Camel - AI Agent Frameworks Tool

Overview

Camel is an open-source AI agent framework from camel-ai designed to orchestrate multi-step agent workflows across multiple LLM providers and runtime sandboxes. It emphasizes multi-model integration (for example routing tasks to advanced LLMs such as GPT-5 or Anthropic Claude 4.1), fine-grained workforce prompting, and secure custom sandboxing to safely execute code, browse, or interact with external systems. Camel's architecture is intended for building production-grade agent pipelines that combine planner, executor, and verifier roles, letting teams mix and match models and runtime environments to match task requirements. According to the GitHub repository, Camel is actively maintained (15,289 stars, 1,691 forks, 179 contributors) under the Apache-2.0 license, with frequent releases visible on the project's releases page. Typical use-cases include multi-model orchestration for complex automation, running untrusted code in isolated sandboxes, and scaling human-in-the-loop or synthetic workforce prompting patterns for evaluation and quality control.

GitHub Statistics

• Stars: 15,289
• Forks: 1,691
• Contributors: 179
• License: Apache-2.0
• Primary Language: Python
• Last Updated: 2026-01-09T17:16:29Z
• Latest Release: v0.2.82

The GitHub project shows strong community interest with 15,289 stars, 1,691 forks, and 179 contributors, indicating broad adoption and contributor diversity. The repository uses an Apache-2.0 license and remains actively developed (last commit referenced 2026-01-09). Frequent tagged releases on the releases page and a relatively large contributor base suggest ongoing feature development, bug fixes, and responsiveness to issues. For detailed changelogs and release notes, refer to the repository's releases.

Installation

Install via pip:

git clone https://github.com/camel-ai/camel.git

cd camel && pip install -e .

Key Features

• Multi-model routing: dispatch tasks across different LLMs (e.g., GPT-5, Claude 4.1) based on capability or cost
• Agent orchestration: compose planners, executors, and verifiers into multi-step workflows
• Custom sandboxing: run untrusted code and tools in isolated execution environments
• Workforce prompting: coordinate human-in-the-loop or synthetic workforce evaluation workflows
• Extensible connectors: integrate multiple model providers and external APIs
• Release-driven stability: formal releases and changelogs for production upgrades
• Apache-2.0 license: permissive licensing for commercial and research use

Community

Active and growing community with 15,289 stars, 1,691 forks, and 179 contributors on GitHub. The project posts frequent releases and commits; community discussions, issue reports, and PR activity are visible on the repository. For real-time support and contribution guidelines, consult the GitHub repo and its README/release notes.

Key Information

• Category: Agent Frameworks
• Type: AI Agent Frameworks Tool