Ship great agents fast with our open source frameworks – LangChain, LangGraph, and Deep Agents. Maintained by @LangChain.
Joined January 2026
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LangChain Community Spotlight: LangChain Explained by our Poland Ambassador 🇵🇱
Simon Budziak (@simon_budziak), our Poland Ambassador, covers messages in under a minute for episode 8 of LangChain Explained. He shows that everything a LangChain agent knows lives in one list of messages, and the model rereads all of it on every turn. His advice is to put your effort into deciding what stays in that list.
📺 Watch the episode: youtube.com/shorts/OmlkmNum0…
LangChain Community Spotlight: Querit Search Tools 🔍
The Querit team (@QueritAi) built LangChain tools for their search API. WebSearchTool returns up to 50 results for a query, and WebContentsTool fetches the full page behind up to 10 URLs per call. A page that fails to crawl comes back marked as failed instead of breaking the whole call.
🔗 GitHub: github.com/querit-ai/langcha…
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LangChain Community Spotlight: Enterprise RAG on LangChain 🔎📚
When a RAG answer comes back wrong, Kacper Klusek of Soba Labs reads what retrieval returned before blaming the model. His guide builds the retrieval stack on LangChain step by step, and in Anthropic's published evaluation, the chain he recommends cut the top-20 retrieval failure rate from 5.7% to 1.9%.
📖 Read the full guide: sobalabs.ai/blog/enterprise-…
Async subagents keep your agent responsive to you while background workers handle the long tasks.
langchain.com/blog/agents-ar…
LangChain Community Spotlight: Which User Is Costing You Money? 💸
Your agent bill went up and you want to know which user did it. Soba Labs shows how to find out by passing a user_id in each run's metadata, then grouping LangSmith's dashboard by user to see cost per customer. They also found that behind an OpenAI proxy, streamed token usage can read zero, so check one trace by hand before you trust the split.
📺 Watch the episode: youtube.com/shorts/vcoxAV7nN…
LangChain Community Spotlight: 50 Advanced Generative AI Concepts 🧠
Gustaf, a senior AI engineer, explains 50 GenAI concepts and their trade-offs. In the section on agent skills, he points out that an agent picks what to load from each skill's short description, so a skill with a weak description might never be found. He suggests using LangSmith to check whether your agent actually loads the skills it's supposed to.
📺 Watch the full breakdown: youtube.com/watch?v=mqPd8vcL…
LangChain Community Spotlight: How Deep Agents Run the Porting Loop 🎮🤖
Bart Ludera (CEO, Harnessloop) writes up a production game port where an agent built on LangChain's Deep Agents took over the build-and-test loop a programmer used to drive by hand. It reruns the same scenario before calling a defect fixed, and the programmer now steers several of these agents at once.
📖 Read the full case study: harnessloop.dev/blog/how-dee…
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Async subagents keep your agent responsive to you while background workers handle the long tasks.
Try them out in Deep Agents today!
Give your agents the web w/ browser use in deepagents!
agentic browser requests jumped ~5x in september alone... browser use is becoming a key primitive for agents!
icymi, we shipped it in (managed) deep agents last week
now your agent can do anything interactive on the web!
xitter.twitterapi.xyz/sydneyrunkle/status/21…
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LangChain Community Spotlight: 50 Advanced GenAI Concepts 🧠
Gustaf, an AI Engineer, walks through 50 concepts that come up in AI engineer interviews, from KV cache and GraphRAG to agent harnesses and LLM-as-a-judge evals. The agent section covers using LangSmith to check whether your agent actually loads its skills.
📺 Watch the full breakdown: youtube.com/watch?v=mqPd8vcL…
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use the @OpenAI decisions model in @LangChain today!
the @OpenAI decisions API is now in @LangChain!
we shipped:
1. OpenAIDecisions: ask yes/no, choice, and score questions
2. model routing middleware
3. auto mode middleware
docs: docs.langchain.com/oss/pytho…
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with the @link CLI your agent can make payments (either through tools or with browser use), gated on your approval!
this agent
1. browses the web with stagehand from @browserbase
2. prepares payment with the link CLI from @stripe
3. asks for approval before the charge
4. places the order, ready for pickup!
official @LangChain / link integration coming soon!
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MCP usage is growing... and growing fast!
check out our latest blog on our revamped support for MCP and the new, stateless spec!
ICYMI -- yesterday we released support for the new MCP protocol in @LangChain !! what do you need to know?
1. MCP support is now in the main langchain package! get started with `uv pip install 'langchain[mcp]'`
2. it's now built on top of FastMCP v4; FastMCP has ergonomic support for all things MCP (clients, servers, and apps). FastMCP handles things like protocol negotiation, tool name conflicts, connection lifecycle, transport specs, auth, and caching!
3. langchain now supports elicitation (MCP's human in the loop concept) via interrupts!
4. langchain now supports list_tools caching (so you don't have to re-fetch on every agent run; this was a common pain point for TTFT!)
5. the new protocol is stateless, which means the agent <-> server interactions are more efficient, and much more scale-able.
new MCP spec support is coming soon in @LangChain (and deepagents)
try it out and let us know what you think!
we're adding support for the new MCP spec in @LangChain open source!
the new API is built on top of FastMCP, so you can take advantage of their excellent devx for building MCP servers and clients.
an early version is available in langchain==1.4.0a2; let us know what you think!
github.com/langchain-ai/lang…
What's new in LangChain? 🚀
🧯 Standard exceptions: chat models now raise standard exception types across providers, so you can distinguish retryable errors (timeouts, rate limits, etc.) in a consistent way. Fully backward compatible.
🧩 Agent middleware: custom token_counter in ContextEditingMiddleware, plus non-retryable exceptions now skip retries in ModelRetryMiddleware (github.com/syyy44, github.com/Yigtwxx)
🔥 Fireworks document reranking: a new reranker integration (github.com/11adyy)
📊 Token & usage accounting: bug fixes for xAI and DeepSeek (github.com/aryansk, github.com/nazsats)
⚡ Portability & perf: lazy transformers import, and tighter grep scope for Anthropic (github.com/jtoman, github.com/Haaaarry)
This is your harness! Thanks to everyone who made it better. 🙏
What's new in LangChain? 🚀
🔌 Support for OpenAI's 3.0 SDK (using httpx2)
✨ Support for gemini-3.7-flash
Plus a wave of core reliability fixes from external contributors:
🛠️ Tool calling & structured output: clearer errors, respect for pydantic aliases when validating tool inputs (github.com/RinZ27, github.com/kinch-tech, github.com/charan16)
📊 Usage metadata fixes: token usage callback bugs, and cost metadata when streaming OpenRouter (github.com/Solaris-star, github.com/iroiro147)
🧱 Content & prompt hardening: guard malformed Anthropic content blocks, and preserve non-str/non-dict items in prompt templates for content blocks (github.com/Oxygen56, github.com/zerafachris)
Huge thanks to everyone who contributed! 🙏
New langchain python release! 🚀 Highlights:
• Internal middleware model calls no longer leak into messages / event stream (github.com/devbyteai, reported by github.com/nick-youngblut)
• SummarizationMiddleware retries failed summaries (reported by github.com/Harshxth) + faster token counting for tool schemas
• Batch of langchain-anthropic bug fixes (github.com/Mohith26, github.com/Functionhx)
• OpenRouter preserves provider info in response metadata (github.com/broskell)
This release also includes cross-provider standardization of reasoning effort, including supported values per-model in model.profile: docs.langchain.com/oss/pytho…
Huge thanks to our external contributors for helping to shape and improve our harness!
