aws-samples/remote-swe-agents

Autonomous SWE agent working in the cloud!

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aws-samples/remote-swe-agents has 200+ stars and 50+ forks on GitHub, is written in TypeScript, and was last pushed on Oct 6, 2026. Data as of . Signed-in members get hourly updates — create a free account.

Summary Information

Updated 5 minutes ago
Added to GitGenius on August 31st, 2026
Created on April 1st, 2025
Open Issues & Pull Requests: 25 (+0)
GitHub issues: Enabled
Number of forks: 50
Total Stargazers: 244 (+0)
Total Subscribers: 6 (+0)

Repository Insights (GitGenius)

Median issue/PR response: 18.2 hours
Mean response time: 13.2 days
90th percentile: 50.0 days
Tracked items: 94

How this project is maintained

100% of open issues come from outside the core team, so the backlog reflects real-world use rather than internal planning. Three people close 98% of everything that gets resolved.

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Issue Activity (beta)

Open issues: 20
New in 7 days: 1
Closed in 7 days: 0
Avg open age: 371 days
Stale 30+ days: 19
Stale 90+ days: 18

Recent activity

Opened in 7 days: 1
Closed in 7 days: 0
Comments in 7 days: 0
Events in 7 days: 0

Top labels

  • p1 (7)
  • p2 (6)

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Detailed Description

Remote SWE Agents is a framework for building autonomous software engineering agents that operate in cloud environments.

The project addresses the challenge of creating AI agents capable of performing software engineering tasks without local execution constraints. It works by integrating with AWS Bedrock for large language model capabilities and implements the Model Context Protocol as a client to enable structured communication between the agent and external tools. The agent can reason about code, plan engineering work, and execute tasks across cloud infrastructure rather than being confined to a single machine.

Developers should consider this tool if they are building systems that need autonomous code generation, analysis, or modification at scale within AWS environments. It suits projects where distributed, cloud-native agent execution provides advantages over local alternatives. The framework is particularly relevant for teams already invested in the AWS ecosystem and those exploring agentic AI patterns for software development workflows.

The project shows active development with recent commits addressing core functionality. Work has focused on implementing agent orchestration patterns and refining the integration between the language model backend and tool execution layer. The codebase demonstrates ongoing refinement of how agents interact with cloud-based development tasks.