durag 2.1.12

Du-RAG: Durable RAG — persistent memory for AI agents

Du-RAG

Durable RAG - a persistent memory layer for AI agents built on retrieval-augmented generation.

---

pip install durag

from durag import Memory

m = Memory()

m.add("Alice loves Python and open source", user_id="alice")
m.add("Alice built Du-RAG", user_id="alice")

history = m.get_all(filters={"user_id": "alice"})
print(history)

Why Du-RAG?

Du-RAG is the fastest memory library for AI agents. Unlike other libraries that block for 1-2 seconds calling an LLM on every write, Du-RAG stores memories instantly and lets you consolidate in the background.

# Fast path - returns in ~50ms, no LLM call
m.add("Alice switched to Enterprise plan", user_id="alice")

# Search works immediately on raw text
results = m.search("What plan is Alice on?", user_id="alice")

# Consolidate later - batch-extract facts with one LLM call
m.consolidate(user_id="alice")

API Keys

Du-RAG requires a provider API key. Set the env var for your preferred provider before first use:

Provider	Env Var	Used For
OpenAI (default)	OPENAI_API_KEY	Embeddings + LLM
Anthropic	ANTHROPIC_API_KEY	LLM
Google Gemini	GOOGLE_API_KEY	Embeddings + LLM
DeepSeek	DEEPSEEK_API_KEY	LLM
Together AI	TOGETHER_API_KEY	Embeddings + LLM
Groq	GROQ_API_KEY	LLM
MiniMax	MINIMAX_API_KEY	LLM
Sarvam AI	SARVAM_API_KEY	LLM
vLLM	VLLM_API_KEY	LLM

export OPENAI_API_KEY="sk-..."

Features

• Fast writes - add() returns in ~50ms (no blocking LLM call)
• Async consolidation - batch-extract facts with consolidate() when idle
• Persistent memory across conversations - agents remember what they learn
• Semantic search via vector embeddings - find the right context fast
• Multiple backends - OpenAI, Anthropic, Gemini, DeepSeek, Ollama, vLLM, and more
• Vector stores - Qdrant (default), FAISS, Chroma, Pinecone, Weaviate, and others

License

Apache 2.0