# Cerebro 🧠
[](https://crates.io/crates/cerebro)
[](https://opensource.org/licenses/MIT)
**Cerebro** is a blazing-fast, universal, and storage-agnostic "Memory Layer" for AI Agents and LLM Applications, written in pure Rust.
## Why Cerebro?
While typical vector database wrappers just push raw vectors into a database, `Cerebro` functions as the **Hippocampus** for autonomous AI. It natively understands Agentic Memory structures:
- **Short-Term Episodic Memory** (Conversations)
- **Working Memory** (KV State)
- **Long-Term Semantic Memory** (Vector Search with Temporal Decay)
It bridges the gap between raw document parsing and multi-agent frameworks (like LangChain, Auto-GPT, Cursor).
## Key Features
- 🚀 **Zero-Cost Abstractions**: Powered by Rust, designed for massive async parsing and embedding workloads.
- 🔌 **Universal Storage**: Trait-based backends — swap between `MemoryVectorStore`, `PgVectorStore`, or `Qdrant`.
- 🧠 **Pluggable Compute**: Route embeddings through local models (`Candle`) or remote APIs (`OpenAI`, `Anthropic`).
- 🔄 **Active Consolidation**: Background "Sleep Cycle" worker for autonomous memory pruning and semantic organization.
- 🔍 **Hybrid Search**: Native RRF (Reciprocal Rank Fusion) combining keyword and vector retrieval for highest precision.
- 🌐 **MCP Ready**: Native Model Context Protocol server (`cerebro-mcp`) for AI desktop apps.
- 🦀 **Multi-Language**: Native Python (`PyO3`) and WASM bindings.
- 📄 **Complex Ingestion**: PDF extraction and HTML-aware semantic chunking.
## Getting Started
```toml
[dependencies]
cerebro = "0.1.7"
```
### Basic Example
```rust
use cerebro::prelude::*;
use std::sync::Arc;
#[tokio::main]
async fn main() {
let chunker = Arc::new(RecursiveCharacterChunker::new(512, 50));
let embedder = Arc::new(MockEmbedder::new(1536));
let store = Arc::new(MemoryVectorStore::new());
let engine = MemoryEngine::new(chunker, embedder, store);
let doc = Document::new("The Rust programming language ensures memory safety.");
engine.ingest_document(doc).await.unwrap();
let memories = engine.query("What language is safe?", 5).await.unwrap();
for (node, score) in memories {
println!("Match: {} (Score: {})", node.chunk.text, score);
}
}
```
## Documentation
For in-depth guides and technical details:
- **[USER_GUIDE.md](docs/USER_GUIDE.md)**: Implementation examples and usage guides.
- **[ARCHITECTURE.md](docs/ARCHITECTURE.md)**: Structural layout and data pipelines.
- **[CHANGELOG.md](docs/CHANGELOG.md)**: Release history and version updates.
- **[CEREBRO.md](docs/CEREBRO.md)**: Core registry documentation.
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