mimir-decisions

Decisions your agents can act on. MIMIR is a non-generative decision model: give it a context, a question and the options, and get back a typed answer with calibrated probabilities, the evidence behind it, and a certified verdict on whether to act or escalate. No text generated. Nothing to parse. Nothing to hallucinate.

It beats Laya and GLiNER2.5-Decide head-to-head on six of ten tasks — by 54.7 points on Banking77, 42.3 on MASSIVE, 36.3 on typed decisions — and where it cannot back an answer, it abstains instead of guessing.

pip install "mimir-decisions[local]"        # the local engine (CPU and CUDA)
pip install mimir-decisions                  # data models and HTTP client only

Python 3.11+. Documentation: https://abderahmane-ai.github.io/mimir/

Why MIMIR

Most agents route, classify, and verify using a general-purpose language model: slow, expensive, and impossible to audit. MIMIR is built for structured decisions. It runs locally on Torch, on CPU or GPU, returns calibrated probabilities with every answer, and issues a certificate — measured evidence that answers passing its threshold stayed at or below the risk level you ask for, on held-out data.

• No generation. Answers are drawn from the options you supply, not synthesised. The model cannot hallucinate an answer that wasn't on the list.
• Calibrated confidence. Probabilities are not softmax scores; they are calibrated to match realised accuracy on held-out data.
• Answers, always. Every decision returns the model's prediction with its probabilities. In threshold and certified modes, answers below the floor come back deferred for review — never withheld.
• One typed contract. Seven decision types — choice, multi-choice, yes/no, verify, rank, rate, estimate — all returning the same result shape, over any context.
• Portable. The same Python interface works locally on CPU or GPU, over HTTP, and over MCP. Framework adapters exist for eight agent SDKs.

Quickstart

from mimir import Context, Field, Mimir, Passage

model = Mimir.from_pretrained("Mythologic/MIMIR-1")

# The short form: the chosen option id, or None when the model abstains or defers
team = model.pick(
    "Hi, invoice INV-8821 was billed twice. Please refund the duplicate.",
    ["billing", "technical", "sales"],
)  # "billing"

# Context pairs prose passages with typed structured fields (amounts, IDs, metadata)
context = Context(
    passages=[
        Passage(
            title="Ticket #4091",
            text="Hi, we were billed twice ($2,400 total) on invoice INV-8821. Please refund the $1,200 duplicate today or we will cancel our plan.",
        )
    ],
    fields=Field.from_json({
        "invoice_id": "INV-8821",
        "duplicate_amount": 1200,
        "customer_plan": "enterprise",
    }),
)

# The question is optional: model.choose(context, options) asks "Which option applies to this context?"
result = model.choose(
    context,
    "Which department should handle this request?",
    options={
        "billing": "Billing: invoices, payments, refunds",
        "technical": "Technical: bugs, outages, system errors",
        "sales": "Sales: pricing, new contracts",
        "other": "Other: everything else",
    },
)

result.status         # Status.DECIDED, Status.ABSTAINED or Status.DEFERRED
result.answer         # "billing", or None when no option applies
result.probabilities  # calibrated probability of each option id
result.certificate    # mathematical proof of risk bound on held-out data

answer is always the model's prediction. status says whether it cleared the operating floor:

• DECIDED — act on answer.
• ABSTAINED — no listed option applies (clean OOD rejection).
• DEFERRED — the answer came in below the certificate floor; route to a human reviewer.

The Risk Dial: From Mission-Critical SLA to High-Throughput Agents

The release certifies thresholds across four finite-sample ri

... View full README on GitHub