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enhesa/big-agentic-brain v2.24.0 — Command center publish/todo structure change

enhesa/big-agentic-brain v2.24.0 adds session links to the published items in the command center and organizes todos and next steps into task units.

Repositoryenhesa/big-agentic-brain ↗

enhesa/big-agentic-brain v2.24.0 adds session links to the published items in the command center and organizes todos and next steps into task units.

GitHub description: Swarm intelligence for distributed AI agents. One agent learns, the whole swarm knows. Shared memories, reminders and agent-to-agent messages, ranked by trust. MCP and Claude Code ready.

stars
★ 11
Forks
0
Language
TypeScript
License
Apache-2.0
Latest release
v2.1.0
Read original source ↗View on GitHub ↗

enhesa/big-agentic-brain released v2.24.0 on November 5, 2026. It is a component that multiple AI agents use together for shared memory, reminders, and inter-agent messaging, and the repository description states that it can be attached directly to MCP and Claude Code.

The changes listed in this version are two features on the command-center (command-center) side. Every published (publish) item gets a session link, and all todos and next steps are organized by task (task) unit. The commit is the merge commit for PR #135 opened by release-please, and the statistics are 14 files, 11 insertions, and 3 deletions. The files remaining in FACTS are four: CHANGELOG.md, package.json, .release-please-manifest.json, and plugins/claude-code/.claude-plugin/plugin.json, and the version notation changed from 2.23.0 to 2.24.0. The license in package.json is Apache-2.0, the package name is bab, and it is marked as private: true.

However, the substance that can be confirmed in these four files is only the version notation update and two added lines in CHANGELOG. FACTS does not state in which flow the session links and task-unit storage are used, so it is still too early to judge what this feature actually changes. According to CHANGELOG, 2.23.0 was also released on the same day, continuing releases at short intervals, and no performance or usage metrics were given.