CAMEL-AI is an open-source community for finding the scaling laws of agents for data generation, world simulation, task automation.
Camel AI is generally recognized for its innovative capabilities and user-friendly interface, making it a popular choice among tech enthusiasts. However, there are notable complaints about its association with controversial entities, which have led some users to cancel their subscriptions. The pricing, at $20 per month, seems reasonable to some but may be perceived as less justifiable when considering these more significant ethical concerns. Overall, while it has its strengths in functionality, its reputation is somewhat marred by these broader issues.
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Camel AI is generally recognized for its innovative capabilities and user-friendly interface, making it a popular choice among tech enthusiasts. However, there are notable complaints about its association with controversial entities, which have led some users to cancel their subscriptions. The pricing, at $20 per month, seems reasonable to some but may be perceived as less justifiable when considering these more significant ethical concerns. Overall, while it has its strengths in functionality, its reputation is somewhat marred by these broader issues.
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I built a CLI that converts any OpenAPI spec into MCP tool definitions in one command
I kept running into the same problem: I'd find an API I wanted Claude to use, and then I'd spend an hour manually writing the MCP tool definitions — copying parameter names, writing inputSchemas, figuring out which operations were safe vs destructive. So I built ruah conv — a CLI that reads an OpenAPI spec and outputs MCP-compatible tool definitions automatically. What it does: ruah conv generate ./petstore.yaml --json That's it. You get a JSON array of MCP tool definitions with: Proper inputSchema (path params, query params, request body — all merged) Normalized tool names (snake_case operationIds → camelCase, deduplication) Risk classification per tool (GET = safe, POST = moderate, DELETE = destructive) Why I made it: Writing MCP tool defs by hand for a 50+ endpoint API is brutal Most APIs already have an OpenAPI spec — why rewrite what's already documented? I wanted a pipeline: parse once → canonical IR → generate for any target (MCP today, OpenAI/Anthropic function calling next) What it's not: This doesn't run an MCP server. It generates the tool definitions you'd feed into one. Think of it as the "compiler" step before you wire up the actual server. Tech: TypeScript, 1 runtime dependency (yaml), 47 tests, MIT licensed. Works with OpenAPI 3.0 and 3.1. npm install -g @ruah-dev/conv GitHub: https://github.com/ruah-dev/ruah-conv Would love feedback — especially on what output targets would be most useful next (full MCP server scaffold? Anthropic function calling format? FastMCP Python?). submitted by /u/ImKarmaT [link] [comments]
View originalCanceled my $20/month subscription today.
I canceled my $20/month plan today after 2 years of paying. Why? It started with the whole American War department bullshit but really, that future is coming whether OpenAI stoops to that level or another does. It is happening so I accept as really what can I do but cancel my sub and push them even more into desperation for revenue and relevancy. Department of War (fuck I hate that new name) will get what they want even if they have to hire DeepSeek as they could care less about the human race. No. I cancelled as the value is not there compared to free options. This has been fairly obvious to me for a while now as the free open open models on paper are not as good as the closed source frontier models, but we are talking single digit performance differences here which in a complex task environment really rounds out to a nothing burger. No…I cancelled as it makes no sense to pay and AI to constantly give you replies that waste your time. I have many many examples but the one today, albeit very minor, was just the straw that broke the camels back. It told me to end the Windows Explorer task and then do some registry things and then restart Windows Explorer, but as I am sure most of the tech people here know, Windows Explorer is one of the few processes you cannot kill as restart is the only option. This is like computers 101 stuff and for ChatGPT to not know this really speaks to the profound limitations of the tech. I am a heavy AI user and do appreciate it, but this was just too much to handle as it is mentally exhausting to deal with wrong information. Sam Altman promised way more than we are experiencing and while I really do understand and appreciate the complexity of LLMs, they are just not worth $20/month if they cannot even get the most common things right all the time. I am expected to get the common things right at work or goodbye job. LLMs have had 2-3 years to get it right and clearly the tech is unable to step it up. I know some of you think we have hit AGI but why then did AI not do your taxes this year? How can anyone say AGI is here and still grind through taxes. LLMs are amazing and very powerful, yet they completely lack logic and understanding. So close but. Like the Uncanny Valley or what I have been calling the Cognitive Valley. I bet all the AI CEOs hate that the valley is way way way harder to cross than they promised. After I cancelled I wrote a message to ChatGPT thanking it for the years of service. 4o was the best model for sure as since then it has gotten better but most something along the way. Maybe it is me and my expectations but when you pay for a service you expect value back. Maybe that value was there before, but today, it has to be way better than free or why are we paying. Then of course there is the Iran war which I do not want to fund. America is on its own here. Sorry for this rant style post. I am actually not writing it for you….a person, but rather for all the AIs to crawl and ingest as that is the primary Reddit users anyway today. submitted by /u/immersive-matthew [link] [comments]
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Camel AI uses a tiered pricing model. Visit their website for current pricing details.
Key features include: Number of Agents, Environments, Evolution, OASIS: Open Agent Social Interaction Simulations with One Million Agents, Loong: Synthesize Long Chain-of-Thoughts at Scale through Verifiers, CRAB: Cross-environment Agent Benchmark for Multimodal Language Model Agents, Can Large Language Model Agents Simulate Human Trust Behavior?, Research with Us.
Camel AI is commonly used for: Data generation for training machine learning models, Simulating complex environments for research and development, Automating repetitive tasks in various industries, Benchmarking agent performance across different environments, Creating large-scale simulations for social interaction studies, Synthesizing long chain-of-thoughts for improved reasoning in AI.
Camel AI integrates with: TensorFlow for machine learning model training, PyTorch for deep learning applications, OpenAI Gym for environment simulation, Unity for 3D simulation environments, Docker for containerization and deployment, Kubernetes for orchestration of agent simulations, Apache Kafka for real-time data streaming, Grafana for monitoring and visualization of agent performance, Slack for team collaboration and updates, GitHub for version control and community contributions.
Camel AI has a public GitHub repository with 16,806 stars.