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Claude Code vs OpenCode vs Oh My Pi: speed, cost, success. Advertisements Key takeaways. Is Pi the Best Coding Agent? Pi vs OpenCode vs Claude Code * Claude Code completes tasks in 122 seconds but costs $0.195 per successful task * OpenCode is cheapest at $0.073 per task but slower at 202 seconds * Oh My Pi achieved the highest success rate (17/30) but was slowest at 272 seconds Claude Code is the fastest of four major agent frameworks tested against DeepSeek V4 Flash, completing tasks in 122 seconds on average. It also costs nearly three times more than the cheapest alternative. AI tooling company Composio ran 30 real-world tasks across Claude Code, Codex, OpenCode, and Oh My Pi, using integrations with Gmail, GitHub, Slack, and Notion. The results show that the wrapper around a model matters as much as the model itself. | Framework | Success Rate | Avg. Time/Task | Cost/Successful Task | | Claude Code | ~15-16/30 | 122 seconds | $0.195 | | Oh My Pi | 17/30 | 272 seconds | Not disclosed | | OpenCode | 14/30 | ~202 seconds | $0.073 | | Codex | ~15-16/30 | Not disclosed | Not disclosed | Advertisements What did the benchmark actually test? Composio held the model constant, running DeepSeek V4 Flash through all four frameworks. The 30 tasks involved real-world tool usage: sending emails, interacting with GitHub repositories, posting to Slack, and managing Notion pages. This setup isolates framework behavior from model capability. Seven tasks passed or failed based solely on which framework ran them. That's a 23% variance from the framework alone. The model never changed. The prompts never changed. Only the orchestration layer differed. Why does Claude Code cost so much more? Claude Code clocked $0.195 per successful task despite using the fewest tool calls and generating the least output tokens. OpenCode hit $0.073. That's a 2.7x price gap for comparable success rates. The cost difference likely traces to how each framework structures its context window, manages retries, and batches API calls. Claude Code's speed advantage (122 seconds versus OpenCode's estimated 202 seconds) suggests it parallelizes aggressively. But parallelization inflates token usage if the framework isn't pruning redundant context between calls. For teams running hundreds of agent tasks daily, that 2.7x multiplier compounds. A workflow that costs $73 per thousand successful tasks on OpenCode would cost $195 on Claude Code. Over a month, that's real budget pressure. Oh My Pi won on success rate but lost on time. Oh My Pi completed 17 of 30 tasks, the highest success rate in the test. It also took 272 seconds per task, more than double Claude Code's 122 seconds. For interactive use cases (a developer waiting for code generation, a support agent needing a quick answer), that latency kills the workflow. For batch processing where correctness matters more than speed, Oh My Pi's higher success rate might justify the wait. Composio didn't publish Oh My Pi's cost-per-task, so the full tradeoff picture remains incomplete. Advertisements What should teams optimize for? The answer depends on the job. A startup automating internal workflows with Zapier or Make cares about cost per task. An enterprise shipping customer-facing agents cares about latency. A compliance team cares about success rate. Disclosure. Some links in this post are affiliate links - Logicity earns a commission if you sign up, at no extra cost to you. Logicity only link products Logicity has used or actively recommend. No framework dominated all three metrics. That's the finding that matters. Claude Code is not "the best" agent framework. It's the fastest. OpenCode is not "bad." It's cheap. Oh My Pi is not "slow." It's thorough. The choice is a tradeoff, not a ranking. Logicity's take. This benchmark confirms what many teams suspected: the orchestration layer is a cost center, not a commodity. Teams evaluating agent frameworks should run their own task mix before committing. Composio's test used a specific set of integrations; your Slack-heavy or GitHub-heavy workflow may skew results differently. Also missing: error recovery behavior, which matters when agents fail mid-task. What the test didn't cover. Composio published success/fail, time, and cost. They didn't publish error types, retry behavior, or partial-success rates. A task that fails on the final step after nine correct ones is not the same as a task that fails immediately. Framework error handling matters for production reliability, and this benchmark doesn't measure it. The test also held the model constant at DeepSeek V4 Flash. Different frameworks may perform differently with Claude models, GPT-4, or Gemini. Framework-model pairing effects are real; this benchmark doesn't capture them. Frequently asked questions. Which agent framework is cheapest per task? OpenCode, at $0.073 per successful task in Composio's benchmark. Which agent framework is fastest? Claude Code, at 122 seconds per task on average. Which framework has the highest success rate? Oh My Pi completed 17 of 30 tasks, the highest in the test. Need help implementing this? Evaluating agent frameworks for your stack? Reach out to Logicity's consulting team for a custom benchmark tailored to your integrations and cost constraints. Advertisements Huma Shazia Senior AI & Tech Writer Produced with AI assistance and reviewed by the Logicity editorial team. Learn more in its Editorial Policy.

Unlocking the power of Claude: essential skills and tools for AI enthusiasts. July 22, 2026 4 min min read Source: GitHub Trending ComposioHQ launches awesome Claude Skills repository. In a significant move for developers and AI enthusiasts alike, ComposioHQ has just unveiled the awesome-claude-skills repository on GitHub. This curated list is designed to enhance Claude AI workflows by providing an extensive collection of skills, resources, and tools specifically tailored for Claude, which has been gaining traction in the AI community. As of now, the repository has attracted 155 stars, indicating a strong interest from the developer community. This is not just another list; it's a well-organized collection that aims to empower users to customize and optimize their interactions with Claude AI. Elevate your AI interactions with a curated toolkit designed for Claude enthusiasts. Features of the awesome Claude Skills repository. The repository includes several key features that make it stand out: * Curated Skills: A comprehensive list of Claude Skills that users can easily implement and customize. * Resources: Links to documentation, tutorials, and external tools that facilitate deeper integration and understanding of Claude AI. * Community Contributions: Open for contributions, allowing developers to share their own skills and enhancements, fostering a collaborative environment. This initiative is crucial for anyone looking to leverage Claude AI's capabilities in their applications, as it streamlines the process of finding and implementing useful skills. Technical details and community engagement. The repository is primarily built using Python, making it accessible to a vast number of developers familiar with this language. This choice is strategic, as Python is widely recognized for its simplicity and versatility in AI development. Current stats: * Stars: 155 (and counting) * Language: Python * Date Released: July 22, 2026 The engagement from the community is palpable, with numerous developers already exploring the skills available. This quick uptake highlights the repository's potential to become a go-to resource for enhancing AI workflows. Why this matters. The launch of the awesome-claude-skills repository is timely, as the demand for customizable AI solutions continues to grow. Here's why this development is significant: * Empowerment for Developers: It provides developers with the tools they need to create tailored solutions, increasing productivity and innovation. * Accelerates Adoption: By making it easier to integrate Claude AI into various applications, it could lead to broader adoption across different sectors. * Community-Driven Growth: The open nature of the repository encourages collaboration, ensuring that the skills and resources remain relevant and up-to-date. "Empowering developers with customizable tools is key to driving innovation in AI." - ComposioHQ What's Next for Claude AI? As the repository gains momentum, there are several avenues to watch: * New Contributions: Keep an eye on how quickly the community adds new skills and resources. This will indicate the repository's growth and relevance. * Integration with Other Tools: Future developments may include integrations with popular frameworks and platforms, making it even easier to adopt Claude AI. * Updates and Enhancements: Regular updates from ComposioHQ could introduce new features or improvements based on user feedback. Implications for Developers and practitioners. For developers and practitioners, the awesome-claude-skills repository represents a significant opportunity to enhance their AI projects. With a curated list of skills at your fingertips, you can: * Reduce Development Time: Quickly find and implement skills tailored to your specific use cases. * Stay Ahead of Trends: Engage with a community of like-minded individuals, keeping you informed about the latest developments and best practices in AI. * Expand Your Toolbox: The diverse resources available can help you explore new capabilities within Claude AI that you may not have considered before. This launch is a pivotal moment in the AI landscape, particularly for those working with Claude. As more developers engage with the repository, Trakin AI Labs can expect exciting advancements and innovative applications of Claude AI in the near future. claude-ai ai-workflows customization-tools machine-learning resources

How Protegrity and Composio secure Agentic AI workflows. By Muneeb Hasan, Senior Partner and Alliances Solution Engineer Jul 7, 2026 Share: The enterprise rush to adopt agentic AI is hitting a massive roadblock: data security. While standard Large Language Models (LLMs) can answer simple prompts, true enterprise value lies in AI agents - autonomous systems that can actively read, reason, and write across your corporate software ecosystem. But giving an AI agent the "hands" to access Salesforce, Jira, Slack, or any other platform introduces unprecedented risk. How do you leverage the reasoning power of an AI agent without exposing your most sensitive corporate assets - PII, PHI, and PCI - to the LLM or third-party platforms? The answer lies in a groundbreaking architectural integration between Composio, the AI agent orchestration platform, and Protegrity, the global leader in data protection. Together, they introduce the "Privacy Sandwich" - a design pattern that enables a true Zero Trust AI workflow. The business value: unleashing AI productivity without the risk. For CXOs and security leaders, this integration solves the ultimate AI paradox: balancing aggressive innovation with strict regulatory compliance, including GDPR and HIPAA. By embedding Protegrity's robust data protection directly into Composio's flexible agentic workflows, enterprises can realize three core business benefits: Zero Trust AI reasoning. The core philosophy of this integration is simple: an LLM does not need to know a customer's real name or credit card number to understand their intent. Protegrity sanitizes data by finding and redacting sensitive PII, PCI, PHI, and other protected information. By feeding the AI sanitized, format-preserving semantic protection instead of raw data, your business can securely use public, private, or hybrid LLMs. Elimination of third-party data leakage. When AI agents leverage Composio to connect with external applications, there is a constant risk of data exposure. The "Privacy Sandwich" ensures that data is neutralized by applying Protegrity's data protection before it leaves your secure perimeter. Even if an agent interacts with an external cloud service, it passes secure semantic protection rather than sensitive data. Dynamic, identity-aware compliance. Not all human users are created equal in the eyes of compliance. When an AI agent formulates a response, Protegrity automatically evaluates the role and privileges of the specific human interacting with the agent. An HR manager can see unprotected data they are authorized to view, while a general support agent sees semantically protected data - all from the same AI workflow. The technical blueprint: how it works under the hood. Achieving this level of security requires tightly coordinated choreography between Composio's orchestration capabilities and Protegrity's centralized policy engine. [External Sources] | (Composio Ingestion) | [Protegrity Gateway] | (Sanitized Data) | [LLM Processing] | [Authorized Human] | (Identity Check) | [Protegrity Gateway] | (Tokenized Output) | + The process spans three fundamental technical phases: Phase 1: Ingestion and initial protection - The inbound shield. * Multi-source orchestration: Composio acts as the "hands" of the enterprise, triggering APIs to gather raw datasets from platforms like Salesforce or Jira. * Automated discovery: Before this raw payload touches the LLM, it passes through the Protegrity Gateway, which scans unstructured text to automatically identify sensitive entities. * Format-preserving tokenization: Protegrity replaces sensitive elements with format-preserving tokens. For example, John Doe becomes Person_Token_882. The LLM receives an entirely sanitized context. Phase 2: secure reasoning and secondary action. * Context preservation: The LLM processes the request using tokenized data. If it decides to execute a secondary task, such as "Update ticket for User_Token_123," Composio passes that exact token to maintain context across systems. * Intermediate validation: If Composio's secondary actions pull new data into the conversation loop, the Protegrity Gateway immediately intercepts, discovers, and sanitizes it before the LLM processes it again. Phase 3: egress and role-based access control. * Response interception: The LLM generates its final output, which still contains semantic protection. * Dynamic de-identification: The Protegrity Gateway intercepts the outbound message, queries Protegrity Policy Management, and checks the user's active directory role. The gateway either dynamically semantically unprotects the data for authorized users or leaves it masked for unauthorized users. Advanced deployment: the vault approach. For highly strict regulatory environments, enterprises can opt for the Local Execution Pattern. Rather than relying on hosted cloud infrastructure, the Composio Local Runner is deployed directly within your private network. It hooks directly into your local data vault, ensuring PII is only unmasked in memory at the final moment before being pushed to a secure API destination. Conclusion. The future of enterprise productivity belongs to autonomous AI agents, but innovation cannot come at the cost of security. By combining Composio's powerful integration fabric with Protegrity's Zero Trust data protection, organizations no longer have to compromise. Are you ready to safely accelerate your agentic AI roadmap? Reach out to learn more about implementing the Protegrity and Composio integration in your environment. Frequently asked questions. What is Agentic AI, and why does it introduce new data security risks? How does the Protegrity and Composio integration ensure Zero Trust AI processing? What is format-preserving tokenization in the context of LLM workflows? Can different users see different data outputs from the same AI agent workflow? How do Protegrity and Composio support strict local or on-premises data compliance? Summary. * Protect Data Before the LLM Protegrity and Composio use a "Privacy Sandwich" architecture to sanitize sensitive data before AI processing and enforce policy before output. * Use AI Agents With Less Data Exposure Teams can connect agents to enterprise tools while reducing exposure of PII, PHI, and PCI through tokenization, semantic protection, and role-aware controls. Build safer AI with protected data. Get practical insights on securing sensitive data across AI pipelines, analytics workflows, GenAI systems, and regulated environments. This site is protected by reCAPTCHA. Recommended next read. Blogs. 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