LangChain LLM-System Development Framework
(Redirected from LangChain LLM-system development framework)
Jump to navigation
Jump to search
A LangChain LLM-System Development Framework is an 3rd-party open source component-based LLM orchestration framework by LangChain, Inc..
- AKA: LangChain, LangChain Framework, LangChain Python Framework, LangChain (the LLM framework), LangChain framework, Langchain, LangChain LLM-system development framework, LangChain Agent Framework, LangChain LLM framework, Langchain Framework.
- Context:
- It can typically provide LangChain LLM-System Development Framework Composability through LangChain LLM-system development framework modular abstractions and LangChain LLM-system development framework chain-based architectures.
- It can typically support LangChain LLM-System Development Framework Component Integrations via LangChain LLM-system development framework tool interfaces, LangChain LLM-system development framework model providers, and LangChain LLM-system development framework vector stores.
- It can typically enable LangChain LLM-System Development Framework Chain Construction by combining LangChain LLM-system development framework LLM calls with LangChain LLM-system development framework data retrieval steps and LangChain LLM-system development framework processing logic.
- It can typically facilitate LangChain LLM-System Development Framework Agent Development through LangChain LLM-system development framework agent executors and LangChain LLM-system development framework tool-calling capabilityes.
- It can typically implement LangChain LLM-System Development Framework Memory Management for LangChain LLM-system development framework conversational context and LangChain LLM-system development framework state persistence.
- It can typically support LangChain LLM-System Development Framework Retrieval-Augmented Generation via LangChain LLM-system development framework document loaders and LangChain LLM-system development framework retrieval chains.
- It can typically establish LangChain LLM-System Development Framework Model Abstraction through LangChain LLM-system development framework unified interfaces and LangChain LLM-system development framework provider-agnostic design.
- It can typically maintain LangChain LLM-System Development Framework Prompt Engineering through LangChain LLM-system development framework prompt templates and LangChain LLM-system development framework few-shot examples.
- It can typically provide LangChain LLM-System Development Framework Linear Workflows through LangChain LLM-system development framework sequential chains and LangChain LLM-system development framework pipeline patterns.
- ...
- It can often provide LangChain Expression Language (LCEL) for LangChain LLM-system development framework declarative chain composition.
- It can often integrate with LangChain Ecosystem Products including LangSmith, LangGraph, and LangServe.
- It can often support LangChain LLM-System Development Framework Multi-Language Implementations in Python and JavaScript/TypeScript.
- It can often enable LangChain LLM-System Development Framework Streaming Support for LangChain LLM-system development framework real-time token generation and LangChain LLM-system development framework intermediate results.
- It can often facilitate LangChain LLM-System Development Framework Testing Frameworks through LangChain LLM-system development framework evaluation chains and LangChain LLM-system development framework debugging tools.
- It can often implement LangChain LLM-System Development Framework Callback Systems for LangChain LLM-system development framework event handling and LangChain LLM-system development framework execution monitoring.
- It can often provide LangChain LLM-System Development Framework Async Support through LangChain LLM-system development framework asynchronous execution and LangChain LLM-system development framework concurrent processing.
- It can often enable LangChain LLM-System Development Framework Custom Components through LangChain LLM-system development framework extensible interfaces and LangChain LLM-system development framework plugin architecture.
- It can often support LangChain LLM-System Development Framework Data-Aware Applications through LangChain LLM-system development framework data source connections and LangChain LLM-system development framework context integration.
- It can often facilitate LangChain LLM-System Development Framework Agentic Applications through LangChain LLM-system development framework environment interactions and LangChain LLM-system development framework action execution.
- ...
- It can range from being a Simple LangChain LLM-System Development Framework to being a Complex LangChain LLM-System Development Framework, depending on its LangChain LLM-system development framework application sophistication.
- It can range from being a Standalone LangChain LLM-System Development Framework to being an Integrated LangChain LLM-System Development Framework, depending on its LangChain LLM-system development framework ecosystem utilization.
- It can range from being a Research LangChain LLM-System Development Framework to being a Production LangChain LLM-System Development Framework, depending on its LangChain LLM-system development framework deployment maturity.
- It can range from being a Single-Model LangChain LLM-System Development Framework to being a Multi-Model LangChain LLM-System Development Framework, depending on its LangChain LLM-system development framework model diversity.
- It can range from being a Synchronous LangChain LLM-System Development Framework to being an Asynchronous LangChain LLM-System Development Framework, depending on its LangChain LLM-system development framework execution model.
- It can range from being a Basic-Feature LangChain LLM-System Development Framework to being a Full-Feature LangChain LLM-System Development Framework, depending on its LangChain LLM-system development framework capability scope.
- It can range from being a Python-Only LangChain LLM-System Development Framework to being a Multi-Language LangChain LLM-System Development Framework, depending on its LangChain LLM-system development framework implementation languages.
- It can range from being a Linear-Chain LangChain LLM-System Development Framework to being a Graph-Based LangChain LLM-System Development Framework, depending on its LangChain LLM-system development framework workflow complexity.
- It can range from being a Stateless LangChain LLM-System Development Framework to being a Stateful LangChain LLM-System Development Framework, depending on its LangChain LLM-system development framework memory capability.
- It can range from being a Developer-Tool LangChain LLM-System Development Framework to being a Enterprise-Solution LangChain LLM-System Development Framework, depending on its LangChain LLM-system development framework deployment scale.
- ...
- It can integrate with LLM Providers including OpenAI, Anthropic, Google, Hugging Face, Cohere, AI21 Labs, and Together AI.
- It can utilize Vector Databases such as Pinecone, Weaviate, Chroma, FAISS, Qdrant, Milvus, and Elasticsearch.
- It can employ Document Loaders for PDFs, web pages, structured data sources, CSV files, JSON files, and databases.
- It can leverage Embedding Models for semantic search, similarity matching, document retrieval, and contextual understanding.
- It can connect to External Tools including search engines, calculators, web browsers, and custom APIs.
- It can support Data Processing Tools for text splitting, data transformation, and content filtering.
- It can work alongside LangChain Ecosystem Frameworks like LangGraph for stateful orchestration and multi-agent workflows.
- ...
- Example(s):
- LangChain LLM-System Development Framework Release Versions, such as:
- LangChain Initial Release (October 2022), launched by Harrison Chase at Robust Intelligence:
- LangChain v0.0.1 (October 16-25, 2022), as initial Python wrapper for LangChain LLM-system development framework prompt templates.
- Early implementations including LLM Math (PR #8), Self-Ask With Search (PR #9), and NatBot (PR #18).
- LangChain Company Formation (January 2023), incorporating as LangChain Inc with Ankush Gola as co-founder.
- LangChain Funding Rounds:
- LangChain Seed Funding (April 2023), raising $10 million from Benchmark.
- LangChain Series A (April 2023), raising $20+ million from Sequoia Capital at $200+ million valuation.
- LangChain Stable Releases, such as:
- LangChain v0.1.0 (February 9, 2024), as first stable version with LangChain LLM-system development framework backward compatibility.
- LangChain v0.2.0 (May 2024), removing langchain-community dependency and adding LangChain LLM-system development framework enhanced features.
- LangChain v0.3.0 (October 9, 2024), upgrading to Pydantic 2 and dropping Python 3.8 support.
- LangChain v0.3.26 (Current), representing latest stable release.
- LangChain Initial Release (October 2022), launched by Harrison Chase at Robust Intelligence:
- LangChain LLM-System Development Framework Core Libraryes, such as:
- langchain-core, containing LangChain LLM-system development framework base abstractions and LangChain LLM-system development framework runnable interfaces.
- langchain, providing LangChain LLM-system development framework high-level chains and LangChain LLM-system development framework agent architectures.
- langchain-community, hosting LangChain LLM-system development framework third-party integrations.
- [[langchain-[partner]]], offering LangChain LLM-system development framework dedicated partner packages (e.g., langchain-openai, langchain-anthropic).
- LangChain Ecosystem Platform Products (distinct from the framework), such as:
- LangSmith (July 2023)]], providing observability platform and debugging tools for LangChain LLM-system development framework applications.
- LangGraph, offering hosted service for stateful agent deployment and production scaling.
- LangServe (October 2023)]], facilitating API deployment of LangChain LLM-system development framework applications.
- LangChain Templates, offering reference architectures for common LangChain LLM-system development framework use cases.
- LangChain Ecosystem Frameworks (complementary to LangChain), such as:
- LangChain LLM-System Development Framework Production Deployments, such as:
- Klarna Customer Support Implementation serving 85 million users with 80% query resolution time reduction.
- Financial Institution RAG Systems for LangChain LLM-system development framework document analysis.
- Enterprise Chatbots with LangChain LLM-system development framework multi-modal capabilityes.
- Healthcare Document Processing Systems using LangChain LLM-system development framework medical data extraction.
- LangChain LLM-System Development Framework Milestones, such as:
- Fastest-Growing GitHub Project (June 2023), achieving LangChain LLM-system development framework community recognition.
- 96K+ GitHub Stars (December 2024), demonstrating LangChain LLM-system development framework developer adoption.
- 28 Million Monthly Downloads (2024), showing LangChain LLM-system development framework production usage.
- ...
- LangChain LLM-System Development Framework Release Versions, such as:
- Counter-Example(s):
- Standalone LLM API, which provides direct model access without LangChain LLM-system development framework orchestration capability.
- Traditional NLP Framework, which lacks LLM integration and LangChain LLM-system development framework prompt engineering capabilityes.
- Pure Agent Framework, which focuses on agent behavior without LangChain LLM-system development framework comprehensive LLM tooling.
- Simple Prompt Library, which offers templates without LangChain LLM-system development framework chain composition.
- Model-Specific SDK, which supports single provider without LangChain LLM-system development framework model abstraction.
- Hosted LLM Platform, which provides managed services without LangChain LLM-system development framework developer framework.
- LangGraph, which offers graph-based workflows without LangChain LLM-system development framework linear chain focus.
- See: LLM Development Framework, AI Orchestration Framework, LangSmith Evaluation Platform, LangGraph Framework, Prompt-Programming Framework, AI System Development Framework, Multi-Agent Development Framework, LLM-based System Development Framework, Open-Source 3rd-Party LLM-based System Development Framework, LLM Orchestration Framework, RAG Framework, Vector Database, Embedding Model, LLM Provider, Python Framework, JavaScript Framework, Software Development Framework, Open Source Framework.
References
2024
- https://github.com/langchain-ai/langchain
- NOTES:
- Framework Architecture Layer
- LangChain Core: This component serves as the foundation for developing and managing workflows using large language models. It provides the necessary core abstractions and tools to create chains, handle data transformations, and integrate.
- LangGraph Orchestration: This component enhances the capabilities by enabling the development of complex, multi-step workflows that involve stateful and multi-agent systems. LangGraph is especially useful for scenarios that require real-time interaction and dynamic decision-making, with support for features like streaming to improve application responsiveness.
- Component Integration Layer: Third-Party Integrations: This component represents the ecosystem's extensibility, allowing for seamless integrations with various third-party services. These integrations expand the functionality of LangChain and LangGraph, enabling them to work with LLM providers, databases, and other critical services for more sophisticated application development.
- Deployment Infrastructure Layer: LangGraph Cloud Deployment: This service is designed for the deployment and scaling of complex LLM applications. It provides a managed environment with features such as auto-scaling, persistent storage, and fault tolerance, making it easier to transition applications from development to production with tools for easy deployment and real-time monitoring.
- Development Tools Layer: LangSmith DevOps Platform: LangSmith functions as the DevOps backbone within the LangChain ecosystem, offering a range of tools that streamline the development, testing, and monitoring of LLM-based applications. It integrates seamlessly with both LangChain and LangGraph, providing developers with a unified platform from prototype to production.
- Framework Architecture Layer
2024-12-05
- Perplexity.ai
- Question: What are the best Python frameworks and tools for building AI agent applications? I'm particularly interested in understanding the key features of PydanticAI and how it compares to other frameworks like LangChain and AutoGen. Please include information about their main capabilities, ease of use, and integration with large language models. Also, what makes these frameworks suitable for production-grade applications? I'd like to know about their validation features, type safety, and debugging capabilities.
- Answer: Leading Python frameworks for building AI agent applications include PydanticAI, LangChain, and AutoGen, each offering distinct capabilities and advantages for development.
- Framework Overview:
- LangChain:
- Provides modular architecture.
- Excels in language model integration.
- Features efficient prompt management.
- Suitable for building intelligent chatbots.
- Enables personalized recommendations.
- Supports automated content generation.
- AutoGen:
- Implements multi-agent architecture.
- Offers customizable agents.
- Features conversable agents.
- Specializes in AI workflows.
- Facilitates agent collaboration.
- Uses conversational approach.
- LangChain:
- Production Suitability:
- LLM Integration:
- Framework Overview:
2023
- https://python.langchain.com/docs/modules/
- Modules: LangChain provides standard, extendable interfaces and external integrations for the following modules, listed from least to most complex:
- Model I/O: Interface with language models
- Retrieval: Interface with application-specific data
- Chains: Construct sequences of calls
- Agents: Let chains choose which tools to use given high-level directives
- Memory: Persist application state between runs of a chain
- Callbacks: Log and stream intermediate steps of any chain
2023
- Google Bard (2023-09-11)
- LangChain is a framework for orchestrating the interaction between an LLM and other components of an LLM-based application. It provides a way to define a sequence of steps that the LLM will take to complete a task, as well as a way to manage the data that is passed between the LLM and the other components.
- Data-aware applications are able to connect to other sources of data, such as databases or APIs. This allows them to use the language model to process and understand information from a wider range of sources.
- Agentic applications are able to interact with their environment. This means that they can take actions in the world, such as controlling robots or devices.
- LangChain provides a number of features that make it easier to develop data-aware and agentic applications:
- Abstractions: LangChain provides abstractions for the components that are needed to work with LLMs, such as data sources, actions, and memories. This makes it easier to write code that is modular and reusable.
- Implementations: LangChain provides implementations for a variety of these abstractions, so you don't have to write them yourself.
- Off-the-shelf chains: LangChain provides a number of off-the-shelf chains that you can use to accomplish specific tasks, such as summarization, question answering, and code generation.
- LangChain is written in Python and JavaScript. It is open source and available on GitHub.
- Here are some examples of applications that can be built with LangChain:
- A chatbot that can answer questions about a product or service.
- A virtual assistant that can control smart home devices.
- A summarizer that can generate a summary of a long piece of text.
- A question answering system that can answer questions about a specific topic.
- A code generator that can generate code from natural language descriptions.
- LangChain is a framework for orchestrating the interaction between an LLM and other components of an LLM-based application. It provides a way to define a sequence of steps that the LLM will take to complete a task, as well as a way to manage the data that is passed between the LLM and the other components.
2023
- chat
- LangChain is a framework for building applications around large language models (LLMs) such as GPT-3, BLOOM, etc. It allows you to chain together different components such as prompt templates, LLMs, agents, and memory to create more advanced use cases. For example, you can use LangChain to create chatbots, question-answering systems, summarizers, and more12.
- Some of the features of LangChain include:
- A standard interface for prompt templates, which are templates for different types of prompts that can be input to LLMs
- A selection of LLMs to choose from, either from Hugging Face Hub or OpenAI
- A standard interface for agents, which use LLMs to decide what actions should be taken
- A standard interface for memory, which is the concept of persisting state between calls of a chain or agent
- A collection of toolkits that enable agents to interact with different data sources or APIs234
- LangChain is an open-source project that was created by Harrison Chase in late 2022 and has gained popularity since then. You can learn more about LangChain from its official blog2, documentation3, or GitHub repository.
2023
- https://python.langchain.com/en/latest/
- QUOTE: LangChain is a framework for developing applications powered by language models. We believe that the most powerful and differentiated applications will not only call out to a language model via an API, but will also:
- Be data-aware: connect a language model to other sources of data
- Be agentic: allow a language model to interact with its environment
- The LangChain framework is designed with the above principles in mind.
- This is the Python specific portion of the documentation. For a purely conceptual guide to LangChain, see here. For the JavaScript documentation, see here.
- QUOTE: LangChain is a framework for developing applications powered by language models. We believe that the most powerful and differentiated applications will not only call out to a language model via an API, but will also: