trae-agent vs Agentless
Python LLM tools for software engineering compared
trae-agent and Agentless use language models to help with software engineering tasks. trae-agent is a configurable CLI agent for general project work, while Agentless follows a staged pipeline for locating and repairing bugs, with a focus on SWE-bench.
trae-agent: Delegate Software Engineering Tasks to an AI Agent
Trae Agent is a configurable Python CLI that uses language models and tools to carry out software engineering tasks. It suits developers and researchers who want an extensible agent they can run against a project, inspect through recorded trajectories, and adapt for experiments.
Agentless: Find and Repair Bugs with LLMs
Agentless uses language models to localize bugs, generate candidate code patches, and validate them without an agent-based workflow. It is aimed at developers and researchers evaluating automated software repair, especially on SWE-bench.
| trae-agent | Agentless | |
|---|---|---|
| Language | Python | Python |
| License | MIT | MIT |
| Stars | 12.1k | 2.1k |
| Forks | 1.4k | 239 |
| Last analyzed | Oct 3, 2026 | Oct 3, 2026 |
Key differences
- trae-agent accepts general natural-language tasks and uses tools such as file editing and shell execution; Agentless localizes faults, generates patch candidates, and validates them.
- trae-agent supports multiple hosted and local model providers; Agentless requires an OpenAI API key.
- trae-agent offers interactive sessions, YAML configuration, and recorded trajectories; Agentless provides a staged repair workflow with test-based patch reranking.
- trae-agent can optionally run tasks in Docker; Agentless is oriented toward repository issue repair and running relevant tests.
- Both are Python projects under the MIT license. trae-agent lists 12.1k stars and 1.4k forks, while Agentless lists 2.1k stars and 239 forks.
Choose trae-agent if you…
- need a configurable CLI for varied software engineering tasks.
- want to select among hosted or local model providers and inspect recorded trajectories.
- prefer the option of running tasks in Docker.
Choose Agentless if you…
- want a staged workflow for fault localization, patch generation, and test-based validation.
- are evaluating automated issue repair, especially on SWE-bench.
- can provide OpenAI API access and run relevant tests.
This comparison is generated with AI from the OSRepos analyses of both projects. Always check each project's repository and documentation before choosing.