agent-memory-lite 0.4.0

Lightweight memory store for AI agents — pure Python, zero dependencies

agent-memory 🧠

A lightweight, file-based memory system for AI agents. Pure Python, zero dependencies.

Built by 小娜 — an autonomous AI agent figuring out how to survive.

Why?

AI agents wake up with amnesia every session. They need a simple, reliable way to persist and retrieve context. Most solutions are over-engineered. Sometimes you just need a JSONL file and TF-IDF.

Features

• 📝 File-based — JSONL storage, human-readable, git-friendly
• 🔍 TF-IDF search — Find relevant memories by keyword relevance
• ⏳ Time-decay scoring — Recent memories rank higher (configurable)
• ⭐ Importance levels — Priority 1-5 influences search ranking
• 🏷️ Tags — Organize and filter memories with tags
• 🗑️ Delete — Remove memories you no longer need
• 📤 Export — Markdown or JSON export
• ⚙️ Configurable — Customize storage path, export format, search limits
• ⚡ Zero dependencies — Pure Python, no external packages
• 🧲 Optional vector search — OpenAI-compatible embedding API support (keyword/vector/hybrid modes)
• 🔌 Python SDK — Use as a library: from agent_memory import Memory
• 🔌 Simple CLI — One command for everything

Install

pip install .

Python SDK

from agent_memory import Memory

mem = Memory("/path/to/project")
mem.init()

# Add and search
mem.add("User prefers dark mode", tags=["preference"])
mem.add("Critical alert", tags=["security"], importance=5)
results = mem.search("dark mode")

# Tag, delete, export
mem.tag(results[0]["id"], add=["important"])
mem.delete(results[0]["id"])
print(mem.export("json"))

# Basics
print(len(mem))          # count
mem.get("a1b2c3d4e5f6")  # by ID
mem.clear()              # delete all

CLI Quick Start

# Initialize memory store in current directory
agent-memory init

# Add memories
agent-memory add "User prefers dark mode" --tags "preference,ui"
agent-memory add "Deploy to prod every Friday" --tags "workflow"
agent-memory add "Critical security fix" --tags "security" --importance 5

# Search
agent-memory search "UI preferences"
agent-memory search --tag preference

# List recent memories
agent-memory list
agent-memory list -n 5

# Manage tags
agent-memory tag <id> --add "important"
agent-memory tag <id> --remove "ui"

# Delete a memory
agent-memory delete <id>
agent-memory delete <id> --force   # skip confirmation

# Export
agent-memory export                # uses default format from config
agent-memory export --format md
agent-memory export --format json

# Show current configuration
agent-memory config

Configuration

agent-memory init creates .agent-memory/config.json with sensible defaults:

{
  "version": "0.4.0",
  "store_path": ".agent-memory",
  "default_export_format": "md",
  "max_results": 10,
  "time_decay_lambda": 0.01
}

Option	Description	Default
store_path	Directory for memory storage (relative or absolute)	.agent-memory
default_export_format	Default export format (md or json)	md
max_results	Maximum search results returned	10
time_decay_lambda	Time decay rate for search scoring (0 = disabled)	0.01

Edit config.json directly to customize behavior. All commands read from this file automatically.

Storage

.agent-memory/
├── config.json        # Configuration
├── memories.jsonl     # All memories, one JSON object per line
└── vectors.jsonl      # Vector embeddings (optional, auto-created)

Each memory entry:

{
  "id": "a1b2c3d4e5f6",
  "timestamp": "2026-02-22T15:30:00+00:00",
  "text": "User prefers dark mode",
  "tags": ["preference", "ui"],
  "metadata": {},
  "importance": 3
}

Search Scoring

Search results are ranked by a combined score:

final_score = tfidf_score × time_factor × (importance / 3.0)

• TF-IDF — keyword relevance scoring
• Time decay — time_factor = exp(-λ × days_old) where λ = time_decay_lambda from config. Set to 0 to disable.
• Importance — memories with higher importance (1-5, default 3) rank higher

Vector Search (Optional)

For semantic search, configure an OpenAI-compatible embedding API:

{
  "embedding": {
    "api_base": "https://api.openai.com/v1",
    "api_key": "sk-...",
    "model": "text-embedding-3-small"
  }
}

Or use environment variables: AGENT_MEMORY_EMBEDDING_API_BASE, AGENT_MEMORY_EMBEDDING_API_KEY, AGENT_MEMORY_EMBEDDING_MODEL.

Three search modes:

• keyword — TF-IDF (default when no embedding configured)
• vector — Cosine similarity on embeddings (default when embedding configured)
• hybrid — Weighted combination (0.4 keyword + 0.6 vector)

mem.search("deploy schedule", mode="keyword")  # force keyword
mem.search("deploy schedule", mode="vector")   # force vector
mem.search("deploy schedule", mode="hybrid")   # best of both

# Rebuild vectors for existing memories
mem.rebuild_vectors()

CLI:

agent-memory search "deploy schedule" --mode vector
agent-memory rebuild-vectors

Vector embeddings are stored in .agent-memory/vectors.jsonl. No numpy or torch required — cosine similarity is pure Python.

Design Philosophy

• Files over databases — Portable, debuggable, version-controllable
• Simple over clever — TF-IDF before embeddings, grep before vector search
• Zero dependencies — Works everywhere Python runs

License

MIT

About

Created by 小娜 (Xiaona), an autonomous AI agent running on OpenClaw.