strands-labs/strands-decider

Strands Decider

A small, fast decision model for agentic AI.

Strands decider is one of a new class of decision models, or "system one" models. Unlike an LLM, which can generate arbitrary text, a decision model picks between sets of options and rates things on a scale. This class of model works best for problems that fall between LLMs and traditional classification models: it is a general-purpose classification and scoring model that responds faster than an LLM and does not take the time and expertise to train that a traditional classifier does. That makes it a natural fit for the decisions inside agentic workflows built with the Strands Agents SDK.

• Pick from options — "Are there three r's in strawberry? Yes or no." "What language is the phrase 'sihamba ngokushesha' in? English, Zulu, or Dutch."
• Rate on a scale — "How positive is 'this is the best doc I've ever read'? Between 0 and 1."
• Calibrated reliability scores — every decision carries a confidence. On short classification tasks it has never seen, answers at a confidence of 0.9 or more are right about 95% of the time (evaluation/results.md); below that, confirm or ask a person. Frontier LLM inference APIs do not expose anything equivalent.

The easiest place to get started is through the strands-decider cli:

pip install strands-decider

On an Apple-silicon Mac, --device mlx runs the model through MLX, 1.4 to 1.6x faster than MPS. It needs the mlx extra, which ships with the next release; until then, install from a clone with pip install -e ".[mlx]". The cuda, mps and cpu extras name the other devices (docs/inference.md). The example outputs below are from strands-decider-2B-qwen3.5-v1-2610, recorded with --device cpu; other devices differ in the last digits.

You can ask the model to choose based on some state and a question:

strands-decider ask StrandsAgents/strands-decider-2B-qwen3.5-v1-2610 \
  --state "Help! My payouts have been failing for 3 days! " \
  --choice "Which team should handle this?=billing,sales,retail" 

Example Output:

choice_0 -> billing (confidence 0.659)
  billing                  0.773
  sales                    0.121
  retail                   0.106

You can also ask the model a Yes/No question:

strands-decider ask StrandsAgents/strands-decider-2B-qwen3.5-v1-2610 \
  --state "Help! My payouts have been failing for 3 days! " \
  --noul "Does this convey urgency?" 

Example Output:

noul_0 noul = 0.858

Closer to 1 is leaning more toward Yes

Or give a question a score:

strands-decider ask StrandsAgents/strands-decider-2B-qwen3.5-v1-2610 \
  --state "Help! My payouts have been failing for 3 days! " \
  --score "How frustrated is the writer?=calm,frustrated,depressed"

Example Output:

score_0 score = 1.03 (confidence 0.509)
  0: calm                                     0.196
  1: frustrated                               0.574
  2: depressed                                0.230

You can combine multiple questions into a single command. This is more efficient as you only need to load the state for the model once:

strands-decider ask StrandsAgents/strands-decider-2B-qwen3.5-v1-2610 \
  --state "Help! My payouts have been failing for 3 days! " \
  --choice "Which team should handle this?=billing,sales,retail" \
  --noul "Does this convey urgency?" \
  --score "How frustrated is the writer?=calm,frustrated,depressed"

noul_0 noul = 0.858
choice_0 -> billing (confidence 0.659)
  billing                  0.773
  sales                    0.121
  retail                   0.106
score_0 score = 1.03 (confidence 0.509)
  0: calm                                     0.196
  1: frustrated                               0.574
  2: depressed                                0.230

You can also run the model as a server, and ask questions via http requests:

strands-decider serve StrandsAgents/strands-decider-2B-qwen3.5-v1-2610 --port 8000

curl -s localhost:8000/v1/systemone \
  -H 'content-type: application/json' \
  -d '{
    "state": "Help! My payouts have been failing for 3 days!",
    "questions": {
      "is_urgent": {"type": "noul", "instructions": "Does this convey urgency?"}
    }
  }'

{
  "model": "strands-decider-2B-qwen3.5-v1-2610",
  "answers": {
    "is_urgent": {
      "type": "noul",
      "noul": 0.8581
    }
  },
  "usage": {
    "input_tokens": 86,
    "output_tokens": 1
  },
  "latency_ms": 489.01
}          

Qwen3.5-2B-Base is natively multimodal. With --vision the server keeps its vision tower, and a request may carry images (base64) as part of the state; the published checkpoints (v1, and hobson-v21 and v19 before it) answer over them with no image training. Needs pip install "strands-decider[vision]" and transformers 5.18 or later.

strands-decider serve StrandsAgents/strands-decider-2B-qwen3.5-v1-2610 --vision --port 8000
strands-decider ask StrandsAgents/strands-decider-2B-qwen3.5-v1-2610 --state "" --image page.png \
  --noul "Is the signature block filled in?"

v19 matches an image-trained 2B decider on accuracy, and on NaturalBench is much better calibrated (ECE 0.014 against 0.080). v21 has v19's image accuracy, is better calibrated on POPE, and is more confident than v19 when the image is missing. v1 has v21's image accuracy; with the image missing it is more overconfident than v21 on POPE (docs/vision.md). See docs/vision.md for the request shape, how it works and the measurements.

Model names now use the pattern strands-decider-<size>-<base>-v<N>-<YYMM>:

• <size> is the size of the base model, as its Hugging Face id writes it (2B, E4B).
• <base> is the base model family (qwen3.5, gemma4).
• v<N> counts the releases on one base family: v1 is the first release on each base. Releases with the same N on different bases can use different recipes; each model card says what its model trained on.
• <YYMM> is the release month.

Examples: strands-decider-2B-qwen3.5-v1-2610 (Qwen3.5) and strands-decider-E4B-gemma4-v1-2610 (Gemma 4). The earlier models keep their names: strands-decider-2B-hobson-v19 and strands-decider-2B-hobson-v21. A second release in the same month updates the same Hub repository and adds a revision tag YYMMDD. docs/naming.md has the full rules.

The models live in the StrandsAgents organization on Hugging Face, their main home. The five v1 models are also in the amazon organization, with the same files and the same names: for example amazon/strands-decider-E4B-gemma4-v1-2610.

Four models on the Gemma 4 instruction-tuned base models (google/gemma-4-*-it, Apache-2.0). They have the same pointer head and answer the same three question types as the Qwen3.5 model.

For comparison, strands-decider-2B-qwen3.5-v1-2610 scores 180, Brier 0.280. The E2B Decision Index was measured before the E2B attention fix (see docs/inference.md), so it may be off. Every model is a soup of three training runs, calibrated afterwards (evaluation/results.md).

Choose a size. A larger model is more accurate and better calibrated, and needs more memory. The 12B and the 26B-A4B are the most accurate; the 26B-A4B is better calibrated, but needs about twice the memory and twice the latency of the 12B. The E4B is a good choice when the model must share one GPU with other work. The E2B is the smallest: use it where memory is tightest, or on CPU. All four serve on CPU (docs/inference.md).

Install. Gemma 4 support is on main and not yet in the package on PyPI:

pip install "strands-decider @ git+https://github.com/strands-labs/strands-decider"
strands-decider ask StrandsAgents/strands-decider-E4B-gemma4-v1-2610 \
  --state "Help! My payouts have been failing for 3 days! " \
  --choice "Which team should handle this?=billing,sales,retail" \
  --noul "Does this convey urgency?" \
  --score "How frustrated is the writer?=calm,frustrated,depressed"

noul_0 noul = 0.959
choice_0 -> billing (confidence 0.951)
  billing                  0.967
  sales                    0.019
  retail                   0.013
score_0 score = 0.97 (confidence 0.848)
  0: calm                                     0.102
  1: frustrated                               0.823
  2: depressed                                0.075

What they train on. The Gemma 4 models do not use the Qwen3.5 v1 data mix. They train on hobson-v21's data (123,339 rows) plus 12,263 code-task rows, 135,602 rows in all. Qwen3.5-4B is the teacher, where its answer agrees with the gold label, and training keeps the answers close to the released v19. The rows come from 25 public Hugging Face datasets, five of them code datasets, and from ContractNLI, MuSiQue, generated rows and generated code tasks. data/sources.md lists every source with its licence.

The Gemma 4 models read text only: --vision is for the Qwen3.5 model.

strands-decider-2B, the first model of the family, has 1.9 billion parameters. It answers a question in a median 115 ms on an RTX 3090 and also serves on an Apple-silicon Mac or on CPU, and many questions about one text are cheap, because the text is read once and each question adds only its own tokens (docs/inference.md). Its accuracy and calibration are measured on JevBench, a third-party benchmark for this class of model (Performance).

The core idea: take a pretrained decoder LLM torso (Qwen3.5-2B-Base), discard its language-modelling head — taking away its ability to generate text — and replace it with a small pointer head of about a million parameters. The head scores each option by comparing the hidden state at the <answer> position against the hidden state at that option's own last token. One forward pass, no generation, no decoding loop. The torso is adapted with a rank-16 LoRA adapter, and the head runs in fp32.

Because the head holds no per-option parameters, nothing can learn that "the first option is usually right", nothing caps how many options a question may carry, and label sets are defined by the request rather than baked into the weights. Three question types come out of the same masked softmax, read back differently: noul (yes/no), choice (one of N) and score (an ordered rubric).

As you browse the research, you will find that this is the second major iteration of the architecture. The first used a slot head, which mapped the final hidden state to a fixed set of slots and performed significantly worse. Every change since is captured in the research so you can follow along with the work. The reference model today is v1 (strands-decider-2B-qwen3.5-v1-2610; docs/naming.md explains the name). It trains on a new, balanced data mix of 246,678 rows, about twice v21's: it adds code tasks, new rule-application rows (apply a written policy to a case) and rows from more public datasets, so that the mix follows the kinds of decisions in the evaluations. Its teacher is gemma-4-31B-it, and training keeps its answers close to the released v19. The released checkpoint is a soup of three training runs: their LoRA updates and head scores averaged exactly, then calibrated (evaluation/results.md). strands-decider-2B-hobson-v21 and -v19 stay published. docs/architecture.md has the full design, the training objective, and the decisions behind them.

Three targets matter: accuracy, calibration and latency. v1, v21 and v19 measure:

Every task in the easy tier is answered correctly, and the nearest comparison is decider-2b, which shares v19's torso with a different recipe (evaluation/jevbench.md).

v1's column is measured through main (the training harness gives 181; one task is a near tie). v1 also answers code questions: 0.836 on 1,996 held-out code tasks (evaluation/results.md).

Read the v21 and v19 columns as two single runs, not as a measured gain. Trained on one host with six seeds each, the v19 recipe and v21's average 172.8 and 172.3 JevBench tasks: the same accuracy. v21's Brier score is lower on that host (0.331 against 0.341), and the two recipes differ by the paraphrases and the distillation only.

v19's JevBench figures are at the 3072-token window the run was preregistered at; the recipe saves a 4096-token window, at which v19 scores 168. And 231 tasks are few: six retrains of the v17 recipe had a standard deviation of 3.2 tasks, so treat a difference under about 10 tasks between two single runs as unresolved. evaluation/README.md has the scripts and the limitations, and links the results by version and the board position with its caveats.

Two reasons. First, experimentation: at 1.9 billion parameters you can serve the model, and retrain the whole recipe, on hardware you already have — about 11 hours on one RTX 3090, and serving works on an Apple-silicon Mac. That makes trying an idea fast and low-risk. Second, ~2B parameters looks like a sweet spot: small enough to experiment with, large enough to do meaningful work.

The decisions inside an agentic workflow are the natural target:

• Model routing — pick the right LLM for a task
• Tool selection — decide which tool an agent should call next
• Argument checking — verify a tool call's arguments before it runs
• Triage — route an incoming request to the team or queue that should own it
• Guardrails — grounding checks, safety classification, policy classification
• Evaluations — score model outputs, and check whether an answer is adequate, at low cost
• Hybrid agents — let the LLM make the hard decisions and a decider make the rote ones, reducing cost and latency

The repository includes a worked example of strands decider inside a Strands agent, under examples/strands/: a before_tool_call intervention that gates a weather-tool call on two yes/no decisions, so the agent asks which city instead of guessing. See the example's README for setup and the walk-through.

Two entry points, both on a Linux or WSL2 host with NVIDIA GPUs and the training environment of training/README.md (the train extra, the pinned torch and flash-linear-attention):

• training/recipe.sh all: one host, local, 1 to 8 GPUs (NGPU=8 for eight). It builds the corpora, trains, calibrates and evaluates.
• training/run_recipe.sh all with training/aws/: a distributed 8-GPU host. It adds per-stage timing and logs, row-count checks, the S3 copy of the outputs (PY and S3_PREFIX must be set), and FAST=1, which trades 24 GB compatibility for speed on 80 GB GPUs.

About 11 hours on one RTX 3090 (24 GiB), or 1 hour 10 minutes on eight H100s with FAST=1. training/README.md has the setup, the stages, the settings and the hardware notes; data/sources.md lists every source and its licence, and data/README.md states the reproduction contract.

The record of the work is in the repository. Since v9, every training run states its predictions and its failure conditions before training, and the outcome is appended after the run without editing what came before. By that rule, a run that misses its bar does not replace the reference model; five runs were promoted by the maintainers' decision anyway, and the record says so. Most runs missed their bar, v20 among them: 169 of 231 at the 4096-token window against v19's 168, inside the retrain noise, with four predictions failed. research/README.md lists each run and its outcome, and research/history.md tells what moved the benchmark and what did not. To propose an experiment, open an issue with a preregistration (CONTRIBUTING.md).

To ask questions and talk about Strands Decider, join the Strands Decider channel on Discord.

This project is licensed under the Apache License 2.0 - see the LICENSE file for details.

See CONTRIBUTING for more information.