rig vs deepagents
LLM application frameworks compared
rig is a Rust library with shared interfaces for model providers, vector stores, and agent workflows. deepagents is a Python harness with built-in planning, delegation, file access, and context management, so the main difference is composable integrations versus a ready-to-extend agent structure.
rig: Build LLM Applications in Rust
Rig is a Rust library for building modular LLM applications, with shared interfaces for model providers, vector stores, and agent workflows. It suits Rust developers who want to integrate multiple LLM services without building each integration from scratch.
deepagents: Build Agents with Planning and Tooling Built In
Deep Agents is a Python agent harness for building LLM agents with planning, delegation, file access, and context management built in. It suits developers who need a more capable starting point than a minimal agent loop, while retaining control over models and tools.
| rig | deepagents | |
|---|---|---|
| Language | Rust | Python |
| License | MIT | MIT |
| Stars | 8.8k | 29.9k |
| Forks | 989 | 4.2k |
| Last analyzed | Oct 3, 2026 | Oct 3, 2026 |
Key differences
- rig focuses on reusable interfaces across more than 20 model providers and more than 10 vector store integrations; deepagents focuses on built-in capabilities for multi-step agents.
- rig is written in Rust, while deepagents is written in Python.
- rig separates portable core contracts from agent orchestration and offers optional integrations through Cargo features; deepagents bundles agent behavior and is built on LangGraph.
- deepagents includes file operations, shell commands through a sandbox, sub-agent delegation, and persistent memory; rig describes agent prompting and streaming alongside completion, embedding, transcription, and media generation capabilities.
- Both projects use the MIT license. The supplied project data lists 8.8k stars and 989 forks for rig, and 29.9k stars and 4.2k forks for deepagents.
Choose rig if you…
- need a Rust library with interchangeable model-provider and vector-store integrations.
- want composable LLM building blocks, including completion, embedding, and agent workflows.
- need the portable core or classic runtime to support browser WASM, within the stated limitations.
Choose deepagents if you…
- want a Python agent harness with planning, delegation, and context management built in.
- need agents to work with files or run shell commands through a chosen sandbox.
- want configurable state and store backends for memory across sessions.
Compare the numbers side by side, or add more projects
This comparison is generated with AI from the OSRepos analyses of both projects. Always check each project's repository and documentation before choosing.