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LangSmith Agent Engineering Platform (Self-Hosted)
LangSmith provides tools for developing, debugging, and deploying LLM applications. It helps you trace requests, evaluate outputs, test prompts, and manage deployments in one place.
LangSmith is framework agnostic, so you can use it with or without LangChain open-source libraries langchain and langgraph.
Prototype locally, then move to production with integrated monitoring and evaluation to build more reliable AI systems.
LangSmith provides:
Observability to see exactly how your agent thinks and acts with detailed tracing and aggregate trend metrics.
Evaluation to test and score agent behavior on production data and offline datasets for continuous improvement.
Deployment to ship your agent in one click, using scalable infrastructure built for long-running tasks.
Reviews (146)
Financial Services
Easy to learn: syntax is quickly mastered
Reviewed on Oct 27, 2026
Review provided by G2
What do you like best about the product?
It's easy to learn, the syntax is quickly learned
What do you dislike about the product?
It requires installing many Python components on Windows
What problems is the product solving and how is that benefiting you?
Created an agent with Ollama for cybersecurity activities
Abinash D.
Langchain Makes Agent Orchestration and Observability Easy
Reviewed on Oct 21, 2026
Review provided by G2
What do you like best about the product?
Langchain helps me create Agent Orchestrations with single API Call making Tool calling, state, retries, and more importantly observability easy, i love its community support and sdk
What do you dislike about the product?
it has a lot of Abstractions, and every package update brings a lot of breaking Changes, and Docs sometimes are not up to date sometimes feel instability to maintain the app.
What problems is the product solving and how is that benefiting you?
Langchain fills the gap between my API vendor and my app, helping me build faster and try llm features and move to different Api vendor with slight to very no code modifications
Shashank S.
LangChain Simplifies Building and Connecting LLM Applications
Reviewed on Oct 15, 2026
Review provided by G2
What do you like best about the product?
I like LangChain because it makes building LLM-powered applications more flexible and organized. Its prompt templates, model integration, documents loaders, text splitters, retrievers, and vector store integrations help connect different components into a single workflow. I also find its RAG capabilities useful for working with external data, while tool calling helps connect language models with other services. The documentations and integration options make it easier to experiment with different approaches when developing AI applications.
What do you dislike about the product?
One downside of LangChain is that the learning curve can be steep when working with complex chains, agents, and integration. Debugging workflows sometimes takes extra time, especially when multiple components are connected. Frequent updates and changes between versions can also require code adjustments. Clearer error messages and more consistent documentation across integrations would make the development experience smoother.
What problems is the product solving and how is that benefiting you?
LanChain helps me build and experiment with LLM-powered application by connecting language models with prompts, external data, and tools. It is useful for organizing multi-step workflows, working with document retrieval, and building retrieval-augmented generation (RAG) pipelines. It also makes it easier to integrate different model providers and manage chains of operations, reducing the amount of custom code needed for common tasks.
Roshan P.
LangChain Makes Building Flexible AI Workflows Fast and Straightforward
Reviewed on Oct 14, 2026
Review provided by G2
What do you like best about the product?
What I like most about LangChain is how straightforward it makes building AI applications and connecting different language models to external tools and data sources. I also appreciate the flexibility it provides for creating workflows and trying out different models as I iterate. Overall, it saves me development time and makes it easier to manage and improve AI projects without having to build everything from scratch.
What do you dislike about the product?
One thing I dislike about LangChain is that it can feel a bit complicated to get started, especially when you’re dealing with different integrations and configuration options. At times, debugging takes longer than I expect, partly because the framework is updated frequently and things change from version to version. The documentation is helpful overall, but I think some examples could be simpler and more beginner-friendly, with clearer steps that are easier to follow.
What problems is the product solving and how is that benefiting you?
LangChain helps address the challenge of building AI applications that need to connect language models with external data, APIs, and other tools. It makes it easier to design AI-powered workflows without having to write everything from scratch. For me, that means less development time spent on boilerplate, smoother integration across components, and a more efficient way to build and test AI solutions.
Mahwish Q.
Speeds Up RAG App Builds, but Abstractions and Breaking Changes Can Frustrate
Reviewed on Oct 12, 2026
Review provided by G2
What do you like best about the product?
Langchain's greatest strength is how dramatically it speeds up AI application development through its flexible, model-agnostic framework
What I like best is its rich ecosystem of pre-built integrations, which makes connecting LLMs to vector database and external data sources for RAG workflows virtually effortless. While the high level of abstraction cam occasionally make debugging tricky, the massive amount of boilerplate code it saves makes Langchain an essential toolkit for building scalable AI applications.
What I like best is its rich ecosystem of pre-built integrations, which makes connecting LLMs to vector database and external data sources for RAG workflows virtually effortless. While the high level of abstraction cam occasionally make debugging tricky, the massive amount of boilerplate code it saves makes Langchain an essential toolkit for building scalable AI applications.
What do you dislike about the product?
What developers typically dislike about LangChain is its heavy reliance on over-engineered abstractions, which often wraps straightforward API calls in multiple layers of complex classes and makes debugging stack traces unnecessarily difficult. Additionally, the fast-moving nature of the framework frequently introduces breaking changes and outdated documentation, while adding dependency bloat and friction for simple use cases that would otherwise be handled in a few lines of raw python.
What problems is the product solving and how is that benefiting you?
Langchain directly addresses the critical bottleneck of context, engineering and operational friction when bridging raw Large language Models with production software. By providing unified abstractions, it solves the fragmentation of model APIs, the complexity of data ingestion for Retrieval-Augmented Generation (RAG), and the tedious state management required for conversational memory and multi-step agent workflows.
Shobha D.
Build AI Apps Fast with Less Glue Code
Reviewed on Oct 12, 2026
Review provided by G2
What do you like best about the product?
What I like most is how fast you can build things. It handles all the messy glue work between the AI model, prompts, and external data. Instead of writing tons of code from scratch, you can connect the pieces together and test a working app in hours.
What do you dislike about the product?
It feels too bloated and complicated for simple tasks. They hide basic things under too many layers, so when something breaks, debugging is painful. Plus, the library changes so fast that old code breaks constantly, and finding clear, up-to-date documentation can be a real headache.
What problems is the product solving and how is that benefiting you?
Before LangChain, connecting an AI model to your own files and databases took endless repetitive code. LangChain solves that by providing ready-made building blocks. It benefits me by cutting down setup time, letting me focus on the actual app logic rather than wiring everything manually.
RaHul K.
Great modular framework for RAG and agents, though documentation can be confusing
Reviewed on Oct 10, 2026
Review provided by G2
What do you like best about the product?
What I Like (Pros):
Massive collection of integrations with various vector stores, models, and APIs.
Speeds up prototyping and building complex LLM-powered applications significantly.
Strong community support and pre-built components for standard AI tasks.
Massive collection of integrations with various vector stores, models, and APIs.
Speeds up prototyping and building complex LLM-powered applications significantly.
Strong community support and pre-built components for standard AI tasks.
What do you dislike about the product?
What I Dislike
Rapid updates and shifting abstractions mean that breaking changes can happen frequently, requiring constant code maintenance.
Documentation can sometimes be tricky to navigate due to the fast-paced evolution of the framework.
Rapid updates and shifting abstractions mean that breaking changes can happen frequently, requiring constant code maintenance.
Documentation can sometimes be tricky to navigate due to the fast-paced evolution of the framework.
What problems is the product solving and how is that benefiting you?
The Problem It Solves:
Our development team needed a structured, modular way to connect large language models with external data sources, prompt templates, and memory components without writing complex boilerplate code from scratch.
How We Are Using It (How it's solving the problem):
We use LangChain at our workplace to orchestrate our LLM workflows, build retrieval-augmented generation (RAG) pipelines, and manage multi-step agent interactions efficiently
Our development team needed a structured, modular way to connect large language models with external data sources, prompt templates, and memory components without writing complex boilerplate code from scratch.
How We Are Using It (How it's solving the problem):
We use LangChain at our workplace to orchestrate our LLM workflows, build retrieval-augmented generation (RAG) pipelines, and manage multi-step agent interactions efficiently
Anonymous
Streamlined Multi-Agent AI with Langchain
Reviewed on Oct 10, 2026
Review provided by G2
What do you like best about the product?
I really like Langchain's standardized abstractions and stateful orchestration management. It makes building agentic AI applications and multi-agent AI workflows so much easier. Its lightweight API calling and the flexibility it provides are fantastic, allowing me to switch or compare LLM providers and vector databases with minimal code changes. Langchain streamlines API calls and speeds up development from initial client demo to production-ready applications. As an AI engineer who's used many frameworks, the predictable execution loops and complex multi-agent workflows of Langchain, without having to build custom state management, make my work simpler and more flexible than other frameworks.
What do you dislike about the product?
I don't have any significant dislikes at the moment, but I think Langchain could improve its documentation clarity. Since the ecosystem changes so quickly, breaking changes and deprecated methods can lead to extra maintenance overhead when upgrading packages. Better documentation and smoother version upgrades would help, especially with highlighting changed methods.
What problems is the product solving and how is that benefiting you?
Langchain simplifies building production-grade agentic applications. It standardizes abstractions and offers stateful orchestration with Langgraph, tackling complexity, debugging, and workload management without custom state code. These features streamline API calls, speed up development, and provide flexibility.
Rohit K.
Saves time while creating multi agent system
Reviewed on Oct 04, 2026
Review provided by G2
What do you like best about the product?
It's the best among the other applications for the role of making advanced multi agent systems and to easily create a loops. And if you want to switch from one model to another model then it only needs 1 to 2 lines of code depending on the model
What do you dislike about the product?
It's good for the advance but not for the basic things like it requires the unnecessary code for it and also important the framework takes a massive third party integration which you even not use and which leads to the increase in the size of the project and as we are using the third party so it has the risk also
What problems is the product solving and how is that benefiting you?
It's helping me in like I am working the side by side on the AI and machine learning so it is useful for me when I am learning about the LLM, API creation etc and while creating the advance code and working on a project it's a lot of time and also they gave us a very high security like they don't have any access to our private data like files if you are doing something on our private project so they don't interfere there
Chaitanya T.
Simplifies Building LLM Apps with Reusable Prompt, Model, and Tool Components
Reviewed on Sep 30, 2026
Review provided by G2
What do you like best about the product?
I like it because it simplifies building LLM-powered applications by providing reusable components for prompts, models and tools.
What do you dislike about the product?
It can sometimes feel complex because it has many abstractions and components and its API changes frequently.
What problems is the product solving and how is that benefiting you?
It simplifies the development of LLM-powered applications by providing reusable components for prompts, model integration and retrieval. It helps me connect different AI components into a complex workflow more easily.