Framework
An agent evaluation framework built for production tasks
Teams comparing agent evaluation frameworks should look past leaderboard scores. AgentClash gives you repeatable workloads, same-task eval runs, replay evidence, and release gates you can audit in git.
Candidate
Baseline
Control
replay timeline
ci verdict
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.
OpenTelemetry-compatible trace import
Pinned datasets and golden test cases
Baseline versus candidate regression checks
Replay trails for tool calls, outputs, and artifacts
Scorecards for correctness, cost, latency, and evidence
CI gates for prompt, model, RAG, and tool changes
Workflow
Framework workflow
Import the evidence
Start from OpenTelemetry traces, curated datasets, support transcripts, or a real failure your team already saw.
Pin the baseline
Record the current accepted behavior so every prompt, model, RAG, or tool change has a fair comparison point.
Replay the evidence
Inspect tool calls, outputs, artifacts, latency, cost, and judge evidence when a candidate gets worse.
Gate the release
Compare candidate and baseline runs, then fail CI before a regression reaches users.
Evaluate before you commit
Bring your first workload into the loop
Use AgentClash alongside prompt-eval tools when you need end-to-end agent behavior, not single-call scoring.
Agent evals
Real-task agent evals with replay evidence and CI gates.
LLM agent evaluation
Evaluate LLM agents on full trajectories, not one-shot answers.
Compare tools
See how AgentClash differs from prompt-eval platforms.
Datasets overview
Import examples, record baselines, sync regression suites, and gate CI.
Dataset CI gates
Fail builds when a candidate regresses against a pinned baseline.
CI/CD agent gates
Block pull requests when agent behavior gets worse.
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.