Automatic memory ingestion
Stores, indexes, and continually reasons over messages and interaction history.
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An AI-native memory platform for building stateful agents with persistent context, retrieval, and continual reasoning.
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Stores, indexes, and continually reasons over messages and interaction history.
Returns useful memory rather than forcing an agent to load its entire history.
Controls context size to reduce unnecessary usage and cost.
Offers multiple chat reasoning tiers for targeted questions.
Finds patterns and inferences asynchronously between active sessions.
Scopes memory across users, agents, NPCs, groups, and relationships.
Works with OpenAI, Anthropic, and custom model stacks.
Supports CLI and integrations for coding agents and orchestration tools.
Remember team conventions, architecture preferences, and prior code reviews.
Maintain relationships, preferences, and emotional continuity across conversations.
Give characters opinions, memories, and evolving relationships.
Preserve customer context across sessions, channels, and handoffs.
Track misconceptions and adjust explanations and difficulty over time.
Honcho gives AI agents a durable memory layer. It stores and indexes messages, reasons over interactions through its Neuromancer models, and returns curated context through a simple context() method.
The platform is model-agnostic and supports token budgets, unlimited retrieval, peer scoping, background dreaming, and on-demand reasoning. It can represent users, agents, NPCs, groups, and relationships, helping developers build systems that preserve preferences, conventions, and continuity instead of starting from an empty context each session.
