Contexto provides a context engine that curates and compacts the information an AI agent processes, ensuring relevant data remains accessible while irrelevant output is filtered out. By managing what the model sees at each step, it prevents performance degradation over long runs without relying on larger model windows, enabling more reliable multi‑agent and sub‑agent workflows.
Funding
Funding not disclosed
Founders
Product
Problem
AI agents that rely on large language models accumulate tool outputs and intermediate data over time, causing context bloat that degrades decision quality. Simply increasing the model’s context window does not prevent important information from being lost during compaction.
Solution
Contexto offers a dedicated context engine that curates the information presented to an AI agent at each processing step. It selectively persists critical data, compacts less relevant content, and retrieves stored context when needed, ensuring the model consistently works with the most pertinent information. By managing context rather than expanding model windows, Contexto maintains decision quality throughout long-running workflows. The platform also lays the groundwork for advanced capabilities such as sub‑agent scoping and multi‑agent handoff, enabling more efficient and modular AI system designs.
Target Audience
Primary customers are developers and enterprises building autonomous AI agents or multi‑agent systems that require reliable, long‑term reasoning and efficient use of LLM resources.
Features
- Real-time context curation that decides which data to retain, compact, or discard during agent execution
- Persistent storage of curated context with automatic retrieval based on relevance triggers
- Model‑agnostic integration that works with any LLM without requiring larger context windows
- Compacting algorithms that preserve essential information while reducing token usage
- Architecture designed for future extensions like sub‑agent scoping and multi‑agent handoff