Agenta vs DeepEval
Agenta
agenta.ai
DeepEval
deepeval.comSide by side
| Figure | Agenta | DeepEval |
|---|---|---|
| Price from | $29/mo | $200/mo |
| Free plan | Yes | Yes |
| Free trial | — | — |
| Open source | Yes | Yes |
| Web | Yes | Yes |
| Windows | — | Yes |
| macOS | — | Yes |
| Linux | — | Yes |
| API | Yes | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 8 | 5 |
| Spec score · Prompt Engineering Tools | 9.3 (#1) | 9.3 (#2) |
| Framework integrations | Official integration guides cover Agno, DSPy, Google ADK, LangChain, LangGraph, LlamaIndex, OpenAI Agents SDK, PydanticAI and Vercel AI SDK. | Official integrations include LangChain, LlamaIndex, CrewAI, OpenAI Agents, LangGraph, PydanticAI, Google ADK, AgentCore, Strands and OpenTelemetry. |
| Headquarters | Berlin, Germany | San Francisco, California, United States |
| Integrations | The documentation describes more than 1,000 integrations through built-in connectors, MCP servers and Composio. | Listed integrations include LangChain, Pydantic AI, OpenAI Agents, LangGraph, AWS AgentCore, Strands, Google ADK, LlamaIndex, and CrewAI. |
| Model providers | The pricing page lists OpenAI, Anthropic, Gemini, Mistral, Groq, MiniMax, Together AI, OpenRouter, Azure OpenAI, AWS Bedrock, Google Vertex AI, and OpenAI-compatible endpoints. | Evaluation model integrations include OpenAI, Azure OpenAI, Ollama, OpenRouter, Anthropic, Amazon Bedrock, Gemini, DeepSeek, Vertex AI, Grok, Moonshot, Portkey, vLLM, LM Studio, and LiteLLM. |
| Self-hosting | Self-hosting uses Docker and Docker Compose, with an optional Kubernetes Helm deployment documented separately. | Confident AI's enterprise service can be deployed on the customer's own infrastructure or used in the vendor cloud. |
| Tracing | Agenta records model and tool calls with token counts, latency, estimated costs, logs and errors. | DeepEval traces agent steps so they can be graded and inspected in the terminal and test runner. |
| What it does | Agenta is a collaborative workspace for building AI agents and automations through chat, with cloud or self-hosted deployment. | DeepEval is an open-source framework for testing and evaluating LLM applications locally or in CI/CD. |
Plans
Agenta
- HobbyFree
- Open sourceFree
- Pro$29 a month
- Business$299 a month
- EnterprisePriced on request
- Enterprise (Cloud)Priced on request
- Enterprise (self-hosted)Priced on request
- Enterprise self-hostedPriced on request
DeepEval
- DeepEvalFree
- FreeFree
- Starter$200 a month
- Team$2,000 a month
- EnterprisePriced on request
In their own words
Agenta
Agenta is an open-source workspace for teams building and automating AI coworkers. Teams can interact with agents in Slack or Telegram, start from templates for engineering, customer support, sales, company knowledge, and operations, and run them on a schedule or in response to events. Its observability tools include traces, logs, errors, version history, evaluations, and token usage with estimated model costs. Agenta connects with services such as Slack, Notion, GitHub, HubSpot, Google Drive, Linear, Zendesk, and Discord, and supports more than 1,000 integrations. Listed model providers include OpenAI, Anthropic, Gemini, Mistral, and others. Cloud Hobby is free and allows two team members and 5,000 runs monthly; runs stop at that limit. Pro costs $29 a month and includes 10,000 runs monthly, with extra runs priced at $5 per additional 10,000. Model-provider charges are separate. Self-hosting is available, with users operating the application, storage, runner, upgrades, and backups in their own infrastructure. A mobile app supports agent conversations, approvals, and workspace management.
DeepEval
DeepEval is a free, open-source framework for building evaluation pipelines to test AI systems. Its Pytest-native evaluations run in CI/CD or as Python scripts, and the framework lists more than 50 research-backed metrics, including checks for hallucination, faithfulness, answer relevancy, summarization, toxicity and bias. It evaluates text, images and audio, including conversational and voice interactions, using methods such as G-Eval, DAG, QAG and JevEval. DeepEval can generate synthetic goldens from a knowledge base and model conversations across user personas. It also traces agent steps so they can be graded and inspected in a terminal or test runner. Integrations cover agent frameworks including LangChain, Pydantic AI, OpenAI Agents, LangGraph, LlamaIndex and CrewAI, alongside a range of model providers. By default, it sends basic telemetry to PostHog, excluding personally identifiable information and stored results; users can opt out with an environment variable. The maker describes DeepEval OS as for pre-production testing, with results in local files and an engineer-owned test runner. Enterprise capabilities are offered through Confident AI Evals, with self-hosted or maker-cloud deployment options.