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Adapt it to the work

Keep long-running agents within context limits

Control how much information the model receives with each request.

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Budget instructions, history, knowledge, memory, and tool schemas before every model call.

What this changes for your team.

Set how much information to include with each model request. Yekar.AI fits instructions and selected history, knowledge, and tool details into that limit. The provider’s model limit still applies.

How it works in practice.

  1. 01

    Set the context budget supported by the selected model and your cost expectations.

  2. 02

    Plan space across instructions, session history, knowledge, memory, tool schemas, and the response reserve.

  3. 03

    Trim or retrieve selectively when all candidate material cannot fit the planned turn.

What you can plan around.

What to expect, including limits that matter to your team.

Context planning happens before the provider request and uses model-token estimates.

The selected limit is saved with the agent’s settings.

Tokenizer estimates and provider accounting can differ, so provider limit errors remain possible and visible.

Complete the operating path

View all capabilities
Adapt it to the workTest changes against real past cases

Save a finished conversation as a test, and check every later draft against it before you trust the new release.

Adapt it to the workCarry useful context forward

Let an agent retain selected memory entries and working context beyond one conversation thread.

Build the agentBuild agents with AI

Turn a working brief into a reviewable agent draft without starting from a blank setup.

Bring one real process

See how Yekar.AI fits the way you work.

Start with a job your team already owns, plus the tools and decisions around it.

Get started