Interacting with the agent in the studio is the easiest way to get a user friendly UI! I can share it with others in my LangSmith org, and they can chat with it there
AGENTS
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Deploy AI Agents to LangSmith Using deepagents and Docker
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After that, it's go time! `deepagents deploy` This will use Docker under the hood, so make sure you have that installed It will build an agent server image, and then deploy it to LangSmith
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Building Documentation Assistant Agent Core Instructions
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Next up – the agents core instructions These live in AGENTS.md. I'm going to write some instructions for how this agent can be the best documentation assistant agent! You can see the full text here: https://
github.com/langchain-ai/d
eepagents/blob/main/examples/deploy-mcp-docs-agent/AGENTS.md
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LangChain adds MCP server tool for Mintlify documentation
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Now let's give it a tool! We host our docs on @mintlify
, which means we get an MCP server out of the box. Let's add that to our mcp.json file (You can see our full docs here: https://
docs.langchain.com) -
Initializing DeepAgents Project with Repository Setup
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Then I'm going to set up my repo. I'm going to run `deepagents init` It will ask me to give my project a name. I'm going to call it `lc-docs`. It will then set up scaffolding for my project
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Configuring AI Agent with GLM5 Model via Baseten
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After switching into the new directory (`cd lc-docs`) I'm going to set the name of my agent, and the model to use This is done in deepagents.toml I'm going to use @Zai_org GLM5 model, served via @baseten
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DeepAgents Deploy: No-Code Agent Production Deployment Platform
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DeepAgents Deploy is the easiest way to bring agents to production, with just a few markdown and configuration files (no code!) Here's me building an agent that connects to LangChain Docs. Powered by @Zai_org GLM5 (via @baseten
) and @mintlify MCP -
Installing DeepAgents CLI for agent deployment
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First I'm going to install deepagents cli. This is a CLI which will help me set up and then deploy my agent `uv tool install deepagents-cli` DeepAgents CLI is open source – see it here: https://
github.com/langchain-ai/d
eepagents/tree/main/libs/cli
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Agent Latency Optimization: Router and Manager Bottlenecks
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Agreed on both. Latency depends mostly on the agent blocks, not the connections themselves (those are basically free). With a decent local model the bottleneck is inference time per agent call, so the router and manager agents dominate. Most of the 29 edges are just data
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Error Handling and Retry Mechanisms in AI Agent Blocks
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Good question. Sim has retries and error paths built into each block, so you can set retry counts and route failures to a fallback branch. For the agent blocks, I also keep prompts strict on output format so downstream parsing doesn't break. Haven't needed heavy fallback