@LangChain: Alex recently joined the @LangChain_OSS team, and he published his first article on how Deep Agents uses prompt caching…
Summary
Alex, a new LangChain team member, published an article explaining how Deep Agents uses prompt caching to reduce API costs.
View Cached Full Text
Cached at: 06/26/26, 06:14 PM
Alex recently joined the @LangChain_OSS team, and he published his first article on how Deep Agents uses prompt caching to cut API costs.
Give it a read, and give @its_ao a follow!
Similar Articles
@LangChain_OSS: LangChain Community Spotlight: Deep Agents + ACP Coding Agent Jacob Lee built a custom AI coding agent with Deep Agents…
Jacob Lee created an open-source AI coding agent using Deep Agents and ACP that supplants Claude Code, offering multi-model support, LangSmith observability, and human-in-the-loop safeguards.
@LangChain: Deep Agents explained in <90 seconds by @sydneyrunkle
A short explanation of Deep Agents by Sydney Runkle, presented by LangChain.
@LangChain: En route to improving your agents
LangChain announces a resource for improving AI agents.
@LangChain: Everything you need to know about Managed Deep Agents:
LangChain announces Managed Deep Agents in private beta, a hosted API-first runtime for building, running, and operating deep agents in production, leveraging the open-source Deep Agents harness and integrating with LangSmith for durable execution, streaming, and human-in-the-loop workflows.
@LangChain: Deep Agents now supports dynamic subagents. Instead of invoking subagents with tool calls, the main agent writes orches…
LangChain's Deep Agents now supports dynamic subagents, where the main agent writes orchestration code to coordinate work at scale, enabling workflows like processing hundreds of documents with deterministic coverage.