Airweave Search vs Local RAG (shinpr)
Two MCP servers from our AI & ML ranking, compared on live data - updated nightly, never sponsored.
Bottom line · 2026-08-25
Local RAG (shinpr) records more monthly package installs (13,923 vs 6,559); Local RAG (shinpr) is maintained more actively (last commit 2026-08-23 vs 2026-06-05).
How they differ in kind
Airweave Search focuses on: Open-source context retrieval layer for AI agents and RAG systems. Local RAG (shinpr) focuses on: Search private documents from an MCP client or the terminal without sending them to an embedding API. Airweave Search offers a remote endpoint; Local RAG (shinpr) is local stdio. Environment-variable extraction: Airweave Search yielded no variables extracted; Local RAG (shinpr) yielded 3 variables. Implementation languages differ (Python vs TypeScript).
What each one does
Airweave Search
Open-source context retrieval layer for AI agents and RAG systems. Airweave connects to your apps, tools, and databases, continuously syncs their data, and exposes it through a unified, LLM-friendly search interface. AI agents query Airweave to retrieve relevant, grounded, up-to-date context from multiple sources in a single request.
From the project's README.
Local RAG (shinpr)
Search private documents from an MCP client or the terminal without sending them to an embedding API. It runs locally over stdio via the published package.
From the project's README.
14 signals, side by side
| Signal | Airweave Search | Local RAG (shinpr) |
|---|---|---|
| Monthly installs | 6,559 | 13,923 |
| GitHub stars | 6,564 | 371 |
| Last commit | 2026-06-05 | 2026-08-23 |
| Releases · last 90 days | 2 | 10+ |
| In the registry since | Feb 2026 | Oct 2025 |
| Registry versions | 72 | 47 |
| Documented tools | not extracted - see README | not extracted - see README |
| Env vars extracted | none extracted - see README | 3 |
| Runs | local or remote | local (stdio) |
| Endpoint auth | yes (verified) | local only |
| Maintenance | actively maintained | actively maintained |
| License | MIT | MIT |
| Language | Python | TypeScript |
| Category rank | #3 of 95 | #1 of 95 |
Environment variables found in the READMEs
These are extracted names, not a requiredness check. Project docs may mark them optional or require other setup.
Airweave Search
No environment variables were extracted from the README setup. Check the project documentation for other authentication or configuration steps.
Local RAG (shinpr)
- BASE_DIR
- RAG_HYBRID_WEIGHT
- BASE_DIRS
Which one, for what
Derived from the signals above, not hands-on testing.
Pick Airweave Search if…
- → you want a hosted endpoint instead of running a local process
- → you prefer a Python codebase to extend or audit
Pick Local RAG (shinpr) if…
- → release velocity matters - 10+ releases in 90 days vs 2
- → you prefer a TypeScript codebase to extend or audit
Who's behind them
Airweave Search - airweave-ai: 1 MCP server tracked, 1 maintained, 6,564 combined stars.
Local RAG (shinpr) - shinpr: 3 MCP servers tracked, 3 maintained, 622 combined stars. Full record.
Not sold on either?
The next-ranked AI & ML servers we track:
Quick answers
Is Airweave Search better than Local RAG (shinpr)?
Package installs favor Local RAG (shinpr): 13,923 monthly installs to 6,559. Airweave Search still ranks #3 of 95 maintained AI & ML servers. Data as of 2026-08-25; we haven't hands-on tested either.
Can I use Airweave Search and Local RAG (shinpr) together?
Yes - MCP clients accept multiple servers in one config, so you can enable both AI & ML 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?
Airweave Search: no environment variables extracted from the README setup; Local RAG (shinpr): 3 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 AI & ML servers · Python servers · TypeScript 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.