AgentClash

Framework

An agent evaluation framework built for production tasks

Teams comparing agent evaluation frameworks should look past leaderboard scores. AgentClash gives you repeatable workloads, head-to-head races, replay evidence, and release gates you can audit in git.

live race
gate: pass

Candidate

92correct patch, low cost

Baseline

88stable reference run

Control

73missed edge case

replay timeline

1loaded task inputs and tool policy
2ran sandbox actions and captured artifacts
3scored trajectory and validator evidence
4attached scorecard and release verdict

ci verdict

Candidate clears release gate

Correctness improved, latency within budget, and required artifacts were preserved for review.

agentclash run create --follow

What a serious framework includes

Built for reviewable agent decisions

A useful agent evaluation framework packages tasks, enforces fair constraints, scores trajectories, and makes failures reusable.

Sandboxed real-tool execution

Head-to-head runs with fair constraints

Scorecards for correctness, cost, latency, and tool strategy

Replay trails for every important action

Challenge packs that turn failures into reusable tests

CI gates for baseline versus candidate decisions

Workflow

Framework workflow

Package the task

Describe the workload as a challenge pack with inputs, tools, scoring rules, and artifacts.

Race the agents

Run every candidate against the same task with the same constraints.

Replay the evidence

Inspect tool calls, outputs, artifacts, latency, cost, and judge evidence after the run.

Gate the release

Compare candidate and baseline runs, then fail CI before a regression reaches users.

FAQ

Framework comparison FAQ

How is AgentClash different from prompt-evaluation frameworks?

Prompt-evaluation frameworks score isolated model outputs. AgentClash is an agent-evaluation framework for multi-turn tool-using runs in a sandbox.

Can we compare AgentClash with other tools?

Yes. See the compare hub for side-by-side notes with Braintrust, LangSmith, Promptfoo, Langfuse, Arize Phoenix, and OpenAI Evals.

Does the framework support custom scoring?

Yes. Challenge packs carry scoring rules, validators, and judge configuration so teams can encode domain-specific pass conditions.