camel-ai/camel

★ 16,945PythonAudience · researcherComplexity · 3/5Setup · moderate

Mindmap

mindmap
  root((camel))
    What it does
      Multi-agent systems
      Data generation
      World simulation
    Design principles
      Evolvable agents
      Scalable simulation
      Stateful memory
    Key modules
      Workforce roles
      RAG pipelines
      Benchmarks
    Use cases
      Agent research
      Synthetic datasets
      Task automation

mindmap root((camel)) What it does Multi-agent systems Data generation World simulation Design principles Evolvable agents Scalable simulation Stateful memory Key modules Workforce roles RAG pipelines Benchmarks Use cases Agent research Synthetic datasets Task automation

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Things people build with this

USE CASE 1

Build a team of AI agents with different roles that collaborate to research a topic and produce a report

USE CASE 2

Generate synthetic training datasets using agent-based techniques like Chain-of-Thought and Self-Instruct

USE CASE 3

Run large-scale agent simulations to study how AI societies behave as they scale up

USE CASE 4

Create a RAG pipeline where agents search through your documents to complete multi-step tasks

Tech stack

Python

Getting it running

Difficulty · moderate Time to first run · 30min

Requires an LLM API key such as OpenAI or a compatible provider to power the agents.

License type not specified in the explanation.

In plain English

CAMEL is an open-source Python framework for building systems where many AI agents (programs powered by large language models) talk to each other to get things done, rather than relying on a single chatbot. It comes from a community research effort studying how agent societies behave as you scale them up, what the project calls finding the scaling laws of agents. The framework is built around four design principles. Agents are evolvable, learning by interacting with environments and from data via reinforcement learning with verifiable rewards or supervised learning. They are scalable: CAMEL claims it can simulate up to one million agents at once. They are stateful, with each agent keeping a memory so it can carry out multi-step tasks. It also follows a code-as-prompt idea: code and comments themselves act as instructions agents read. What you build with it falls into three buckets. The first is data generation: producing synthetic datasets with techniques the README names, such as Chain-of-Thought, Self-Instruct, Source2Synth, and Self-Improving CoT. The second is task automation, where agents take roles and cooperate on a job, role-playing societies, a Workforce module, and RAG pipelines that let agents look things up in your data are included. The third is world simulation, modelling environments populated by agents. Reach for CAMEL if you are researching multi-agent systems, want to generate synthetic data to train or evaluate models, or want to build automation that needs several agents working together. It ships as a Python package with cookbooks, benchmarks, and an active community. The full README is longer than what was provided.

Copy-paste prompts

Prompt 1

Using the CAMEL framework, show me how to set up two AI agents, a researcher and a coder, that collaborate to analyze a dataset and write Python code

Prompt 2

How do I use CAMEL's Workforce module to break a complex task into subtasks and assign them to different agents automatically?

Prompt 3

Give me a CAMEL example that generates a synthetic dataset of 100 Chain-of-Thought reasoning examples for model fine-tuning

Prompt 4

Show me how to build a CAMEL RAG pipeline that lets agents search through a PDF document to answer questions

Prompt 5

How do I add memory to a CAMEL agent so it remembers context across multiple steps of a long task?

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