HKUDS/ClawTeam

"ClawTeam: Agent Swarm Intelligence" (One Command → Full Automation)

Hosted by HKU Data Science Lab

View on GitHub ↗Jump to charts ↓Open shareable report →

HKUDS/ClawTeam has 5.5k+ stars and 750+ forks on GitHub, is written in Python, and was last pushed on May 9, 2026. Data as of . Signed-in members get hourly updates — create a free account.

Summary Information

Updated 39 minutes ago
Added to GitGenius on August 31st, 2026
Created on March 17th, 2026
Open Issues & Pull Requests: 34 (+0)
GitHub issues: Enabled
Number of forks: 766
Total Stargazers: 5,537 (+0)
Total Subscribers: 43 (+0)

Repository Insights (GitGenius)

Median issue/PR response: 7.0 hours
Mean response time: 2.7 days
90th percentile: 8.7 days
Tracked items: 51

How this project is maintained

About 16% of issues opened in the past year have never received a reply. 75% of issues opened in the past year have been closed, leaving a working backlog. Three people close 96% of everything that gets resolved.

Charts & Analytics

Fetching additional details & charts...

Issue Activity (beta)

Open issues: 15
New in 7 days: 0
Closed in 7 days: 0
Avg open age: 138 days
Stale 30+ days: 15
Stale 90+ days: 15

Recent activity

Opened in 7 days: 0
Closed in 7 days: 0
Comments in 7 days: 0
Events in 7 days: 0

Top labels

  • enhancement (12)

Most active issues this week

Sign in to see which issues are moving.
Sign in

Detailed Description

ClawTeam is an agent swarm framework that orchestrates multiple AI agents to work together on complex tasks through a single command interface.

The framework addresses the challenge of coordinating multiple specialized agents to solve problems that require diverse capabilities or perspectives. It operates by allowing developers to define agent roles and behaviors, then coordinate their interactions to accomplish automation tasks. The swarm approach enables agents to collaborate, share information, and delegate work across the team rather than relying on a single monolithic agent.

ClawTeam suits projects where task complexity benefits from decomposition across specialized agents, such as multi-step workflows, research tasks requiring different expertise areas, or automation scenarios involving parallel work streams. Teams building systems that need flexible agent coordination without managing individual agent calls separately would find this approach valuable. The framework is particularly relevant for developers seeking to move beyond single-agent patterns toward collaborative multi-agent systems.

The project shows active development with regular commits and ongoing refinement of the swarm coordination mechanisms. The codebase maintains focus on the core agent orchestration functionality rather than expanding into tangential areas. Documentation and examples are present to support adoption, though the project remains in active evolution as the swarm intelligence patterns are refined.