AI agents can call tools, execute workflows, and generate complex outputs—but how do we know whether they are actually reliable?
I built AgentLab, an open-source platform for testing, evaluating, and understanding AI agents locally.
What AgentLab does
AgentLab lets developers define evaluation cases using YAML, execute a local agent or application, and inspect clear PASS, FAIL, and ERROR results.
Current capabilities include:
- YAML-based test suites
- Secure local command execution
- Explicit permission before executing programs
- Exact output evaluation
- PASS / FAIL / ERROR analysis
- JSON reports
- Standalone HTML reports
- Local web GUI
- No database, cloud upload, or telemetry
Example test suite
yaml
suite: Greeting Tests
tests:
- id: greeting_001
input: "Hello"
expected: "Hello"
- id: greeting_002
input: "How are you?"
expected: "I'm fine."
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