AutoGPT is a pure Rust framework that makes building, deploying, and orchestrating intelligent AI agents as natural as writing idiomatic Rust code. It solves the hard problems of agentic AI so you can focus on what your agents actually do.
What Problem Does AutoGPT Solve?
Building production-grade AI agents is painful. You need to wire up LLM API calls, manage conversation state, handle retries, persist long-term memory, coordinate multiple agents, secure inter-agent communication, and integrate version control, all before writing any business logic.
AutoGPT eliminates this boilerplate. Define a persona and a behavior. The framework handles everything else.
use autogpt::prelude::*;
#[tokio::main]
async fn main() {
let agent = ArchitectGPT::new(
"Lead UX/UI Designer",
"Generate a Kubernetes architecture diagram with Prometheus monitoring.",
).await;
AutoGPT::default()
.with(agents![agent])
.build()
.expect("Failed to build AutoGPT")
.run()
.await
.unwrap();
}That is the entire program. AutoGPT initializes the LLM client, constructs the task, runs the agent asynchronously, and writes the output to the workspace.
Core Capabilities
Blazing Speed
Written entirely in safe Rust with async/await and Tokio. Agents run concurrently with zero-cost abstractions.
Composable Agents
Mix built-in agents or bring your own via the Auto derive macro and Executor trait.
Multi-Provider LLMs
Gemini, OpenAI, Anthropic Claude, XAI Grok, Cohere, and HuggingFace, switch with a single env var.
Secure Networking
IAC protocol over QUIC/TLS with Ed25519 cryptographic agent identity for distributed deployments.
Long-Term Memory
Pinecone vector database integration to persist and recall agent knowledge across sessions.
9 Built-in Agents
ManagerGPT, ArchitectGPT, BackendGPT, FrontendGPT, DesignerGPT, GitGPT, MailerGPT, OptimizerGPT, and GenericGPT.
Collaborative Agents
Route tasks across multiple LLM providers with CollabPool. Automatic model-level fallback on quota/rate-limit errors.
Metacognition
Agents record task outcomes and inject strategy context into prompts. Self-corrects strategy after consecutive failures.
Architecture at a Glance
flowchart TD
User(["đ¤ User"])
subgraph Runtime["AutoGPT Runtime"]
direction TB
MG["đŠ ManagerGPT"]
AG["đˇ ArchitectGPT"]
BG["âī¸ BackendGPT"]
FG["đĨī¸ FrontendGPT"]
DG["đ¨ DesignerGPT"]
end
subgraph Network["Orchestrated Mode (IAC / TLS)"]
direction LR
Orch["đ orchgpt Orchestrator"]
end
subgraph Providers["LLM Providers"]
Gemini["Gemini"]
OpenAI["OpenAI"]
Claude["Claude"]
XAI["XAI"]
HF["HuggingFace"]
end
DB[("đ§ Pinecone Vector DB")]
User -->|"goal prompt"| MG
MG --> AG
MG --> BG
MG --> FG
MG --> DG
User -->|"--net"| Orch
Orch <-->|"IAC + Ed25519"| MG
Runtime <-->|"API calls"| Providers
Runtime <-->|"save_ltm / get_ltm"| DB
Operating Modes
| Mode | Command | Description |
|---|---|---|
| Interactive Shell | autogpt | Conversational AI shell with session management |
| Direct Prompt | autogpt -p "..." | One-shot LLM query from the terminal |
| Standalone Agent | autogpt arch / back / front | Run a single specialized agent |
| Orchestrated | autogpt --net | Networked multi-agent mode via IAC/TLS |
| Collaborative | autogpt --collab | Round-robin multi-provider collab mode |
Quick Links
- Installation â: Get AutoGPT running in minutes.
- Quickstart â: Build your first agent.
- Custom Agents â: Compose your own agent from scratch.
- Collaborative Agents â: Route tasks across multiple LLM providers.
- Metacognition â: Self-correcting strategy via recorded outcomes.
- IAC Protocol â: Understand the communication layer.
- GitHub: Source code and releases.
0.x API is stabilizing. For the latest changes, follow the GitHub releases.