mira

name	mira
description	Author and run Mira evaluations — the Rust-first, code-first eval framework for agents and tools. Use when writing eval suites, adding scorers/subjects, running evals across a model matrix, wiring evals into CI, or driving the `mira` host CLI. Covers in-process (`subject_fn`), polyglot (`CliSubject`), and everruns runtime subjects.

Mira evals

Mira is a developer tool shaped like a test runner.

Eval = Dataset(Sample…) + Subject + [Scorer…]  ×  model matrix

• Subject — what's under test: subject_fn (in-process), CliSubject (any external binary, the polyglot path), or mira_everruns::RuntimeSubject.
• Scorer — grades a Transcript: built-ins (text, tools, budgets, files), combinators (all_of/any_of/not), closures, or model_graded.
• Matrix — Targets plus extra .axis(name, values); missing API keys skip, so runs are green offline.

Install

The framework is a library (mira-eval, imported as mira); the runner is a binary (mira-cli, installed as mira).

CLI (mira host) — install the prebuilt binary; don't build from source:

brew install everruns/tap/mira      # prebuilt binary (recommended)

Prebuilt binaries for macOS (arm64/x86_64) and Linux (x86_64) are also attached to each GitHub Release: https://github.com/everruns/mira/releases. If Homebrew enforces tap trust checks, run brew trust --tap everruns/tap once first. Building from source (cargo install mira-cli --locked) is the fallback only.

Framework (Rust studies) — add the library to your crate:

cargo add mira-eval                 # the eval framework, used as `mira::…`
cargo add mira-everruns             # + everruns runtime subject (optional)

Cross-language studies need no Rust framework at all — see SDKs.

Authoring an eval study

A study is a program that defines evals and calls mira::Study::registered().serve(); register factories with #[eval]. The lightest form is a single file (study.rs) with cargo-script frontmatter for its deps — run with mira run --study study.rs, no Cargo.toml. The same code also works as a crate [[bin]] (--study-bin NAME) or examples/*.rs (--study-example NAME).

#!/usr/bin/env -S cargo +nightly -Zscript
---
[package]
edition = "2024"

[dependencies]
mira-eval = "0.3"
tokio = { version = "1", features = ["macros", "rt-multi-thread"] }
---
use mira::scorer::{file_contains, succeeded};
use mira::subject::subject_fn;
use mira::{eval, Eval, Sample, Target, Transcript};

#[eval]
fn coding() -> Eval {
    Eval::new("coding")
        .describe("Edits a file to satisfy an instruction")
        .add_sample(Sample::new("add-fn", "Add a greet function to lib.rs").file("lib.rs", "// here\n"))
        .subject(subject_fn(|sample, cx| async move {
            // Call the real agent/model (cx.target.provider / cx.target.model);
            // report the metrics the budget scorers grade.
            let mut t = Transcript::response("done");
            t.files.insert("lib.rs".into(), "fn greet() {}\n".into());
            t
        }))
        .scorer(succeeded())
        .scorer(file_contains("lib.rs", "fn greet"))
        .targets([Target::sim(), Target::anthropic("claude-opus-4-8")])
        .build()
}

#[tokio::main]
async fn main() -> std::io::Result<()> { mira::Study::registered().serve().await }

The host shims cargo-script onto stable (materializes a throwaway crate from the frontmatter); MIRA_SCRIPT_NATIVE=1 uses cargo -Zscript on nightly.

Full example (tools + budget scorers + main), the polyglot CliSubject, the everruns runtime subject, in-process Runner tests, and custom scorers: references/cookbook.md. A non-Rust study runs via --study-cmd "..." (or --study study.py / --study-python) — see examples/greet-python/.

Running

mira list --study study.rs                 # advertised evals/samples/scorers/targets
mira run --study study.rs                  # whole matrix
mira run --study study.rs add-fn           # substring filter on eval/sample@target
mira run --study study.rs --tag smoke
mira run --study study.rs --targets sim                      # restrict the target axis
mira run --study study.rs --axis effort=low                  # restrict any declared axis
mira run --study study.rs --preset smoke                     # saved selection from mira.toml
mira run --study study.rs --format junit --out results.xml   # CI artifact
mira run --study study.rs --format html  --out report.html   # transcript viewer
mira run --study study.rs                                    # saves a run folder by default
mira run --study study.rs --resume <run_id>                  # resume; run only the missing cases
mira report <run_id>                                          # re-render a saved run's reports
mira run --study-bin NAME                    # a crate study (workspace bin)
mira run --study-cmd "python3 study.py"      # a study written in another language
mira doctor --study-bin coding               # diagnose config/study/saved runs; --fix repairs

Exit code is non-zero if any case failed — drops straight into CI. Run mira help --full for an overview, every flag, examples, and links.

When a setup misbehaves (typo'd mira.toml keys, a preset that selects nothing, duplicate sample ids, unavailable targets, torn run folders), run mira doctor: it lints the config, the study's advertised listing, and the saved-run store, and mira doctor --fix applies the safe repairs (removing leftover temp files, re-rendering missing reports). Errors exit non-zero.

Scorers

A case passes only if every .scorer(...) passes. Families: text/output (succeeded, contains, regex, json_field_equals…), tools (tool_called, tools_used_exactly, tool_called_before…), trajectory structure over the ATIF trajectory — tool arguments, observations, steps (tool_called_with, tool_arg_matches, observation_contains, steps_within; they fail if the subject reported no trajectory), budgets (tokens_within, cost_within, latency_within…), files (file_exists, file_contains), and combinators / custom (all_of, any_of, not, scorer(name, closure), model_graded(rubric, judge)).

Prefer trajectory-based scoring for agent behaviour: Transcript.trajectory (ATIF) is the primary structured contract — produce it via TranscriptSource::AtifFile, the everruns subject, or an SDK study — and it is the only place tool arguments and observations exist. The raw events channel is an advanced fallback for producer-specific data the trajectory doesn't model; don't score against events when the trajectory covers the need.

Full catalog with semantics: references/scorers.md.

Subjects

What's under test — pick one per eval:

• subject_fn(...) — in-process Rust (see Authoring above).
• CliSubject — evaluate any external binary (the polyglot path).
• mira_everruns::RuntimeSubject — a real everruns runtime session.

Recipes for all three (+ in-process Runner tests): references/cookbook.md.

Cross-language studies (SDKs)

Any language that speaks the protocol is a first-class study: the host owns selection, the model matrix, concurrency, saved runs, and reporting; the study owns subjects and scoring. The SDKs are native (not FFI bindings) and generated from the canonical schema, so they never drift from the wire format.

• Python SDK — https://github.com/everruns/mira/blob/main/sdks/python/README.md
• Wire protocol (write your own, any language) — https://github.com/everruns/mira/blob/main/docs/protocol.md
• Worked example — https://github.com/everruns/mira/tree/main/examples/greet-python
• Run it: mira run --study-cmd "python3 study.py"

Examples (runnable, offline)

All run against the sim model with no API keys, so they stay green in CI and cost nothing. Browse: https://github.com/everruns/mira/tree/main/examples

• greet — smallest eval, single-file (--study): #[eval], a closure subject, text + LLM-judge scorers — https://github.com/everruns/mira/blob/main/examples/greet.rs
• coding — single-file (--study): seeded files, a model matrix, structural + file scorers — https://github.com/everruns/mira/blob/main/examples/coding.rs
• cli_subject — crate (--study-bin): the polyglot path, driving an external program — https://github.com/everruns/mira/tree/main/examples/cli_subject
• matrix — crate (--study-bin): a multi-axis matrix (targets × a custom effort axis) — https://github.com/everruns/mira/tree/main/examples/matrix
• greet-python — a whole study in Python via the SDK — https://github.com/everruns/mira/tree/main/examples/greet-python

cargo run -p mira-cli -- run --study examples/greet.rs                             # a single-file Rust example
cargo run -p mira-cli -- run --study-bin matrix                                    # a crate example
cargo run -p mira-cli -- run --study-cmd "python3 examples/greet-python/study.py"  # polyglot

Learn more (read on demand)

Progressive disclosure: this skill is the overview. Bundled references ship with the skill (offline) — read them first:

• references/cookbook.md — recipes for every subject kind, in-process tests, and custom scorers.
• references/scorers.md — the full scorer catalog.

Canonical prose lives in the repo docs — open one only when the task needs that depth:

Doc	When to read
getting-started	First study, end-to-end.
authoring	Evals, samples, targets, axes, presets.
scorers	Every built-in scorer + LLM-as-judge.
subjects	subject_fn, CliSubject, runtime.
metrics	Usage/timing/tools the budget scorers grade.
extensibility	Custom scorers/subjects.
how-it-works	Core model + vocabulary.
protocol	Wire format for non-Rust studies.
specs/architecture	Design of record (the why).

Or run mira help --full for the self-orienting CLI guide (overview, every flag, examples, links).