> ## Documentation Index
> Fetch the complete documentation index at: https://docs.forge.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Router

> Choose the right model and reasoning effort for each AI task.

Forge Router selects the model and reasoning effort for each request across
your configured model providers. It is designed for agent workloads where
different steps in the same workflow can require different levels of speed,
capability, and depth.

Router decisions are optimized for the completed task, not only for the cost or
latency of an individual model request.

## Availability

Forge Router is currently available in beta through Forge LLM Gateway. Add the
managed **Forge Router** destination to an access profile, then send requests
to that profile using the model name `forge/auto`. Forge selects from the model
portfolio reviewed for your organization; applications do not need to choose a
specific upstream model for each request.

Forge Router must be the only destination in its route tier. Use later priority
tiers when you want a separate fallback path.

## What Router considers

Router evaluates the request and its surrounding session context against the
available model portfolio. The decision can account for:

| Factor                | Purpose                                                                                                  |
| --------------------- | -------------------------------------------------------------------------------------------------------- |
| Task characteristics  | Match routine, structured, ambiguous, or complex work to an appropriate model.                           |
| Reasoning effort      | Select the depth of reasoning appropriate for the task instead of applying one setting to every request. |
| Quality requirements  | Prefer a stronger model when the work requires more reliable reasoning, tool use, or output quality.     |
| Latency               | Keep interactive and time-sensitive work responsive.                                                     |
| Inference cost        | Avoid using the most expensive model when a lower-cost model is likely to produce an equivalent result.  |
| Session continuity    | Preserve consistency across related steps when changing models would reduce quality or increase work.    |
| Prompt-cache locality | Consider the value of reusing cached context in long-running sessions.                                   |

The result is not a fixed ranking of models. A model that is effective for one
task category may be inefficient or insufficient for another.

## Company-specific optimization

Router recommendations improve as Forge observes more of your organization’s
work. Signals associated with completed tasks can include durable outputs,
verification, review outcomes, retries, manual rework, and other available
evidence of task quality.

This allows Router to learn patterns specific to your company, such as:

* Which models perform reliably for recurring task types.
* Which tasks need additional reasoning effort.
* Which model choices reduce retries or downstream correction.
* Which workflows benefit from keeping a consistent model path.
* Where a lower-cost or faster model produces comparable results.

Recommendations are based on observed task outcomes rather than only prompt
length, keywords, or generic benchmark results. As models, pricing, and usage
patterns change, the routing policy can adapt to the new evidence.

## Agent-session awareness

Agent sessions are sequences of related requests, tool calls, and intermediate
outputs. Router considers the broader workflow so that an optimization for one
request does not create unnecessary cost or latency elsewhere in the session.

This includes prompt-cache awareness. Long sessions may reuse a large context
across requests, and changing providers or model paths can reduce the value of
that cached context. Router weighs the benefit of selecting a different model
against the benefit of maintaining cache locality and session continuity.

## Routing outcomes

Router is intended to improve the balance of:

* Output quality and task completion
* Reasoning depth and response latency
* Inference spend and session efficiency
* Model flexibility and workflow consistency

The effectiveness of a routing decision can be evaluated through the same
signals used by Forge Optimize, including verification, rework, durable
artifacts, latency, and cost. This provides a basis for comparing routing
behavior against the actual outcomes of your AI workloads.

## Model portfolio

Router works across the models and providers available to your organization.
Adding or removing a model changes the set of eligible routes; observed task
outcomes continue to inform which available model is best suited to each class
of work.