Match the project to your task before installing it.
Vector Retrieval and RAG · Public
memrust
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 guidanceReview the portable capability path.
Before continuingVerify in a sandboxDo not treat a preview pack as a proven local install.
GitHub snapshot1 stars0 forks · 2 contributors
Doramagic.ai Last verification date: 2026-07-29 Verification method: source evidence, semantic profile, public page gate, and static build acceptance.
Publication status · 2026-07-29
What is memrust?
- memrust 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/AIAnytime/memrust, https://github.com/AIAnytime/memrust#readme, Human Manual, Pitfall Log
01
Quick decision
Use this section to decide whether the project is worth a deeper read.Vector retrieval project for checking embedding storage, query semantics, RAG integration, data boundaries, and rollback.
1 stars · 0 forks
02
What it can do
Translate the upstream project into concrete capabilities the user can judge before installing.Getting Started and Overview
Related topics: Architecture and Hybrid Retrieval, Deployment, Namespaces, and Observability
Source: https://github.com/AIAnytime/memrust / Human Manual
Architecture and Hybrid Retrieval
Related topics: Getting Started and Overview, Memory Lifecycle, Multi-Agent Visibility, and Extensibility
Source: https://github.com/AIAnytime/memrust / Human Manual
Deployment, Namespaces, and Observability
Related topics: Getting Started and Overview, Memory Lifecycle, Multi-Agent Visibility, and Extensibility
Source: https://github.com/AIAnytime/memrust / Human Manual
Memory Lifecycle, Multi-Agent Visibility, and Extensibility
Related topics: Architecture and Hybrid Retrieval, Deployment, Namespaces, and Observability
Source: https://github.com/AIAnytime/memrust / 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/AIAnytime/memrust, Human Manual, Project Pack evidence, and downstream validation signals.
03
Community Discussion Evidence
Project-level external discussion stays visible on the detail page, not only inside the manual.Community Discussion Evidence
8 source-linked itemsReview these external discussions before using memrust with real data or production workflows. They are review inputs, not standalone proof that the project is production-ready.
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memrust v0.6.1 — metrics, logs, official Docker image
github / github_release
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memrust v0.6.0 — namespaces and API keys
github / github_release
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memrust v0.5.4
github / github_release
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memrust v0.5.3
github / github_release
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05
memrust v0.5.2
github / github_release
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memrust v0.5.1
github / github_release
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memrust v0.5.0
github / github_release
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Installation risk requires verification
GitHub / issue
04
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.
previewRead the Human Manual
Understand inputs, outputs, limits, and failure modes.
manualTake context to your AI host
Use the compiled assets in your preferred AI environment.
contextRun sandbox verification
Confirm install commands and rollback before using a primary environment.
verifydocker run -p 7700:7700 -v memrust-data:/data aianytime/memrust # Client SDKs pip install memrust # Python npm install memrust-clientOfficial start command · https://github.com/AIAnytime/memrust#readme · verified: yes
05
Human Manual
The English page must expose the real manual, not a short placeholder.8+ sections · Human Manual
memrust Manual
Memory infrastructure for AI agents — agent-native memory engine in Rust (HNSW + BM25 + entity graph + recency behind remember/recall/forget, with MCP)
Open the full manual- https://github.com/AIAnytime/memrust Project Manual
- Table of Contents
- Getting Started and Overview
- Related Pages
- What is memrust
- Core Capabilities
- Architecture at a Glance
- Quick Start
Getting Started and Overview
Related topics: Architecture and Hybrid Retrieval, Deployment, Namespaces, and Observability
Source: https://github.com/AIAnytime/memrust / Human Manual
Architecture and Hybrid Retrieval
Related topics: Getting Started and Overview, Memory Lifecycle, Multi-Agent Visibility, and Extensibility
Source: https://github.com/AIAnytime/memrust / Human Manual
Deployment, Namespaces, and Observability
Related topics: Getting Started and Overview, Memory Lifecycle, Multi-Agent Visibility, and Extensibility
Source: https://github.com/AIAnytime/memrust / Human Manual
Memory Lifecycle, Multi-Agent Visibility, and Extensibility
Related topics: Architecture and Hybrid Retrieval, Deployment, Namespaces, and Observability
Source: https://github.com/AIAnytime/memrust / 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
06
AI Context Pack and portable assets
After deciding to continue, take the project context into your own AI host.Complete pack plus user-owned assets
These files are planning and verification assets for Claude Code, Codex, Gemini, Cursor, ChatGPT, and other AI hosts.
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Preflight checks
Treat this page as a planning asset, not proof that your local environment is ready.- The manual is generated from source-linked project files and Doramagic validation signals.
- Community evidence warnings stay visible instead of being converted into marketing claims.
- This English page is indexable because the locale quality gate passed and explicit English index approval is enabled.
- Use the upstream repository as the final authority for installation commands, license, and version-specific behavior.
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Pitfall Log and verification risks
Doramagic surfaces high-risk items before users treat a candidate capability as verified.Installation risk requires verification
May increase setup, validation, or first-run risk for the user.
Configuration risk requires verification
May increase setup, validation, or first-run risk for the user.
Capability evidence risk requires verification
May increase setup, validation, or first-run risk for the user.
Maintenance risk requires verification
May increase setup, validation, or first-run risk for the user.
Security or permission risk requires verification
May increase setup, validation, or first-run risk for the user.
Security or permission risk requires verification
May increase setup, validation, or first-run risk for the user.
Maintenance risk requires verification
May increase setup, validation, or first-run risk for the user.
Maintenance risk requires verification
May increase setup, validation, or first-run risk for the user.