Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add instructions/verygoodplugins/automem/claude-mdgit clone --depth 1 https://github.com/verygoodplugins/automemWhat 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 | $0.04543 | $0.04543 |
| Opus 5 | $0.02271 | $0.02271 |
| Sonnet 5 | $0.00909 | $0.00909 |
| Haiku 4.5 | $0.00454 | $0.00454 |
Grade A, and why
automem CLAUDE.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 3d 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 — 379 lines — stays where its author put it; the contents beside it link to each section on GitHub.
CLAUDE.md
This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
Project Overview
AutoMem is a Flask-based memory service that provides durable memory storage for AI assistants using FalkorDB (graph database) for relationships and Qdrant (vector database) for semantic search. The service gracefully degrades when Qdrant is unavailable, ensuring graph operations always succeed.
Development Commands
# Setup environment
make install # Create .venv, install deps, and keep venv -> .venv
source .venv/bin/activate
# Development
make dev # Start full stack (FalkorDB + Qdrant + API) via Docker
make stop # Stop local containers (keep volumes)
make test # Run pytest test suite (unit tests only)
make test-integration # Run all tests including integration tests (starts Docker)
make test-live # Run integration tests against live Railway server
make logs # Follow Flask API logs
make clean # Clean up Docker containers/volumes
# Benchmarking
make test-locomo # Run LoCoMo benchmark against local server
make test-locomo-live # Run LoCoMo benchmark against Railway server
# Recall Quality Lab (data-driven scoring tests)
make lab-clone # Clone production data to local Docker
make lab-queries # Generate test queries from local data
make lab-test CONFIG=baseline # Run recall test with a config
make lab-compare CONFIG=fix_v1 BASELINE=baseline # A/B compare configs
make lab-sweep PARAM=SEARCH_WEIGHT_VECTOR VALUES=0.20,0.30,0.40,0.50
# Code quality
black . # Format Python code
flake8 # Lint Python code
# Testing specific features
pytest # Run all tests
pytest tests/test_app.py -v # Run with verbose output
pytest -k test_store_memory # Run specific test by name
pytest tests/test_consolidation_engine.py::TestMemoryConsolidator # Run test class
# Deployment
make deploy # Deploy to Railway
make status # Check deployment status
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.
- 3d ago First seen · 379 lines · 4,543 tokens per session scan A 856b778c74c1
automem CLAUDE.md is an instructions file published in the GitHub repository verygoodplugins/automem (805 stars, last pushed 5d ago), licensed MIT. It adds 4,543 tokens to every session, about $0.0227 per session on Opus 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 2026-08-30.
Other instructions, from other repositories
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AGENTS.md instructions for topoteretes/cognee, covering repository guidelines, project structure & module organization, build, test, and development commands, runtime flags worth knowing and multi-tenancy support by backend.
redis-vl-python CLAUDE.md
Instructions for redis/redis-vl-python, covering claude.md - redisvl project context, frequently used commands, development workflow, redis setup and documentation.
RediSearch AGENTS.md
AGENTS.md instructions for RediSearch/RediSearch, covering redisearch development guide, proposing features and large changes, build commands, testing and header generation.
Dragon-Brain CLAUDE.md
Instructions for iikarus/Dragon-Brain, covering dragon brain — claude.md, the harness, audit remediation (april–may 2026, complete 2026-05-09), the lie this audit closed and the contract that matters now.
redis-tui CLAUDE.md
Instructions for davidbudnick/redis-tui, covering claude.md, project overview, build & test commands, architecture and code conventions.
mcp-automem AGENTS.md
Instructions for verygoodplugins/mcp-automem, covering agents.md, project overview, build & development, build typescript to dist/ and development with hot-reload.