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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchYou can try Strands Decider 2B locally with the strands-decider command-line tool. It is a decision model that chooses among options you define; it is not a general-purpose text generator. The official examples demonstrate a CLI decision and a Strands agent intervention, but they do not provide a ready-made multi-RAG router. This guide separates the documented setup from a proposed design you would need to validate for your own retrieval systems.
What Decider 2B does—and what it does not do
Strands Decider 2B is a 2-billion-parameter decision model. Rather than writing an open-ended answer, it makes a one-pass selection from a defined set of choices and can return scores for those choices. That makes it a possible component for bounded decisions such as selecting a model, tool, or policy action—not a replacement for a generative model that writes explanations, code, chat responses, or document summaries.
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The Strands Agents announcement describes model routing, tool selection, evaluations, guardrails, memory, context management, and policy classification as promising applications. It also cautions that decision models are less suited to complex problems than reasoning models. Keep the decision space explicit, and use a generative model for open-ended synthesis. Read the Strands Decider 2B announcement.
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Try the documented local Decider CLI
The announcement’s basic installation and example use the strands-decider CLI and a named model. Install the package in the Python environment you intend to use:
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pip install strands-deciderThen ask it to choose from a short list of candidate teams based on a state description:
strands-decider ask StrandsAgents/strands-decider-2B-hobson-v19
--state "Help! My payouts have been failing for 3 days!"
--choice "Which team should handle this?=billing,sales,retail"The example returns a selected option, confidence, and scores for the options. Treat these as outputs for that example, not proof that scores are calibrated probabilities or that the selected option will be correct in your domain. Before relying on a decision, define the choices carefully and test it against representative cases, including ambiguous inputs.
Understand the separate local Ollama quickstart
Strands also documents how to run a Strands agent using a local Ollama model. This is a separate local-agent provider path; the quickstart does not establish that Decider 2B itself can be served through Ollama.
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Requirements and commands
The Python quickstart calls for Python 3.10 or newer and a virtual environment. Install the Ollama extra, start the Ollama service, and pull the tool-capable llama3.1 model shown in the documentation:
pip install 'strands-agents[ollama]'
ollama serve
ollama pull llama3.1Configure a Strands agent
Point the documented provider at the local Ollama endpoint, then create an agent:
from strands import Agent
from strands.models.ollama import OllamaModel
model = OllamaModel(host="http://localhost:11434", model_id="llama3.1")
agent = Agent(model=model)
agent("What is an agent harness, in one sentence?")This configures the generative model for the Strands agent. The separate Python quickstart and harness quickstart document Ollama provider use; neither describes it as a Decider 2B serving method.
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Know which parts of the routing example are local
The announcement’s Strands integration illustrates a local Decider intervening before a tool call. It checks whether tool arguments are grounded in the conversation and whether a call is premature, then maps the result to an action such as Proceed, Deny, Confirm, or Guide. The authors say the questions, threshold, and policy in the example were hand-picked and present it as an illustration, not a recommendation.
That integration is hybrid, not fully offline: the announcement says the agent and Decider run locally while the default LLM comes from Amazon Bedrock. A dedicated integration library was described as still in development at the time of the announcement, so distinguish a CLI experiment from custom Strands intervention code when planning implementation. The post’s description of the setup is in the official announcement.
Proposed architecture for multi-RAG routing
The official material supports model routing as an application area, but does not publish a turnkey multi-RAG implementation, retriever-selection schema, or validated recipe. The following is a proposed design, not an AWS or Strands reference architecture:
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- Define bounded retriever choices. Name each retrieval route and specify what it is for—for example, product documentation, support cases, or policy documents. Include an abstain or fallback choice rather than forcing a potentially poor match.
- Give the decision component only the context needed to route. Provide a compact description of the user’s request and any relevant routing metadata. Keep the candidate choices explicit and manageable.
- Call the selected retrieval tool or tools. Decide whether the task should use one retriever or several. If multiple sources are queried, define how conflicting or duplicate evidence will be handled.
- Pass retrieved evidence to a generative model. Use a text-generating model to synthesize an answer grounded in the retrieved material; Decider’s choice is not itself the answer.
- Evaluate against a simple baseline. Compare routing and end-to-end results with a fixed retriever or another straightforward routing rule. Measure routing errors, retrieval quality, abstention behavior, and end-to-end latency on your own representative tasks.
These steps are an implementation proposal. The published examples do not establish the choice schema, retrieval orchestration, or evaluation thresholds that will work for a particular set of RAG systems.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Choose a setup based on privacy, task, and operations
| Option | Execution and privacy | Best fit | Important boundary |
|---|---|---|---|
| Decider CLI experiment | Runs the documented decision command locally. | Trying bounded choices and inspecting outputs. | Does not by itself implement agent intervention or multi-RAG orchestration. |
| Local Strands agent with Ollama | The quickstart connects a Strands agent to Ollama at http://localhost:11434. |
Trying a locally served generative model in a Strands agent. | The documented llama3.1 setup is not a documented way to serve Decider 2B. |
| Local Decider with Bedrock default LLM | The announcement’s integration runs the agent and Decider locally and uses Amazon Bedrock for the default LLM. | Illustrating a local decision component alongside a hosted generative model. | It is hybrid, so the generative-model portion is not fully local or offline. |
| Custom multi-RAG router | Depends on where you run each decision, retrieval, and generation component. | Routing among your own retrieval systems after task-specific design and testing. | No validated multi-RAG recipe is documented in the cited official material. |
Interpret latency and benchmark claims in context
The announcement reports around 115 ms median latency on a local Nvidia RTX 3090 and around 153 ms median latency for small tasks on an M3 MacBook. These are example measurements, not hardware requirements or guarantees for another machine; the post says latency rises approximately linearly with task size. The model is described as suitable for local CPU or GPU execution, so the RTX 3090 is not a stated prerequisite.
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Quick Recap
Practical limits to check before deployment
- Decision quality depends on the choices and input. Poorly defined or overlapping candidates can make a bounded selector hard to evaluate meaningfully.
- Scores are not a deployment policy by themselves. Set and validate any thresholds, abstention rules, or human-review paths for your use case.
- Open-ended work needs a generative model. Decider is not designed to produce arbitrary text or replace reasoning for complex tasks.
- Latency depends on workload and hardware. Measure your task sizes and end-to-end pipeline, not just an isolated decision call.
- Multi-RAG behavior must be validated locally. Test retrieval relevance and failure handling as well as the router’s choice.
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