cerebro-1.1.0 has been yanked.
Cerebro 🧠
A high-performance semantic memory engine + multi-agent swarm orchestrator for AI, written in pure Rust.

Cerebro provides a three-tier cognitive memory system (Working, Episodic, Semantic) with pluggable storage backends and a built-in multi-agent swarming engine. Agents don't just store vectors — they remember, reason, and collaborate.
Quick Start
[dependencies]
cerebro = "1.0.1"
Memory Engine
use cerebro::prelude::*;
use std::sync::Arc;
#[tokio::main]
async fn main() {
let engine = MemoryEngine::new(
Arc::new(RecursiveCharacterChunker::new(512, 50)),
Arc::new(MockEmbedder::new(1536)),
Arc::new(MemoryVectorStore::new()),
);
engine.ingest_document(Document::new("Rust ensures memory safety at compile time.")).await.unwrap();
let results = engine.query("memory safety", 5).await.unwrap();
for (node, score) in results {
println!("[{:.3}] {}", score, node.chunk.text);
}
}
Multi-Agent Swarming
use cerebro::prelude::*;
use cerebro::swarm::prelude::*;
use std::sync::Arc;
#[tokio::main]
async fn main() {
let engine = Arc::new(MemoryEngine::new(
Arc::new(RecursiveCharacterChunker::new(512, 50)),
Arc::new(MockEmbedder::new(8)),
Arc::new(MemoryVectorStore::new()),
));
let memory = Arc::new(CerebroMemoryBus::new(engine, Arc::new(MemoryKVStore::new())));
let mut swarm = SwarmOrchestrator::new(memory);
swarm.register_agent(AgentConfig {
id: "analyst".into(),
name: "Security Analyst".into(),
system_prompt: "Analyze code for security vulnerabilities.".into(),
model: LlmProvider::Ollama { model: "llama3".into(), base_url: "http://localhost:11434".into() },
tools: vec![], handoff_targets: vec![], max_steps: 10,
});
let result = swarm.execute(
SwarmPattern::Sequential { agent_order: vec!["analyst".into()] },
"Review: fn process(data: &[u8]) { unsafe { ... } }",
).await.unwrap();
println!("{}", result.final_output);
}
Features
| Feature |
Description |
| Three-tier memory |
Working (KV), Episodic (conversation), Semantic (vector search) |
| SwarmForge |
Sequential, parallel, and hierarchical multi-agent orchestration |
| Universal LLM |
Ollama, OpenAI, Gemini, Anthropic, any OpenAI-compatible API |
| Hybrid search |
RRF combining keyword + vector retrieval (PgVector) |
| Consolidation |
Background worker for autonomous memory pruning |
| MCP server |
cerebro-mcp binary for AI desktop apps |
Feature Flags
| Flag |
Enables |
local_models |
Local embeddings via HuggingFace Candle |
qdrant |
Qdrant distributed vector storage |
pdf |
PDF text extraction |
graph |
Neo4j knowledge graph persistence |
python |
PyO3 bindings |
wasm |
Browser/Edge worker support |
License
MIT
Author: Suraj Kumar Nanda | surajkumarnanda.com