AgentClash

AgentClash is ranked #10 of 29 in AI agent evaluation tools on The Geeks Club. It runs on API, Self-hosted, Web. There is a free plan. Paid plans start at $49/mo.

AgentClash plans and pricing

All plans

Free Free 1 workspace · 25 eval runs / month · up to 4 models per run · 7-day replay retention · BYO LLM API key · BYO E2B sandbox token · community support agentclash.dev · 1 Oct 2026

Pro $49/mo Billed monthly; annual billing $39 / month ($468 / yr) 500 eval runs / workspace / month · up to 8 models per run · 30-day replay retention · hosted sandbox with included credit · private challenge packs · CI integration · 3 concurrent eval runs · email support < 1 business day agentclash.dev · 1 Oct 2026

Team $100/mo Billed monthly; annual billing $80 / month ($960 / yr) 2,000 eval runs / workspace / month · up to 12 models per run · 90-day replay retention · 10 concurrent eval runs · multiple workspaces · workspace-level audit log · Slack notifications · priority email support < 4 business hours agentclash.dev · 1 Oct 2026

Enterprise Not published Custom SSO / SAML · org-wide audit logs · unlimited replay retention · 99.9% uptime SLA · dedicated support channel · custom MSA / billing terms agentclash.dev · 1 Oct 2026

Compared on AI agent evaluation tools

Free plan: Yes
Paid from: $39/mo
Evaluation methods: hybrid
Tool-call checks: Yes
Trace ingestion: Yes
Safety evaluations: Yes
Regression runs: Yes

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AgentClash

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.
Agent evaluation: It evaluates multi-turn agents that take actions in a real sandbox and scores tool choices, cost, latency, recovery, and the final result.
Sandboxing: Each agent runs in a fresh Firecracker microVM with an isolated filesystem and network, and the sandbox is torn down after the run.
Providers: First-class adapters support OpenAI, Anthropic, Gemini, xAI, Mistral, and OpenRouter, with more than 300 models available through OpenRouter.
Tools: Agents can use file I/O, data queries, HTTP, shell, and test runners, with declarative YAML challenge packs defining tools, policy, scoring, and starting state.
Scoring: Runs combine deterministic, mathematical, behavioural, and LLM-based judges with configurable consensus aggregation and weights.
Regression loop: When a model fails a challenge, AgentClash freezes the failing trace into a permanent test that future evaluations replay.
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.
Security: API keys, database credentials, and OAuth tokens are stored in a scoped secret vault and injected at tool-call time without appearing in prompts, traces, or replays.
Knowledge sources: Knowledge sources include PDFs, wikis, Notion, codebases, and custom APIs, with provenance attached to retrieved facts.
Workloads: The product is positioned for coding, research, SRE, multi-step operations, codebase question answering, and support workloads.
Open source and hosting: AgentClash is MIT licensed, can be self-hosted as a full stack, or used against the hosted backend; its CLI installs from npm as the agentclash package.
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.
Price from: $49/mo

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Where it ranks on The Geeks Club

Best AI Agent Evaluation Tools in 2026#10 of 29

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Sources

agentclash.dev· checked 1 Oct 2026
agentclash.dev/docs· checked 1 Oct 2026