260317 X(트위터) 모음
[10/10] RT by @hwchase17: LangChain just open-sourced a replica of Claude Code. It’s an
![원문](https://nitter.net/pic/media%2FHDdsf5YbkAAK1Og.jpg"){kind=link}
RT by @hwchase17: LangChain just open-sourced a replica of Claude Code.
It’s an MIT-licensed framework that recreates the core workflow behind coding agents like Claude Code but in an open system dev
LangChain just open-sourced a replica of Claude Code.
It’s an MIT-licensed framework that recreates the core workflow behind coding agents like Claude Code but in an open system developers can inspect and modify.
It is called Deep Agents.
I spent a bit of time looking through the repo and it’s actually a pretty helpful reference if you’re trying to understand how these coding agents are structured.
Here's what's inside:
→ Planning tools for breaking down tasks
→ File system access for reading, writing, and editing code
→ Shell command execution with sandboxing
→ Sub-agents for handling complex work in parallel
→ Auto-summarization when context gets too long
Another useful aspect is that it’s model-agnostic, so you can plug in different LLMs and experiment with building your own coding agents on top of the same structure.
If you’re exploring agent frameworks or just curious how tools like Claude Code work under the hood, this is a pretty good repo to bookmark.
Link in the comments.
agent_orchestration claude code llm agent agent framework
출처: https://nitter.net/pic/media%2FHDdsf5YbkAAK1Og.jpg"
점수: 10/10 — 점수 10/10: claude code, claude, agent framework
[8/10] RT by @hwchase17: We’re going live at #NVIDIAGTC in 30 minutes. ⏱️ Join us for
RT by @hwchase17: We’re going live at #NVIDIAGTC in 30 minutes. ⏱️
Join us for GTC Live at 8 a.m. PT as we get ready for Jensen Huang's keynote 11 a.m.
Featuring industry leaders from: @bfl_ml, @Ca
We’re going live at #NVIDIAGTC in 30 minutes. ⏱️
Join us for GTC Live at 8 a.m. PT as we get ready for Jensen Huang's keynote 11 a.m.
Featuring industry leaders from: @bfl_ml, @Cadence, @CaterpillarInc, @cohere, @CoreWeave, @DellTech, @EdisonSci, @FireworksAI_HQ, @IBM, @LangChain, @MistralAI, @MorganStanley, OpenClaw, @EvidenceOpen, @PalantirTech, @perplexity_ai, PhysicsX, @PrimeIntellect, @SkildAI, @Waabi_ai
🔗 nvda.ws/4lx8m0w
Video
agent_orchestration openclaw
출처: https://nitter.net/search?f=tweets&q=%23NVIDIAGTC">#NVIDIAGTC
점수: 8/10 — 점수 8/10: openclaw
[10/10] RT by @hwchase17: the deepagents library is basically our starting point for doi
RT by @hwchase17: the deepagents library is basically our starting point for doing harness engineering and shipping agents
the internal agents used at the company are built on it (background coding,
the deepagents library is basically our starting point for doing harness engineering and shipping agents
the internal agents used at the company are built on it (background coding, GTM/SDR, research)
there’s primitives we find really useful across our evals and dogfooding like filesystems, multi-model, context management like compaction and large tool call offloading
the goal is to give builders a good starting harness and the tools to customize and extend it to any task they want
we have a lot of fun watching the open source community and customers ship with deepagents and also build evals that actually measure and improve their agents over time
reach out if you’re building agents, doing harness engineering, exploring the space - we wanna help!
Hasan Toor (@hasantoxr)
LangChain just open-sourced a replica of Claude Code.
It’s an MIT-licensed framework that recreates the core workflow behind coding agents like Claude Code but in an open system developers can inspect and modify.
It is called Deep Agents.
I spent a bit of time looking through the repo and it’s actually a pretty helpful reference if you’re trying to understand how these coding agents are structured.
Here's what's inside:
→ Planning tools for breaking down tasks
→ File system access for reading, writing, and editing code
→ Shell command execution with sandboxing
→ Sub-agents for handling complex work in parallel
→ Auto-summarization when context gets too long
Another useful aspect is that it’s model-agnostic, so you can plug in different LLMs and experiment with building your own coding agents on top of the same structure.
If you’re exploring agent frameworks or just curious how tools like Claude Code work under the hood, this is a pretty good repo to bookmark.
Link in the comments.
agent_orchestration claude code llm agent agent framework
출처: https://nitter.net/pic/media%2FHDdsf5YbkAAK1Og.jpg"
점수: 10/10 — 점수 10/10: claude code, claude, agent framework
[9/10] RT by @hwchase17: You can now build your own version of Claude Code. Deep Agent
![원문](https://nitter.net/pic/media%2FHDi7wydbIAA3tW9.jpg"){kind=link}
RT by @hwchase17: You can now build your own version of Claude Code.
Deep Agents is a MIT-licensed framework that recreates the core workflow behind top coding agents.
It lets you inspect and modify
You can now build your own version of Claude Code.
Deep Agents is a MIT-licensed framework that recreates the core workflow behind top coding agents.
It lets you inspect and modify the exact architecture that makes these agents work.
- Planning and todo tools for managing tasks.
- File system reading, editing, and shell commands.
- Sub-agents for delegating complex, multi-step tasks.
- 100% model-agnostic so you can plug in any LLM.
100% open source.
agent_orchestration claude code llm agent
출처: https://nitter.net/pic/media%2FHDi7wydbIAA3tW9.jpg"
점수: 9/10 — 점수 9/10: claude code, claude
[9/10] RT by @hwchase17: 早起读了 LangChain 工程师这篇讨论 Agent Harness 的文章，之前一些碎片性的概念被串起来了，也能更好理
RT by @hwchase17: 早起读了 LangChain 工程师这篇讨论 Agent Harness 的文章，之前一些碎片性的概念被串起来了，也能更好理解类似 Claude Code 这些工具为什么这样设计。推荐阅读。
早起读了 LangChain 工程师这篇讨论 Agent Harness 的文章，之前一些碎片性的概念被串起来了，也能更好理解类似 Claude Code 这些工具为什么这样设计。推荐阅读。
Viv (@Vtrivedy10)
agent_orchestration claude code agent
출처: http://x.com/i/article/2031387672686604289">x.com/i/article/203138767268…
점수: 9/10 — 점수 9/10: claude code, claude
[8/10] Great use case for langsmith-cli!
Great use case for langsmith-cli!
<hr /Great use case for langsmith-cli!
Great use case for langsmith-cli!
Ralph👨🏻💻 (@ralphesber)
Just published my first OpenClaw skill 🎉
langsmith-cli — query your LangSmith traces with natural language, right from your AI assistant.
ask "what do failing runs have in common?" and get an answer back. Also: cost breakdowns, latency percentiles, before/after diffs.
No extra API key. No data leaving your machine.
👉 clawhub.com/skills/langsmith…
agent_orchestration openclaw
출처: https://clawhub.com/skills/langsmith-cli">clawhub.com/skills/langsmith…
점수: 8/10 — 점수 8/10: openclaw
[8/10] RT by @hwchase17: Just published my first OpenClaw skill 🎉 langsmith-cli — query
RT by @hwchase17: Just published my first OpenClaw skill 🎉 langsmith-cli — query your LangSmith traces with natural language, right from your AI assistant. ask "what do failing runs have in common?" a
Just published my first OpenClaw skill 🎉
langsmith-cli — query your LangSmith traces with natural language, right from your AI assistant.
ask "what do failing runs have in common?" and get an answer back. Also: cost breakdowns, latency percentiles, before/after diffs.
No extra API key. No data leaving your machine.
👉 clawhub.com/skills/langsmith…
출처: https://clawhub.com/skills/langsmith-cli">clawhub.com/skills/langsmith…
점수: 8/10 — 점수 8/10: openclaw
[10/10] RT by @hwchase17: LangChain just open-sourced a replica of Claude Code. It’s an
RT by @hwchase17: LangChain just open-sourced a replica of Claude Code.
It’s an MIT-licensed framework that recreates the core workflow behind coding agents like Claude Code but in an open system dev
LangChain just open-sourced a replica of Claude Code.
It’s an MIT-licensed framework that recreates the core workflow behind coding agents like Claude Code but in an open system developers can inspect and modify.
It is called Deep Agents.
I spent a bit of time looking through the repo and it’s actually a pretty helpful reference if you’re trying to understand how these coding agents are structured.
Here's what's inside:
→ Planning tools for breaking down tasks
→ File system access for reading, writing, and editing code
→ Shell command execution with sandboxing
→ Sub-agents for handling complex work in parallel
→ Auto-summarization when context gets too long
Another useful aspect is that it’s model-agnostic, so you can plug in different LLMs and experiment with building your own coding agents on top of the same structure.
If you’re exploring agent frameworks or just curious how tools like Claude Code work under the hood, this is a pretty good repo to bookmark.
Link in the comments.
agent_orchestration claude code llm agent agent framework
출처: https://nitter.net/pic/media%2FHDdsf5YbkAAK1Og.jpg"
점수: 10/10 — 점수 10/10: claude code, claude, agent framework
[10/10] RT by @hwchase17: the deepagents library is basically our starting point for doi
RT by @hwchase17: the deepagents library is basically our starting point for doing harness engineering and shipping agents
the internal agents used at the company are built on it (background coding,
the deepagents library is basically our starting point for doing harness engineering and shipping agents
the internal agents used at the company are built on it (background coding, GTM/SDR, research)
there’s primitives we find really useful across our evals and dogfooding like filesystems, multi-model, context management like compaction and large tool call offloading
the goal is to give builders a good starting harness and the tools to customize and extend it to any task they want
we have a lot of fun watching the open source community and customers ship with deepagents and also build evals that actually measure and improve their agents over time
reach out if you’re building agents, doing harness engineering, exploring the space - we wanna help!
Hasan Toor (@hasantoxr)
LangChain just open-sourced a replica of Claude Code.
It’s an MIT-licensed framework that recreates the core workflow behind coding agents like Claude Code but in an open system developers can inspect and modify.
It is called Deep Agents.
I spent a bit of time looking through the repo and it’s actually a pretty helpful reference if you’re trying to understand how these coding agents are structured.
Here's what's inside:
→ Planning tools for breaking down tasks
→ File system access for reading, writing, and editing code
→ Shell command execution with sandboxing
→ Sub-agents for handling complex work in parallel
→ Auto-summarization when context gets too long
Another useful aspect is that it’s model-agnostic, so you can plug in different LLMs and experiment with building your own coding agents on top of the same structure.
If you’re exploring agent frameworks or just curious how tools like Claude Code work under the hood, this is a pretty good repo to bookmark.
Link in the comments.
agent_orchestration claude code llm agent agent framework
출처: https://nitter.net/pic/media%2FHDdsf5YbkAAK1Og.jpg"
점수: 10/10 — 점수 10/10: claude code, claude, agent framework
[9/10] RT by @hwchase17: You can now build your own version of Claude Code. Deep Agent
RT by @hwchase17: You can now build your own version of Claude Code.
Deep Agents is a MIT-licensed framework that recreates the core workflow behind top coding agents.
It lets you inspect and modify
You can now build your own version of Claude Code.
Deep Agents is a MIT-licensed framework that recreates the core workflow behind top coding agents.
It lets you inspect and modify the exact architecture that makes these agents work.
- Planning and todo tools for managing tasks.
- File system reading, editing, and shell commands.
- Sub-agents for delegating complex, multi-step tasks.
- 100% model-agnostic so you can plug in any LLM.
100% open source.
agent_orchestration claude code llm agent
출처: https://nitter.net/pic/media%2FHDi7wydbIAA3tW9.jpg"
점수: 9/10 — 점수 9/10: claude code, claude
[9/10] RT by @hwchase17: 早起读了 LangChain 工程师这篇讨论 Agent Harness 的文章，之前一些碎片性的概念被串起来了，也能更好理
RT by @hwchase17: 早起读了 LangChain 工程师这篇讨论 Agent Harness 的文章，之前一些碎片性的概念被串起来了，也能更好理解类似 Claude Code 这些工具为什么这样设计。推荐阅读。
早起读了 LangChain 工程师这篇讨论 Agent Harness 的文章，之前一些碎片性的概念被串起来了，也能更好理解类似 Claude Code 这些工具为什么这样设计。推荐阅读。
Viv (@Vtrivedy10)
agent_orchestration claude code agent
출처: http://x.com/i/article/2031387672686604289">x.com/i/article/203138767268…
점수: 9/10 — 점수 9/10: claude code, claude
[8/10] RT by @hwchase17: We’re going live at #NVIDIAGTC in 30 minutes. ⏱️ Join us for
RT by @hwchase17: We’re going live at #NVIDIAGTC in 30 minutes. ⏱️
Join us for GTC Live at 8 a.m. PT as we get ready for Jensen Huang's keynote 11 a.m.
Featuring industry leaders from: @bfl_ml, @Ca
We’re going live at #NVIDIAGTC in 30 minutes. ⏱️
Join us for GTC Live at 8 a.m. PT as we get ready for Jensen Huang's keynote 11 a.m.
Featuring industry leaders from: @bfl_ml, @Cadence, @CaterpillarInc, @cohere, @CoreWeave, @DellTech, @EdisonSci, @FireworksAI_HQ, @IBM, @LangChain, @MistralAI, @MorganStanley, OpenClaw, @EvidenceOpen, @PalantirTech, @perplexity_ai, PhysicsX, @PrimeIntellect, @SkildAI, @Waabi_ai
🔗 nvda.ws/4lx8m0w
Video
agent_orchestration openclaw
출처: https://nitter.net/search?f=tweets&q=%23NVIDIAGTC">#NVIDIAGTC
점수: 8/10 — 점수 8/10: openclaw
[8/10] Great use case for langsmith-cli!
Great use case for langsmith-cli!
<hr /Great use case for langsmith-cli!
Great use case for langsmith-cli!
Ralph👨🏻💻 (@ralphesber)
Just published my first OpenClaw skill 🎉
langsmith-cli — query your LangSmith traces with natural language, right from your AI assistant.
ask "what do failing runs have in common?" and get an answer back. Also: cost breakdowns, latency percentiles, before/after diffs.
No extra API key. No data leaving your machine.
👉 clawhub.com/skills/langsmith…
agent_orchestration openclaw
출처: https://clawhub.com/skills/langsmith-cli">clawhub.com/skills/langsmith…
점수: 8/10 — 점수 8/10: openclaw
[8/10] RT by @hwchase17: Just published my first OpenClaw skill 🎉 langsmith-cli — query
RT by @hwchase17: Just published my first OpenClaw skill 🎉 langsmith-cli — query your LangSmith traces with natural language, right from your AI assistant. ask "what do failing runs have in common?" a
Just published my first OpenClaw skill 🎉
langsmith-cli — query your LangSmith traces with natural language, right from your AI assistant.
ask "what do failing runs have in common?" and get an answer back. Also: cost breakdowns, latency percentiles, before/after diffs.
No extra API key. No data leaving your machine.
👉 clawhub.com/skills/langsmith…
출처: https://clawhub.com/skills/langsmith-cli">clawhub.com/skills/langsmith…
점수: 8/10 — 점수 8/10: openclaw