Vectorize
Open SourceOpen-source agent memory that lets AI agents learn from experience
Información de la herramienta
¿Qué es Vectorize?
Vectorize is an open-source agent memory system that gives AI agents persistent, learning memory. It helps agents retain user context, learn from mistakes, detect patterns, and improve judgment across sessions and agents through multi-strategy retrieval and reflection—designed as agent memory rather than traditional RAG or a simple vector database wrapper.
Características principales
Four parallel retrieval strategies
Runs dense vector search, sparse BM25 keyword matching, graph traversal for entity relationships, and temporal search with causal chains in parallel, then merges results under token budgets rather than fixed top-K limits.
Token-budget optimization
Controls how much memory fits into the prompt with predictable context size and API costs, instead of returning a fixed number of results.
Per-user memory and cross-session persistence
Gives every user separate persistent context for preferences, history, and decisions that survive session boundaries.
Learning from mistakes and reflection
Captures tool failures and user corrections as experiences; a reflection layer consolidates facts into patterns and reusable knowledge so agents build judgment over time.
Conflict detection and entity resolution
Preserves both states of changing facts with timestamps and uses batch entity disambiguation to reduce queries significantly.
Fast multi-language and MCP integration
Offers Python and TypeScript clients plus MCP tools for remember, recall, and reflect; works with any LLM and installs via skills for agents such as Claude Code or Cursor.
Open source and managed options
MIT-licensed self-hosted version with no feature gating or telemetry; Hindsight Cloud provides the same API with managed infrastructure, scaling, and backups.
Production security controls
SOC2 Type 2 certified, with tag-based user isolation and no phone-home telemetry on self-hosted deployments.
Casos de uso
- #1
Persistent agent memory across sessions
Agents retain user preferences, history, and decisions so conversations can continue weeks later without cold starts.
- #2
Agents that learn from mistakes
Failed tool calls and user corrections become experiences that help prevent repeating the same errors.
- #3
Shared context across multiple agents
One agent learns a preference or fact and other agents can apply it automatically through compound memory.
- #4
Time-aware and causal recall
Retrieve what happened during a specific period together with cause-and-effect relationships.
Planes de precios
MIT-licensed open-source self-hosted option with no usage limits or feature gating; Hindsight Cloud managed service is available. Specific plans or prices are not clearly published on the official source pages provided.
Alternativas
Preguntas frecuentes
What is Vectorize?
Is Vectorize open source?
How does Vectorize retrieve memories?
Does Vectorize support multi-agent shared memory?
What clients and integrations are available?
Is there a managed cloud option?
Vectorize Contacto y redes sociales
- EmpresaVectorize, Inc.
- Soporte
- Sitio web:
- Twitter
- YouTube
- LinkedIn
- GitHub
Reseñas
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