Vector Retrieval and RAG · Public

agent-memory-server

Vector retrieval project for checking embedding storage, query semantics, RAG integration, data boundaries, and rollback.

Vector databaseRAGEmbeddingsSemantic searchData boundaries

Best fitDevelopers connecting knowledge bases, documents, or app data to semantic retrieval or RAG workflows.

Check whether this project matches your task before installing it.

What it can doVector database setup checks, embedding model boundaries, collection management, query acceptance, and deletion guidance

Review the portable capability path.

Before continuingVerify in a sandbox

Do not treat a preview pack as a proven local install.

GitHub snapshot280 stars

55 forks · 22 contributors

Doramagic.ai Last verification date: 2026-07-22 Verification method: source evidence, semantic profile, public page gate, and static build acceptance.

Official first step Read manual preview Source repository

Publication status · 2026-07-22

What is agent-memory-server?

agent-memory-server is a vector database, retrieval, or RAG storage component for AI applications.
Best fit: Developers connecting knowledge bases, documents, or app data to semantic retrieval or RAG workflows.
Not for: Not for one-off model API calls or environments that cannot isolate indexed data, credentials, and persistence paths.
Capability added to an AI workflow: Vector database setup checks, embedding model boundaries, collection management, query acceptance, and deletion guidance
First safe verification step: Verify create, query, delete, and rollback with a small public text sample before using real data.
Verification state: source, Quick Start, and sandbox install checks are recorded as passed.
Top risk: May increase setup, validation, or first-run risk for the user.
Evidence base: https://github.com/redis/agent-memory-server, https://github.com/redis/agent-memory-server#readme, Human Manual, Pitfall Log

Quick decision

Use this section to decide whether the project is worth a deeper read.

Best forDevelopers connecting knowledge bases, documents, or app data to semantic retrieval or RAG workflows.

Match the project to your task before installing it.

CapabilityVector database setup checks, embedding model boundaries, collection management, query acceptance, and deletion guidance

Vector retrieval project for checking embedding storage, query semantics, RAG integration, data boundaries, and rollback.

Repositoryredis/agent-memory-server

280 stars · 55 forks

What it can do

Translate the upstream project into concrete capabilities the user can judge before installing.

Overview and System Architecture

Related topics: Core Memory Features: Working Memory, Long-Term Memory, Search, and Extraction, Clients, APIs, MCP, and Workbench, Operations, Deployment, LLM/Embedding Integration, and Ex...

Source: https://github.com/redis/agent-memory-server / Human Manual

Core Memory Features: Working Memory, Long-Term Memory, Search, and Extraction

Related topics: Overview and System Architecture, Clients, APIs, MCP, and Workbench, Operations, Deployment, LLM/Embedding Integration, and Extensibility

Source: https://github.com/redis/agent-memory-server / Human Manual

Clients, APIs, MCP, and Workbench

Related topics: Overview and System Architecture, Core Memory Features: Working Memory, Long-Term Memory, Search, and Extraction, Operations, Deployment, LLM/Embedding Integration, and Ext...

Source: https://github.com/redis/agent-memory-server / Human Manual

Operations, Deployment, LLM/Embedding Integration, and Extensibility

Related topics: Overview and System Architecture, Core Memory Features: Working Memory, Long-Term Memory, Search, and Extraction, Clients, APIs, MCP, and Workbench

Source: https://github.com/redis/agent-memory-server / Human Manual

Doramagic Pitfall Log

Source-linked risks stay visible on the manual page so the preview does not read like a recommendation.

Source: Doramagic discovery, validation, and Project Pack records

Sources: https://github.com/redis/agent-memory-server, Human Manual, Project Pack evidence, and downstream validation signals.

Community Discussion Evidence

Project-level external discussion stays visible on the detail page, not only inside the manual.

Stars280 stars

Forks55 forks

Contributors22 contributors

Licenseunknown

Community Discussion Evidence

12 source-linked items

Review these external discussions before using agent-memory-server with real data or production workflows. They are review inputs, not standalone proof that the project is production-ready.

01
docs: deduplicate=True merges distinct-but-similar records — ids are ups
github / github_issue
02
docs/UX: search limit hard-capped at 100 — over-limit reads as empty res
github / github_issue
03
docs: empty-text search as the listing/count primitive; relevance cutoff
github / github_issue
04
docs: long-term search ordering is relevance-based — server_side_recency
github / github_issue
05
Server v0.15.2
github / github_release
06
Server v0.15.1
github / github_release
07
Server v0.15.0
github / github_release
08
Server v0.14.0
github / github_release
09
Java Client 0.1.0
github / github_release
10
Server v0.13.2
github / github_release
11
0.13.1
github / github_release
12
0.13.0
github / github_release

How to start

Only source-backed commands are shown here. Verify them in an isolated environment first.

Try the prompt first

Test the workflow without installing the upstream project.

preview

Read the Human Manual

Understand inputs, outputs, limits, and failure modes.

manual

Take context to your AI host

Use the compiled assets in your preferred AI environment.

context

Run sandbox verification

Confirm install commands and rollback before using a primary environment.

verify

docker run -p 8000:8000 -e REDIS_URL=redis://your-redis:6379 -e OPENAI_API_KEY=your-key redislabs/agent-memory-server:latest agent-memory api

Official start command · https://github.com/redis/agent-memory-server#readme · verified: yes

Human Manual

The English page must expose the real manual, not a short placeholder.

8+ sections · Human Manual