Deepagents
Deepagents is an open-source agent harness that provides an opinionated agent that runs out of the box. It is designed to be extensible, model-agnostic, and production-ready. The harness includes features such as sub-agents, filesystem, context management, shell access, persistent memory, human-in-the-loop, skills, and tools. Deepagents is built on top of LangGraph and is designed to work with any LLM that supports tool calling. It is available as a JavaScript/TypeScript library and can be used in production with LangSmith for tracing, evaluation, and monitoring. The project provides a range of resources, including examples, documentation, API reference, discussions, and contributing guides. Deepagents is inspired by Claude Code and aims to push the boundaries of general-purpose agents. The project follows a 'trust the LLM' model and enforces boundaries at the tool/sandbox level, rather than expecting the model to self-police.
- Category
- AI Agents
- Pricing
- Open Source
Problem solved
Deepagents solves the problem of creating a general-purpose agent that can be used in a variety of applications, from research to production. It provides a flexible and extensible framework for building agents that can interact with their environment and make decisions based on the information they receive. By providing a pre-built harness, Deepagents saves developers time and effort in building their own agents from scratch, and allows them to focus on developing their own tools and skills. Additionally, Deepagents provides a production-ready solution for deploying agents in a variety of environments, making it a valuable tool for businesses and organizations looking to leverage the power of AI.
Use cases
- Research and development
- Production deployment
- Building custom agents
- Integrating with other AI models
- Creating human-in-the-loop systems
Audience
- Developers
- Researchers
- Business Users
Pros
- Extensible and customizable
- Model-agnostic
- Production-ready
- Provides a range of features and tools
- Follows a 'trust the LLM' model for security
Cons
- May require significant development effort to customize
- Dependent on LangGraph and LangSmith for full functionality
- Security model may not be suitable for all applications
Official website: https://github.com/langchain-ai/deepagents
Canonical LambdaBase page: https://www.lambdabase.com/tools/deepagents