Evals

Lock in agent behavior with regression tests.

Evals are regression tests for your agents. Rerun the same prompts against the same agents and behavior drift becomes visible.

/improve-agent generates probes from an agent’s instructions to find new weaknesses. Evals preserve known behavior as repeatable cases.

Cases

Cases live in evals/cases.py. Each case sends one input to an agent (agent=) and optionally checks two things:

• judge: AgentAsJudgeEval scores the response against criteria (binary pass/fail) using an LLM.
• reliability: ReliabilityEval checks which tools fired against expected_tool_calls.

A case looks like this:

evals/cases.py

from os import getenv

# The WebSearch agent calls parallel_search when PARALLEL_API_KEY is set, web_search otherwise.
_WEB_SEARCH_TOOL = "parallel_search" if getenv("PARALLEL_API_KEY") else "web_search"

CASES: tuple[Case, ...] = (
    Case(
        name="web_search_recent_anthropic_research",
        agent=web_search,
        input="What did Anthropic publish about agent research recently?",
        tags=("live",),
        timeout_seconds=120,
        criteria=(
            "Answers the question by citing at least one real Anthropic URL "
            "(anthropic.com domain). The response is grounded in fetched content "
            "rather than refusing to answer."
        ),
        expected_tool_calls=(_WEB_SEARCH_TOOL,),
    ),
    # add more cases here
)

A case can use either check or both. If both are set, the agent runs once and feeds the same response into both.

Add tags to group your cases into suites. The template uses three tags: smoke, release, and live. This case uses the live tag because its answer depends on the open web.

Run the suite

The suite runs on the host, calls the model, and logs results to your local Postgres through eval_db. Start the platform first (docker compose up -d) and make sure .env has your OPENAI_API_KEY.

1. Create a virtual environment

   The eval suite runs on the host and needs a local virtual environment:

   ./scripts/venv_setup.sh
   

   Activate it:

   source .venv/bin/activate
   
2. Run the eval suite

   python -m evals --tag smoke     # fast suite
   python -m evals                # full suite
   

   Other options:

   python -m evals -v             # stream the agent run with full panels
   python -m evals --name <case>  # single case while iterating
   

Each case prints its response and the verdicts for the checks it defines. The run ends with an Eval Summary table.

Results write to Postgres via eval_db. The eval history shows up on os.agno.com alongside your sessions and traces, so you can see when a case started failing and what changed.

Diagnose failures with your coding agent

Run /create-evals to add coverage for an agent. The skill maps the agent’s behavior, proposes cases, writes them to evals/cases.py, and verifies the new cases.

Open your coding agent and run:

/eval-and-improve

The coding agent runs the suite, triages every failure (bad criteria, real regression, flaky LLM judge), and proposes in-scope fixes. It edits the agent or the case and re-runs until the suite is green.

When to run evals

Trigger	Frequency
Before deploying a change to an agent	Every time
As part of CI	Every PR
Against production	On a daily cron
After bumping a model version	Every time

The template registers a daily run-evals schedule in the disabled state because it uses model calls. Enable it from the AgentOS UI when you want the smoke-tagged cases to run daily. See Scheduler for the cron API.

What good cases look like

• Specific. “Returns a JSON object with ticker and price” beats “Returns the right answer”.
• Stable. Avoid prompts whose correct answer changes daily. Use phrasing like “describes a real, recent…” instead of locking in a specific result.
• Scoped to one behavior. One case per behavior makes failures easy to read.
• Anchored to tools. expected_tool_calls catches the failure mode where the agent confidently makes things up instead of calling a tool.

Next

Next steps →