CAMEL-AI.org (@CamelAIOrg) / X

CAMEL-AI.org

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CAMEL-AI.org

@CamelAIOrg

camel-ai.org is working on finding the scaling laws of agents. The first and the best multi-agent framework. Discord: discord.camel-ai.org. Product @Eigent_AI

github.com/camel-ai/camel

Joined June 2023

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CAMEL-AI.org
@CamelAIOrg
Feb 5
Open AI and Anthropic just release GPT-5.3-Codex and Opus 4.6 model, terminal capability is now on top of their list evaluating modal capability. But terminal training hits a wall fast: there aren’t enough high-quality environments. In SETA, we just shipped 1,376 validated
CAMEL-AI.org
@CamelAIOrg
Jan 9
Article
SETA: Scaling Environments for Terminal Agents
— Designing resilient toolkits and scalable RL environments for CAMEL terminal agents Authors: Qijia Shen (@qijia_shen) 1,2, Jay Rainton 3, Aznaur Aliev 4, Ahmed Awelkair (@ahmedawelkair) 1,2,4,5,...
CAMEL-AI.org
@CamelAIOrg
Jul 31
We’re starting in 30 minutes! If you're working on model evaluation, LLM testing, or agent diagnostics, come join CAMEL-AI Community Live Talk by @NishantBalepur! 🐫 Meeting Info👇 us05web.zoom.us/j/85737858776?… Meeting ID: 85737858776 Passcode: Hz3cR1
CAMEL-AI.org
@CamelAIOrg
Jul 30
This Friday on 🐫 CAMEL-AI Live Talk, Nishant is unveiling BenchMarker, and it might change how much you trust your favorite benchmarks. He is an NLP PhD candidate at the University of Maryland who cares about making LLM evaluation actually mean something. The idea is simple but
CAMEL-AI.org
@CamelAIOrg
Jul 30
This Friday on 🐫 CAMEL-AI Live Talk, Nishant is unveiling BenchMarker, and it might change how much you trust your favorite benchmarks. He is an NLP PhD candidate at the University of Maryland who cares about making LLM evaluation actually mean something. The idea is simple but
CAMEL-AI.org
@CamelAIOrg
Jul 27
See you at #AGIPlayground 2026 in Singapore, where @GeekParkHQ is gathering ~500 AI builders 🇸🇬 A playground for AI founders & builders. Aug 3–4. Gardens by the Bay, Singapore. 🔗 luma.com/n9r72dc9
CAMEL-AI.org
@CamelAIOrg
Jul 3
Execution feedback from real tools can help agents refine tool-use plans. However, repeated real-tool calls may incur costs, hit rate limits, or cause side effects. Our ICML 2026 paper introduces Gecko, a pre-execution simulation sandbox for tool-use agents. Given tool schemas
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