New: Automatic Context Window Management Across 17,000+ Models

Backboard now includes Adaptive Context Management, a system that automatically manages conversation state when your application moves between models with different context window sizes.

With access to 17,000+ LLMs on the platform, model switching is common. But context limits vary widely across models. What fits in one model may overflow another.

Until now, developers had to handle that manually.

Adaptive Context Management removes that burden. And it’s included for free with Backboard.

The Problem: Context Windows Are Inconsistent

Different models support different context window sizes. Some allow large conversations. Others are much smaller.

If an application starts a session on a large-context model and later routes a request to a smaller one, the total state can exceed what the new model can handle.

That state typically includes more than just chat messages:

• system prompts

• recent conversation turns

• tool calls and tool responses

• RAG context

• web search results

• runtime metadata

  
  

When that information exceeds the model’s limit, something must be removed or compressed.

Most platforms leave this responsibility to developers. That means writing logic for truncation, prioritization, summarization, and overflow handling.

In multi-model systems, that quickly becomes fragile.

Introducing Adaptive Context Management

Backboard now automatically handles context transitions when models change.

When a request is routed to a new model, Backboard dynamically budgets the available context window.

The system works as follows:

• 20% of the model’s context window is reserved for raw state

• 80% is freed through intelligent summarization
  
  

Backboard first calculates how many tokens fit inside the 20% allocation. Within that space we prioritize the most important live inputs:

• system prompt

• recent messages

• tool calls

• RAG results

• web search context

  
  

Whatever fits inside this budget is passed directly to the model.

Everything else is compressed.

Intelligent Summarization

When compression is required, Backboard summarizes the remaining conversation automatically.

The summarization pipeline follows a simple rule:

1. First we attempt summarization using the model the user is switching to.

2. If the summary still cannot fit within the available context, we fall back to the larger model previously in use to generate a more efficient summary.

This approach preserves the most important information while ensuring the final state fits inside the new model’s limits.

The process happens automatically inside the Backboard runtime.

You Should Rarely Hit 100% Context Again

Because Adaptive Context Management runs continuously during requests and tool calls, the system proactively reshapes the state before a context window is exhausted.

In practice this means your application should rarely reach the full limit of a model’s context window, even when switching models mid conversation.

Backboard keeps the system stable so developers do not need to constantly monitor token overflow.

Developers Can See Exactly What Is Happening

We also expose context usage directly in the msg endpoint so developers can track how their application is using context in real time.

Example response:

"context_usage": {

 "used_tokens": 1302,

 "context_limit": 8191,

 "percent": 19.9,

 "summary_tokens": 0,

 "model": "gpt-4"

}"

"context_usage": {

 "used_tokens": 1302,

 "context_limit": 8191,

 "percent": 19.9,

 "summary_tokens": 0,

 "model": "gpt-4"

}"

"context_usage": {

 "used_tokens": 1302,

 "context_limit": 8191,

 "percent": 19.9,

 "summary_tokens": 0,

 "model": "gpt-4"

}"

This makes it easy to monitor:

• how much context is currently being used

• how close a request is to the model’s limit

• how many tokens were generated by summarization

• which model is currently managing the context

Developers gain visibility without needing to build their own tracking systems.

The Bigger Idea

Backboard was designed so developers can treat models as interchangeable infrastructure.

But that only works if state moves safely with the user.

Adaptive Context Management is another step toward that goal. Applications can move freely across thousands of models while Backboard ensures the conversation state always fits the model being used.

Developers focus on building. Backboard handles the context.

Next Steps

Adaptive Context Management is available today through the Backboard API.

Start building at docs.backboard.io