Table of Contents

Understanding Context Windows (Context Length) for LLMs


TL;DR

A context window is the maximum number of tokens a model can "see" at once (prompt + chat history + recalled memory + tool outputs + the tokens it is currently generating).

In LM-Kit.NET, MultiTurnConversation exposes:

  • ContextSize: the total token budget for the conversation window. (docs.lm-kit.com)
  • ContextRemainingSpace: how many tokens are still available right now. (docs.lm-kit.com)

You can also:

  • Override the context size at construction time (or let LM-Kit pick an optimal size). (docs.lm-kit.com)
  • Cap per-turn output with MaximumCompletionTokens (default 2048). (docs.lm-kit.com)
  • Inject long-term memory via Memory and cap it with MaximumRecallTokens. (docs.lm-kit.com)
  • Choose overflow behaviors using InferencePolicies (trim, shift KV cache, throw, stop generation). (docs.lm-kit.com)

What Exactly is a Context Window?

A model does not "read" your whole app or your whole conversation forever. It processes a sliding window of tokens.

Think of it like a desk:

  • Everything on the desk is visible to the model.
  • Anything that falls off the desk might be forgotten unless you re-inject it (summary, memory recall, retrieval, etc.).

A practical token accounting view:

\[ \text{ContextUsed} = \text{System} + \text{History} + \text{Tools} + \text{RecalledMemory} + \text{CurrentUser} + \text{GeneratedTokens} \]

If ContextUsed > ContextSize, you need a strategy.

LM-Kit explicitly models this in MultiTurnConversation with ContextSize and ContextRemainingSpace. (docs.lm-kit.com)


Why Context Windows Matter

  1. Quality and coherence

    • Longer context helps keep instructions and earlier details consistent.
    • But if the window is full, important instructions may get trimmed out (which looks like "the model forgot").

  2. Performance and memory

    • Larger context usually means more RAM usage and slower inference (KV cache grows with context).
    • LM-Kit exposes configuration like KV cache recycling for efficiency in .NET apps. (docs.lm-kit.com)

  3. Agent behavior

    • Planning, reflection, tool-calling, and memory recall all consume tokens.
    • If you do not budget for these, the "agentic" parts are the first to degrade.

What Happens When You Hit the Limit?

There are two common overflow problems:

1) Prompt too long (input overflow)

In LM-Kit, this is configurable via InferencePolicies.InputLengthOverflowPolicy. (docs.lm-kit.com)

Options include: (docs.lm-kit.com)

  • TrimAuto: automatically trims to fit
  • TrimStart: drop earliest tokens (often best for chat, keeps the most recent turns)
  • TrimEnd: drop the latest tokens
  • KVCacheShifting: shift KV cache to accommodate overflow
  • Throw: raise NotEnoughContextSizeException

2) Context fills up during generation (context overflow)

Controlled by InferencePolicies.ContextOverflowPolicy. (docs.lm-kit.com)

Options include: (docs.lm-kit.com)

  • KVCacheShifting: shifts cache contents to keep going
  • StopGeneration: stops and returns ContextSizeLimitExceeded

Practical Strategies That Actually Work

Budget your output

In MultiTurnConversation, use MaximumCompletionTokens to cap how long the assistant can speak per turn (default 2048). (docs.lm-kit.com) If you intentionally cap output, you can continue later using ContinueLastAssistantResponseAsync. (docs.lm-kit.com)

Use memory recall instead of stuffing the prompt

LM-Kit supports a long-term memory store:

  • Assign Memory (an AgentMemory implementation). (docs.lm-kit.com)
  • Control its budget with MaximumRecallTokens (defaults to ContextSize / 4, capped to ContextSize / 2). (docs.lm-kit.com)

Summarize when the chat grows

LM-Kit includes a Summarizer that can summarize text and also attachments (including PDFs and Microsoft Office formats via Attachment). (docs.lm-kit.com) It also supports an overflow strategy for overly long inputs, with default RecursiveSummarize. (docs.lm-kit.com)

Persist and resume sessions

MultiTurnConversation can persist and restore full chat sessions (save session to file/bytes, restore from them). (docs.lm-kit.com)


Context Window Management in LM-Kit.NET

Example: set context size, budgets, and overflow policies

using LMKit;
using LMKit.TextGeneration;
using LMKit.Inference;

// Optional: performance-oriented global configuration
LMKit.Global.Configuration.EnableKVCacheRecycling = true;

// Load a model by ID (downloads if not cached locally).
var model = LM.LoadFromModelID("gemma3:12b");

// Option A: let LM-Kit pick an optimal context size (contextSize = -1)
// Option B: force a context size (bounded by model limits)
using var chat = new MultiTurnConversation(model, contextSize: 4096);

// Set system prompt BEFORE the first user message
chat.SystemPrompt = "You are a concise technical assistant.";

// Per-turn output budget
chat.MaximumCompletionTokens = 512;

// Overflow handling policies
chat.InferencePolicies.InputLengthOverflowPolicy = InputLengthOverflowPolicy.TrimStart;
chat.InferencePolicies.ContextOverflowPolicy = ContextOverflowPolicy.KVCacheShifting;

Console.WriteLine($"ContextSize: {chat.ContextSize} tokens");
Console.WriteLine($"ContextRemainingSpace: {chat.ContextRemainingSpace}");

while (true)
{
    Console.Write("> ");
    var prompt = Console.ReadLine();
    if (string.IsNullOrWhiteSpace(prompt)) break;

    if (chat.ContextRemainingSpace < 256)
        Console.WriteLine("[Heads up] Context is getting tight. Consider summarizing or saving the session.");

    var result = chat.Submit(prompt);
    Console.WriteLine(result.Content);

    // If you capped MaximumCompletionTokens and want more, you can explicitly continue:
    // var more = await chat.ContinueLastAssistantResponseAsync();
    // Console.WriteLine(more.Content);
}

Example: summarizing older content to free space

using LMKit;
using LMKit.TextGeneration;

// Use a summarizer model
var model = LM.LoadFromModelID("lmkit-tasks:4b-preview");
var summarizer = new Summarizer(model)
{
    GenerateTitle = true,
    GenerateContent = true
};

var summary = summarizer.Summarize(longText);
Console.WriteLine("Title: " + summary.Title);
Console.WriteLine("Summary: " + summary.Content);

You can then inject that summary back into your conversation as a "compressed memory" message (or store it in your AgentMemory implementation), instead of keeping the entire raw history.


Key Terms

  • Context Window / Context Length: max tokens visible at once.
  • Token Budget: the slice of ContextSize reserved for each component (history, tools, memory recall, output).
  • KV Cache: internal attention cache that grows with context; LM-Kit supports overflow policies like KV cache shifting. (docs.lm-kit.com)
  • Recall Tokens: tokens injected from long-term memory, capped by MaximumRecallTokens. (docs.lm-kit.com)
  • Overflow Policy: what happens when input or generation exceeds the limit. (docs.lm-kit.com)


External Resources


Summary

Context windows define the hard boundary of what a language model can consider at any given moment. Every token in your system prompt, chat history, tool outputs, recalled memory, and generated response counts toward this limit. When the window fills up, information is lost unless you actively manage it.

LM-Kit.NET gives you fine-grained control over this budget through MultiTurnConversation. You can set the total context size, cap per-turn output with MaximumCompletionTokens, inject long-term memory with bounded recall tokens, and choose from multiple overflow policies (trimming, KV cache shifting, or stopping generation). For conversations that grow beyond the window, summarization and session persistence offer practical escape hatches.

The key takeaway: treat your context window as a scarce resource. Budget it deliberately across system prompts, history, tools, and output. Use memory recall and RAG instead of stuffing everything into the prompt. Monitor ContextRemainingSpace at runtime, and have a clear overflow strategy in place before your users hit the limit.

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