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    <title>langchain</title>
    <link rel="self" type="application/atom+xml" href="https://links.biapy.com/guest/tags/1268/feed"/>
    <updated>2026-04-18T20:05:09+00:00</updated>
    <id>https://links.biapy.com/guest/tags/1268/feed</id>
            <entry>
            <id>https://links.biapy.com/links/12164</id>
            <title type="text"><![CDATA[Deep Agents]]></title>
            <link rel="alternate" href="https://docs.langchain.com/oss/python/deepagents/overview" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/12164"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[The batteries-included agent harness.

The easiest way to start building agents and applications powered by LLMs—with built-in capabilities for task planning, file systems for context management, subagent-spawning, and long-term memory. You can use deep agents for any task, including complex, multi-step tasks.

 Agent harness built with LangChain and LangGraph. Equipped with a planning tool, a filesystem backend, and the ability to spawn subagents - well-equipped to handle complex agentic tasks. 

Deep Agents is an agent harness. An opinionated, ready-to-run agent out of the box. Instead of wiring up prompts, tools, and context management yourself, you get a working agent immediately and customize what you need.

- [Deep Agents @ GitHub](https://github.com/langchain-ai/deepagents).

Related contents:

- [Your harness, your memory @ LangChain Blog](https://blog.langchain.com/your-harness-your-memory/).]]>
            </summary>
            <updated>2026-04-13T11:13:40+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/11463</id>
            <title type="text"><![CDATA[Headroom]]></title>
            <link rel="alternate" href="https://github.com/chopratejas/headroom" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/11463"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[The Context Optimization Layer for LLM Applications.

 Cut your LLM costs by 50-90% without losing accuracy.]]>
            </summary>
            <updated>2026-01-15T06:47:20+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/10292</id>
            <title type="text"><![CDATA[Open SWE]]></title>
            <link rel="alternate" href="https://swe.langchain.com/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/10292"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[An Open-Source Asynchronous Coding Agent.

Open SWE is an open-source cloud-based asynchronous coding agent built with LangGraph. It autonomously understands codebases, plans solutions, and executes code changes across entire repositories—from initial planning to opening pull requests.

- [Open SWE @ GitHub](https://github.com/langchain-ai/open-swe).]]>
            </summary>
            <updated>2025-09-19T06:05:42+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/1729</id>
            <title type="text"><![CDATA[Langfuse]]></title>
            <link rel="alternate" href="https://langfuse.com/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/1729"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Open Source LLM Engineering Platform.
Traces, evals, prompt management and metrics to debug and improve your LLM application.

🪢 Open source LLM engineering platform: LLM Observability, metrics, evals, prompt management, playground, datasets. Integrates with LlamaIndex, Langchain, OpenAI SDK, LiteLLM, and more. 🍊YC W23 

- [Langfuse @ GitHub](https://github.com/langfuse/langfuse).

Related contents:

- [Self-hosting Langfuse with Kubernetes @ Xebia](https://xebia.com/blog/setting-up-local-langfuse-server/).]]>
            </summary>
            <updated>2025-08-28T20:43:58+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/1748</id>
            <title type="text"><![CDATA[🦜🕸️LangGraph]]></title>
            <link rel="alternate" href="https://langchain-ai.github.io/langgraph/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/1748"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[LangGraph is a library for building stateful, multi-actor applications with LLMs, used to create agent and multi-agent workflows.

- [🦜🕸️LangGraph @ GitHub](https://github.com/langchain-ai/langgraph).

Related contents:

- [Building effective agents @ Anthropic](https://www.anthropic.com/research/building-effective-agents).]]>
            </summary>
            <updated>2025-08-28T20:47:51+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/3427</id>
            <title type="text"><![CDATA[🦜️🔗 LangChain]]></title>
            <link rel="alternate" href="https://www.langchain.com/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/3427"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[The largest community building the future of LLM apps

LangChain’s flexible abstractions and AI-first toolkit make it the #1 choice for developers when building with GenAI.
Join 1M+ builders standardizing their LLM app development
in LangChain&amp;#039;s Python and JavaScript frameworks.

- [LangChain @ GitHub](https://github.com/langchain-ai).

Related contents:

- [\#307.src - Langchain: Faire de l&amp;#039;IA comme des Lego avec Maxime Thoonsen @ &amp;lt;ifttd&amp;gt;](https://www.ifttd.io/episodes/langchain).
- [Tour d&amp;#039;horizon des frameworks pour créer des applications basées sur les LLM @ Data-Crafting.io :fr:](https://datacrafting.substack.com/p/tour-dhorizon-des-frameworks-pour).
- [\#304.bin - Bilan 2024: Le début de la révolution avec Quentin Adam @ &amp;lt;ifttd&amp;gt;](https://www.ifttd.io/episodes/bilan-2024).
- [Construire son RAG (Retrieval Augmented Generation) grâce à langchain: L’exemple de l’Helpdesk d’OCTO @ OCTO talks :fr:](https://blog.octo.com/le-chatbot-docto-langchain-rag-et-code-associe).
- [CLI Chatbot with LangChain and OpenAI in Node.js @ rw;eruch](https://www.robinwieruch.de/langchain-node-js-openai/).
- [Meetup GenAI - Découverte de LangChain @ Flint&amp;#039;s YouTube :fr:](https://www.youtube.com/watch?v=0ImjWIo5fyM).
- [Agents 2.0: From Shallow Loops to Deep Agents @ PHILSCHMID](https://www.philschmid.de/agents-2.0-deep-agents).
- [Production RAG: what I learned from processing 5M+ documents @ Abdellatif Abdelfattah](https://blog.abdellatif.io/production-rag-processing-5m-documents).
- [Intégration de Google Drive avec langchain @ Octo talks! :fr:](https://blog.octo.com/integration-de-google-drive-avec-langchain).
- [Deux techniques pour ingérer des pages web pour le RAG : BeautifulSoup vs Docling @ lbke :fr:](https://links.biapy.com/links/3427/edit).
- [Créer un RAG avec LangChain en 5 étapes @ lbke :fr:](https://www.lbke.fr/formations/rag-introduction-langchain/rag-avec-langchain).
- [RAG Against The Machine @ Quoi de neuf les devs ? :fr:](https://happytodev.substack.com/p/quoi-de-neuf-les-devs-153-veille?open=false#%C2%A7rag-against-the-machine).
- [LangChain, LangGraph Flaws Expose Files, Secrets, Databases in Widely Used AI Frameworks @ The Hacker News](https://thehackernews.com/2026/03/langchain-langgraph-flaws-expose-files.html).]]>
            </summary>
            <updated>2026-03-30T18:57:21+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/4117</id>
            <title type="text"><![CDATA[Auto-News]]></title>
            <link rel="alternate" href="https://github.com/finaldie/auto-news" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/4117"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[An Automatic News Aggregator with LLM.

A personal news aggregator to pull information from multi-sources + LLM (ChatGPT via LangChain) to help us reading efficiently with less noises, the sources including: Tweets, RSS, YouTube, Web Articles, Reddit, and personal Journal notes. 

- [Auto-News – L’agrégateur de news qui vous permet d’éviter le « bruit » @ Korben :fr:](https://korben.info/auto-news-agregateur-news-automatise-ia-lecture-efficace-2.html).]]>
            </summary>
            <updated>2025-08-29T03:23:01+00:00</updated>
        </entry>
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