LangChain Is Dead? 7 AI Agent Frameworks for Programmers in 2026

Every few months, “LangChain is dead” comes back in a new costume: LangChain is bloated, LangGraph is too heavy, serious teams are ripping it all out. Meanwhile, another fact: in October 2025, LangChain the company announced a $125M raise at a $1.25B valuation — and the product actually making that money wasn’t LangChain, and wasn’t LangGraph. It was LangSmith.

Both things are true at the same time. That alone tells you “framework X is dead” is the wrong question.

This article walks the whole story from the top: what real problem LangChain solved, why the chain model breaks under actual agents, what LangSmith’s rise says about where the value is, and how I’d choose in 2026 if I were starting a new LLM project today. Framework facts reference a Medium post from June 2026 and each vendor’s official docs; the judgments and war stories are our own.

LangChain in 2023 solved a real problem

Roll back to early 2023. ChatGPT had just exploded, and every developer wanted to build “an app with AI in it.” But once you sat down to build, you realized the raw model API only gets you halfway.

OpenAI gave you a chat.completions endpoint: one HTTP request in, one reply out. A genuinely useful AI app needs far more than that:

• Look up internal documents before answering — that’s RAG.
• Remember what the user said three turns ago — that’s memory.
• Decide whether to search the web, query a database, or just answer — that’s tool use.
• If the first answer isn’t good enough, try a different angle — that’s a loop.
• Use OpenAI today, swap to Anthropic tomorrow without a rewrite — that’s provider abstraction.

In early 2023, none of this existed in the model APIs. You wrote every piece yourself, from scratch — and everyone wrote the same boilerplate: vector database plumbing, document chunking, prompt templates, output parsing, retry handling.

LangChain went straight at that problem. Founder Harrison Chase was an ML engineer at Robust Intelligence; at a company hackathon in October 2022 he built a bot that could query internal data from Notion and Slack, open-sourced it the same month under MIT, and called it LangChain — Language Model Chains. A month later ChatGPT launched, and developers across the industry suddenly found exactly what they needed sitting on GitHub.

What it did can be said in one sentence: it turned that repetitive work into prefab Lego bricks. A typical chain: take input → retrieve from a vector database → stuff into a prompt template → call the model → parse the output → return it to the user. Every step had a ready-made class, with adapters for vector stores (Pinecone, Weaviate), model providers (OpenAI, Anthropic, local models), and document loaders (PDF, Notion, web pages).

Looking back, LangChain often gets labeled “over-engineered.” I don’t buy it. It was the most pragmatic answer available in 2023. You can criticize how it ballooned later, but the problems it targeted were real — and every one of them is still real today.

Why the chain model breaks under agents

LangChain’s default mental model is a straight line: A → B → C → return. That shape fits the 2023 wave of “chat with your PDF” apps perfectly. But from 2024 onward, teams started building something different: agents that think for minutes, call tools repeatedly, hand work off to other agents, pause for a human to approve, and resume from where they left off.

A straight line can’t hold that.

The canonical failure story is Octomind — a YC company that had built its entire product on LangChain, and published a 2024 postmortem explaining why they ripped it out. The core problem in one sentence: they needed to dynamically change which tools an agent could use based on what the agent had already discovered, and LangChain gave them no way to inspect or modify the agent’s state mid-run. Their final call was to dumb the product down to fit the framework. That postmortem spread far because everyone building serious agents had hit some version of the same wall.

We hit the same wall building our own multi-agent runner. Once a long task is running, “which step is it on, what state is it holding, where did it fail” must be inspectable from the outside — and ideally fixable by a human before it continues. That’s not a nice-to-have; it’s the baseline for production.

The Chase team’s answer was to change the shape. In LangChain you write your app as a recipe: first A, then B, then C. In LangGraph you write it as a map. Nodes do work, edges decide where to go next; arrows can point backward — bad output, rerun the previous node; they can branch — route by user intent; they can pause — everything stops until a human clicks “approve.” With checkpoints, you can pull up an agent’s full state two days later and resume from any checkpoint.

LangGraph launched in early 2024 and now sees tens of millions of monthly downloads (the source article cites 47M+). Directionally, I think it’s right: take state out of the framework’s black box and make it explicit, inspectable, and persistent.

The other side of the “dead” narrative: LangSmith is the product

So where does “dead” come from? If you hang around developer Twitter, you’ve seen the cycle every few weeks: LangChain is dead, LangGraph is bloated, everyone’s moving to Pydantic AI, CrewAI, the Claude Agent SDK, or the raw API.

And at the same time, LangChain the company raised $125M (reported October 2025). What drove that round wasn’t LangChain, and wasn’t LangGraph. It was LangSmith — an observability platform that doesn’t even require you to use its own frameworks.

Put the two together, and here’s my read: the framework itself was never the valuable layer. What’s valuable is runtime data — what each step of an agent run did, which tools it called, how many tokens it burned, where it failed, and whether all of that can be recorded, replayed, and audited. LangSmith happens to sit on that layer, which is why it deserves the funding and why it doesn’t care which framework you use.

We’ve run multi-provider scheduling in our own production systems, and the lesson lands hard: when an agent misbehaves, the first priority is never “switch frameworks” — it’s “pull up exactly what the state looked like.” A system that can’t do that is hopeless no matter what framework it’s built on.

So “LangChain is dead” is a false question. The question that matters: who owns your state? Can it be inspected? That’s the real dividing line for any framework decision.

The 2026 landscape: three categories, seven options

Here are the seven I’d seriously consider, grouped into three categories. First, the yardsticks I measure them by — not feature lists, but three things:

1. Is state explicit, inspectable, and recoverable?
2. Can you swap providers, and at what cost?
3. Is the abstraction thin enough — a framework should never be heavier than your business logic.

Category 1: Provider-native SDKs

Model vendors have built first-party tooling into their own SDKs, and for many use cases you can now skip the framework layer entirely.

Claude Agent SDK. Anthropic’s official SDK for building agents on Claude, extracted from Claude Code. Native support for tool use, MCP, computer use, and long-horizon context management. If your model choice is locked to Claude, this is the lowest-friction path — plugging in MCP tools really is a few lines of code. The cost: you’re tied to Anthropic. And if your app is just a simple chatbot, call the Messages API directly; this is overkill. To start: pip install claude-agent-sdk, and the official quickstart gets a tool-using agent running in under fifty lines.

OpenAI Agents SDK. Launched March 2025, evolved from the experimental Swarm project. Handoffs — one agent delegating to another — are first-class citizens, with guardrails and built-in tracing. The smoothest option for multi-agent division of labor on OpenAI models. The downsides are symmetric: tied to OpenAI; the handoff tree may feel wrong if you want graph-shaped routing; and the built-in tracing is fine for development, but production will likely still want a dedicated observability layer. To start: pip install openai-agents; the docs walk through a multi-agent support bot in about thirty minutes.

AWS Strands Agents. AWS’s open-source agent framework, integrated with Bedrock for inference and AgentCore for managed deployment. Despite the AWS branding it’s genuinely model-agnostic: Bedrock, Anthropic, OpenAI, Ollama all work. If your infrastructure already lives on AWS, AgentCore quietly removes the deployment, scaling, and monitoring layer — real, tangible value. Off AWS, the integration story loses most of its punch, and the docs are thinner than the first two. To start: pip install strands-agents.

Category 2: Orchestration frameworks

When a single agent loop isn’t enough — explicit state, conditional branching, human approval gates, multi-agent coordination — this is the layer that earns its keep.

LangGraph. The graph-based orchestration framework from the LangChain team: nodes do work, edges route, state can be checkpointed and resumed. In the “stateful + branching + human-in-the-loop” quadrant it’s still the cleanest answer on the field — parts of the architectures behind Claude Code and Devin follow the same idea. The cost is a real learning curve: you have to learn to think in nodes, edges, and state schemas, a step up from calling an SDK. And careless imports can drag in more LangChain dependencies than you want. To start: pip install langgraph; the official tutorial builds a support agent with handoffs and approvals, which exercises the framework’s strengths better than a toy example.

Pydantic AI. A typed agent framework from the Pydantic team, released in late 2024. The philosophy: “I’d rather have types than magic.” Inputs and outputs are typed Python objects, tools are typed functions — no chains, no graphs, no abstractions to learn. If you’ve ever debugged a LangChain output parser, you know exactly what I mean. Weaknesses: no first-class multi-agent graph model — you’ll build state machines and approval branches yourself; and it’s Python-only, so TypeScript-first teams are out. To start: pip install pydantic-ai; ten minutes through the first-agent example gives you the feel immediately.

CrewAI. A role-based multi-agent framework: define a crew — a researcher, a writer, a critic — and CrewAI handles the coordination. v1.14 added A2A protocol support. It’s probably the fastest way to prototype a multi-agent idea: from concept to a working demo in an afternoon. Content workflows and research tasks — anywhere the “team of specialists” mental model fits — feel natural. The catch: the role abstraction that makes prototyping fast is also the control ceiling at scale; plenty of teams eventually migrate to LangGraph or bare SDKs when they hit it. To start: pip install crewai.

Category 3: Research-first

CAMEL-AI (with OWL). Born as an academic project at KAUST in 2023 asking “what happens when LLM agents talk to each other in defined roles,” it has grown into a production-capable framework supporting million-agent-scale simulation. OWL is the practical layer on top, and it hit #1 among open-source general agents on the GAIA benchmark in 2025 (per the source article). If your problem looks like a society of agents — synthetic data at scale, agent simulation, multi-agent research workloads — it’s the strongest of the seven. But if you’re shipping a customer-facing feature in three weeks, don’t pick it: it’s research-first that happens to be production-capable, not the other way around. To start: pip install camel-ai.

You may notice AG2, Semantic Kernel, smolagents, Haystack, and LlamaIndex are missing. Not because they’re bad — Haystack owns EU-compliance niches, LlamaIndex is strong for RAG-heavy apps, Semantic Kernel fits .NET shops — but because their ground is either narrower or carrying less production momentum than the seven above. If your context maps cleanly onto one of those niches, by all means go with the niche specialist.

My recommendations

Let’s make it simple. This is the decision table I’d hand my own team:

One last splash of cold water: no row in that table is “always right.” The real dividing line is still the yardstick from earlier — who owns state, whether providers are swappable, whether the abstraction stays thin. Run candidates through those three measures and the answer usually picks itself.

Running this in China

Two of the seven come with a real access hurdle for teams in mainland China: Claude Agent SDK and OpenAI Agents SDK both assume you can reach the vendor’s API reliably, which is not the default on Chinese networks. Three practical routes around it:

1. OpenAI-compatible endpoints with domestic models. Both LangGraph and Pydantic AI let you set a custom base_url. Point it at DeepSeek, Qwen, or Zhipu GLM-5 — domestic models with capable tool calling — and the request shape stays largely the same. The change is small.
2. Local inference. Run an open model (e.g. the Qwen family) via Ollama; the orchestration layer stays unchanged and nothing depends on external networks.
3. Relay gateways. If your company already operates an API relay or gateway, point the SDK’s endpoint at it — swapping upstream providers becomes a config change, invisible to business code.

Our practical takeaway: the more uncertain the provider layer gets — accounts, network, pricing, cutoffs — the more you want to sit on the layer with the thinnest provider dependency. It’s exactly why our own scheduling service can switch between multiple upstreams: the state lives with us, and swapping a provider is a config edit.

FAQ

Is LangChain really dead?

No. “Dead” is mostly developer-community narrative. In October 2025 LangChain the company raised $125M at a $1.25B valuation, driven by LangSmith — the observability platform — not the frameworks. The framework is still maintained, with tens of millions of monthly downloads.

Should a new project use LangChain directly?

Probably not. For simple cases, call the model API directly. For state, branching, and human approval, use LangGraph. If you like typed code, pick Pydantic AI. LangChain’s original chain abstraction fits the straight “retrieve → stuff → call” pipelines of 2023.

What’s the relationship between LangGraph and LangChain?

Two products from the same team. LangChain models an app as a straight chain (A → B → C); LangGraph models it as a graph: nodes do work, edges route, state can be checkpointed and resumed, with loops, branching, and pauses for human approval. For agents, look at LangGraph.

Can you build AI agents in 2026 without any framework?

Yes, and many teams do. Official vendor SDKs have made tool use, MCP, and tracing first-class. Frameworks still earn their keep for explicit state, conditional branching, human-in-the-loop gates, and multi-agent coordination. A single agent loop is usually simpler with a bare SDK.

How should teams in China choose?

Prefer orchestration layers with thin provider dependency (LangGraph, Pydantic AI), point OpenAI-compatible endpoints at DeepSeek, Qwen, or Zhipu GLM-5, or run local inference via Ollama / a relay gateway. Claude Agent SDK and OpenAI Agents SDK are hard to reach directly from mainland China.

References

• Ankita Tripathi, “LangChain and LangGraph are DEAD? So what to USE!!”, Medium, June 2026 (author profile External link ; archived source. Framework facts and figures such as the 47M monthly downloads are cited from this article.)
• LangGraph docs External link
• Claude Agent SDK docs External link
• OpenAI Agents SDK External link
• AWS Strands Agents External link
• Pydantic AI External link
• CrewAI External link
• CAMEL-AI External link

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You may also want to check out our AI coding assistant service — we help teams actually put AI coding tools to work in daily development.

One more thing: AIOS, the local-first agent workflow layer we use in daily development, sits exactly on the “who owns the state” layer this article keeps returning to — it adds cross-session project memory, /team multi-agent collaboration, and aios verify validation & privacy redaction to AI coding assistants like Codex, Claude Code and OpenCode. The framework runs the task; AIOS remembers the state and guards the quality: cli.rexai.top