AgentClash vs DeepEval
AgentClash
agentclash.dev
DeepEval
deepeval.comSide by side
| Figure | AgentClash | DeepEval |
|---|---|---|
| Price from | $49/mo | $200/mo |
| Free plan | Yes | Yes |
| Free trial | — | — |
| Open source | — | Yes |
| Web | Yes | Yes |
| Windows | — | Yes |
| macOS | — | Yes |
| Linux | — | Yes |
| API | Yes | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 4 | 5 |
| Spec score · AI Agent Evaluation Tools | 8.5 (#10) | 9.3 (#1) |
| Documentation | The public documentation covers the CLI, local stack, Fleet eval sets, datasets, regression gates, multi-turn human takeover, security stress harnesses, and runtime components. | The documentation contains installation, quickstarts, metric references, integrations, tracing, datasets, CLI, environment-variable and troubleshooting sections. |
| Integrations | CI/CD integrations can run regression tests from GitHub Actions, a webhook, or the CLI and fail builds when correctness, cost, latency, or required evidence regresses. | Listed integrations include LangChain, Pydantic AI, OpenAI Agents, LangGraph, AWS AgentCore, Strands, Google ADK, LlamaIndex, and CrewAI. |
| Purpose | AgentClash is an open-source AI-agent evaluation platform that runs agents on real tasks, scores outcomes, replays steps, and turns failures into regression tests. | DeepEval is an open-source LLM evaluation framework for building evaluation pipelines to test AI systems. |
Plans
DeepEval
- DeepEvalFree
- FreeFree
- Starter$200 a month
- Team$2,000 a month
- EnterprisePriced on request
In their own words
AgentClash
AgentClash is an open-source platform for evaluating AI agents on real tasks. It runs each agent in a fresh Firecracker microVM with isolated files and network, then removes the sandbox after the run. Multi-turn evaluations score tool choices, cost, latency, recovery, and final outcomes. YAML challenge packs define tools, policies, scoring rules, and starting state; available tools include file operations, data queries, HTTP, shell, and test runners. Scoring can combine deterministic, mathematical, behavioral, and language-model judges with configurable weights and consensus. Failed runs can become permanent regression tests. CI/CD checks run through GitHub Actions, webhooks, or the CLI and can fail builds when correctness, cost, latency, or evidence regresses. Adapters cover OpenAI, Anthropic, Gemini, xAI, Mistral, and OpenRouter. It offers API, web, and self-hosted use. Pricing is freemium: Free, or paid from $39/mo (annual).
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.