What problem it solves

Coding agents need persistent, structured memory to handle long-horizon tasks without losing context. Markdown-based plans can become messy and do not naturally represent dependencies between work items.

Key features

• Dependency-aware task graph — Work is represented as a graph with dependency tracking and automatic detection of tasks that are ready to claim.

  Source: README

• Version-controlled storage — The Dolt-backed database provides native branching, cell-level merging, and synchronization through Dolt remotes.

  Source: README

• Collision-resistant issue IDs — Hash-based identifiers help prevent merge collisions when multiple agents or branches create issues concurrently.

  Source: README

• Persistent memory and compaction — Agents can store durable project facts, while semantic memory decay summarizes older closed tasks to conserve context.

  Source: README

• Multiple workflow modes — The project supports embedded and server-backed databases, an experimental HTTP backend, and operation without Git.

  Source: README

Quick start

Copied verbatim from the project's README.

brew install beads           # macOS / Linux (recommended)
npm install -g @beads/bd     # Node.js users

Who it may suit

Inference: this project may suit coding-agent developers and teams that need persistent task tracking across long-running or multi-agent workflows.

Topics to discuss

• How should coding-agent memory evolve as project context becomes more structured?

• Which graph relationships are most useful for coordinating long-horizon tasks?

• What workflows should be covered first by additional agent and editor integrations?

Written by ReadmeX AI from the project's README, source and public GitHub data; it may contain mistakes.

Read the original README https://github.com/gastownhall/beads#readme

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