CAMEL — AI Agent Framework: Live Stats & TrendScore

CAMEL is a research-grade multi-agent framework with 5k+ stars. We track its GitHub activity and academic citations alongside its practical community adoption.

GitHub data synced: Jun 26, 2026 • Sentiment updated: Jun 23, 2026

GitHub Statistics

Community Sentiment

Community Buzz: As stated on GitHub, 'We should use camel yaml-dsl routes for testing, the pipe stuff is deprecated.' and on Dev.to, 'Wanaku 0.1.1, a significant milestone that showcases how Apache Camel's...'

Pros & Cons

What People Love

Apache Camel's AI integration, Wanaku 0.1.1 milestone, Dev.to community support

Common Complaints

Apache Camel issues, pipelines compatibility

Biggest Positive: Great AI integration

Biggest Negative: Apache Camel issues

Why CAMEL Stands Out

CAMEL stands out from alternative multi-agent frameworks due to its unique combination of evolvability, scalability, and statefulness. By leveraging these design principles, CAMEL enables the creation of complex, dynamic agent systems that can simulate real-world scenarios and facilitate cutting-edge research in cooperative AI. The code-as-prompt approach and support for multiple benchmarks further enhance CAMEL's value, making it an attractive choice for researchers and developers alike. Additionally, CAMEL's focus on finding the scaling laws of agents sets it apart from other frameworks, providing a distinctive technical approach to multi-agent system development.

Built With

Build a large-scale multi-agent system with dynamic communication — CAMEL's framework design principles enable evolvability, scalability, and statefulness, making it ideal for complex agent interactions, Build a research agent that simulates up to 1M agents to study emergent behaviors — CAMEL's support for large-scale agent systems and dynamic communication facilitates seamless collaboration among agents, Build a task automation system with real-time interactions among agents — CAMEL's stateful memory and support for multiple benchmarks enable agents to retain historical context and make informed decisions, Build a synthetic dataset generation system with diverse agent types — CAMEL's code-as-prompt approach and support for different agent types facilitate the creation of complex, realistic datasets, Build a cooperative AI system with verifiable rewards and supervised learning — CAMEL's framework design principles and support for reinforcement learning enable the development of sophisticated, cooperative AI systems

Getting Started

  1. Install CAMEL using pip: `pip install camel-ai`
  2. Configure the CAMEL environment by setting the `CAMEL_HOME` variable: `export CAMEL_HOME=/path/to/camel`
  3. Initialize a new CAMEL project using the `camel init` command: `camel init my_project`
  4. Configure the agent types and tasks in the `config.yaml` file: `vim config.yaml`
  5. Try running a simple agent simulation using the `camel run` command to verify that CAMEL is working correctly: `camel run --agent-type=chatagent`

About

🐫 CAMEL: The first and the best multi-agent framework. Finding the Scaling Law of Agents. https://www.camel-ai.org

Official site: https://docs.camel-ai.org/

Category & Tags

Category: multi-agent

Tags: agent, ai-societies, artificial-intelligence, communicative-ai, cooperative-ai, deep-learning, large-language-models, multi-agent-systems, natural-language-processing

Market Context

Competing with other integration frameworks