deepseek-ai/awesome-deepseek-agent

Awesome DeepSeek Agent is a curated collection of integration guides for AI agent and coding-assistant tools.

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Summary Information

Updated 35 minutes ago
Added to GitGenius on August 16th, 2026
Created on April 27th, 2026
Open Issues & Pull Requests: 310 (+0)
GitHub issues: Enabled
Number of forks: 702
Total Stargazers: 5,887 (+0)
Total Subscribers: 45 (+0)

Repository Insights (GitGenius)

Median issue/PR response: 7.3 hours
Mean response time: 3.5 days
90th percentile: 8.2 days
Tracked items: 76

How this project is maintained

Around half of the issues opened in the past year never receive a reply. 100% of open issues come from outside the core team, so the backlog reflects real-world use rather than internal planning. 21% of tracked open issues have had no activity in three months. Only 3% of issues opened in the past year have been closed.

Charts & Analytics

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Issue Activity (beta)

Open issues: 107
New in 7 days: 3
Closed in 7 days: 1
Avg open age: 70 days
Stale 30+ days: 84
Stale 90+ days: 23

Recent activity

Opened in 7 days: 3
Closed in 7 days: 1
Comments in 7 days: 1
Events in 7 days: 4

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Detailed Description

Awesome DeepSeek Agent is a curated collection of integration guides for AI agent and coding-assistant tools.

The project addresses the practical challenge of connecting DeepSeek language models to existing development workflows. Rather than requiring developers to build integrations from scratch, it provides step-by-step guides that cover installation, configuration, and initial setup for popular AI agent frameworks and coding assistants. This approach lets developers quickly incorporate DeepSeek-V4-Pro or DeepSeek-V4-Flash models into tools they already use.

The collection suits developers who want to experiment with DeepSeek models within their current toolchain without investing time in custom integration work. It is most valuable for those using mainstream AI agent platforms or coding assistants that lack built-in DeepSeek support. The guides are designed to get users operational in minutes rather than hours, making it accessible to developers of varying experience levels with model integration.

The project maintains documentation in multiple languages to serve a broad audience. The repository structure emphasizes practical, actionable guides rather than theoretical discussion, with each entry focused on concrete steps to enable a working integration.