deepagents vs agent.cpp
LLM agent frameworks compared
deepagents is a Python harness for multi-step agents with built-in planning, delegation, file tools, and context management. agent.cpp is a C++ library for local agents powered by llama.cpp; the main difference is deepagents' broader ready-to-extend harness versus agent.cpp's focus on local model inference and lifecycle customization.
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.
agent.cpp: Build Local AI Agents in C++
agent.cpp is a C++ library for building agents that run small language models locally through llama.cpp. It provides an agent loop, tools, callbacks, and support for GGUF models, grammar constraints, and LoRA adapters.
| deepagents | agent.cpp | |
|---|---|---|
| Language | Python | C++ |
| License | MIT | MIT |
| Stars | 29.9k | 210 |
| Forks | 4.2k | 31 |
| Last analyzed | Oct 3, 2026 | Oct 3, 2026 |
Key differences
- deepagents is written in Python and built on LangGraph, while agent.cpp is a C++ library built around llama.cpp.
- deepagents bundles planning, sub-agent delegation, file operations, and context management; agent.cpp provides an agent loop, tools, and callbacks for developers to customize.
- deepagents supports tool-calling models with flexible model choice, while agent.cpp is designed for local inference using GGUF models and does not target external LLM APIs.
- deepagents includes configurable memory and file backends, while agent.cpp highlights GBNF grammar constraints, LoRA adapters, and model cache management.
- Both projects use the MIT license and are not archived. deepagents has 29.9k stars and 4.2k forks, compared with 210 stars and 31 forks for agent.cpp.
Choose deepagents if you…
- need a ready-to-extend structure for multi-step agents with planning, delegation, and file tools.
- want configurable persistence, context management, and reusable skills in a Python harness.
- prefer model choice flexibility and can provide a tool-calling model and suitable tool security boundaries.
Choose agent.cpp if you…
- want to build local model-backed agents in C++ using llama.cpp and GGUF models.
- need callbacks to customize context handling, validation, logging, or approval in the agent lifecycle.
- want to explore grammar-constrained outputs or separate LoRA adapters for models sharing base weights.
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.