AgentClash

AgentClash is an open-source platform for evaluating AI agents on real tasks in a sandbox. It assesses multi-turn agents as they act, scoring tool choices, cost, latency, recovery, and the final result. Each run uses a fresh Firecracker microVM with isolated filesystem and network, and the sandbox is removed afterward. Challenge packs written in YAML can define tools, policy, scoring, and starting state; agents can use file I/O, data queries, HTTP, shell, and test runners. Scoring combines deterministic, mathematical, behavioural, and language-model judges, with configurable weights and consensus. When an agent fails a challenge, its trace can be frozen as a permanent test for later evaluations. CI checks can run through GitHub Actions, a webhook, or the CLI, and fail builds when correctness, cost, latency, or required evidence regresses. Provider adapters include OpenAI, Anthropic, Gemini, xAI, Mistral, and OpenRouter. AgentClash is MIT licensed and can be self-hosted as a full stack or used with its hosted backend. The Free plan includes 25 monthly evaluation runs; paid plans start at $39/month with annual billing.

AgentClash links sandboxed agent evaluation with replayable traces and regression checks. The free plan has a monthly run limit, while paid plans start at $39/month with annual billing.

AgentClash may suit teams evaluating coding, research, SRE, multi-step operations, codebase question answering, or support agents. It is relevant to teams that want to run evaluations in CI or self-host the platform.

AgentClash is an open-source platform for evaluating AI agents while they perform real, multi-step tasks in disposable sandboxes. It is best for engineering, research, SRE, and operations teams that need evidence about actions as well as answers. The standout use is converting failures into repeatable tests that can gate future releases.

Overview

AgentClash measures tool choices, cost, latency, recovery behavior, and final outcomes, making it more useful for action-oriented agents than a basic response grader. Workloads include coding, research, support, codebase question answering, and multi-step operations. The MIT license, self-hostable full stack, hosted backend, and npm CLI accommodate teams that want either control of their infrastructure or a managed route.

Each evaluation starts in a fresh Firecracker microVM with isolated filesystem and network access, then the sandbox is destroyed. That clean boundary improves repeatability and reduces residue between runs, while challenge authors still have to model the production situations they care about.

Key features

Tasks, tools, and scoring

Agents can use file I/O, data queries, HTTP, shell, and test runners. YAML challenge packs define the available tools, policy, scoring rules, and starting state, giving teams a reproducible operating context instead of a prompt-only test. Evaluation combines deterministic, mathematical, behavioural, and LLM-based judges; configurable weights and consensus aggregation let teams decide how correctness, evidence, and behavior should count.

First-class adapters cover OpenAI, Anthropic, Gemini, xAI, Mistral, and OpenRouter. OpenRouter access to more than 300 models is particularly useful for comparative model runs, although broader choice also increases the work of controlling cost and latency.

Regression, security, and knowledge

When an agent fails a challenge, its trace can become a permanent test that later evaluations replay. GitHub Actions, webhooks, and the CLI can run those regression checks and fail builds when correctness, cost, latency, or required evidence worsens. That creates a practical bridge from evaluation to release discipline.

Secrets such as API keys, database credentials, and OAuth tokens reside in a scoped vault and are injected at tool-call time without appearing in prompts, traces, or replays. Knowledge sources can include PDFs, wikis, Notion, codebases, and custom APIs, with provenance attached to retrieved facts. Safety evaluations, tool-call checks, trace ingestion, and regression runs are supported.

Pricing

The Free plan costs nothing and includes one workspace, 25 eval runs per month, up to four models per run, seven-day replay retention, a BYO LLM API key, a BYO E2B sandbox token, and community support. It is suitable for learning the workflow or maintaining a small test set, but 25 runs and short retention constrain active teams.

Pro costs $49 a month billed monthly, or $39 / month ($468 / yr) with annual billing. It raises capacity to 500 eval runs per workspace per month and eight models per run, adds 30-day replay retention, hosted sandbox credit, private challenge packs, CI integration, three concurrent runs, and email support. This is the sensible tier for a single product team that needs managed execution and release checks.

Team costs $100 a month billed monthly, or $80 / month ($960 / yr) annually. It provides 2,000 runs per workspace per month, up to 12 models per run, 90-day retention, 10 concurrent runs, multiple workspaces, workspace-level audit logs, Slack notifications, and priority email support. It fits organizations running parallel evaluations and requiring stronger coordination.

Enterprise uses custom pricing and adds SSO/SAML, organization-wide audit logs, unlimited replay retention, a 99.9% uptime SLA, a dedicated support channel, and custom MSA and billing terms. The paid tiers require a larger budget, and annual billing trades flexibility for lower monthly rates.

Platforms

AgentClash is available through an API, a web interface, and self-hosted deployment. The self-hosted option and MIT license suit teams with infrastructure or data-control requirements; hosted plans are more convenient when sandbox capacity and support matter.

Who it's for

Choose AgentClash when your agent must use tools, recover from problems, and produce auditable evidence across several steps. It is a strong match for teams willing to author YAML challenges and maintain regression suites. It is less suitable for simple single-turn text evaluation or projects whose testing volume cannot fit the free allowance and whose budget does not cover paid capacity.

Pros and cons

• Pro: Fresh microVM sandboxes make runs isolated and repeatable.
• Pro: Failed traces become CI-enforced regression tests, connecting evaluation to shipping decisions.
• Pro: Broad provider adapters and OpenRouter model access support structured comparisons.
• Pro: Vault-injected secrets keep credentials out of prompts and replay artifacts.
• Con: The Free plan's 25 monthly runs and seven-day retention are restrictive for frequent evaluation.
• Con: Challenge-pack design and scoring weights demand meaningful engineering judgment.

Alternatives

Browse the AI Agent Evaluation Tools list for a wider comparison. Pick Arklex when a free, Apache-2.0 open-source agent testing framework is the priority. Choose Future AGI AI Evaluation SDK for Apache-2.0 self-hosting with Docker, Python, and Node SDK options. Noveum is a freemium alternative with a free tier offering 2.5K credits, 1M spans, 2 GB storage, three members, and 30-day retention. Braintrust suits teams wanting a free starter with 1 GB processed data, 10,000 scores, 14-day retention, and unlimited users, projects, and datasets. Choose DeepEval for a free Apache-2.0 local and CI/CD test runner. Galileo is a paid hosted option with a $100.00 monthly, yearly-billed Pro plan and 50,000 traces per month. Giskard is a free open-source local option with basic vulnerability scanning and RAG evaluation. Google Cloud Agent Evaluation is a paid, usage-priced API and web service instead.

Verdict

AgentClash is a strong choice for teams building tool-using agents that need repeatable, sandboxed evidence and regression gates in CI. Choose it for the failure-to-test workflow and deployment flexibility; look elsewhere if you need only lightweight text checks or cannot justify paid run capacity beyond the small free quota.