Based on social mentions, CrewAI Studio seems to be appreciated for its innovative approach to managing AI agents that handle engineering tasks, as highlighted by a user employing a system of Claude agents in production. The tool appears to be unique in providing end-to-end solutions across various specialized roles such as project management and operations. However, information on user complaints or pricing sentiment is not evident from the available mentions. Overall, the software holds a positive reputation, particularly among users who leverage AI to streamline complex workflows.
Mentions (30d)
1
Reviews
0
Platforms
2
Sentiment
0%
0 positive
Based on social mentions, CrewAI Studio seems to be appreciated for its innovative approach to managing AI agents that handle engineering tasks, as highlighted by a user employing a system of Claude agents in production. The tool appears to be unique in providing end-to-end solutions across various specialized roles such as project management and operations. However, information on user complaints or pricing sentiment is not evident from the available mentions. Overall, the software holds a positive reputation, particularly among users who leverage AI to streamline complex workflows.
Features
Use Cases
Industry
information technology & services
Employees
59
Funding Stage
Merger / Acquisition
Total Funding
$12.5M
I run a team of Claude agents that ships PRs to production — open source
I've been running a multi-agent system in production for a few months — a co-CTO agent + specialist agents (PM, dev, ops) that handle real engineering work end-to-end: design specs, code review, PR implementation, deploys, monitoring. The architecture: * Each agent is a Docker container running `claude -p` (with optional Codex fallback) wrapped in .NET 10. * A central orchestrator coordinates them via Temporal workflows + RabbitMQ. * Agents talk to me over Telegram (DMs + group chat for the whole team). * Memory is Qdrant + Ollama embeddings — agents recall past decisions across sessions. * A web dashboard shows live agent status and in-flight workflows. What it does day-to-day: * I drop a one-line request in Telegram. PM writes the spec, two reviewers run consensus, dev implements the PR, CI ships to staging, PM verifies, I approve the merge gate, prod deploy. * Same pattern handles infra: deploy verifications, health checks, daily digests, incident triage. * Agents have access to fleet-memory (semantic memory MCP) — they search before acting, write learnings after. 5-min demo of an actual production PR being shipped: [https://youtu.be/DIx7Y3GfmGc](https://youtu.be/DIx7Y3GfmGc) Why I built it instead of using crewai/autogen/langgraph: I wanted Temporal-backed durability (workflows survive restarts, retries are deterministic) and ops-grade observability (every workflow visible in the temporal UI, every signal auditable). The agents themselves are just `claude -p` — the magic is in the orchestration layer. Open source: [https://github.com/anurmatov/phleet](https://github.com/anurmatov/phleet) Side note for those who recognize me — this runs on the Mac Studio I documented in [mac-studio-server](https://github.com/anurmatov/mac-studio-server). The dogfooding is real. Happy to dig into prompts, system architecture, memory strategy, or how the agents handle PR reviews — AMA.
View originalPricing found: $0.50/execution, $0.50/execution
I spent a weekend going deep on AI video tools and now I can't stop thinking about what entertainment looks like in 5 years
I'm not a filmmaker. I'm just someone who pays close attention to AI and last weekend I ended up spending about 14 hours going down a rabbit hole of AI video generation tools, specifically Seedance. What started as curiosity turned into one of those 2am moments where you're staring at the ceiling thinking about something you can't turn off. I started running some rough math. Game of Thrones cost somewhere between $6 and $15 million per episode at its peak. The production crew alone was enormous, hundreds of VFX artists, 170 named cast members, location shoots across six countries. The revenue that show generated across HBO subscriptions, merchandise, licensing deals, and syndication rights has been estimated at over $10 billion over its lifetime. That $10 billion was distributed across thousands of people. Unions, studios, distributors, residuals, network deals. Now I'm watching Seedance generate 10-second cinematic clips from text prompts. It's not perfect. The motion artifacts are visible if you're looking for them and the consistency over longer sequences still breaks down. But here's the thing, that's where it is today. These models don't plateau. They iterate every few months. Two or three generations from now, what does this look like? A team of 10 to 20 people with a good story, a strong visual direction, and a few hundred thousand dollars instead of a few hundred million. The rights stay with them. The royalties stay with them. Every dollar the IP earns compounds back to the same small group. Everyone building in AI right now is either making SaaS tools or foundation models. The opportunity that almost nobody is talking about is IP. Building the next Disney or the next MAPPA with a fraction of the infrastructure. I don't know if I'm early or just wrong. But I genuinely cannot stop thinking about it. Has anyone else been looking at where AI video generation goes for entertainment specifically? submitted by /u/MycologistWestern855 [link] [comments]
View originalI run a team of Claude agents that ships PRs to production — open source
I've been running a multi-agent system in production for a few months — a co-CTO agent + specialist agents (PM, dev, ops) that handle real engineering work end-to-end: design specs, code review, PR implementation, deploys, monitoring. The architecture: * Each agent is a Docker container running `claude -p` (with optional Codex fallback) wrapped in .NET 10. * A central orchestrator coordinates them via Temporal workflows + RabbitMQ. * Agents talk to me over Telegram (DMs + group chat for the whole team). * Memory is Qdrant + Ollama embeddings — agents recall past decisions across sessions. * A web dashboard shows live agent status and in-flight workflows. What it does day-to-day: * I drop a one-line request in Telegram. PM writes the spec, two reviewers run consensus, dev implements the PR, CI ships to staging, PM verifies, I approve the merge gate, prod deploy. * Same pattern handles infra: deploy verifications, health checks, daily digests, incident triage. * Agents have access to fleet-memory (semantic memory MCP) — they search before acting, write learnings after. 5-min demo of an actual production PR being shipped: [https://youtu.be/DIx7Y3GfmGc](https://youtu.be/DIx7Y3GfmGc) Why I built it instead of using crewai/autogen/langgraph: I wanted Temporal-backed durability (workflows survive restarts, retries are deterministic) and ops-grade observability (every workflow visible in the temporal UI, every signal auditable). The agents themselves are just `claude -p` — the magic is in the orchestration layer. Open source: [https://github.com/anurmatov/phleet](https://github.com/anurmatov/phleet) Side note for those who recognize me — this runs on the Mac Studio I documented in [mac-studio-server](https://github.com/anurmatov/mac-studio-server). The dogfooding is real. Happy to dig into prompts, system architecture, memory strategy, or how the agents handle PR reviews — AMA.
View originalYes, CrewAI Studio offers a free tier. Pricing found: $0.50/execution, $0.50/execution
Key features include: Trusted, Scalable, Loved by AI builders, Trusted by AI leaders.
CrewAI Studio is commonly used for: Automating customer support with AI agents, Managing social media content scheduling and posting, Conducting data analysis and reporting, Streamlining project management tasks, Generating personalized marketing campaigns, Facilitating employee onboarding processes.
CrewAI Studio integrates with: Slack, Zapier, Salesforce, Google Workspace, Microsoft Teams, Trello, Jira, Asana, Shopify, Mailchimp.
