Use the OAP interface’s tool browser to click on each tool for its call name and description or run tests directly within the UI to accelerate discovery and prototyping.
Build a RAG system by following structured steps: chunking and splitting data, vectorizing, storing embeddings, using out‐of‐the‐box templates, deploying a RAG server to create collections, upload files, manage embeddings, and give agents access.
Two LangChain projects may not reference each other in documentation but are closely connected in functionality, suggesting a pattern for mapping interdependent components.
The LangChain Open Agent Platform enables developers to rapidly prototype and build proof-of-concept AI agents by integrating open source packages with new product offerings.
Implement a demo-driven workflow by querying the feature card, developing the functionality, recording a demo, and sharing it in a dedicated demos channel to gather rapid feedback.
Start by building a Retrieval-Augmented Generation (RAG) agent to interact seamlessly with vault data, enabling contextual querying and real-time insights from secure stores.
Developers can connect to the Inspector Agent Vault demo build to quickly surface configuration errors, such as stray spaces, for efficient troubleshooting.
Using registries like the MCP Registry, companies can maintain private registries in addition to public, centralized ones to manage internal AI components and data flows.
Integrating an agent like Gemini deep research with the A2A protocol requires defining agent cards, exposing endpoints, and addressing multiple hidden complexities beyond basic specifications.
Adopting a common agent protocol as an internal standard enables developers to communicate consistently and manage an organized agent stack through shared skill definitions.
