Beads is a lightweight memory layer designed for coding agents, providing persistent context and efficient retrieval to enhance AI-assisted development. It converts important conversation snippets and code context into embeddings, stores them in an efficient index, and enables reliable retrieval during multi-turn interactions or long-lived sessions.
Persistent Memory
- Stores key conversation snippets, code fragments, and metadata across sessions
- Survives agent restarts and context window resets
- Decouples memory management from model inference for cleaner architecture
- Lightweight storage footprint optimized for coding assistant workloads
Embedding-Based Retrieval
- Vector index for fast semantic search across stored memories
- Metadata filtering to narrow results by file, language, or topic
- Low-latency queries tuned for real-time coding assistant interactions
- Relevant context returned in compact form for direct injection into the prompt
Integration Design
- Simple, extensible API for plugging into existing agent runtimes and toolchains
- Works alongside any LLM by appending retrieved context to the model's input
- Modular architecture that separates memory concerns from reasoning logic
- Compatible with popular coding agent frameworks and IDE extensions
When to Use Beads
- Coding assistants that need to maintain conversational state across sessions
- Recovering relevant past changes, annotations, and debugging history
- Enriching code generation and debugging with historical project context
- Reducing context engineering complexity in long-lived agent workflows