Aura: The Universal Context Compiler

Compile any document into AI-ready knowledge bases with built-in agent memory.

Quick Start • Agent Memory • Integrations • RAG Support • Website

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Context is the new Compute.

Aura compiles messy, real-world files (PDFs, DOCX, HTML, code, spreadsheets — 60+ formats) into a single optimized binary (.aura) ready for RAG retrieval and AI agent memory.

One command. No JSONL scripting. No parsing pipelines.

pip install auralith-aura
aura compile ./my_data/ --output knowledge.aura

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⚡ Quick Start

1. Install

pip install auralith-aura

# For full document support (PDFs, DOCX, etc.)
pip install 'aura-core[all]'

2. Compile

# Basic compilation
aura compile ./company_data/ --output knowledge.aura

# With PII masking (emails, phones, SSNs automatically redacted)
aura compile ./data/ --output knowledge.aura --pii-mask

# Filter low-quality content
aura compile ./data/ --output knowledge.aura --min-quality 0.3

3. Use

For RAG (Knowledge Retrieval):

from aura.rag import AuraRAGLoader

loader = AuraRAGLoader("knowledge.aura")
text = loader.get_text_by_id("doc_001")

# Framework wrappers
langchain_docs = loader.to_langchain_documents()
llama_docs = loader.to_llama_index_documents()

For Agent Memory:

from aura.memory import AuraMemoryOS

memory = AuraMemoryOS()

# Write to memory tiers
memory.write("fact", "User prefers dark mode", source="agent")
memory.write("episodic", "Discussed deployment strategy")
memory.write("pad", "TODO: check auth module")

# Search memory
results = memory.query("user preferences")

# End session (flushes to durable shards)
memory.end_session()

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🧠 Agent Memory

Aura includes a 3-Tier Memory OS — a persistent memory architecture for AI agents:

Tier	Purpose	Lifecycle
/pad	Working notes, scratch space	Transient
/episodic	Session transcripts, conversation history	Auto-archived
/fact	Verified facts, user preferences	Persistent

The Memory OS is included free when you install from PyPI (pip install auralith-aura).

# CLI memory management
aura memory list       # View all memory shards
aura memory usage      # Storage usage by tier
aura memory prune --before 2026-01-01  # Clean up old memories

v2.1 Performance Enhancements

Memory OS v2.1 (auralith-aura>=0.2.2) adds six performance enhancements designed for zero RAM overhead — no embedding models, no vector databases, no background services:

Enhancement	What It Does
Temporal Decay	Recent memories rank higher (14-day half-life recency boost)
Noise Filtering	Blocks meta-questions and agent denials from storage and search
Entry Dedup	SHA-256 + SimHash near-duplicate detection prevents redundant writes
Bloom Filters	~1KB per shard — skips irrelevant shards entirely during query
SimHash	64-bit locality-sensitive hashing for fuzzy text matching without embeddings
Tiered Scoring	Facts rank above episodic, episodic above pad in search results

Upgrade: pip install --upgrade auralith-aura

Data Provenance & Trust

Every memory entry stores explicit metadata — you always know what's in memory and where it came from:

Field	What It Tells You
source	Who wrote it — agent, user, or system
namespace	Which tier — pad, episodic, or fact
timestamp	Exact ISO 8601 time of the write
session_id	Which session created it
entry_id	Unique content hash for traceability

Nothing is inferred or synthesized. Memory contains only what was explicitly written via write(). No hidden embeddings, no derived data, no background processing.

Full user control over memory:

memory.show_usage()                              # Inspect what's stored per tier
memory.query("topic")                            # See exactly what's in memory
memory.prune_shards(before_date="2026-01-01")    # Prune by date
memory.prune_shards(shard_ids=["specific_id"])   # Delete specific shards
# Or delete ~/.aura/memory/ to wipe everything

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🤖 Agent Integrations

Aura works natively with the major AI agent platforms:

Platform	Repo	Use Case
OpenClaw	aura-openclaw	Persistent RAG + memory for always-on agents
Claude Code	aura-claude-code	Context-aware coding with /aura commands
OpenAI Codex	aura-codex	Knowledge-backed Codex agents
Gemini CLI	aura-gemini-cli	Gemini CLI extension for RAG

How It Works (Agent RAG Flow)

You: "Learn everything in my /docs/ folder"
  → Agent runs: aura compile ./docs/ --output knowledge.aura
  → Agent loads: AuraRAGLoader("knowledge.aura")
  → You: "What does the auth module do?"
  → Agent queries the .aura file and responds with cited answers

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🌟 Key Features

Feature	Description
Universal Ingestion	Parses 60+ formats: PDF, DOCX, HTML, MD, CSV, code, and more
Agent Memory OS	3-tier memory (pad/episodic/fact) with instant writes
PII Masking	Automatically redacts emails, phones, SSNs before compilation
Instant RAG	Query any document by keyword or ID. LangChain + LlamaIndex wrappers
Quality Filtering	Skip low-quality content with configurable thresholds
Cross-Platform	macOS, Windows, and Linux
Secure by Design	No pickle. No arbitrary code execution. Safe to share.

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📁 Supported File Formats

Documents - PDF, DOCX, HTML, and more

• .pdf, .docx, .doc, .rtf, .odt, .epub, .txt, .pages, .wpd
• .html, .htm, .xml
• .eml, .msg (emails)
• .pptx, .ppt (presentations)

Data - Spreadsheets and structured data

• .csv, .tsv
• .xlsx, .xls
• .parquet
• .json, .jsonl
• .yaml, .yml, .toml

Code - All major programming languages

• Python: .py, .pyi, .ipynb
• Web: .js, .ts, .jsx, .tsx, .css
• Systems: .c, .cpp, .h, .hpp, .rs, .go, .java, .kt, .swift
• Scripts: .sh, .bash, .zsh, .ps1, .bat
• Backend: .sql, .php, .rb, .cs, .scala
• Config: .ini, .cfg, .conf, .env, .dockerfile

Markup - Documentation formats

• .md (Markdown)
• .rst (reStructuredText)
• .tex, .latex

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🔧 CLI Reference

aura compile <input_directory> --output <file.aura> [options]

Options:
  --pii-mask           Mask PII (emails, phones, SSNs)
  --min-quality SCORE  Filter low-quality content (0.0-1.0)
  --domain DOMAIN      Tag with domain context
  --no-recursive       Don't search subdirectories
  --verbose, -v        Verbose output

Memory Management

aura memory list                        # List all memory shards
aura memory usage                       # Show storage by tier
aura memory prune --before 2026-01-01   # Remove old shards
aura memory prune --id <shard_id>       # Remove specific shard

Inspect an Archive

aura info knowledge.aura

📦 Aura Archive: knowledge.aura
   Datapoints: 1,234
   
   Sample datapoint:
     Tensors: ['raw_text']
     Source:  legal/contract_001.pdf

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🔌 RAG Support

from aura.rag import AuraRAGLoader

loader = AuraRAGLoader("knowledge.aura")

# Text retrieval
text = loader.get_text_by_id("doc_001")

# Filter documents
pdf_docs = loader.filter_by_extension(".pdf")
legal_docs = loader.filter_by_source("legal/")

# Framework wrappers
langchain_docs = loader.to_langchain_documents()  # LangChain
llama_docs = loader.to_llama_index_documents()     # LlamaIndex
dict_list = loader.to_dict_list()                  # Universal

# Statistics
stats = loader.get_stats()

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📐 File Format Specification

The .aura format is a secure, indexed binary archive:

[Datapoint 1][Datapoint 2]...[Datapoint N][Index][Footer]

Each Datapoint:
  [meta_length: 4 bytes, uint32]
  [tensor_length: 4 bytes, uint32]
  [metadata: msgpack bytes]
  [tensors: safetensors bytes]

Footer:
  [index_offset: 8 bytes, uint64]
  [magic: 4 bytes, 'AURA']

Security: Uses safetensors (not pickle) — safe to load untrusted files.

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💻 Runs Locally

Aura compiles entirely on your local machine — no cloud uploads, no external APIs, no telemetry.

• Runs on your local hardware — any modern laptop or desktop, your setup, your choice
• Fully offline — zero internet required after install
• Cross-platform — macOS, Windows, Linux
• Python 3.8+

Your documents never leave your hardware.

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🚀 Scale Up with OMNI

Aura handles local compilation. For enterprise-scale training pipelines, model fine-tuning, and production-grade agent infrastructure — there's OMNI.

• Cloud-scale data compilation & training pipelines
• Supervised model fine-tuning with emphasis weighting
• Production agent memory infrastructure
• Team collaboration & enterprise compliance

Explore OMNI →

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📜 License

• Compiler, RAG, Loader, Binary Format: Apache License 2.0
• Memory OS: Proprietary — free to use, included in PyPI package. See LICENSE-MEMORY.

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🔗 Links

• Website: aura.rtalabs.org
• PyPI: pypi.org/project/auralith-aura
• GitHub: github.com/Rtalabs-ai/aura-core
• OpenClaw Skill: github.com/Rtalabs-ai/aura-openclaw

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Made with ❤️ by Rta Labs