Borrowing it
Nothing to install: this file belongs to NeerajG03/vector-memory. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/NeerajG03/vector-memory/main/AGENTS.mdgit clone --depth 1 https://github.com/NeerajG03/vector-memoryWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/instructions/neerajg03/vector-memory/agents-md)<a href="https://agentmods.dev/instructions/neerajg03/vector-memory/agents-md"><img src="https://agentmods.dev/badge/instructions/neerajg03/vector-memory/agents-md/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/instructions/neerajg03/vector-memory/agents-md"><img src="https://agentmods.dev/badge/instructions/neerajg03/vector-memory/agents-md.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.03436 | $0.03436 |
| Opus 5.5 | $0.01374 | $0.01374 |
| Sonnet 5.5 | $0.00687 | $0.00687 |
| Haiku 4.5 | $0.00344 | $0.00344 |
Grade A, and why
vector-memory AGENTS.md scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 536 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent Guide - Vector Memory MCP Server
This document provides AI agents with comprehensive information about the codebase architecture, design decisions, and development guidelines.
Table of Contents
- Project Overview
- Architecture
- File Structure
- Core Components
- Design Decisions
- Development Guidelines
- Testing Strategy
- Publishing Workflow
Project Overview
Purpose: MCP server that provides semantic memory capabilities for AI assistants using Redis vector store and HuggingFace embeddings.
Key Features:
- Save files (PDF, TXT, MD) to vector memory
- Recall information using natural language queries
- Automatic duplicate removal when re-saving files
- Smart chunking based on file type
- Memory management tools
Tech Stack:
- Language: Python 3.12+
- Framework: FastMCP (MCP server framework)
- Vector Store: Redis with RedisVectorStore
- Embeddings: HuggingFace sentence-transformers
- Build Tool: Hatchling
- Package Manager: uv
Architecture
High-Level Flow
User/AI Client
↓
MCP Protocol (stdio)
↓
FastMCP Server (vector_memory.py)
↓
├─→ HuggingFace Embeddings (sentence-transformers)
└─→ Redis Vector Store (mcp_vector_memory:*)
Data Flow
-
Save to Memory:
File Path → Check Existence → Determine File Type → Get Optimal Chunk Size → Load Document → Remove Old Versions → Chunk Content → Generate Embeddings → Store in Redis -
Recall from Memory:
Query → Generate Query Embedding → Similarity Search in Redis → Retrieve Top K Results → Format Output
File Structure
vector-memory/
├── vector_memory.py # Main MCP server (2 tools)
├── manage_memory.py # Interactive management CLI
├── cleanup.py # Quick cleanup CLI
├── main.py # Entry point (if needed)
├── test_connection.py # Connection test script
├── validate_server_json.py # Schema validation script
├── pyproject.toml # Package configuration
├── server.json # MCP registry metadata
├── README.md # Quick start guide
├── USAGE.md # Complete usage documentation
├── AGENTS.md # This file
├── LICENSE # MIT license
└── .github/workflows/
└── publish-mcp.yml # Automated publishing workflow
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 536 lines · 3,436 tokens per session scan A c26bb0507933
vector-memory AGENTS.md is an instructions file published in the GitHub repository NeerajG03/vector-memory (0 stars, last pushed 11mo ago), licensed MIT. It adds 3,436 tokens to every session, about $0.0137 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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next.js AGENTS.md
AGENTS.md instructions for vercel/next.js, covering next.js development guide, codebase structure, monorepo overview, core package: packages/next and other important packages.
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langchain AGENTS.md
AGENTS.md instructions for langchain-ai/langchain, covering global development guidelines for the langchain monorepo, corridor security analysis, project architecture and context, monorepo structure and development tools & commands.
vscode copilot-instructions.md
Copilot instructions for microsoft/vscode, covering vs code copilot instructions, project overview, root folders, core architecture (src/ folder) and built-in extensions (extensions/ folder).
deepseek-harness AGENTS.md
AGENTS.md instructions for deepseek-ai/deepseek-harness, covering agents.md, pre-stable apis and released session data, repository layout, commands and host sandbox failures.