Sold by: LangChain
Deployed on AWS
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.
4.5
Overview
LangSmith Observability and Evals is a unified observability & evals platform where teams can debug, test, and monitor AI app performance - whether building with LangChain or not.
Find failures fast with agent observability. Quickly debug and understand non-deterministic LLM app behavior with tracing. See what your agent is doing step by step, then fix issues to improve latency and response quality.
Evaluate your agent's performance. Evaluate your app by saving production traces to datasets, then score performance with LLM-as-Judge evaluators. Gather human feedback from subject-matter experts to assess response relevance, correctness, harmfulness, and other criteria.
Experiment with models and prompts in the Playground, and compare outputs across different prompt versions. Any teammate can use the Prompt Canvas UI to directly recommend and improve prompts.
Track business-critical metrics like costs, latency, and response quality with live dashboards, then get alerted when problems arise and drill into root cause.
LangSmith Deployments is a purpose-built infrastructure and management layer for deploying and scaling long-running, stateful agents -- offering:
- 1-click deployment to go live in minutes,
- 30 API endpoints for designing custom user experiences that fit any interaction pattern
- Horizontal scaling to handle bursty, long-running traffic
- A persistence layer to support memory, conversational history, and async collaboration with human-in-the-loop or multi-agent workflows
- Native LangSmith Studio, the agent IDE, for easy debugging, visibility, and iteration
LangSmith Agent Builder: Give every team the ability to build, use, and improve AI agents with the security your org requires.
Highlights
- LangSmith Observability and Evals is a unified observability & evals platform where teams can debug, test, and monitor AI app performance - whether building with LangChain or not. Quickly debug and understand non-deterministic LLM app behavior with tracing. See what your agent is doing step by step, then fix issues to improve latency and response quality.
- LangSmith Deployments is a purpose-built infrastructure and management layer for deploying and scaling long-running, stateful agents offering 1/1-click deployment to go live in minutes, 2/Horizontal scaling to handle bursty, long-running traffic 3/A persistence layer to support memory, conversational history, and async collaboration with human-in-the-loop or multi-agent workflows.
- Please note: there is a $150k annual Platform License plus a minimum $150k annual usage commitment to access this package. To discuss enterprise pricing or to activate your commitment and obtain your license key after signup, please contact us at https://www.langchain.com/contact-sales - alternatively, our self-serve cloud-based products are available at https://www.langchain.com
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Pricing is based on actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Cost/unit |
|---|---|
Per Trace | $0.01 |
Per Agent Run | $0.01 |
Metered Usage Amount | $0.01 |
Minimum annual usage commitment, billed in advance | $150,000.00 |
Per Agent Builder Run | $0.10 |
Dimensions summary
You pay for what you use across five independent metering dimensions. Per Trace charges cover observability data captured when your agents run. Per Agent Run and Per Agent Builder Run charge for agent executions on the platform. Metered Usage Amount tracks normalized compute and storage consumption from deployments, engine analysis, and related services. The Minimum annual usage commitment is billed in advance, setting a baseline you draw down against as usage accrues. These dimensions add together based on actual consumption, so your total scales with trace volume, agent activity, and resources used.
Top-of-mind questions for buyers
A trace is a single execution of your application—an agent run, evaluator, or playground session. One trace can include many steps, such as model calls and other tracked events. All those steps roll up into the single trace you are billed for.
It tracks normalized units of work and storage across services. Compute-related work is measured in LangChain Compute Units, and data stored or managed is measured in LangChain Storage Units. Deployments, engine analysis, and sandboxes all consume these units at different rates based on the resources they use.
It depends on your workload. High trace volume from heavy observability pushes Per Trace charges up. Running agents in production drives Per Agent Run and Metered Usage Amount through deployment uptime and engine analysis. All dimensions bill independently and add together on one invoice based on actual consumption.
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https://www.langchain.com/terms-of-service#:~:text=Customer%20will%20pay%20LangChain%20all ,Fees%20paid%20are%20non%2Drefundable.
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LangSmith Helm Chart
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- Amazon EKS
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Helm charts are Kubernetes YAML manifests combined into a single package that can be installed on Kubernetes clusters. The containerized application is deployed on a cluster by running a single Helm install command to install the seller-provided Helm chart.
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LangSmith 0.13.14 release
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See https://docs.smith.langchain.com/self_hosting for full installation and configuration instructions.
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Similar products
LangSmith is an agent engineering platform to build, test, deploy and observe your agents. 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.
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Customer reviews
Aswindev P.
LangChain 1.0: Mature, Modular Framework with Powerful Provider-Agnostic Integrations
Reviewed on Aug 05, 2026
Review provided by G2
What do you like best about the product?
If you strip away the massive hype cycle from the early days of Generative AI, LangChain has matured into a genuinely formidable framework. For a while, enterprise architects actively avoided it because the abstractions were too heavy, the documentation was a maze, and the agent loops were fragile black boxes.
However, with the massive architectural overhaul in their 1.0 release in October 2025, LangChain fixed its biggest flaws by streamlining its core packages and introducing LangGraph as its underlying execution engine.
Here is what I like best about LangChain in its current state, and where it provides the highest upside for enterprise engineering:
1. Zero-Friction Provider Agnosticism What is most helpful: The AI landscape changes weekly. OpenAI, Anthropic, Google, and open-source models constantly leapfrog each other in capabilities and price. LangChain provides a standardized abstraction layer over 80+ model providers.
The Upside: You completely avoid vendor lock-in. You can build an entire Retrieval-Augmented Generation (RAG) pipeline optimized for OpenAI, and if Anthropic releases a cheaper, faster model tomorrow, you can swap the LLM out by changing a single import statement. You do not have to rewrite your API calls, tool schemas, or prompt templates.
2. LCEL (LangChain Expression Language) What is most helpful: Writing nested functional code to chain together prompts, models, and output parsers used to result in messy, unreadable scripts. LangChain introduced LCEL, which uses a clean, declarative pipe syntax (prompt | model | parser) heavily inspired by Unix pipelines.
The Upside: It turns sequential LLM operations into highly readable, composable Directed Acyclic Graphs (DAGs). This makes it incredibly easy for developers to stream outputs, implement fallbacks, and trace data flow without writing boilerplate orchestration logic.
3. The LangGraph Execution Engine (Fixing the Agent Loop) What is most helpful: The legacy LangChain AgentExecutor was notoriously brittle if an agent got stuck in a reasoning loop, it would just crash. Now, LangChain's primary agent abstractions (like create_agent) run internally on LangGraph.
The Upside: LangGraph treats agent execution as a cyclic state machine rather than a linear script. This gives you durable execution meaning agents can maintain state, pause for human-in-the-loop approvals, recover from failures, and execute highly complex multi-agent workflows reliably. You get the fast start of LangChain with the production-grade reliability of LangGraph.
4. The "Batteries-Included" Ecosystem What is most helpful: An LLM is useless without enterprise context. Because of its massive community head start, LangChain possesses over 600 integrations for document loaders, vector stores, and tools.
The Upside: Whether your data lives in a legacy Oracle database, a secure Confluence space, or unstructured PDF repositories, there is almost certainly a pre-built LangChain community loader for it. You don't have to waste expensive backend engineering cycles writing custom API wrappers just to ingest data into your vector store.
Ultimately, the biggest upside of LangChain today is its modularity. You can use it as a massive scaffolding library to prototype in days, and then selectively drop down into LangGraph for granular control when you move to production.
However, with the massive architectural overhaul in their 1.0 release in October 2025, LangChain fixed its biggest flaws by streamlining its core packages and introducing LangGraph as its underlying execution engine.
Here is what I like best about LangChain in its current state, and where it provides the highest upside for enterprise engineering:
1. Zero-Friction Provider Agnosticism What is most helpful: The AI landscape changes weekly. OpenAI, Anthropic, Google, and open-source models constantly leapfrog each other in capabilities and price. LangChain provides a standardized abstraction layer over 80+ model providers.
The Upside: You completely avoid vendor lock-in. You can build an entire Retrieval-Augmented Generation (RAG) pipeline optimized for OpenAI, and if Anthropic releases a cheaper, faster model tomorrow, you can swap the LLM out by changing a single import statement. You do not have to rewrite your API calls, tool schemas, or prompt templates.
2. LCEL (LangChain Expression Language) What is most helpful: Writing nested functional code to chain together prompts, models, and output parsers used to result in messy, unreadable scripts. LangChain introduced LCEL, which uses a clean, declarative pipe syntax (prompt | model | parser) heavily inspired by Unix pipelines.
The Upside: It turns sequential LLM operations into highly readable, composable Directed Acyclic Graphs (DAGs). This makes it incredibly easy for developers to stream outputs, implement fallbacks, and trace data flow without writing boilerplate orchestration logic.
3. The LangGraph Execution Engine (Fixing the Agent Loop) What is most helpful: The legacy LangChain AgentExecutor was notoriously brittle if an agent got stuck in a reasoning loop, it would just crash. Now, LangChain's primary agent abstractions (like create_agent) run internally on LangGraph.
The Upside: LangGraph treats agent execution as a cyclic state machine rather than a linear script. This gives you durable execution meaning agents can maintain state, pause for human-in-the-loop approvals, recover from failures, and execute highly complex multi-agent workflows reliably. You get the fast start of LangChain with the production-grade reliability of LangGraph.
4. The "Batteries-Included" Ecosystem What is most helpful: An LLM is useless without enterprise context. Because of its massive community head start, LangChain possesses over 600 integrations for document loaders, vector stores, and tools.
The Upside: Whether your data lives in a legacy Oracle database, a secure Confluence space, or unstructured PDF repositories, there is almost certainly a pre-built LangChain community loader for it. You don't have to waste expensive backend engineering cycles writing custom API wrappers just to ingest data into your vector store.
Ultimately, the biggest upside of LangChain today is its modularity. You can use it as a massive scaffolding library to prototype in days, and then selectively drop down into LangGraph for granular control when you move to production.
What do you dislike about the product?
If you talk to engineering teams running high-scale AI applications in 2026, you will hear a consistent theme: many are actively ripping LangChain out of their production environments.
The fundamental problem with LangChain is what the industry refers to as the "abstraction tax." It makes the easy prototyping phase look effortless, but makes the hard production edge-cases incredibly difficult to solve.
Here are the biggest technical and operational downsides to relying on LangChain in a production enterprise environment:
1. The Debugging Black Hole
LangChain wraps simple API calls in deep, bespoke layers of custom classes and middleware. If you write a direct API call to an LLM and it fails, you get a clear error. If a LangChain AgentExecutor loops out or a complex Retrieval chain breaks, the resulting stack trace is an absolute nightmare. Engineers frequently complain that to figure out why an agent failed, they have to abandon their own application logic and spend hours reading LangChain's internal framework source code.
2. Brutal API Churn and Documentation Decay
The framework moves at a breakneck pace, which creates massive operational liability. LangChain has a history of shipping aggressive restructuring updates such as splitting the monolithic package into langchain-core and langchain-community, deprecating original agent patterns, and heavily forcing the newer LangChain Expression Language (LCEL). An approach that was officially documented one month can be completely deprecated the next, instantly breaking production pipelines and rendering tutorials or Stack Overflow answers obsolete.
3. The Illusion of Seamless Vendor Agnosticism
LangChain markets the ability to swap from OpenAI to Anthropic to Google with a single line of code. In reality, this is often a "leaky abstraction". Because different models have fundamentally different internal behaviors for tool calling, prompt caching, and structured JSON outputs, LangChain tries to force them all into a lowest-common-denominator interface. When you attempt a swap on a complex pipeline, you inevitably hit edge cases where the abstraction breaks, forcing you to write custom workaround code anyway.
4. The Shrinking Value Proposition (Native SDKs Caught Up)
In 2023, LangChain was strictly necessary because the native SDKs provided by AI companies were bare-bones. Going into 2026, that landscape has changed completely. OpenAI, Anthropic, and Google now offer highly robust native Python and Node SDKs that handle function calling, structured outputs, and prompt caching right out of the box.
For many teams, the abstraction that LangChain provides no longer justifies the latency overhead and complexity it adds. Many enterprise architectures are shifting toward writing thin, custom routing layers directly over the native SDKs, gaining total control over their data flow and massively reducing debugging time.
Ultimately, LangChain is an incredible tool for prototyping, integrating obscure data sources, and getting a demo to market in days. But for highly optimized, stable production systems, its heavy abstractions frequently become the bottleneck.
The fundamental problem with LangChain is what the industry refers to as the "abstraction tax." It makes the easy prototyping phase look effortless, but makes the hard production edge-cases incredibly difficult to solve.
Here are the biggest technical and operational downsides to relying on LangChain in a production enterprise environment:
1. The Debugging Black Hole
LangChain wraps simple API calls in deep, bespoke layers of custom classes and middleware. If you write a direct API call to an LLM and it fails, you get a clear error. If a LangChain AgentExecutor loops out or a complex Retrieval chain breaks, the resulting stack trace is an absolute nightmare. Engineers frequently complain that to figure out why an agent failed, they have to abandon their own application logic and spend hours reading LangChain's internal framework source code.
2. Brutal API Churn and Documentation Decay
The framework moves at a breakneck pace, which creates massive operational liability. LangChain has a history of shipping aggressive restructuring updates such as splitting the monolithic package into langchain-core and langchain-community, deprecating original agent patterns, and heavily forcing the newer LangChain Expression Language (LCEL). An approach that was officially documented one month can be completely deprecated the next, instantly breaking production pipelines and rendering tutorials or Stack Overflow answers obsolete.
3. The Illusion of Seamless Vendor Agnosticism
LangChain markets the ability to swap from OpenAI to Anthropic to Google with a single line of code. In reality, this is often a "leaky abstraction". Because different models have fundamentally different internal behaviors for tool calling, prompt caching, and structured JSON outputs, LangChain tries to force them all into a lowest-common-denominator interface. When you attempt a swap on a complex pipeline, you inevitably hit edge cases where the abstraction breaks, forcing you to write custom workaround code anyway.
4. The Shrinking Value Proposition (Native SDKs Caught Up)
In 2023, LangChain was strictly necessary because the native SDKs provided by AI companies were bare-bones. Going into 2026, that landscape has changed completely. OpenAI, Anthropic, and Google now offer highly robust native Python and Node SDKs that handle function calling, structured outputs, and prompt caching right out of the box.
For many teams, the abstraction that LangChain provides no longer justifies the latency overhead and complexity it adds. Many enterprise architectures are shifting toward writing thin, custom routing layers directly over the native SDKs, gaining total control over their data flow and massively reducing debugging time.
Ultimately, LangChain is an incredible tool for prototyping, integrating obscure data sources, and getting a demo to market in days. But for highly optimized, stable production systems, its heavy abstractions frequently become the bottleneck.
What problems is the product solving and how is that benefiting you?
From a business and operational standpoint, the fundamental problem LangChain solves is the "orchestration tax."
When enterprise leaders mandate the integration of AI into their products, they quickly realize that calling a Large Language Model (LLM) API is only 5% of the work. The other 95% is the expensive, grueling process of connecting that model to proprietary databases, securing it, giving it memory, and orchestrating multi-step reasoning.
LangChain acts as the standardized scaffolding for that 95%. Here is how that architecture translates into direct business ROI:
1. Eliminating the "Glue Code" Tax (Time-to-Market) The Problem: Without a framework, businesses waste hundreds of expensive backend engineering hours writing custom API wrappers just to get an LLM to read a PDF from SharePoint or query a PostgreSQL database. The Benefit (Velocity): LangChain provides hundreds of pre-built integrations for data loaders, vector stores, and tools. Developers can plug an LLM into an enterprise data source in a few lines of code. This dramatically accelerates time-to-market, allowing teams to prototype applications like automated compliance checkers or customer support bots in days rather than quarters.
2. Mitigating Vendor Lock-In (Agility and Cost Control) The Problem: The AI landscape is incredibly volatile. If an enterprise hardcodes its entire application infrastructure around OpenAI's native SDK, they are trapped. If Anthropic or Google suddenly releases a faster, drastically cheaper model, the business cannot pivot without a massive codebase rewrite. The Benefit (Optionality): LangChain provides a standardized, provider-agnostic abstraction layer. A business can seamlessly swap models across 80+ providers by changing a single variable. This allows procurement and DevOps teams to continuously route traffic to the most cost-effective models, ensuring the business is never held hostage by a single vendor's pricing changes.
3. Contextualizing AI (Accuracy & Deflection) The Problem: Raw LLMs suffer from complete amnesia and hallucinate facts when disconnected from your company's reality. A support bot that confidently gives a customer the wrong refund policy is a massive liability. The Benefit (Risk Mitigation): LangChain standardized the architecture for Retrieval-Augmented Generation (RAG). By easily chaining document retrieval to generation, the business can ground the AI strictly in its own verified knowledge bases. This directly impacts the bottom line by enabling high-confidence support ticket deflection and dramatically reducing time-to-resolution, without eroding customer trust.
4. Solving Agent Reliability (Compliance and Scalability) The Problem: Early AI agents were unpredictable. They would get stuck in infinite reasoning loops or fail silently, making them impossible to deploy in regulated industries like finance or healthcare. The Benefit (Operational Control): With the integration of LangGraph as its core execution engine in late 2025, LangChain solved the reliability problem. It allows businesses to build complex, stateful multi-agent systems with explicit conditional routing. More importantly, it enables built-in "Human-in-the-Loop" pause states. An AI can do the heavy lifting of parsing a 200-page contract, pause its execution, and wait for a human compliance officer to click "Approve" before sending an email.
Ultimately, LangChain and its surrounding ecosystem (LangGraph and LangSmith) allow a business to graduate from building toy AI chat interfaces to deploying durable, auditable, and reliable autonomous workflows that actually reduce operational expenditure.
When enterprise leaders mandate the integration of AI into their products, they quickly realize that calling a Large Language Model (LLM) API is only 5% of the work. The other 95% is the expensive, grueling process of connecting that model to proprietary databases, securing it, giving it memory, and orchestrating multi-step reasoning.
LangChain acts as the standardized scaffolding for that 95%. Here is how that architecture translates into direct business ROI:
1. Eliminating the "Glue Code" Tax (Time-to-Market) The Problem: Without a framework, businesses waste hundreds of expensive backend engineering hours writing custom API wrappers just to get an LLM to read a PDF from SharePoint or query a PostgreSQL database. The Benefit (Velocity): LangChain provides hundreds of pre-built integrations for data loaders, vector stores, and tools. Developers can plug an LLM into an enterprise data source in a few lines of code. This dramatically accelerates time-to-market, allowing teams to prototype applications like automated compliance checkers or customer support bots in days rather than quarters.
2. Mitigating Vendor Lock-In (Agility and Cost Control) The Problem: The AI landscape is incredibly volatile. If an enterprise hardcodes its entire application infrastructure around OpenAI's native SDK, they are trapped. If Anthropic or Google suddenly releases a faster, drastically cheaper model, the business cannot pivot without a massive codebase rewrite. The Benefit (Optionality): LangChain provides a standardized, provider-agnostic abstraction layer. A business can seamlessly swap models across 80+ providers by changing a single variable. This allows procurement and DevOps teams to continuously route traffic to the most cost-effective models, ensuring the business is never held hostage by a single vendor's pricing changes.
3. Contextualizing AI (Accuracy & Deflection) The Problem: Raw LLMs suffer from complete amnesia and hallucinate facts when disconnected from your company's reality. A support bot that confidently gives a customer the wrong refund policy is a massive liability. The Benefit (Risk Mitigation): LangChain standardized the architecture for Retrieval-Augmented Generation (RAG). By easily chaining document retrieval to generation, the business can ground the AI strictly in its own verified knowledge bases. This directly impacts the bottom line by enabling high-confidence support ticket deflection and dramatically reducing time-to-resolution, without eroding customer trust.
4. Solving Agent Reliability (Compliance and Scalability) The Problem: Early AI agents were unpredictable. They would get stuck in infinite reasoning loops or fail silently, making them impossible to deploy in regulated industries like finance or healthcare. The Benefit (Operational Control): With the integration of LangGraph as its core execution engine in late 2025, LangChain solved the reliability problem. It allows businesses to build complex, stateful multi-agent systems with explicit conditional routing. More importantly, it enables built-in "Human-in-the-Loop" pause states. An AI can do the heavy lifting of parsing a 200-page contract, pause its execution, and wait for a human compliance officer to click "Approve" before sending an email.
Ultimately, LangChain and its surrounding ecosystem (LangGraph and LangSmith) allow a business to graduate from building toy AI chat interfaces to deploying durable, auditable, and reliable autonomous workflows that actually reduce operational expenditure.
Neelanjana M.
LangChain Makes Building AI Apps Fast with Powerful Integrations
Reviewed on Aug 04, 2026
Review provided by G2
What do you like best about the product?
What I like best about LangChain is how easily it connects language models with external data sources, APIs, databases, and tools. It provides reusable components that make it faster to build AI applications such as chatbots, document assistants, and automated workflows. The wide range of integrations and active community support are also very helpful when developing and testing new use cases.
What do you dislike about the product?
What I dislike about LangChain is that it can become complex when building larger applications. The documentation and framework structure may feel overwhelming for beginners, and frequent updates can sometimes introduce changes that require existing code to be modified. Debugging multi-step chains or agent workflows can also be difficult because it is not always easy to identify where an issue occurred. For simpler AI use cases, the framework may feel heavier than necessary.
What problems is the product solving and how is that benefiting you?
LangChain helps solve the complexity of building AI applications that need to connect language models with documents, databases, APIs, and external tools. Instead of developing every integration and workflow from scratch, it provides reusable components for creating chatbots, document-based question-answering systems, agents, and automated processes.
This benefits me by reducing development time and making it easier to test different AI use cases. It also helps organize multi-step workflows, manage prompts, connect multiple data sources, and build prototypes more efficiently. As a result, I can focus more on the business requirement and user experience rather than spending too much time on basic technical integration.
This benefits me by reducing development time and making it easier to test different AI use cases. It also helps organize multi-step workflows, manage prompts, connect multiple data sources, and build prototypes more efficiently. As a result, I can focus more on the business requirement and user experience rather than spending too much time on basic technical integration.
Mihir M.
LangChain’s Intuitive, High-Performance AI Integrations Deliver Exceptional ROI
Reviewed on Aug 04, 2026
Review provided by G2
What do you like best about the product?
LangChain stands out for its AI capabilities and seamless integrations. The UI/UX feels intuitive, performance is robust, and the onboarding support is genuinely helpful. Together, these strengths save development time and deliver exceptional ROI when building intelligent applications.
What do you dislike about the product?
Frequent breaking API updates hurt performance and make UI/UX debugging harder. Complex third-party integrations, limited onboarding support and documentation, and high observability costs all impact ROI, even though the core AI intelligence tools are strong.
What problems is the product solving and how is that benefiting you?
LangChain helps solve complex LLM integration challenges by standardizing how workflows are developed, which improves overall performance and the intelligence of the AI. The UI/UX feels intuitive, and the onboarding support is strong, saving time and resources. Overall, it delivers solid ROI by accelerating deployment and making the build process more efficient.
Shaquashia A.
Brings PDF Sources into Context Effortlessly
Reviewed on Aug 04, 2026
Review provided by G2
What do you like best about the product?
Helps me bring into context from sources such as pdfs.
What do you dislike about the product?
Sometimes I experience difficulty when using through goggle drive.
What problems is the product solving and how is that benefiting you?
Providing the resources I need.
Ram K.
Rapid Prototyping with LangChain and Extensive Integrations
Reviewed on Aug 04, 2026
Review provided by G2
What do you like best about the product?
Best experience with LangChain offers rapid prototyping, model agnosticism, and extensive integrations, but it also introduces heavy abstraction layers, complex debugging, and frequent API changes
What do you dislike about the product?
Abstraction and Debugging Pain: You can build a Retrieval-Augmented Generation (RAG) pipeline or an agent framework in just a few hours, rather than spending that time writing custom boilerplate code.
What problems is the product solving and how is that benefiting you?
Switching between different LLM providers (OpenAI, Anthropic, and Google Gemini) feels seamless thanks to the unified interface.