langgraph
cost control for AI agent loops. Your AI agent loops don't know when to stop. LoopGain does.
When an agent keeps checking and revising its own work, it often spins on a problem it can't solve — or talks itself into an answer worse than one it already had. LoopGain watches the loop as it runs, stops it at the right iteration, and keeps the best version, not the last.
Reusable GitHub Action that reviews Terraform PRs for security, cost, and style using a LangGraph multi-agent system, posting a single severity-ranked comment.
The batteries-included agent harness.
The easiest way to start building agents and applications powered by LLMs—with built-in capabilities for task planning, file systems for context management, subagent-spawning, and long-term memory. You can use deep agents for any task, including complex, multi-step tasks.
Agent harness built with LangChain and LangGraph. Equipped with a planning tool, a filesystem backend, and the ability to spawn subagents - well-equipped to handle complex agentic tasks.
Deep Agents is an agent harness. An opinionated, ready-to-run agent out of the box. Instead of wiring up prompts, tools, and context management yourself, you get a working agent immediately and customize what you need.
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An Open-Source Asynchronous Coding Agent.
Open SWE is an open-source cloud-based asynchronous coding agent built with LangGraph. It autonomously understands codebases, plans solutions, and executes code changes across entire repositories—from initial planning to opening pull requests.