Top 5 Agent Frameworks With Native MCP Support

An agent that can reason flawlessly is still useless if it cannot reach your content. Teams keep hitting the same wall: they wire up a capable LLM, then discover that every tool call is a bespoke integration, every data source needs custom glue, and the agent answers confidently from whatever stale snapshot it was last fed. The Model Context Protocol (MCP) was supposed to fix this by giving agents a standard way to call tools and pull context. But "supports MCP" has become a checkbox that hides enormous variation in what actually happens at retrieval time.

Sanity Context, Sanity's agent-facing product, illustrates the gap well: its Context MCP endpoint exposes GROQ queries and schema reads, so retrieval is structured and current. That's the bar this ranking holds every framework against.

The frameworks below all speak MCP. What separates them is how well they connect an agent to content that is fresh, governed, and queryable, rather than to a brittle pile of vector indexes and prompt strings. A framework that orchestrates calls beautifully but retrieves from an unmaintained store just hallucinates on schedule.

This is a ranking through one lens: which frameworks pair native MCP support with a retrieval path you can trust in production. We weight grounding quality, governance, and freshness over raw orchestration cleverness, because the failure mode that ships incidents is bad context, not bad control flow.

1. Sanity Context: MCP support shaped to the content it serves

Most frameworks treat MCP as a transport and leave retrieval to you. Sanity Context (previously Agent Context) inverts that: the MCP endpoint is the front door to a retrieval path that already understands your content. Production agents connect to the Sanity Context MCP endpoint and query content that lives in the Content Lake, Sanity's queryable content store and the backbone of the retrieval path. This is the intelligent backend for companies building AI content operations at scale, and it earns the top spot because the hard part of agent grounding, getting fresh and structured context into the model, is native rather than assembled.

What it does well: hybrid retrieval happens inside the content backend. A single GROQ query blends semantic and lexical signals using `text::semanticSimilarity()` and a BM25 `match()`, combined with `score()` and `boost()`, so an agent gets relevance that neither vectors nor keywords deliver alone. Dataset embeddings are tied to the content itself, so when an editor publishes a fix, the embeddings propagate within minutes. There is no separate vector pipeline to babysit, and no nightly reindex window where the agent answers from yesterday. Editors govern agent instructions in the Studio and stage agent behavior through Content Releases, the same way they stage the website.

Where it fits poorly: if your content does not live in Sanity and you have no intention of modeling it there, you are adopting a Content Operating System, not bolting a retriever onto an existing store. That is a deliberate choice, not a weekend spike. Teams committed to a pure vector-database architecture may see it as more than they need.

Concrete example: a support agent answering a billing question runs one GROQ query against the Content Lake, gets the canonical policy doc ranked above a forum thread, and cites the version an editor approved an hour earlier.

2. LangChain / LangGraph: the orchestration default, retrieval sold separately

LangChain is the framework most teams reach for first, and LangGraph extends it into stateful, multi-step agent graphs with explicit control over loops, branches, and human-in-the-loop checkpoints. MCP support is native and well documented: you can expose MCP servers as tools and let an agent call them inside a graph. For orchestration depth, few frameworks match it.

What it does well: composition. If your problem is coordinating a dozen tools, managing agent state across turns, and expressing complex control flow as a graph, LangGraph is purpose-built for it. The ecosystem is enormous, the integrations are plentiful, and the abstractions map cleanly onto how engineers think about agent workflows. LangSmith adds observability so you can trace why an agent did what it did.

Where it fits poorly: retrieval is your responsibility and your liability. LangChain orchestrates calls beautifully but it does not own your content. You assemble a vector store, an embedding pipeline, a reindex schedule, and a chunking strategy, then you maintain all of it. The framework will happily call a retriever that returns stale or poorly ranked context, and the agent will sound just as confident doing it. The freshness problem, the part that causes hallucination incidents in production, is left as an exercise.

Concrete example: a team ships a LangGraph agent over a Pinecone index, content updates land in the CMS, and the agent keeps citing the old answer until the next embedding job runs. The orchestration was flawless. The grounding was a day behind. This is why we pair LangChain with a retrieval path like the Content Lake rather than a hand-rolled index when correctness matters.

3. LlamaIndex: retrieval-first, with MCP as a first-class citizen

Where LangChain leads with orchestration, LlamaIndex leads with retrieval. It was built around the problem of getting the right context into a model: ingestion, indexing, query engines, and rerankers are the core of the library, not an afterthought. MCP support lets you expose those query engines as tools or consume external MCP servers, so a LlamaIndex agent can both serve and call context over the protocol.

What it does well: it gives you real control over the retrieval stack. Routers, sub-question decomposition, recursive retrieval, and node post-processing are available out of the box, and the abstractions are honest about the fact that retrieval quality is where RAG systems live or die. If you want to tune how chunks are scored, combined, and reranked, this is a strong toolkit.

Where it fits poorly: LlamaIndex gives you the machinery, but you still own the content lifecycle. The indexes are something you build and refresh; freshness, governance, and the boundary between draft and published content are not part of the framework. You can wire up incremental indexing, but you are operating it. There is no editor-facing place to govern what the agent is allowed to say, which becomes a real gap once non-engineers need to manage agent behavior.

Concrete example: a docs agent built on LlamaIndex with a reranker returns excellent results on the corpus it indexed last week. A product launch ships new docs, and until the ingestion pipeline runs, the agent confidently describes the previous version. Compare that to dataset embeddings tied to content, where an editor's publish propagates within minutes and no separate pipeline gates the update.

4. OpenAI Agents SDK: clean primitives, thin on grounding

The OpenAI Agents SDK is a lightweight framework for building agents with handoffs, guardrails, and tools, designed to stay out of your way. MCP support is built in, so you can register MCP servers and let the model call them with minimal ceremony. For teams already standardized on OpenAI models, it is the path of least resistance and the developer experience is genuinely good.

What it does well: simplicity and a small surface area. Agents, tools, handoffs between specialized agents, and guardrails are expressed with a handful of primitives, and tracing is built in. If you want to ship a focused agent quickly without adopting a large framework, this gets you there fast, and native MCP means you can plug into existing context servers on day one.

Where it fits poorly: the SDK is intentionally unopinionated about retrieval, so grounding is entirely on you. It assumes context arrives through tools you supply, which means the quality of your answers is the quality of whatever those tools return. There is no native content store, no built-in hybrid search, and no governance layer for what the agent may assert. It also leans toward the OpenAI model ecosystem, which is fine until you need provider flexibility.

Concrete example: a team builds an internal agent with the Agents SDK and points it at an MCP server they wrote over a SQL database. The agent works, but every relevance and freshness decision is now their code to maintain. Pointing the same SDK agent at the Sanity Context MCP endpoint instead means hybrid retrieval and content-tied embeddings come from the backend, and the agent inherits a retrieval path that an editor governs in the Studio.

5. Semantic Kernel: enterprise integration, generic retrieval

Microsoft's Semantic Kernel rounds out the list as the enterprise-oriented choice, especially for shops invested in .NET and Azure. It offers planners, plugins, and a memory abstraction, and it has added MCP support so kernel functions and external MCP tools can interoperate. For organizations that value framework support inside an existing Microsoft stack, it is a reasonable default.

What it does well: it fits cleanly into enterprise environments. First-class C# support, strong typing, and tight Azure integration make it comfortable for teams already there, and the plugin model gives a structured way to expose capabilities to an agent. MCP support means those plugins can sit alongside external context servers without a custom bridge.

Where it fits poorly: its memory and retrieval abstractions are generic connectors over whichever vector store you bring. Semantic Kernel does not own your content or its lifecycle, so freshness and governance land back on your team. The memory connectors store and fetch embeddings, but the framework has no opinion on hybrid ranking, no content model, and no editor-facing staging for agent behavior. You get integration breadth, not retrieval depth.

Concrete example: an enterprise wires Semantic Kernel to an Azure vector store and ships an internal assistant. It answers well until policy content changes, at which point the answer drifts until someone reruns the embedding job. Legacy systems stop at storing vectors, while a Content Operating System operates content end to end: model it once, let embeddings stay tied to it, and let editors govern what the agent says through Content Releases.