toolkami

effective long context, at lower cost.

Research-backed context folding for LLM agents with an easy to use drop-in API.

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Measured savings

Toolkami reduces context bloat and improves recall across long-running agent workflows.

Context length

Up to 100x

Recall quality

~28-33%

Cost reduction

Up to 2x

How it works

Context is treated as part of environment that the LLM can symbolically interact with.

Why Toolkami?

Built for teams that need reliable long context with predictable token costs.

Research based folding

Context folding cuts token bloat while preserving the details that matter to the task.

Dev friendly API

Drop in endpoints for retrieval, folding, and summaries that keep agents focused.

Secure implementation

Locked down REPL with zero data retention.

Integration

Drop-in replacement for existing LLM calls with the same interface.

Examples

Drop-in 
import llm

response = llm.completion(prompt, model)
export OPENAI_BASE_URL="https://toolkami.com/${endpoint_id}/openai/v1"

codex
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