Home/Compare/rag_api vs memsearch

Comparison

rag_api vs memsearch

Verdict

Pick rag_api if key Insights for Using rag_api as an ID-based RAG FastAPI Tool with Langchain and PostgreSQL/pgvector Integration; pick memsearch if memsearch is a hybrid memory management solution for AI agents with Markdown and Milvus backing, ideal for rich semantic search and long-term data storage.

Markdown twin · rag_api alternatives · memsearch alternatives

GraphCanon updated 4d

rag_api logo

rag_api

danny-avila/rag_api

885pushed Aug 15, 2026
vs
memsearch logo

memsearch

zilliztech/memsearch

2.5kpushed Aug 21, 2026

Trust & integrity

Signalrag_apimemsearch
Maintenance
Very active (6d since push)
As of 4d · github_public_v1
Very active (0d since push)
As of 4d · github_public_v1
Provenance
Not a fork · Personal account
As of 4d · github_public_v1
Not a fork · Organization account
As of 4d · github_public_v1
OSV dependency advisories
No lockfile (source not queried)
As of 1mo · osv@v1
No lockfile (source not queried)
As of 1mo · osv@v1
deps.dev advisories
Not queried
deps.dev@v1
Not queried
deps.dev@v1
OpenSSF Scorecard
Not queried
openssf-scorecard@v1
Not queried
openssf-scorecard@v1

Tagline

rag_api
ID-based RAG FastAPI: Integration with Langchain and PostgreSQL/pgvector
memsearch
A persistent, unified memory layer for all your AI agents backed by Markdown and Milvus.

Stars

rag_api
885
memsearch
2.5k

Forks

rag_api
387
memsearch
231

Open issues

rag_api
44
memsearch
240

Language

rag_api
Python
memsearch
Python

Adopt for

rag_api
Key Insights for Using rag_api as an ID-based RAG FastAPI Tool with Langchain and PostgreSQL/pgvector Integration
memsearch
memsearch is a hybrid memory management solution for AI agents with Markdown and Milvus backing, ideal for rich semantic search and long-term data storage.

Persona

rag_api
-
memsearch
-

Runtime

rag_api
-
memsearch
-

License

rag_api
MIT
memsearch
MIT

Last pushed

rag_api
Aug 15, 2026
memsearch
Aug 21, 2026

Categories

rag_api
Data & Retrieval, Vector Databases
memsearch
AI Agents, Data & Retrieval, Vector Databases

Trust and health

Days since push

rag_api
6d
memsearch
0d

Open issues (now)

rag_api
44
memsearch
240

Stars delta

rag_api
+19 (30d)
memsearch
+155 (30d)

Open issues delta

rag_api
-3 (30d)
memsearch
+9 (30d)

Owner type

rag_api
User
memsearch
Organization

Full report

memsearch
Trust report

Choose rag_api if…

  • Tags unique to rag_api: api, api-rest, embeddings, fastapi.
  • rag_api ships Docker support for self-hosted deployment.
  • When you need rapid integration of REST API services for Retrieval-Augmented Generation (RAG) with robust vector storage.

When NOT to use rag_api

  • Avoid using if your project cannot leverage PostgreSQL/pgvector due to license or compatibility constraints.
  • Not recommended for scenarios where high-level orchestration of multiple APIs and services is necessary without a direct need for FastAPI's simplicity.

Choose memsearch if…

  • Tags unique to memsearch: agent-memory, long-term-memory, milvus, semantic-search.
  • Also covers AI Agents.
  • When you need robust integration with AI agents like Claude Code or Codex

When NOT to use memsearch

  • If your application doesn't require integration with specific AI agents like Claude Code
  • In cases where only simple text data storage without semantic search is needed

Explore

Sources

Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.

GitHub stars on cards: rag_api 885 · memsearch 2.5k (synced Aug 21, 2026).

Common questions

What is the difference between rag_api and memsearch?
rag_api: ID-based RAG FastAPI: Integration with Langchain and PostgreSQL/pgvector. memsearch: A persistent, unified memory layer for all your AI agents backed by Markdown and Milvus.. See the comparison table for live GitHub stats and shared categories.
When should I choose rag_api over memsearch?
Choose rag_api over memsearch when Tags unique to rag_api: api, api-rest, embeddings, fastapi; rag_api ships Docker support for self-hosted deployment; When you need rapid integration of REST API services for Retrieval-Augmented Generation (RAG) with robust vector storage.
When should I choose memsearch over rag_api?
Choose memsearch over rag_api when Tags unique to memsearch: agent-memory, long-term-memory, milvus, semantic-search; Also covers AI Agents; When you need robust integration with AI agents like Claude Code or Codex.
When should I avoid rag_api?
Avoid using if your project cannot leverage PostgreSQL/pgvector due to license or compatibility constraints. Not recommended for scenarios where high-level orchestration of multiple APIs and services is necessary without a direct need for FastAPI's simplicity.
When should I avoid memsearch?
If your application doesn't require integration with specific AI agents like Claude Code In cases where only simple text data storage without semantic search is needed
Is rag_api or memsearch more popular on GitHub?
memsearch has more GitHub stars (2,491 vs 885). Stars measure visibility, not whether either tool fits your constraints.
Are rag_api and memsearch open source?
Yes - both are open-source projects on GitHub (rag_api: MIT, memsearch: MIT).
Where can I find alternatives to rag_api or memsearch?
GraphCanon lists graph-backed alternatives at rag_api alternatives and memsearch alternatives (rag_api markdown twin, memsearch markdown twin), ranked by typed relationship edges rather than popularity votes.
Is there a machine-readable version of this comparison?
Yes. The markdown twin at this comparison mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.
Which is better maintained, rag_api or memsearch?
rag_api: Very active. memsearch: Very active. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance.
Where are the full trust reports for rag_api and memsearch?
GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: rag_api trust report; memsearch trust report.

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