Challenges & Criticisms of LangChain
LangChain burst onto the scene in early 2023 as a popular framework for building applications with large language models (LLMs). It promised to let developers chain model calls, tools, and data sources with ease. If you’re unfamiliar with LangChain or need a quick refresher before diving into its challenges, I previously wrote a Medium article titled LangChain — A Quick Refresher, which covers the basics and fundamental concepts of the framework.
However, as the ecosystem matured, many developers began voicing significant challenges and criticisms. Key concerns include dependency bloat and complexity, frequent breaking changes and unstable APIs, outdated documentation and guidance gaps, and overcomplicated abstractions that can slow down development. Beyond technical issues, there’s growing developer frustration and signs of declining adoption, with many comparing LangChain to alternative approaches (like LlamaIndex or custom orchestration) and sharing real-world cases where LangChain fell short.
Dependency Bloat & Excessive Complexity
“Added unnecessary complexity”
One common complaint is that LangChain introduces dependency bloat — pulling in a large number of integrations and packages that inflate project complexity. The framework bundles support for many vector databases, model providers, and tools. Even though these integrations are “optional,” in practice many LangChain features require installing a bucketload of dependencies that feel excessive for basic use cases [1]. Developers have described LangChain as “bloated” and prone to dependency hell [2]. In other words, a simple application using LangChain might end up installing numerous libraries (from HTTP clients to ML toolkits) that wouldn’t be needed if one wrote a lightweight custom solution.
This bloat isn’t just about installation size — it also affects maintainability and performance. Each extra layer or package is another point of potential conflict or failure. In legacy or constrained environments, LangChain’s heavy dependency chain can be overkill. As one data scientist noted, not every project needs all the “bells and whistles” LangChain offers; in a recent small chatbot project, LangChain “added unnecessary complexity” whereas a simpler approach provided exactly what was needed “without the extra weight” [3].
These sentiments underline that LangChain’s all-in-one nature can translate to a high complexity overhead, especially for projects that only require a subset of its functionality.
Frequent Breaking Changes and Unstable Interfaces
“Break first, fix later”
LangChain’s rapid development pace led to frequent breaking changes and version incompatibilities throughout 2023. Many developers felt the framework’s interfaces were a moving target, an update could suddenly break existing code. Complaints about “things break often between updates” [4] were common. In online discussions, users vented that LangChain’s maintainers often introduced breaking API changes without clear communication, leaving developers to scramble and fix code unexpectedly. The project remained in a 0.x version phase for a long time, which in semantic versioning usually signals unstable APIs. Indeed, the LangChain team acknowledged that as long as the library was on 0.0.* versions, “users couldn’t be confident that updating would not have breaking changes.” [5]
This instability eroded trust. Developers implementing LangChain in production or larger projects grew wary of upgrades, sometimes pinning to old versions or forking the code. It became a common refrain that LangChain’s pace of change outstripped its documentation and tests, leading to a “break first, fix later” impression. In response to these concerns, the LangChain maintainers took steps in late 2023 to refactor and move towards a more stable release.
In January 2024 they announced version 0.1.0 as the “first stable version,” promising that going forward any breaking change would trigger a minor version bump and be clearly communicated. This move was explicitly aimed at earning back developer trust by systematically stabilizing the API and reducing surprise breakages. While this shift is a positive sign, much of the criticism was rooted in the churn experienced before this initiative, a pain that many early adopters remember vividly.
Outdated Documentation and Lack of Clear Guidance
“Atrocious and inconsistent”
Another major pain point has been LangChain’s documentation quality and clarity. As the framework rapidly evolved, the docs often lagged behind or contained inconsistencies. Developers frequently struggled with outdated or confusing documentation, which made the learning curve even steeper.
Some frustrated users have called the official docs “messy, sometimes out of date.” Others went further, describing LangChain’s documentation as “atrocious and inconsistent,” which made it hard to figure out how to do things the “LangChain way”. This lack of clear guidance is especially problematic given LangChain’s complex abstractions, without good docs, developers are left guessing how components are intended to be used or how they fit together.
Real-world accounts highlight the documentation gap. For example, one engineer recounted spending a week reading LangChain’s comprehensive docs and examples, only to find that “after a week of research, I got nowhere.” Even when the demo code ran, any attempt to modify it for a custom use case would break, and the docs didn’t provide enough help to resolve the issues [6]. This led to wasted time and even self-doubt, until the team abandoned LangChain for a simpler approach.
The takeaway is that documentation and tutorial materials struggled to keep up with LangChain’s feature set and changes, leaving many users without the clear guidance needed to effectively use the framework. For an open-source project aiming to accelerate development, such doc issues significantly undermined its usability.
In April 2024, LangChain attempted to address this with a “Rethinking Our Documentation” initiative. While this effort signals an acknowledgment of the problem, for many developers, it felt like too little, too late, as they had already resorted to alternative resources or abandoned LangChain altogether.
Overcomplicated Abstractions Making Development Harder
“More difficult to understand and frustrating to maintain”
LangChain’s core design centers on abstractions — Chains, Agents, Tools, Prompts, Memory, etc. — intended to simplify common LLM usage patterns. However, a frequent critique is that these layers of abstraction often make development more complicated, not less. Developers have found that LangChain’s high-level constructs can obscure what is actually happening under the hood, introduce non-intuitive patterns, and require adapting one’s thinking to the framework’s way of doing things. One software engineer quipped that using LangChain felt like “going through 5 layers of abstraction just to change a minute detail,” making any non-standard use case a struggle [1]. In practice, if your needs strayed even slightly from the happy-path examples, you might have to dig through multiple wrapper classes to implement a small tweak.
The complexity curve in software development often follows a predictable pattern. Early on, developers write simple, straightforward code. As they gain experience, they embrace design patterns, object-oriented principles, and excessive abstractions, believing this adds structure and scalability. However, over time, many realize that excessive abstraction leads to unnecessary complexity, making code harder to understand and maintain.
LangChain follows a similar trajectory. While its abstractions — Chains, Agents, Memory, and Tools were designed to simplify LLM orchestration, they often add more complexity than they remove. Developers frequently find themselves fighting the framework rather than benefiting from it, as the sheer number of layers obscures what’s actually happening under the hood. Many developers start with simple code, overcomplicate things in an attempt to “do things the right way,” and eventually come full circle, embracing simplicity over abstraction ⬇️
Several examples illustrate this over-engineering. The team at Octomind (an AI testing startup) initially adopted LangChain but discovered that its rigid, high-level abstractions became a source of friction. At first their simple requirements aligned with LangChain’s presumptions, but soon the abstraction layers made their code “more difficult to understand and frustrating to maintain.” The developers were spending as much time deciphering and debugging LangChain internals as building new features [7], a clear sign that the abstractions were getting in the way. They give a simple example of translating text: using the raw OpenAI API was straightforward, while doing the same with LangChain required introducing prompt template objects, output parser classes, and a custom chain syntax. “All LangChain has achieved is increased the complexity of the code with no perceivable benefits,”. Good abstractions should reduce cognitive load, but in this case LangChain added ceremony around a basic task without a clear payoff.
Moreover, LangChain tends to stack abstractions on top of abstractions — you often find yourself thinking in terms of nested layers (prompts inside chains inside agents, etc.). This can lead to deeply nested calls and difficult debugging, as developers must peel back each layer to see where something went wrong. As the Octomind team noted, using LangChain meant comprehending huge stack traces and debugging framework code one didn’t write, instead of focusing on application logic. A common community complaint is the proliferation of classes and methods that achieve similar ends. One experienced developer remarked that LangChain frequently has “the same feature being done in three different ways,” adding to confusion about which approach to use.
In short, many feel LangChain’s design is over-engineered: it tries to provide an abstraction for everything, but this one-size-fits-all approach can backfire by making simple things complex. This has driven some developers to label LangChain as an “unnecessary abstraction” altogether in cases where a few straightforward function calls would do.
Developer Frustrations and Declining Adoption Trends
The issues above have led to palpable frustration in the developer community. Initially, LangChain rode a wave of hype — it was the “go-to” framework in spring 2023, garnering stars, tutorials, and integration offers. Newcomers to LLMs often started with LangChain due to its popularity. However, by late 2023 a backlash had grown. Experienced engineers began questioning whether LangChain’s overhead was worth it. On forums and social media, more and more voices chimed in with negative experiences. As one Reddit user put it bluntly: “Out of everything I tried, LangChain might be the worst possible choice — while somehow also being the most popular.” This captures the sentiment that LangChain’s reputation ran ahead of its reality. Common grievances circulating among developers (including team leads and CTOs) included unnecessary abstractions, difficulty customizing behavior, and poor maintainability due to frequent breakage.
Some developers describe feeling “burnt” by trying to make LangChain work for their needs. After struggling with the framework’s quirks and constant changes, their goodwill has been exhausted. In Hacker News discussions, a few comments suggest that LangChain was useful as an early experiment, but its time has passed. “LangChain had a time and place… That was Spring of 2023,” one user writes, implying that the community has since moved on to simpler, more robust patterns. Another comment advises to “keep it simple” and notes that even the LangChain creators appear to be shifting focus away from the original library. Indeed, LangChain Inc. started promoting LangSmith (their tool for tracing and evaluation) and emphasizing the now-separated langchain-core module, which some take as a recognition of the main package’s problems.
In terms of adoption trends, there are hints that developers are exploring alternatives and custom solutions more frequently. Posts and articles with titles like ‘Why we no longer use LangChain’ began appearing, reflecting real companies deciding to pull LangChain out of their stacks. One blog by the Octomind team detailed how they used LangChain for 12 months in production but ultimately removed it in 2024, citing that as their requirements grew, LangChain turned from a help into a hindrance.
Anecdotally, some open-source maintainers report an uptick in interest for leaner libraries that do less. The initial fervor (LangChain’s GitHub stardom and funding news) has given way to a more cautious outlook, with developers prioritizing stability and clarity over buzz. While LangChain is still widely known and used, the tone of community discussions has shifted — it’s no longer uncritically praised, and many are openly skeptical of using it for new projects.
Comparisons with Alternative Frameworks
The dissatisfaction with LangChain has naturally led developers to explore alternative frameworks and approaches for LLM orchestration. One prominent alternative is LlamaIndex (formerly GPT Index), which focuses on connecting LLMs with external data sources and indices. LlamaIndex is often mentioned as a more specialized, perhaps more lightweight option for retrieval-augmented generation and data ingestion tasks. Even some LangChain critics acknowledge that LangChain “has some use-cases, especially around data ingestion”, but suggest that “llamaindex might do better anyway.” In practice, LlamaIndex provides tools for indexing documents and querying them with LLMs without the broader abstraction overhead that LangChain carries. Developers choosing LlamaIndex often cite its relative simplicity for building question-answering over documents, versus piecing together LangChain components.
Aside from LlamaIndex, there are other frameworks and libraries trying to fill the gap for LLM application development. For instance, Haystack (by deepset) is an established open-source framework for search and question-answering that some prefer, especially when integrating with existing enterprise systems (it tends to be more modular and focused on IR + LLM pipelines). Another category of alternatives are prompt orchestration libraries like Guidance or Microsoft’s Prompt Engine, and minimalist frameworks like Instructor or Atomic Agents. These tools often aim to provide just one piece of the puzzle (e.g., better handling of prompt templates or agent loop logic) without dictating an entire architecture.
A Reddit discussion noted that LangChain’s developers chose to integrate “every single prompt-engineering paper (ReAct, CoT, etc.) rather than just providing the tools to let you build your own approach,” which resulted in a bloated feature set that isn’t easily customizable. In contrast, smaller libraries let you implement those methods in a plug-and-play fashion or tweak them as needed.
Perhaps the most significant “alternative” to LangChain is the do-it-yourself approach — simply using the raw LLM APIs and standard programming constructs. Many seasoned developers advocate for this approach: writing your own glue code with the OpenAI (or other model) SDK, using Python control flow for chaining logic, and pulling in only the specific utilities you need (such as an embedding model client or a vector database SDK). For the majority of LLM applications, this custom approach can be surprisingly straightforward. As one engineer observed, “Most LLM applications require nothing more than string handling, API calls, loops, and maybe a vector DB if you’re doing RAG. You don’t need several layers of abstraction and a bucketload of dependencies to manage [basic tasks].” By avoiding an over-engineered framework, developers gain more control and can more easily debug or extend their systems in whatever direction they need. This DIY method is essentially what many teams have reverted to after hitting limitations with LangChain — in Octomind’s case, “once we removed it, we no longer had to translate our requirements into LangChain-appropriate solutions. We could just code.”
Real-World Cases Where LangChain Fell Short
It’s fun to look at a few real examples where LangChain failed to deliver as expected, prompting teams to rethink their approach:
- Octomind’s AI Agent Platform: Octomind used LangChain for a year to power AI agents that create and fix software tests, but they encountered numerous issues as they scaled up. They found that LangChain’s abstractions were too inflexible for more complex agent architectures (like agents spawning sub-agents or specialized agents coordinating). In one instance, they needed to dynamically adjust which tools an agent could use based on business logic and the agent’s state — but LangChain provided no mechanism to observe or control an agent’s state mid-run. This limitation forced them to downgrade their design to fit LangChain’s capabilities. After growing frustrations, Octomind’s team decided to remove LangChain entirely in 2024. The result? They could implement the needed features directly and gain freedom: “Once we removed it… we could just code,” they report, noting that no longer being constrained by LangChain made their team far more productive. The move also simplified their codebase and reduced the mental overhead for new team members.
- My Experience: Another firsthand example of LangChain’s instability comes from my own experience. I had to present LangChain as a topic and also wrote a Medium article, LangChain — A Quick Refresher [8]. While preparing the content, I initially demonstrated how to use Router Chains with normal syntax, following LangChain’s official documentation and examples. However, within just a week, this approach completely broke due to an update. Suddenly, Router Chains could only be implemented using LCEL (LangChain Expression Language), and the previous method was deprecated. This unexpected breaking change caused significant frustration, especially since there was no clear migration guide or warning in advance. It made my existing content obsolete almost instantly, forcing me to rewrite parts of my article. If an essential feature like Router Chains can change so drastically within a short period, developers using LangChain in production will constantly find themselves rewriting code, fixing broken dependencies, and struggling with unannounced modifications.
- BuzzFeed’s Recipe Chatbot Project: Developer Max Woolf recounted his experience trying to use LangChain for a ChatGPT-based recipe assistant at BuzzFeed. Despite LangChain being “the popular tool of choice for RAG” at the time, his attempt was fraught with difficulty. He spent about a month learning and experimenting with LangChain, only to hit roadblocks. The LangChain demo code worked on toy problems, but when he tried to adapt it to his real use case (integrating recipe search with chat), things kept breaking. Debugging through LangChain’s layers yielded no clear answers for improving the bot’s performance. Ultimately, the team abandoned LangChain and re-implemented the chatbot using a lower-level ReAct pattern with direct API calls. The simpler solution “immediately outperformed” the LangChain-based approach in both conversation quality and accuracy. In retrospect, the month spent wrestling with LangChain was largely wasted, and the experience served as a lesson that the most hyped tool isn’t always the right one for the job.
- Developer Community Stories: Countless individual developers have shared similar tales. One Reddit user mentioned helping a client completely “rewrite their codebase from LangChain to a minimalist framework” (Atomic Agents) because the client’s CTO had serious maintainability concerns. On Hacker News, an intern described building a retrieval-augmented Q&A system without LangChain (using basic Python instead) and facing skepticism from colleagues for not using the popular library, but in the end, his project succeeded and validated his choice to avoid LangChain’s complexity.
These real-world accounts consistently highlight that when projects moved beyond trivial prototypes, LangChain often became a liability. Teams encountered either outright technical limitations or a productivity drag due to the framework’s complexity and instability, leading them to either strip LangChain out or avoid it altogether in favor of more direct approaches.
Conclusion
LangChain’s rise and the subsequent backlash reflect the growing pains of a fast-moving field. Early on, LangChain provided a valuable springboard, a way for developers to experiment quickly with chaining LLM prompts, tools, and data. But as use cases expanded and production needs grew, the framework’s flaws became more apparent. Dependency bloat, unstable APIs, poor documentation, and excessive abstraction have collectively damaged its standing among many in the developer community. In response, the LangChain team has started addressing some issues (for example, reorganizing the codebase and improving version stability), but it may be a case of too much, too soon — the framework’s design philosophy itself might be mismatched to what many developers actually need.
The broader lesson is that in the rapidly evolving LLM ecosystem, simpler solutions often prevail over complex, one-size-fits-all frameworks. Developer sentiment has shifted towards a preference for minimal, transparent, and composable tools. Many are choosing alternatives like LlamaIndex, Haystack, or custom orchestration with plain SDKs to retain greater control and reliability. LangChain, once the default choice, is now one of many options — and one that comes with a lot of caveats. Its story serves as a caution that abstractions in a nascent domain must be designed very carefully: if they leak too much or add too little value, developers will happily bypass them.
Going forward, LangChain’s adoption will likely hinge on how well it can stabilize and prove its worth in real-world scenarios. For now, the industry conversation around LangChain is a mix of acknowledgment for what it attempted and critical analysis of where it fell short, with an eye towards more robust patterns for building the next generation of AI applications.
Sources
[1] Why we no longer use LangChain for building our AI agents | Hacker News
[2] NO, YOU DO NOT NEED LANGCHAIN — Olaf Górski
[4] A rant about LangChain, and a minimalist alternative : r/LangChain
[5] LangChain v0.1.0
[6] The Problem With LangChain | Max Woolf’s Blog
[7] Why we no longer use LangChain for building our AI agents
