Ship great agents fast with our open source frameworks – LangChain, LangGraph, and Deep Agents. Maintained by @LangChain.
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LangChain OSS retweeted
quick explainer on what it means to be a "system one" model (like jev from @typesafeai)
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LangChain OSS retweeted
some questions re context engineering for jev:
* what should jev do, what should jev not do?
* how to represent a problem as state and questions?
* how to present state and questions optimally?
* how can i calibrate next steps on jev's confidence / probabilities?
what else?
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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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LangChain OSS retweeted
i'm using semif (like jev, but open source) to label new issues that come into our open source repos so we have a pulse on popular features and areas where we need to spend more time
current request model:
* state -- issue title/body
* questions -- one noul per label, we apply ones w/ p>0.8
much faster than our previous LLM classifier thus far, currently monitoring to make sure we're calibrated at the right threshold
🆓 Free Open-Source Jev 🤝 LangSmith Gateway
Decision models are becoming a first-class part of the LangSmith Gateway!
We're serving SemIf, an open-source decision model, free for the next week.
Compatible with the TypeSafe SDK - just change a string to try it!
Docs in 🧵
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LangChain OSS retweeted
why is jev called a "system one" model?
this actually comes from @kahneman_daniel's book "thinking, fast and slow" that outlines two ways of thinking:
system one: fast, cheap, almost automatic (jev)
system two: slow, deliberate, and analytical (llms)
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LangChain OSS retweeted
building an agent (model + harness) is about 2 things:
1. picking the right model for the job. this means finding the sweet spot on the cost/intelligence curve.
2. building a harness that's fit to the agent's task(s). this means the harness can get the right context to the model at any step.
here's a guide on how to build a domain specific harness: langchain.com/blog/how-to-bu…
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LangChain OSS retweeted
The biggest challenge facing an agent harness is context engineering, or what information an agent gets, and when.
The longer the task, the harder it is to keep the context window from overloading.
Deep Agents ships with this built in: filesystems, subagents, and skills, already wired up for context management.
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LangChain OSS retweeted
we just changed how deepagents read files!
our evals show that this new format reduces edit_file errors by 15% AND total input token usage by 10%
the little things really add up w/ agents; we're hard at work on these small but mighty optimizations at @LangChain
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LangChain OSS retweeted
we just added a model router to our internal coding agent! this is an experiment in:
1. reducing coding agent costs
2. developing an effective task-specific router
hopefully this router design (sans domain-specific routing information) will be generalizable across agents!
is anyone having success w/ routers in the harness? if so, how did you design yours? how are you improving it?
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LangChain OSS retweeted
subagents are great for parallelizing work and delegation to specialized agents, but it's hard to figure out exactly what context to pass to and return from subagents!
this guide details the two input context modes we now support
1. isolated -- subagent gets a completely new prompt
2. forked -- starts exactly where the main agent left off, with a copy of the old message history
as well as when you might want to use each!
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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.
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RT @sydneyrunkle: we now support the new stateless MCP spec in @LangChain!
this revamp includes moving MCP support into the main langchain…
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LangChain OSS retweeted
you can now build deepagents on top of @MongoDB Atlas!!
this backend, built on top of an underlying object store, supports more robust search including MongoDB search, vector search, and hybrid search!
check out the blog to learn more!
MongoDB now powers the virtual file system behind @LangChain's Deep Agents. 🚀
This new integration lets you implement Deep Agents' BackendProtocol against MongoDB Atlas instead of building your own storage layer. And agent code calling read_file, write, glob, and grep don’t have to change.
Your retrieval and storage run through one backend instead of a maze of stitched-together tools. Vector, full-text, and hybrid search (via $rankFusion) route through Atlas; file reads and writes forward directly to S3.
For builders of long-running or multi-agent workflows: Plans, intermediate outputs, and knowledge artifacts persist across sessions, deployments, and sub-agent handoffs, so file-based context survives between runs instead of resetting each time.
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LangChain OSS retweeted
@LangChain has day 0 support for Gemini 3.8 flash!
Introducing Gemini 3.8 Flash, bringing significant improvements in agentic + coding capabilities from 3.7 Flash, available at the same price.
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LangChain OSS retweeted
you can now build @LangChain agents w/ agentic video understanding!
google is consistently at the forefront of multimodal processing; this is the video analog of "agentic vision"
token usage + cost is also way down w/ this new approach!
docs: docs.langchain.com/oss/pytho…
We’re introducing a new capability to our latest Gemini models: agentic video understanding.
This allows developers to process long-form video content with more accuracy, while using up to 88% less tokens.
See how it works 🧵
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LangChain OSS retweeted
we are rolling support for the new MCP spec in langchain and need feedback on our new API!
with this new pattern you connect langchain agents to multiple MCP servers, hook up oauth, use elicitation w/ langchain interrupts!
we're building on top of FastMCP which is the easiest way to get started building MCP clients and servers
github.com/langchain-ai/lang…
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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…
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