AI Agents
AI agents are software systems that use reasoning, planning, and tools to assist or automate complex tasks. We compare the top open-source and commercial agents.
Best 50+ Open Source AI Agents Listed
Everyone is building AI agents these days. So after hands-on testing with popular AI coding agents, AI agent builders and tools use benchmarks to evaluate their real-world capabilities, we put together a curated list of the best 50+ open source AI agents.
Agentic CLI Tools Compared: Claude Code vs Cline vs Aider
Explore leading agentic CLI tools to streamline and elevate your code-editing workflow. [aim_list] AI coding tools can be grouped into three categories: Claude Code vs Cline vs Aider Tools are listed based on their GitHub scores: Claude Code Claude Code is a CLI interface that connects Claude models, including Claude 3.5, 3.7 and 4.
10 AI Coding Challenges I Face While Managing AI Agents
From what I have observed, AI agents are particularly helpful during the exploratory phases of work, assisting with implementations or outlining potential approaches. However, they fall short in contexts that require consistent judgment or strategic reasoning. Below, I outlined the most common AI coding challenges. Click the links to jump to each section: [aim_list] 1.
Building Local AI Agents: Goose, Observer AI, AnythingLLM
We spent three days mapping the ecosystem of local AI agents that run autonomously on personal hardware without depending on external APIs or cloud services. We organized the tools we evaluated into five categories: Local AI agent stack See category descriptions.
AI Agents in Marketing: Tools & Examples
Research shows that 50% of organizations already using generative AI plan to launch agentic AI pilot programs in 2025.AI agents in marketing represent a significant shift in the industry, introducing systems that can reason, make decisions, and act with minimal human oversight.
Low/No-Code AI Agent Builders: n8n, AgentKit, make, Zapier
We spent three days configuring AI-agent workflows, manually setting up LLM actions, document parsers, search tools, and building pipelines with triggers, conditional steps, tool calls, and webhooks to compare how each system handles multi-step agent automation.
AI Agents for Competitive Intelligence: Tools and Applications
The competitive intelligence landscape is shifting rapidly. MarketsandMarkets projects the global AI agent market will grow from USD 5.1 billion in 2024 to USD 47.1 billion by 2030, at a CAGR of 44.8%. Traditional methods, quarterly reports, and manual research are being replaced by AI agents that continuously track competitors, delivering insights in real-time.
15 Security Threats to LLM Agents (with Real-World Examples)
Even a few years ago, the unpredictability of large language models (LLMs) would have posed serious challenges. One notable early case involved ChatGPT’s search tool: researchers found that webpages designed with hidden instructions (e.g., embedded prompt-injection text) could reliably cause the tool to produce biased, misleading outputs, despite the presence of contrary information.
We Tested Mobile AI Agents Across 65 Real-World Tasks
We spent 3 days benchmarking four mobile AI agents (DroidRun, Mobile-Agent, AutoDroid, and AppAgent) across 65 real-world tasks using an Android emulator with applications such as calendar management, contact creation, photo capture, audio recording, and file operations.
Compare Best AI Agents in Customer Service
AI agents powered by large language models (LLMs) can respond to customer queries in natural language, interpret context, and generate human-like responses. These agents can process and synthesize large volumes of information from sources such as knowledge bases. We compiled a list of the best use cases for the top 4 customer service AI agents.