# Agenta vs Caveman Agenta records 45,760 monthly visits with +29.5% month-over-month growth and Freemium pricing. Caveman records 171,826 monthly visits with -15.5% growth and Freemium, Contact for Pricing pricing. | Metric | [Agenta](https://openaitools.app/tool/agenta) | [Caveman](https://openaitools.app/tool/caveman) | | :--- | :--- | :--- | | Website URL | https://www.agenta.ai | https://caveman.so | | Categories | AI Developer Tools, Prompt Engineering, Large Language Models (LLMs), AI Agent | AI Developer Tools, AI Agent, AI Monitor, AI Productivity Tools | | Pricing Model | Freemium | Freemium, Contact for Pricing | | Monthly Visits | 45,760 | 171,826 | | MoM Growth Rate | +29.5% | -15.5% | | Primary Market | United States | United States | | Direct Visits | 17,206 | 64,225 | | Review Score | 4.4 | 4.7 | | Saved Count | 0 | 0 | | Supported Platforms | Website | Website | ## Agenta Agenta positions itself as an open-source workspace where teams build AI agents and AI co-workers rather than one-off prompts. You can start from pre-built agent templates, give an agent access to models and tools, and run it from a chat surface, Slack or Telegram so the work happens where your team already communicates. Agents can browse, read and write files, and run scheduled automations that trigger from your connected systems. What sets Agenta apart is its focus on operational control. Agents connect to more than a thousand integrations and MCP servers, secrets live in a vault the agent can use but not read, and permissions and approval steps decide which actions run automatically and which need a human sign-off. Every run is logged with duration, token usage and cost, so you can trace why an agent produced a certain result and iterate on it with feedback. For engineering teams, Agenta ships as MIT-licensed software you can self-host with Docker Compose or Helm, keeping agents and data on your own infrastructure with no vendor lock-in. A managed cloud option is available for teams that prefer not to run the stack themselves, and both share the same workspace concepts of agents, skills, sessions and automations. **Pricing & Plans:** Freemium. The open-source edition is MIT-licensed and free to self-host; cloud access is offered alongside it, with a demo available for larger teams. Exact seat and usage limits are listed on the pricing page. - **Agent and co-worker builder:** Assemble agents from templates or from scratch, give them models, skills and tools, and run them from a chat UI, Slack or Telegram. - **MCP connections and 1,000+ integrations:** Wire agents into Notion, GitHub, HubSpot, Slack, Linear, Drive and more over MCP, and swap tools or skills without rewriting the agent. - **Evaluations and run observability:** Score agent outputs, collect feedback and inspect every run with duration, token usage and cost logging so improvements are measurable. - **Guardrails, approvals and a secret vault:** Decide which actions run unattended and which require approval, while credentials stay in a vault your agent can use but cannot read. - **Self-hosting with MIT license:** Deploy the full workspace on your own infrastructure with Docker Compose or Helm charts and keep agents and data on-premises. **Strengths:** Open-source and MIT-licensed, so it can be self-hosted without vendor lock-in; Connects to over a thousand tools and MCP servers instead of a fixed integration list; Built-in evaluation and full run logging with token and cost tracking; Approval gates and a secret vault keep agents within human control **Limitations:** The workspace concept is broad, so new users need time to map it to their workflow; Self-hosting requires someone comfortable running containers and Helm charts; Some enterprise features are only fully explained after contacting sales [Profile (Markdown)](https://openaitools.app/tool/agenta.md) ## Caveman Caveman addresses a very concrete problem for teams running AI agents: token bills grow faster than the value the agents produce. The core agent and SDK are free and MIT-oriented, sitting entirely on your machine or inside your production app. When a turn happens, Caveman scans logs, test output, JSON, diffs and documents, shrinks them before the model ever sees them, and keeps the original on disk so the model can ask for it when it genuinely needs the exact text. Beyond reading, Caveman also trims what the agent writes and picks the model per turn. Its output compressor strips filler prose while returning code, commands and error text byte-for-byte, and its routing layer hands each sub-agent task to the cheapest model and effort level that still gets it right. The company publishes benchmark figures such as around 33% fewer input tokens across full sessions and roughly 0.46x cost per task compared with always-frontier-model routing. Caveman Cloud is the paid, hosted layer. It watches live traffic, surfaces where tokens are still wasted, tests each proposed fix against your own work, and only then ships it with proof. The Cloud Free plan covers a single developer, Team is $300 a month with $300 of usage included, and Enterprise runs inside your own VPC via a Helm chart with your KMS and storage. **Pricing & Plans:** Freemium, Contact for Pricing [Profile (Markdown)](https://openaitools.app/tool/caveman.md) ## Frequently Asked Questions ### How do Agenta and Caveman compare in monthly traffic? Agenta has 45,760 monthly visits, while Caveman has 171,826. Traffic estimates describe audience size, not product quality. ### How do their growth rates compare? Agenta has a month-over-month growth rate of +29.5%; Caveman has -15.5%. ### How does pricing compare? Agenta: Freemium. Freemium. The open-source edition is MIT-licensed and free to self-host; cloud access is offered alongside it, with a demo available for larger teams. Exact seat and usage limits are listed on the pricing page. Caveman: Freemium, Contact for Pricing. Freemium, Contact for Pricing ## Alternatives | # | Tool Name | Monthly Visits | MoM Growth Rate | Pricing Model | Summary | Profile (Markdown) | | :--- | :--- | :--- | :--- | :--- | :--- | :--- | | 1 | [Ollama](https://openaitools.app/tool/ollama) | 9,421,973 | +4.3% | Free, Freemium, Paid | Run and build with open AI models locally or in the cloud | [.md](https://openaitools.app/tool/ollama.md) | | 2 | [Orca](https://openaitools.app/tool/orca) | 1,025,891 | +1% | Free, Open Source, Enterprise on request | Runs parallel CLI coding agents in isolated worktrees, lacking dedicated context compression and cost routing tools. | [.md](https://openaitools.app/tool/orca.md) | | 3 | [Dify](https://openaitools.app/tool/dify-ai) | 819,691 | -15% | Freemium | Build, deploy, and govern production-ready agentic AI workflows | [.md](https://openaitools.app/tool/dify-ai.md) | | 4 | [LobeHub](https://openaitools.app/tool/lobehub) | 654,548 | -25% | Freemium | Put always-on AI agents on your team chat | [.md](https://openaitools.app/tool/lobehub.md) | | 5 | [Augment Code](https://openaitools.app/tool/augment-code) | 326,215 | +24.1% | Freemium, Free Trial, Contact for Pricing | Provides an enterprise context engine for coding agents, but lacks open-source token compression and custom model routing. | [.md](https://openaitools.app/tool/augment-code.md) | | 6 | [Arize AI](https://openaitools.app/tool/arize-com) | 249,028 | +3.2% | Paid, Contact for Pricing | Agent observability, evaluation, and continuous improvement for production AI | [.md](https://openaitools.app/tool/arize-com.md) | | 7 | [Sim AI](https://openaitools.app/tool/sim-ai) | 140,953 | +20.6% | Freemium, Paid (per user/month), and Enterprise with custom pricing; self-hosting free under Apache 2.0 | Open-source workspace to build, deploy and govern AI agents | [.md](https://openaitools.app/tool/sim-ai.md) | | 8 | [Agno](https://openaitools.app/tool/agno) | 140,302 | -3.3% | Contact for Pricing, Free | Provides an open-source Python framework for building model-agnostic agents without built-in context compression. | [.md](https://openaitools.app/tool/agno.md) | | 9 | [Coworker AI](https://openaitools.app/tool/coworker-ai) | 129,346 | -1.8% | Free Trial, Paid, Contact for Pricing | Builds permission-aware context graphs and routes tasks to models, though targeted specifically at enterprise agent workflows. | [.md](https://openaitools.app/tool/coworker-ai.md) | | 10 | [Maxim AI](https://openaitools.app/tool/maxim-ai) | 121,351 | +10% | Contact for Pricing, Free Open Source | Offers an enterprise LLM gateway and monitoring tools, but focuses less on local context compression and agent routing. | [.md](https://openaitools.app/tool/maxim-ai.md) | --- Source: https://openaitools.app/compare/agenta-vs-caveman More Markdown: [Categories](https://openaitools.app/categories.md) · [Traffic rankings](https://openaitools.app/rankings.md) · [llms.txt](https://openaitools.app/llms.txt)