no environment variables extracted from the README setup; Superlocalmemory: 4 environment variables extracted from the README setup. Extraction does not rule out other authentication or configuration steps; check each project's current documentation.
Keep exploring: best memory servers · Python servers · all comparisons · every server
Methodology: package-usage signal = npm/PyPI installs (last month, platform APIs) · maintenance = commit recency + release cadence (GitHub) · tool lists and environment-variable names extracted from each project's README · endpoint auth from our own nightly probes. We haven't hand-tested these servers; everything here is data as of 2026-08-25.