Model-Context-Protocol (MCP) for Agentic AI Workflows

The Model-Context-Protocol (MCP) is an open standard introduced by Anthropic in late 2024 to enable AI systems (like large language model agents) to seamlessly connect with external data sources and tools. This report provides a deep dive into MCP’s architecture and its role in agentic workflows – multi-step, tool-using AI “agents” that coordinate tasks. We will cover MCP’s core concepts, how to develop MCP-compliant agents (both client and server sides), strategies for orchestrating multiple MCP-based agents (coordination, conversation state management, and tool chaining), ensuring interoperability and schema compliance, and finally compare MCP’s approach to other leading agent frameworks (LangGraph, CrewAI, OpenDevin, AutoGen, etc.), evaluating compatibility, strengths, and limitations.

MCP Architecture and Core Concepts in Agentic Systems

MCP Overview: MCP is not a programming framework or a single toolchain – it is a protocol (akin to HTTP or SMTP) that defines how AI applications (“hosts”) and external integrations (“servers”) communicate (What is Model Context Protocol (MCP): Explained - Composio). It standardizes the way large language model (LLM) applications obtain context and execute operations on external systems, solving the “M×N integration” problem – instead of writing custom adapters for every model–tool pair, developers can target one common protocol (Anthropic Publishes Model Context Protocol Specification for LLM App Integration - InfoQ). Anthropic describes MCP as “the USB-C port for agentic systems” (What is Model Context Protocol (MCP): Explained - Composio), meaning any MCP-compliant host can plug into any MCP-compliant server to exchange information and capabilities.

(What is Model Context Protocol (MCP): Explained - Composio) MCP architecture illustrated as a universal connector (like USB-C) between AI assistants and diverse data/tools. Hosts (right, e.g. Claude) run MCP client connectors (client.py) that plug into MCP servers (left) exposing services like Slack, Gmail, Calendar (remote APIs) or local files. The protocol ensures any client can talk to any server securely and uniformly (What is Model Context Protocol (MCP): Explained - Composio).

Host–Client–Server Architecture: MCP follows a three-tier architecture with clear separation of concerns (Architecture – Model Context Protocol Specification) (Architecture – Model Context Protocol Specification):

Host: The host is the AI application or agent runtime that initiates connections to one or more MCP servers. It acts as a container and coordinator for agent interactions (Architecture – Model Context Protocol Specification). The host creates MCP client instances for each server it wants to use, manages their lifecycles, and aggregates context from them. The host also maintains the overall conversation state with the user and the LLM. In an agentic system, the host is essentially the “brain” or orchestrator that decides when to query a tool or data source. It enforces security boundaries (ensuring servers only see what they should) and handles user authorizations for tool use (Architecture – Model Context Protocol Specification).
Client: In MCP, a “client” is the component (usually a library or SDK within the host process) that manages a single connection to an MCP server (Architecture – Model Context Protocol Specification). Each client speaks JSON-RPC with one server, forming a 1:1 session. The client handles protocol negotiation, message routing, and maintains session state (e.g. open subscriptions) for that server (Architecture – Model Context Protocol Specification). Think of it as the adapter inside the host that translates host requests into MCP requests and vice versa. The host may spawn multiple clients (one per server) to incorporate different data/tools in one overall workflow (Architecture – Model Context Protocol Specification) (Architecture – Model Context Protocol Specification).
Server: An MCP server is an external service (which could run locally or remotely) that provides specific context or capabilities to the AI agent (Architecture – Model Context Protocol Specification). Servers can expose three types of features (called primitives): Prompts, Resources, and Tools (explained shortly) (Anthropic Publishes Model Context Protocol Specification for LLM App Integration - InfoQ). Each server focuses on a well-defined domain or function – for example, one server might interface with a file system to provide documents, another might wrap an API like Slack or GitHub, and another might offer computational tools. Servers operate independently and do not see the entire conversation or other servers’ data (they only receive queries relevant to their function) (Architecture – Model Context Protocol Specification). This isolation is by design for security and modularity – it prevents a tool-oriented server from inadvertently leaking data from an unrelated database server, for instance (Architecture – Model Context Protocol Specification). The host is the mediator that combines their contributions.

JSON-RPC Messaging: MCP communication uses JSON-RPC 2.0 as the message format for all requests, responses, and notifications (Model Context Protocol specification – Model Context Protocol Specification). This provides a lightweight, language-agnostic way to do remote procedure calls. Messages can be bidirectional – the host’s client can call methods on the server, and the server can call certain methods on the client as well (for example, to request an AI completion) (Anthropic Publishes Model Context Protocol Specification for LLM App Integration - InfoQ). The core message types are JSON-RPC requests (with methods and params), responses (results or errors tied to request IDs), and one-way notifications (Architecture – Model Context Protocol Specification). Using JSON-RPC means MCP can run over various transports (local sockets, TCP, WebSockets, etc.) without redefinition (Model Context Protocol specification – Model Context Protocol Specification). It also makes schema validation easier – the MCP spec includes a formal TypeScript schema defining all permitted message fields and structures (Model Context Protocol specification – Model Context Protocol Specification). This schema can be used to validate at runtime that a server and client are speaking the same protocol version and message format, ensuring robust interoperability.

Capability Negotiation: An important aspect of MCP’s architecture is that it is capability-driven. When a client and server initiate a session, they exchange a list of capabilities (feature flags) they each support (Architecture – Model Context Protocol Specification). This includes which primitives are available and optional features (like whether a server can send update notifications, support streaming, etc.). For example, a server might advertise that it supports the resources primitive and can send listChanged notifications when new resources become available (Resources – Model Context Protocol Specification). The client might advertise that it supports the sampling feature (allowing the server to ask it for language model completions) (Anthropic Publishes Model Context Protocol Specification for LLM App Integration - InfoQ). This handshake ensures both sides know what’s possible in the session and won’t invoke features the counterpart doesn’t support (Architecture – Model Context Protocol Specification). It’s analogous to how web servers and browsers negotiate capabilities (or how Language Server Protocol negotiates supported features between IDE and language server). MCP’s design principle is extensibility – new capabilities can be added in future revisions, negotiated at runtime, without breaking older implementations (Architecture – Model Context Protocol Specification).

MCP Primitives (Server and Client Features): MCP defines three server-side primitives (Prompts, Resources, Tools) and two client-side primitives (Roots, Sampling) as the building blocks of functionality (Anthropic Publishes Model Context Protocol Specification for LLM App Integration - InfoQ):

Prompts: Predefined prompt templates or instructions that a server can provide to the host. These are typically user-triggered or suggested interactions, not automatically applied (Prompts – Model Context Protocol Specification) (Prompts – Model Context Protocol Specification). For instance, a coding helper server might offer a prompt called "code_review" which, when invoked with a snippet of code, returns a formatted prompt (as a system or user message) instructing the LLM to review the code (Prompts – Model Context Protocol Specification). Prompts are discoverable via prompts/list and retrievable via prompts/get RPC calls (Prompts – Model Context Protocol Specification) (Prompts – Model Context Protocol Specification). They allow the host (or end-user) to inject expert-written instructions dynamically. Importantly, prompts are not automatically executed by the model; the user (or host logic) must choose to use them (e.g. via a UI command) (Prompts – Model Context Protocol Specification).
Resources: Pieces of data or content that the server makes available for the model’s context (Anthropic Publishes Model Context Protocol Specification for LLM App Integration - InfoQ). These are typically read-only information sources (documents, database records, emails, etc.). Resources are identified by URIs and can be listed (resources/list) and fetched (resources/read) via RPC (Resources – Model Context Protocol Specification) (Resources – Model Context Protocol Specification). For example, a Google Drive MCP server might list files and allow reading their contents, or a database server might list queryable tables/rows. Hosts decide how to include resources into the LLM’s prompt (e.g. showing a file tree for selection, or auto-including relevant snippets based on a query) (Resources – Model Context Protocol Specification). MCP supports subscriptions for resources: if a server declares the subscribe capability, the client can subscribe to a resource and get notified if it changes (useful for live-updating context, like “notify me if this file is updated”) (Resources – Model Context Protocol Specification). Resources are an essential part of building Retrieval-Augmented Generation (RAG) style agents, where an LLM retrieves external knowledge mid-conversation.
Tools: Functions or actions that the server can perform on request (Anthropic Publishes Model Context Protocol Specification for LLM App Integration - InfoQ). Tools are the way an AI agent can act on the world or query services. Each tool has a name, description, and a JSON schema for its input arguments (Tools – Model Context Protocol Specification) (Tools – Model Context Protocol Specification). For example, a weather server might expose a tool "get_weather(location:str)", or a calendar server might have "create_event(...)". The host can list available tools (tools/list) (Tools – Model Context Protocol Specification) and when the AI decides to invoke one, the host sends tools/call with the tool name and arguments (Tools – Model Context Protocol Specification). The server executes the function and returns a result (which could be text or structured data). MCP is designed so that LLMs can autonomously decide to use tools – i.e. model-controlled tool use (Tools – Model Context Protocol Specification). In practice, the host will typically inject the tool list and usage format into the model’s system prompt, so the model knows what functions exist. When the model’s output indicates a tool call (e.g. a special JSON indicating the tool name and args), the host’s client will perform the actual RPC to the MCP server and then feed the result back to the model (Tools – Model Context Protocol Specification) (Tools – Model Context Protocol Specification). (This is similar to OpenAI Function Calling or LangChain’s tool invocation loop, but standardized externally.) Because tools let an AI agent take actions, MCP strongly recommends a human-in-the-loop for oversight – users should be able to see and approve (or veto) tool calls, and UIs should highlight when a tool is being used (Tools – Model Context Protocol Specification) (Tools – Model Context Protocol Specification), especially for sensitive operations.
Roots (Client-side): A “Root” is essentially an entry point to a filesystem or data store that the host grants to the server (Anthropic Publishes Model Context Protocol Specification for LLM App Integration - InfoQ). For example, a host could expose a directory of local files as a root, allowing an MCP file-server to index and read files from that directory. This is how servers can access client-side data securely – the host controls which paths (or which database connection, etc.) the server sees. In agentic terms, Roots let you connect your AI agent to your own data sources by running a server that has access to them. A server might list URIs like file:///root1/notes/note1.txt if a root named root1 was provided. The concept cleanly separates what data is offered (host decision) from how it’s used (server provides interface).
Sampling (Client-side): This allows the server to ask the host’s LLM to generate a completion given some prompt or partial conversation (Anthropic Publishes Model Context Protocol Specification for LLM App Integration - InfoQ). In other words, the server can offload language generation tasks back to the primary model. For example, imagine a complex tool where the logic itself involves reasoning – the server could formulate a question and call completion/create (part of MCP Utilities (Prompts – Model Context Protocol Specification) (Prompts – Model Context Protocol Specification)) via the client, which triggers the host’s model to respond. This effectively lets a server chain an LLM call inside a tool action. Anthropic notes this could be used to implement more advanced agent behaviors (like a tool that internally does a few reasoning steps with the model) (Anthropic Publishes Model Context Protocol Specification for LLM App Integration - InfoQ). However, this powerful feature should be used cautiously – the spec suggests always requiring user consent for server-initiated completions (Anthropic Publishes Model Context Protocol Specification for LLM App Integration - InfoQ) to avoid infinite self-looping or unauthorized LLM use. In an agentic workflow, Sampling means an agent tool can itself prompt the AI – enabling things like a planning tool that asks the AI sub-questions and returns a synthesized result. This blurs the line between tool and agent, and highlights that MCP is flexible enough to support agents calling agents in principle.

Design Principles: MCP’s architecture was guided by principles that align well with building agentic systems (Architecture – Model Context Protocol Specification) (Architecture – Model Context Protocol Specification):

Ease of Implementation: Servers should be simple to write and focus only on their niche capability (Architecture – Model Context Protocol Specification). The host (or client library) handles heavy tasks like orchestrating multiple calls, combining outputs, maintaining conversation memory, etc. This means you can quickly create a new “agent plugin” (MCP server) for a data source without reinventing the orchestration logic each time. This is crucial in agentic ecosystems where you might have dozens of small specialized agents.
Composability: Each server is a modular component that can be combined with others (Architecture – Model Context Protocol Specification). Because all servers speak the same protocol and stay isolated, you can mix and match them in one application. This encourages an “assembly kit” approach to building agents – e.g. plug in a documentation server + a database server + a math tool server to create a more capable assistant. The standardization removes integration friction, much like how multiple browser plugins can coexist.
Isolation & Security: By design, no MCP server sees the whole conversation or the internals of other servers (Architecture – Model Context Protocol Specification). They only get the minimal input needed (like the user query relevant to that server’s function) and return results to the host. This prevents leakage of private info between tools and enforces the idea that the host orchestrator is in control. In agent terms, this is like having each tool be a specialist that only knows about its task, while the “lead” agent (the LLM plus host logic) holds the high-level picture. Safety mechanisms (like requiring user approval for certain actions) are implemented at the host layer (Tools – Model Context Protocol Specification).
Incremental Evolution: MCP is meant to evolve without breaking existing agents – features are optional and negotiated. Clients and servers can progressively implement more capabilities over time (Architecture – Model Context Protocol Specification). This is important because agentic AI is a fast-moving space; MCP’s extensibility means it can adapt (for example, adding new message types or primitives) while maintaining backward compatibility (Architecture – Model Context Protocol Specification).

How MCP Fits Agentic Systems: In summary, MCP provides the standard interface for an AI agent (the LLM-based host application) to sense and act on the world: sensing via Resources (reading context) and acting via Tools (performing operations), with Prompts as predefined skills or directives. The host remains the coordinator that keeps the conversation state and decides when to invoke these capabilities. This maps naturally onto agent frameworks: an autonomous agent needs knowledge of its environment (MCP resources can supply it), and the ability to affect the environment (MCP tools enable that). By using JSON-RPC and a strict schema for these interactions, MCP ensures any agent or tool that follows the spec can work together – enabling an ecosystem of interoperable AI agents and services rather than siloed implementations (What is Model Context Protocol (MCP): Explained - Composio) (What is Model Context Protocol (MCP): Explained - Composio).

Developing MCP-Compliant Agents (Clients and Servers)

Building an MCP-compliant agent involves implementing the protocol on both sides of the interaction: creating MCP servers for the tools/data sources you want to expose, and an MCP client/host that your AI agent will run in to connect to those servers. Anthropic has published open-source SDKs in multiple languages (Python, TypeScript, etc.) to simplify this process (Anthropic Publishes Model Context Protocol Specification for LLM App Integration - InfoQ). Below, we detail the development of each component:

Implementing an MCP Server

An MCP server can be thought of as a standalone “agent plugin” that provides a set of prompts, resources, and/or tools. The server listens for JSON-RPC calls from a client and responds according to the MCP spec. Developers have flexibility in language and framework, as long as they follow the protocol – many reference implementations exist (e.g. in Python, Node.js, etc.) (Anthropic Publishes Model Context Protocol Specification for LLM App Integration - InfoQ). Key steps to building a server:

Define Server Capabilities: Decide which primitives your server will support. For example, a read-only knowledge base might only implement resources (to list/search documents), whereas an API integrator might implement a couple of tools, and a complex agent might do all three. During the initial handshake, your server will declare these capabilities in the JSON it returns (e.g. including "prompts": {...} or "tools": {...} in the capabilities field) (Prompts – Model Context Protocol Specification) (Tools – Model Context Protocol Specification). The SDKs usually handle this declaration once you register the corresponding handlers.
Implement the Handlers: You need to write the functions that execute the actual logic when the client calls prompts/list, resources/read, tools/call, etc. The MCP SDKs let you register functions or classes to handle each primitive type. For instance, using the Python SDK, you can create a server and use decorators to register tools and resources:
```
from mcp.server.fastmcp import FastMCP

# Create an MCP server instance named "Demo"
mcp = FastMCP("Demo")

# Define a tool via decorator
@mcp.tool()
def add(a: int, b: int) -> int:
    """Add two numbers"""
    return a + b

# Define a resource via decorator with a custom URI scheme
@mcp.resource("greeting://{name}")
def get_greeting(name: str) -> str:
    """Get a personalized greeting"""
    return f"Hello, {name}!"
```
Code snippet: Example of using the MCP Python SDK to define a simple server with a tool (add) and a dynamic resource (greeting://{name}) (MCP Developer Quick Start. Introduction | by Guangya Liu | Mar, 2025 | Medium) (MCP Developer Quick Start. Introduction | by Guangya Liu | Mar, 2025 | Medium).

In this example, the server exposes one tool (“add”) that the AI can call with two integers, and one resource (accessible via URI like greeting://Alice) that returns a greeting string. The SDK takes care of wiring these to the appropriate JSON-RPC methods (tools/call for the tool, resources/read for the resource, and likely resources/list automatically for the resource scheme).
Run the Server: MCP servers can run as local processes or web services. In development, you might use the provided CLI (for example, mcp dev demo.py) which runs your server and also launches an MCP Inspector UI for testing (MCP Developer Quick Start. Introduction | by Guangya Liu | Mar, 2025 | Medium). In production, you could deploy the server as a microservice accessible via a port. Each server will typically listen on a local socket/port (or STDIO) for JSON-RPC messages. Notably, Anthropic’s Claude Desktop app can spawn local servers automatically via a config (e.g. using an npx command to run a Node package for a server) (GitHub - hideya/mcp-server-weather-js: Simple Weather MCP Server Example) – meaning that from a user perspective, an MCP server can be as simple as an npm or Python package that they point the host to, which then runs and connects.
Testing: Because MCP is a standardized protocol, you can use tools like the MCP Inspector or even generic JSON-RPC clients to test your server. The Inspector provided by Anthropic allows sending test requests and seeing the responses, ensuring your server conforms to the spec. Also, since servers declare their capabilities and provide lists of prompts/tools/resources, you can verify that your server advertises the correct “API” to the client (for example, check that your tool names and schemas appear correctly via tools/list).

MCP servers are designed to be secure and focused. For example, if you implement a server that wraps a database, you might implement the resources primitive such that resources/list lists available tables or queries, resources/read executes a query and returns results, and maybe provide a tool for writing to the database. All of this is done without the server ever seeing the user’s full conversation – the server just gets specific method calls like “read this resource” or “execute this tool with these params”. This specialization aligns with MCP’s principle that servers should be easy to build and maintain, each handling one slice of functionality (Architecture – Model Context Protocol Specification). Indeed, Anthropic reported that Claude 3.5 can generate basic MCP server implementations quickly from spec descriptions (Introducing the Model Context Protocol \ Anthropic), highlighting how straightforward the server roles are.

Implementing an MCP Client / Host Integration

On the other side, to use MCP servers, you need an MCP client embedded in your host application. If you are using an existing AI platform (e.g. Claude’s own interface, which has MCP support built-in (Introducing the Model Context Protocol \ Anthropic)), the client piece may be provided for you. But for custom agent hosts (say you are coding your own orchestrator or integrating into a new product), you will use an SDK to connect to servers.

Key considerations for the client/host side:

Connection Management: The host must spawn a connection for each server, often at startup or when the user enables a given integration. Using the SDK, this might be as simple as providing the server’s address or execution command. For example, in Claude Desktop’s config you can specify a server by a name and how to launch it (command, args) (GitHub - hideya/mcp-server-weather-js: Simple Weather MCP Server Example), and the app will start that process and establish JSON-RPC communication. The host should also handle reconnecting or shutting down connections gracefully according to the lifecycle protocol (initialize, possibly an exit message, etc.) (LangGraph + MCP + Ollama: The Key To Powerful Agentic AI | by Gao Dalie (高達烈) | Data Science Collective | Mar, 2025 | Medium).
Permissions and Security: At client initialization time, the host can decide what to allow the server to do. For instance, for a local file server, the host might pass along a “root” directory (as mentioned above) giving it read-access only to a specific folder (and not to other parts of the system). The host can also enforce that certain sensitive tools require user confirmation before execution (MCP encourages surfacing a confirmation UI for any destructive actions (Tools – Model Context Protocol Specification)). The host is effectively the policy enforcement point, making sure the agent’s autonomy stays within user-approved bounds.
Capability Negotiation & Schema Validation: When the client starts a session with a server, it sends an initialize request with its supported features and receives the server’s supported features (Architecture – Model Context Protocol Specification). The host developer should ensure to check these – e.g., if the server doesn’t support tools, perhaps disable the tool-related UI or logic; if the server does support streaming or progress updates, the client should be ready to handle those (progress notifications, etc.). The MCP schema and SDK help by parsing this negotiation into a client-side object you can query. This negotiation process ensures interoperability even as not all servers or clients implement every feature.
Incorporating Outputs into Agent Workflow: Once connected, the host needs to make use of the server’s outputs in the agent’s reasoning loop. For Prompts, this might mean adding a UI element (like a slash-command or menu) for the user to inject a prompt template from the server (Prompts – Model Context Protocol Specification). For Resources, the host could display a list or allow searching through the server’s resources, and then when the user (or the agent itself) selects one, the host fetches the content via resources/read and inserts it into the LLM’s context (e.g. as part of the system prompt or as quoted text for the assistant to see) (Resources – Model Context Protocol Specification) (Resources – Model Context Protocol Specification). For Tools, the host must decide how to let the model know about them – typically by describing available tools in the system prompt and then monitoring the model’s output for a tool call. If the model (the AI agent) decides to invoke a tool, the host’s code will catch that (either via a parser or via a model’s function call API) and then trigger the tools/call on the appropriate MCP server, wait for the result, and feed the result back into the model’s next prompt. Essentially, the host orchestrator wraps the perception-action loop: the model perceives context (possibly obtained via resources) and can output an action (tool call) which the host executes via MCP, then the cycle repeats.
Maintaining Conversation State: The host is also responsible for maintaining the dialogue history and state across turns. MCP servers themselves do not store the ongoing conversation (apart from any context you explicitly send them in a request). Thus, the host may need to cache or remember previous server outputs if they are relevant. For example, if in turn 1 the agent retrieved a file from a server, and in turn 3 the agent says “using the same file from earlier…”, the host should either keep that file content available or be prepared to call the server again. Some MCP servers might implement their own short-term caching (and MCP supports an optional pagination utility for chunking large outputs (Anthropic Publishes Model Context Protocol Specification for LLM App Integration - InfoQ) (Resources – Model Context Protocol Specification)), but generally the host is the keeper of memory. In practice, this means the host might concatenate relevant content into the LLM prompt each turn (subject to token limits), or summarize it, etc., just like any LLM application with tools.
Error Handling and Schema Validation: The MCP spec defines error response formats and expectations (built on JSON-RPC error standards). As a client developer, you should handle cases like tool execution failures (the server will return an error object and possibly an isError: true in the result (Tools – Model Context Protocol Specification)). Schema validation is largely handled by the SDK – for example, if the server returns data not matching the prescribed schema (say a tool’s output schema), a robust client could validate that and handle discrepancies. Additionally, because all tool inputs/outputs have JSON schemas, the host could even validate user-provided tool parameters against the schema to catch mistakes before calling the server.

Development Example: Anthropic’s Quickstart example shows how to connect an MCP server providing weather data to an LLM client (Anthropic Publishes Model Context Protocol Specification for LLM App Integration - InfoQ). The steps involved writing a simple Python server with a get_weather tool (wrapping a weather API) and then launching Claude Desktop (which has an MCP client built-in) to ask for a forecast. When the user asks, “What’s the weather in X?”, Claude (the LLM) sees that it has a tool named “get_weather” available, and it produces a JSON calling that tool. The Claude app’s MCP client receives this request, calls the weather server’s tools/call method with the location parameter, gets back a JSON result containing (e.g.) “It’s 72°F and sunny in X” (Tools – Model Context Protocol Specification), and inserts that into the chat for Claude to incorporate into its answer. From the developer’s perspective, once the pieces were set up, the actual orchestration was handled by following MCP’s patterns rather than custom code – illustrating the benefit of the standard.

Summary: Developing MCP-compliant agents means you implement a server for each capability area you want to plug in (many are available off-the-shelf – Anthropic provided servers for Google Drive, Slack, Git, Postgres, etc. (Introducing the Model Context Protocol \ Anthropic)), and ensure your host/client can launch and talk to those servers. The heavy lifting of multi-step communication is taken care of by the protocol and SDK. This uniformity speeds up development (you don’t write custom integration code for each new tool) and makes your agent extensible – e.g., if a new MCP server for “Jira issues” comes out, you could connect it to your agent with minimal effort since it speaks the same language.

Orchestrating a System of MCP-Compatible Agents

One of MCP’s greatest strengths in agentic systems is enabling orchestration of multiple agents/tools in a coordinated way. Orchestration involves handling the protocol-level coordination, managing the conversation state, leveraging agent capabilities, and performing tool-use chaining across possibly many steps. Here we describe how an agent developer or an AI orchestration platform can use MCP to run a system of interconnected agents:

Protocol-Level Coordination: MCP provides a structured, stateful communication channel with each server, which helps in complex coordination. For example, MCP supports progress notifications and cancellations (Architecture – Model Context Protocol Specification) (Architecture – Model Context Protocol Specification). If an agent server is doing a long-running task (say searching a large corpus), it can send periodic progress updates to the client, which the host might surface to the user (progress bar) or to the LLM (perhaps to decide to continue waiting or not). The host can also send a cancel request if the user or orchestrator decides to stop a tool mid-execution (Architecture – Model Context Protocol Specification) (Architecture – Model Context Protocol Specification). These protocol utilities ensure that when multiple agents are working in tandem, the system remains responsive and can recover from hung or slow operations (a crucial aspect when chaining tools).
Maintaining Conversation State: In an orchestrated multi-agent system, global conversation state typically lives in the host. The user’s query, the assistant’s prior answers, and any intermediate results all accumulate to form context for each new turn. MCP’s design ensures that each server only sees what part of that state is necessary. For instance, if the user asks, “Find any urgent emails from today and draft a reply,” the host might break this task down: query an email server (MCP server providing email data) for today’s emails, filter those marked urgent, then send the content of those emails to a draft-writing tool (another MCP server, or maybe the host’s LLM itself to compose the reply). The email server doesn’t need to know about the reply drafting step, and the drafting tool doesn’t need to know the entire inbox, only the selected email. The host mediates the flow, storing the list of urgent emails as a variable in state, and providing the relevant pieces to each server in turn. This aligns with MCP’s principle that the host aggregates context across clients (Architecture – Model Context Protocol Specification). In practice, an orchestrator might maintain an object that tracks all active knowledge (from resources) and actions taken, which is then used to construct the LLM’s prompt each cycle. There are also patterns like data augmentation: e.g., after retrieving resources via MCP, the host could summarize them (perhaps using the LLM) to reduce token usage, then feed the summary into the next step. MCP doesn’t dictate how the host uses the data – just that the data can be retrieved in a standard way – so conversation state management is up to the host’s strategy (vector databases for long-term memory, summary of older turns, etc., can all be slotted in without the servers needing to know).
Agent Capability Utilization: In a multi-agent orchestration, different MCP servers provide different capabilities, and the host should leverage each where appropriate. Because servers declare their capabilities at init, the orchestrator can make decisions: e.g., one server might declare a search tool, another provides a calculate tool. If the user’s ask is mathematical in nature, the host should route to the calculator; if it’s about looking up information, use the search tool. This can be done by the LLM agent itself (if it is aware of both tools and can choose) or by some planning logic outside the model. Some sophisticated hosts might implement a meta-policy: for example, first always use a retrieval agent (one server) to get relevant docs, then feed those to the LLM for answering. In MCP terms, that means always calling resources/search or a custom tool (like knowledge_base_query) on a particular server before answering. Coordinating multiple servers may involve calling them in sequence or in parallel. MCP allows parallel calls since each client-server pair is independent (JSON-RPC requests are asynchronous). However, if using a single LLM to process results, typically you’d do sequential steps (retrieve, then incorporate, then decide next action). The capability negotiation also helps avoid mis-coordination: if a server doesn’t support something, the host won’t attempt it – for example, if a server doesn’t support subscribe on resources, the host knows it must poll for changes or not expect updates (Resources – Model Context Protocol Specification).
Tool-Use Chaining: Chaining tools means an agent uses outputs from one tool invocation as inputs to another in a multi-step fashion. With MCP, there are a few ways to achieve this:
1. LLM-directed chaining (implicit): Give the LLM access to multiple MCP tools at once. The model could decide a plan: e.g. call Tool A (via tools/call on Server A), get result, then immediately call Tool B (Server B) with that result. The host’s job is to faithfully execute each call as the model outputs them. If the model isn’t inherently capable of multi-step planning, the host can prompt it with a chain-of-thought approach or use few-shot examples to encourage multi-step. Because MCP tools return structured results, the host can inject those results into the model’s context clearly (e.g. “Output from Tool A: ...”). Many agentic frameworks built on ReAct (Reason+Act) loops could be implemented using MCP tools under the hood – the difference is the model need not have any custom code for each tool, just follow the generic protocol of outputting a JSON for a tool call.
2. Host-directed chaining (explicit): The orchestrator outside the model can break a task into sub-tasks and call the appropriate MCP servers in a predetermined sequence. After each call, the host might compose an intermediate prompt for the LLM. For example, the host could say: “First, I will use the Search server to gather data, then provide it to the LLM.” It calls tools/call on the Search MCP server (perhaps using a query derived from the user question), gets back search results as text (resource content), then feeds a new prompt to the LLM that includes those results and asks the question again. Here the LLM doesn’t explicitly know it called a tool; the orchestration logic did it behind the scenes and just gave the model additional context. This approach can be useful if the model isn’t trusted to decide when to use tools, or if we want a strict order of operations (sometimes called a hardcoded chain as opposed to agentic free-form).
3. Server-initiated chaining: Using the sampling capability, an MCP server itself can invoke the host’s LLM mid-action (Anthropic Publishes Model Context Protocol Specification for LLM App Integration - InfoQ). This is a more advanced pattern – effectively an agent within an agent. For example, imagine a “Research” MCP server that has a tool investigate(topic); when called, this server might autonomously use the LLM (via completion/create requests back to the client) to break the topic into sub-questions, search for each (maybe using its own sub-tools or resources), and compile an answer, which it then returns to the host. To the host, it looks like a single tool call returning a result, but internally that server ran a mini agent workflow. This kind of chaining is powerful for encapsulating complex skills in a server, but it requires careful design to avoid infinite loops or overly long runtimes. The host should generally supervise or put limits on sampling calls (the spec suggests requiring a human to approve each sampling request from a server) (Anthropic Publishes Model Context Protocol Specification for LLM App Integration - InfoQ).
Multi-Agent Conversation Coordination: In scenarios where you truly have multiple LLM agents (not just one LLM using tools, but potentially two or more LLMs collaborating), MCP can still be the glue. For example, one could imagine two MCP servers each wrapping an LLM (say, different specialized models), and the host passes messages between them. However, a more common case is one principal LLM (the “assistant”) using tools. If you wanted, say, a separate planning agent and execution agent, you could implement that by having a planning server (with sampling so it can think using the LLM) and an execution server with tools, coordinating via the host. While MCP doesn’t provide a high-level semantic of “multiple agents talking to each other” out of the box, it provides the infrastructure to build that: since any server can act as an independent process (could even be an LLM), you could set up a pipeline where the host hands off to different servers in turn. The key challenge is managing the shared state – often, you’d have a common memory or context that the host updates and gives to each agent in sequence. For example, a “critic” agent could be an MCP server that takes the conversation transcript as a resource and returns a critique (prompt primitive or resource). The host would call that after the main assistant answers, then feed the critique back to the assistant (perhaps via a system message) for improvement. This kind of orchestration (sometimes called a chain-of-thought with self-reflection or using a “Critic & Proposer” pair of agents) could be cleanly implemented with MCP servers for each role, coordinated by the host’s logic.
Ensuring Coherent Orchestration: When chaining and coordinating multiple steps, the host should ensure that the transitions are smooth and conversation state remains consistent. For example, if multiple tools provide overlapping information, the host might consolidate it before presenting to the LLM to avoid confusion or repetition. Additionally, the host might label or format inserted content to help the LLM distinguish it (e.g., “Here are search results from [Tool]. …”). MCP doesn’t specify these UI/UX aspects, but provides the raw data and events; it’s on the orchestrator to design prompts that make good use of them. Best practices from agent frameworks (such as ReAct prompting, tool output markers, etc.) can be applied on top of MCP.

In summary, orchestrating a system of MCP agents involves a mix of automated planning and developer-defined flow. MCP gives you the hooks (multiple clients, each with isolated session and known capabilities) to call on various services when needed. The developer or the AI model (or a combination of both) then decide when and how to invoke each agent’s capability in service of the user’s goal. Thanks to MCP’s structured approach, much of the nitty-gritty (keeping track of which tool outputs what, dealing with async calls, error states, etc.) is handled in a uniform way. This consistency makes it easier to scale up to many tools: an orchestrator can loop through all connected MCP servers asking each for relevant info, for instance, without needing custom code per integration. The result is a more scalable agentic workflow, where adding a new tool doesn’t exponentially increase complexity – it simply adds another node the host can call, still using the same MCP message patterns.

Interoperability, Composability, and Schema Validation in an MCP Agent Ecosystem

A major goal of MCP is to foster an interoperable and composable ecosystem of AI agents and tools. By adhering to a common protocol and schema, disparate systems can work together like puzzle pieces. Here’s how MCP achieves interoperability, composability, and reliable schema validation:

Standard Interface = Interoperability: MCP establishes a universal set of rules and message formats that any client and server must follow (What is Model Context Protocol (MCP): Explained - Composio). This means an MCP-compliant tool (server) built by one team can be used by a completely different MCP client (host) built by another team, without custom adapters. It’s akin to how any USB-C accessory can plug into any brand of laptop – the protocol abstracts away the specifics. For example, Anthropic’s Claude, OpenAI’s ChatGPT, or Google’s Gemini could all implement MCP clients, and they would immediately gain access to the same pool of MCP servers (Slack, Gmail, databases, etc.) provided by the community (LangGraph + MCP + Ollama: The Key To Powerful Agentic AI | by Gao Dalie (高達烈) | Data Science Collective | Mar, 2025 | Medium) (LangGraph + MCP + Ollama: The Key To Powerful Agentic AI | by Gao Dalie (高達烈) | Data Science Collective | Mar, 2025 | Medium). Conversely, an enterprise can build one MCP server for their proprietary database and have multiple AI products connect to it. This decoupling of tool providers and AI developers breaks the vendor lock-in on both sides (Anthropic Publishes Model Context Protocol Specification for LLM App Integration - InfoQ). We no longer need N different plugins for N platforms – just one MCP connector that works everywhere. Interoperability is further ensured by MCP’s versioning system; each message exchange can include the protocol version, and the spec is updated with backward compatibility in mind (Architecture – Model Context Protocol Specification) (Architecture – Model Context Protocol Specification).
Composable “Agent Plugins”: MCP’s architecture encourages a plug-and-play model for AI capabilities. Need a new skill for your AI agent? Plug in another MCP server. The host can run multiple servers concurrently, each isolated, which makes system design modular (Architecture – Model Context Protocol Specification). You can compose an agent with precisely the capabilities you need by selecting the appropriate servers. This also means capabilities can be developed and improved independently. For instance, if a better “web browsing” MCP server comes along, you can swap out the old one for the new one without changing your host’s core logic (as long as both implement the MCP tools interface for browsing). Composability extends to orchestrating multi-step workflows: an AI solution might involve a chain of MCP servers (one for retrieval, one for transformation, one for actuation), which can be rearranged or replaced as requirements change. The clear separation of concerns (each server for one purpose) not only makes the system easier to maintain (What is Model Context Protocol (MCP): Explained - Composio), but also safer – you can tightly scope what each piece can do.
Isolation Aids Composability: Because MCP servers don’t share state with each other and can’t see each other’s data (except via the host), you can add a new server without worrying about it interfering with existing ones (Architecture – Model Context Protocol Specification). This isolation is crucial for composability; it’s analogous to microservices architecture in distributed systems – small services that communicate through a well-defined API (here, the host and JSON-RPC) can be composed into larger systems. The host becomes the composition layer, merging inputs from various sources. As the spec points out, “each server provides focused functionality in isolation; multiple servers can be combined seamlessly” (Architecture – Model Context Protocol Specification). This also allows incremental upgrades: you could start with one MCP server (say a vector database for retrieval) and later add another (like a calculator tool) to extend your agent, without redesigning from scratch.
Consistent Discovery & Schema: MCP defines uniform ways to discover what a server offers – prompts/list, resources/list, tools/list all return structured listings of available capabilities (Prompts – Model Context Protocol Specification) (Resources – Model Context Protocol Specification) (Tools – Model Context Protocol Specification). This means an MCP client can generically present these to a user or utilize them, without hard-coding for specific servers. For example, a generic agent UI could list all tools available across all connected MCP servers in a single menu for the user, because it knows how to get each server’s tool list and combine them. The listings include machine-readable schemas and human-readable descriptions for prompts/tools, enabling both introspection and validation. The JSON Schemas for tool inputs/outputs and resource identifiers ensure that when a client calls a tool, it can validate the arguments against the schema (preventing type mistakes) (Tools – Model Context Protocol Specification), and when a server returns a result, it matches the expected structure (e.g., a text or an image payload). The MCP specification’s authoritative schema (available in the GitHub repo (Model Context Protocol specification – Model Context Protocol Specification) (Model Context Protocol specification – Model Context Protocol Specification)) can be used to validate message structures at runtime. Many SDKs likely include built-in validation: for instance, if your tool returns a Python object not matching the declared output schema (say you returned a dict where a string was expected), the SDK could flag that as an error before it ever reaches the client.
Testing and Conformance: Because of the formal specification, one can build conformance tests. Anthropic and the community have reference tests and example implementations (Anthropic Publishes Model Context Protocol Specification for LLM App Integration - InfoQ) (Anthropic Publishes Model Context Protocol Specification for LLM App Integration - InfoQ). If you implement your own MCP component, you can use these resources to verify it adheres to the spec. This is analogous to how web browsers have test suites for HTML/CSS standards. Such validation ensures that when you plug a new component into your agent system, it “just works” with others. The MCP community’s emphasis on open-source and collaboration means over time we’ll see a rich library of proven connectors and likely certification of compliance.
Cross-Platform and Vendor-Neutral: MCP is designed to be model-agnostic and platform-neutral. A source describes it as a “single protocol for connecting any LLM to any tool” (What is Model Context Protocol (MCP): Explained - Composio) and highlights “cross-platform compatibility: tools built for one system work with others” (What is Model Context Protocol (MCP): Explained - Composio). This openness is critical for a healthy ecosystem. For example, you might run an MCP server on an AWS Lambda, connect to it from an OpenAI-based app today, and tomorrow connect to it from a self-hosted open-source LLM – no rework needed. For companies and developers, this means investments in building MCP integrations are future-proof and not tied to a single AI provider. It lowers the barrier for new entrants: a new LLM or agent framework can adopt MCP and instantly gain a whole suite of integrations that users have already built.
Schema Evolution and Versioning: MCP includes a versioning scheme for the protocol (the spec document is versioned, e.g. 2024-11-05 initial, 2025-03-26 latest, etc.) (Architecture – Model Context Protocol Specification). Changes are documented and tooling can adapt. The use of capability flags for new features means older clients/servers will ignore what they don’t understand, preventing crashes. For instance, if a future version adds a vision capability for image data, a server might declare it; an older client that doesn’t know about vision will simply not use it, but can still interact on other levels. This careful design for evolution ensures the ecosystem remains interoperable over time, even as it grows to cover new use cases.

In essence, MCP’s emphasis on a well-defined schema and protocol is about creating a healthy AI agent ecosystem akin to the web. Instead of monolithic “AI platforms” where only first-party plugins work, MCP envisions a world where any AI assistant can tap into a universal pool of tools and data by speaking the same language. This interoperability and composability accelerate innovation (developers can build on each other’s work) and gives end-users more choice (mix and match the best-of-breed components). As one analysis put it, MCP “is not revolutionary but brings much-needed standardization to the otherwise chaotic space of agentic development” (What is Model Context Protocol (MCP): Explained - Composio) – a crucial step as we integrate AI deeper into all software systems.

Comparison with Other Agentic Workflow Frameworks

MCP arrives in a landscape where many frameworks and systems are already tackling the challenge of orchestrating LLM-based agents and tool use. It’s important to understand that MCP is a protocol specification (a low-level standard for interoperability), whereas others like LangGraph, CrewAI, OpenDevin, AutoGen (and frameworks such as LangChain, Semantic Kernel, etc.) are higher-level libraries or architectures that provide patterns for building agent behaviors. In many cases, MCP can complement these frameworks rather than directly compete. Below we compare MCP’s approach to these leading systems, addressing each framework’s compatibility with MCP, its strengths/limitations, and how each handles conversation state, tool use, and inter-agent messaging:

LangGraph (by LangChain team)

Overview: LangGraph is a newer orchestration framework built by the LangChain team to enable complex, graph-structured agent workflows (The Agentic Imperative Series Part 3 — LangChain & LangGraph: Building Dynamic Agentic Workflows | by Adnan Masood, PhD. | Mar, 2025 | Medium) (The Agentic Imperative Series Part 3 — LangChain & LangGraph: Building Dynamic Agentic Workflows | by Adnan Masood, PhD. | Mar, 2025 | Medium). Whereas LangChain originally focused on linear sequences or single-step tool use, LangGraph allows branching, loops, and multi-agent flows with explicit control over execution. It represents an agent’s plan as a directed graph of nodes, where each node could be an LLM call, a tool action, a conditional, or even another agent. This provides fine-grained control and the ability to persist state across steps in a long-running process (The Agentic Imperative Series Part 3 — LangChain & LangGraph: Building Dynamic Agentic Workflows | by Adnan Masood, PhD. | Mar, 2025 | Medium) (The Agentic Imperative Series Part 3 — LangChain & LangGraph: Building Dynamic Agentic Workflows | by Adnan Masood, PhD. | Mar, 2025 | Medium).

MCP Compatibility: LangGraph as a framework is model-agnostic and could integrate MCP tools, though it doesn’t natively “speak MCP” out of the box. In practice, one would use MCP within LangGraph by implementing a node that calls an MCP client. For example, you could have a LangGraph tool node that, when executed, triggers an MCP tools/call to an external server. In fact, developers have demonstrated using LangGraph together with MCP – one tutorial shows creating a multi-agent chatbot that uses LangGraph for orchestration and MCP for connecting to external services (LangGraph + MCP + Ollama: The Key To Powerful Agentic AI | by Gao Dalie (高達烈) | Data Science Collective | Mar, 2025 | Medium) (LangGraph + MCP + Ollama: The Key To Powerful Agentic AI | by Gao Dalie (高達烈) | Data Science Collective | Mar, 2025 | Medium). This indicates complementary use: LangGraph handles the agent’s decision logic, while MCP provides the standardized interface to tools and data. So while LangGraph doesn’t require MCP, it can certainly leverage MCP servers as integration points (instead of, say, using only LangChain’s built-in integrations).

Conversation State & Memory: LangGraph emphasizes state persistence and transparent control of the agent’s memory. It can maintain a structured state (variables, intermediate results, full message history, etc.) across the graph’s execution (The Agentic Imperative Series Part 3 — LangChain & LangGraph: Building Dynamic Agentic Workflows | by Adnan Masood, PhD. | Mar, 2025 | Medium) (The Agentic Imperative Series Part 3 — LangChain & LangGraph: Building Dynamic Agentic Workflows | by Adnan Masood, PhD. | Mar, 2025 | Medium). This is a strong point: you can design an agent that remembers earlier steps or user instructions because LangGraph allows storing and passing along that state. It even supports checkpointing and human-in-the-loop breaks (The Agentic Imperative Series Part 3 — LangChain & LangGraph: Building Dynamic Agentic Workflows | by Adnan Masood, PhD. | Mar, 2025 | Medium) (The Agentic Imperative Series Part 3 — LangChain & LangGraph: Building Dynamic Agentic Workflows | by Adnan Masood, PhD. | Mar, 2025 | Medium). For example, an agent might gather info with several tools, store those in variables, and later on use all that data in a final answer node. LangChain’s basic agents, by contrast, often just stuff info into the prompt or rely on the LLM’s short-term memory, but LangGraph gives more explicit control. This is ideal for complex workflows where you might dynamically decide to branch or loop based on accumulated info (for instance, a QA agent that keeps asking clarifying questions until certain of an answer, tracking each Q&A pair in state).

Tool Use Integration: LangGraph doesn’t reinvent tool interfaces; it often uses LangChain’s tool abstractions under the hood (since it’s by the same team) (What is crewAI? | IBM). This means it can use all of LangChain’s existing tools (web search, calculators, etc.) as nodes in the graph. It also allows custom tool nodes. Essentially, each action node in a LangGraph is like a function call that can either be an LLM invocation or a tool call. The framework provides control logic around these calls (e.g., conditions, loops). In terms of MCP, an MCP server’s functionality could be wrapped as a LangChain tool (LangChain tools are basically Python callables with a name/description), which then LangGraph could call. The benefit of LangGraph here is that you can organize multiple tool calls in a sophisticated sequence. For example, you could explicitly construct a subgraph: [Ask user -> Use Tool A -> If result meets condition, use Tool B -> Else, ask user for clarification -> Then use Tool B] – something that would be hard to enforce with a purely LLM-driven flow. LangGraph essentially gives a deterministic scaffold around the LLM’s nondeterministic reasoning, which can greatly improve reliability in production (The Agentic Imperative Series Part 3 — LangChain & LangGraph: Building Dynamic Agentic Workflows | by Adnan Masood, PhD. | Mar, 2025 | Medium) (The Agentic Imperative Series Part 3 — LangChain & LangGraph: Building Dynamic Agentic Workflows | by Adnan Masood, PhD. | Mar, 2025 | Medium).

Inter-agent Messaging: LangGraph can support multi-agent setups by having multiple LLM nodes that pass messages between them or by structuring prompts that simulate dialogues. However, it mainly excels at orchestrating a single agent with many tools or sub-tasks. It doesn’t inherently provide a “multi-agent chatroom” where agents autonomously converse (like AutoGen does), but you can certainly design a graph where two LLM nodes call each other in turn. Usually, though, LangGraph’s multi-agent support is realized by treating each agent’s role as part of the graph (for instance, a “Validator agent” node that evaluates the main agent’s output). The strong point is you can insert coordination logic between agents – e.g., run Agent A, then run Agent B to critique A, then decide if A should iterate again. All of this can be encoded in the graph with conditional edges.

Strengths: LangGraph’s strengths lie in robustness and control. It brings formalism (graphs) to what was a loose procedure, making complex agent workflows more transparent and debuggable (The Agentic Imperative Series Part 3 — LangChain & LangGraph: Building Dynamic Agentic Workflows | by Adnan Masood, PhD. | Mar, 2025 | Medium) (The Agentic Imperative Series Part 3 — LangChain & LangGraph: Building Dynamic Agentic Workflows | by Adnan Masood, PhD. | Mar, 2025 | Medium). It supports features like state inspection, limits on iterations, and easy integration of human approval steps (The Agentic Imperative Series Part 3 — LangChain & LangGraph: Building Dynamic Agentic Workflows | by Adnan Masood, PhD. | Mar, 2025 | Medium). In enterprise settings, this is valuable: you can ensure an agent doesn’t loop forever or goes through a risk review node before executing a destructive action. LangGraph has been used to reduce latency and errors by structuring the agent’s decision process explicitly (e.g., Uber using LangGraph to orchestrate code-modification agents reliably) (The Agentic Imperative Series Part 3 — LangChain & LangGraph: Building Dynamic Agentic Workflows | by Adnan Masood, PhD. | Mar, 2025 | Medium) (The Agentic Imperative Series Part 3 — LangChain & LangGraph: Building Dynamic Agentic Workflows | by Adnan Masood, PhD. | Mar, 2025 | Medium). It’s complementary to simpler frameworks – often one might prototype with LangChain’s basic agent and then upgrade to a LangGraph design for production.

Limitations: The power of LangGraph comes with complexity. Designing the optimal graph requires understanding the problem deeply; it’s more involved than writing a quick prompt for an autonomous agent. Over-specifying a graph can also reduce the flexibility of the agent – if something unexpected comes up, a rigid graph might not handle it unless you accounted for that path. There’s also overhead in managing the graph execution engine. Moreover, LangGraph still relies on the underlying LLM for reasoning; if the model outputs garbage, LangGraph can catch it or loop, but the developer must anticipate failure modes (e.g., decide how many retries, etc.) (The Agentic Imperative Series Part 3 — LangChain & LangGraph: Building Dynamic Agentic Workflows | by Adnan Masood, PhD. | Mar, 2025 | Medium) (The Agentic Imperative Series Part 3 — LangChain & LangGraph: Building Dynamic Agentic Workflows | by Adnan Masood, PhD. | Mar, 2025 | Medium). In terms of integration standards, before MCP one downside was each integration (tool) was custom-coded – but with MCP, one could mitigate that by writing generic MCP call nodes.

Comparison to MCP: MCP and LangGraph operate at different layers. MCP is about how to connect to external functions/data uniformly, whereas LangGraph is about how to organize an agent’s internal decision process. They can work together: MCP could supply the content and actions that LangGraph’s nodes use. If LangGraph is the orchestra conductor, MCP provides a standardized instrument set. The main advantage MCP would give LangGraph is easier interoperability of tools – LangGraph could call an MCP server for Slack, another for Google Calendar, without the LangChain team having to implement those integrations themselves. Conversely, LangGraph provides something MCP doesn’t address: a way to explicitly script complex agent behavior rather than relying on implicit LLM decisions. In summary, LangGraph is compatible with MCP (through integration code) and is strong in orchestrating multi-step workflows with persistent state (The Agentic Imperative Series Part 3 — LangChain & LangGraph: Building Dynamic Agentic Workflows | by Adnan Masood, PhD. | Mar, 2025 | Medium), whereas MCP ensures those multi-step workflows can include a wide array of external capabilities in a standardized way.

CrewAI

Overview: crewAI is an open-source multi-agent orchestration framework that focuses on collaborative “crews” of AI agents working together (What is crewAI? | IBM). It was created by João Moura and gained popularity for enabling teams of agents with different roles to collectively solve tasks. The core idea is to have a crew of specialized agents (e.g., a “Researcher”, a “Solver”, a “Planner”) that communicate and delegate tasks among themselves, analogous to a human team (What is crewAI? | IBM) (What is crewAI? | IBM). CrewAI builds on top of LangChain, using its tools and memory primitives, but adds an architecture for agent collaboration and a hierarchical management of tasks (What is crewAI? | IBM) (What is crewAI? | IBM).

MCP Compatibility: CrewAI does not natively incorporate MCP, but it can interoperate at the tool level. Since CrewAI leverages LangChain for tools, one could wrap an MCP client call as a LangChain Tool and thereby allow CrewAI agents to call MCP-exposed functions. For example, a CrewAI agent could have a tool “DatabaseQuery” that internally calls an MCP server for a database. However, CrewAI itself doesn’t provide an MCP client – integration would require custom glue code. There isn’t a known example of CrewAI with MCP yet (as of early 2025), but conceptually they address complementary aspects: CrewAI deals with multi-agent coordination logic, whereas MCP could standardize how each agent accesses external systems. So, while possible to integrate, out-of-the-box compatibility is not there. A CrewAI user would have to manually incorporate MCP, perhaps by treating the whole CrewAI as an MCP host (with one client for each external server needed).

Conversation State & Memory: CrewAI manages conversation state through each agent’s memory (often using LangChain’s memory classes for per-agent histories) and via a shared context when needed. Each agent in the crew has its own role, goal, and “backstory” (initial prompt) (What is crewAI? | IBM), and will maintain a dialogue (sequence of messages) as it works on its tasks. Agents communicate by sending messages to each other which are routed through the framework’s controller (the “manager agent” or orchestrator) (What is crewAI? | IBM) (What is crewAI? | IBM). This means there is a global chat transcript of sorts, containing the dialogue among agents (and possibly with the user, if the user is represented as an agent). CrewAI is built so that these communications are explicit – one agent can “ask” another for help, and that becomes part of the interaction record (What is crewAI? | IBM) (What is crewAI? | IBM). From a state perspective, CrewAI thus has multiple threads of conversation (one per agent, and an overarching one that ties them together). It also introduces a hierarchical control: typically a Manager agent oversees the others, assigns subtasks, and collates results (What is crewAI? | IBM) (What is crewAI? | IBM). This manager maintains the state of task assignments (which agent is doing what, what’s completed). The CrewAI design also mentions processes and tasks as components – essentially, a process is a high-level job that might require multiple agents and tools, broken into tasks that agents take on (What is crewAI? | IBM).

In terms of memory durability, CrewAI doesn’t inherently persist conversation beyond the session (unless you add vector stores or logging), but it does maintain a memory during the multi-agent session so agents can refer to what each other said. This is key for agents to collaborate: e.g., the Researcher agent can inform the Writer agent of findings via a message. CrewAI ensures those messages are in context when each agent generates its next response.

Tool Usage: CrewAI agents can use any LangChain tools just like single agents do (What is crewAI? | IBM). Additionally, CrewAI provides a toolkit of built-in tools and integrates with the LangChain tool library. Each agent can be configured with a subset of tools relevant to its role. For example, a “Coder” agent might have a terminal tool and a Python execution tool, while a “DataCollector” agent might have a web search tool. CrewAI’s framework likely handles execution of these tools similar to LangChain’s agent loop: the agent produces an action and the framework executes it and feeds back the observation. The difference in CrewAI is that after one agent finishes, another agent can react to that output.

The communication among agents in CrewAI could be seen as a form of tool itself – one agent’s message to another is like using a “communication tool” that results in the second agent receiving new input. In fact, crewAI’s delegation mechanism effectively treats other agents as helpers that can be invoked. This requires the system to schedule agent runs: presumably, CrewAI might cycle through agents or trigger them when addressed by name in a message.

Inter-agent Messaging: This is CrewAI’s forte. It provides inherent mechanisms for agents to ask each other questions, delegate subtasks, and share intermediate results (What is crewAI? | IBM) (What is crewAI? | IBM). The crew paradigm means every agent is aware it’s part of a team. They are given a protocol for how to communicate (likely via a special format or just natural language messages prefixed with the agent’s name). The framework handles routing: if Agent A “says” something that is directed to Agent B, the system captures that and feeds it into Agent B’s input on its next turn. CrewAI basically implements a multi-agent conversation loop on top of LangChain. This is similar to what some research (like AutoGPT with multiple agents, or Camel agents) attempted, but CrewAI formalized it with roles and manager oversight. The communication can be free-form or structured. CrewAI literature suggests it encourages a human-like collaboration, where agents might brainstorm together or divide work (What is crewAI? | IBM) (What is crewAI? | IBM).

Strengths: CrewAI’s strength is in multi-agent collaboration out-of-the-box. It abstracts the complexity of managing multiple LLMs and their dialogues. For a user who has a scenario where different expertise or perspectives are needed, CrewAI provides a ready paradigm (e.g., an agent “crew” solving a complex problem might have a Strategist, an Executor, a Validator, etc.). By giving agents defined roles and the ability to talk to each other, CrewAI can produce richer and more diverse problem-solving behavior than a single agent alone. It also naturally parallelizes some work – agents can work semi-independently on sub-tasks (though in practice, true parallel execution might be limited by computing resources). The hierarchical manager concept is a clever solution to avoid chaos: it’s like having a project manager that keeps the AI agents on track, which is something early multi-agent experiments lacked (often resulting in agents looping or chatting aimlessly). CrewAI also integrates with existing tools and has monitoring hooks (for observability, one can integrate with logging/metric tools to watch the agents) (What is crewAI? | IBM). Real-world tasks like complex planning or coding could benefit: one agent can generate code, another reviews it – all automated.

Limitations: Orchestrating multiple agents is resource-intensive – each agent is an LLM invocation, so 5 agents collaborating might use 5× the tokens of a single agent iteration. This can be slow and costly. There’s also the challenge of coherence: multiple agents can sometimes lead to confusion or contradictions if not managed well. CrewAI’s reliance on prompt-engineered roles means a lot hinges on how well those roles are defined (garbage in, garbage out). Ensuring the agents actually complement rather than derail each other is non-trivial; it might require tuning prompts or adding rules (like “only one agent speaks at a time” or timeouts for idle agents). Another limitation is debugging complexity – when something goes wrong, you have to sift through a multi-agent dialogue to figure out which agent or which interaction was the issue. As a relatively new framework, CrewAI might also have less tooling maturity around these issues compared to older single-agent frameworks. Lastly, CrewAI by itself doesn’t magically integrate external data – you still must give agents tools or context; without something like MCP, you’d use LangChain connectors or vector stores as usual. So it inherits some limitations of integration there.

Comparison to MCP: CrewAI is addressing a different layer: coordination of multiple reasoning entities. MCP doesn’t handle multi-LLM orchestration internally – it assumes one primary “assistant” (host model) that uses tools. However, one could imagine using MCP to implement CrewAI’s functions. For example, each CrewAI agent could run as an MCP server (with a sampling capability, meaning it can generate responses via the host LLM, or each agent could even be a separate model behind an MCP interface). Then the host (manager) could route messages between them via MCP calls. This would be an ambitious but conceptually possible integration, essentially using MCP as a message bus for agents. In current practice, though, CrewAI with MCP would more straightforwardly mean CrewAI agents use MCP for accessing external info. So, if a CrewAI agent needs to query something, instead of using a LangChain tool, you use an MCP tool call. The complementarity is: CrewAI manages who talks when and what they say to each other, MCP standardizes how they fetch or act on external data.

In simpler terms, MCP vs CrewAI: MCP is a standard interface for tools, CrewAI is a framework for multiple AI agents. CrewAI is not directly interoperable with other systems (except those using LangChain under the hood), whereas MCP aims for any system interoperability. If a future version of CrewAI adopted MCP, it could become easier to plug in any data source to any agent in the crew. As of now, a senior engineer might see CrewAI as a way to harness multiple models together for a task, and MCP as a way to connect to external systems; both are relevant to advanced AI orchestration, but at different layers (coordination vs integration).

OpenDevin

Overview: OpenDevin (recently evolving into “OpenHands” per some sources (Carlos E. Perez on X: "1/n OpenDevin's Radical Approach to Agentic ...)) is an open-source platform focused on autonomous software development agents. It provides an environment where AI agents act as software developers – writing code, running code, using a CLI, browsing documentation, etc., to build or debug software. The core of OpenDevin includes a sandboxed OS and browser environment for agents (Harnessing OpenDevin: A Versatile Platform for AI-Driven Software Development and Multi-Agent Collaboration | by QvickRead | Medium), an event-driven interaction mechanism, and specialized agents (or agent modes) for coding tasks (Harnessing OpenDevin: A Versatile Platform for AI-Driven Software Development and Multi-Agent Collaboration | by QvickRead | Medium) (Harnessing OpenDevin: A Versatile Platform for AI-Driven Software Development and Multi-Agent Collaboration | by QvickRead | Medium). It was introduced via research that positioned it as a “generalist coder agent” framework, allowing multiple specialized agents to collaborate on software engineering tasks (Harnessing OpenDevin: A Versatile Platform for AI-Driven Software Development and Multi-Agent Collaboration | by QvickRead | Medium).

MCP Compatibility: OpenDevin’s focus is narrower (software dev), and it includes its own tools for code and web. It does not natively support MCP, and since it is quite domain-specific, MCP integration hasn’t been a primary discussion. However, like others, it could potentially use MCP to extend beyond its default tools – for example, if an OpenDevin agent needed to access a database or a Slack channel as part of a dev task, an MCP server could provide that. Achieving this would require adding an MCP client capability to OpenDevin’s agent controller. Given OpenDevin’s architecture, it might treat MCP calls as just another type of action in its event stream. There’s no evidence of such integration yet in documentation. In general, OpenDevin is more akin to a contained “AI developer workstation,” whereas MCP is about connecting to external systems. So the overlap is limited. If one were to combine them, it might be to allow an OpenDevin agent to easily leverage external knowledge (via MCP resources) or coordinate with other agents outside the coding domain. For now, we can consider OpenDevin largely separate from MCP, with possible integration only through custom development.

Conversation State & Workflow: OpenDevin’s interaction model is more event-driven and modal than a linear conversation. The “conversation” in OpenDevin is effectively the sequence of events and states in the development environment: code changes, execution outputs, error messages, etc., which the agent observes and responds to (Harnessing OpenDevin: A Versatile Platform for AI-Driven Software Development and Multi-Agent Collaboration | by QvickRead | Medium). The platform implements a flexible event stream connecting the UI, the agents, and the environment (Harnessing OpenDevin: A Versatile Platform for AI-Driven Software Development and Multi-Agent Collaboration | by QvickRead | Medium). This means the agent’s state includes things like the current file contents, the result of the last command run, and so on. It’s less about natural language dialogue and more about the state of the project and the environment. The agent does still likely use an LLM for reasoning (e.g., writing code or interpreting errors), but it prompts the LLM with a context that includes these events (like a running log of what’s happened, similar to a REPL transcript).

OpenDevin ensures the environment is sandboxed – an agent can run code (like tests) safely, and browse the web in a controlled way (Harnessing OpenDevin: A Versatile Platform for AI-Driven Software Development and Multi-Agent Collaboration | by QvickRead | Medium). The conversation state is thus multi-modal: it has code text, terminal text, possibly browser text, etc. The platform probably maintains an internal memory structure capturing all relevant info for the task. When multiple agents are involved (OpenDevin mentions multi-agent delegation for specialized tasks (Harnessing OpenDevin: A Versatile Platform for AI-Driven Software Development and Multi-Agent Collaboration | by QvickRead | Medium)), each agent might focus on a certain aspect (one agent writes code, another agent plans the project structure, etc.). Coordination in OpenDevin is possibly orchestrated by the system or a main agent deciding which sub-agent should handle which event (the details are a bit scarce in summary). There might not be a concept of “speaking” to each other like in CrewAI; instead it could be passing artifacts (one agent writes a function, another agent reviews it).

Tool Usage: OpenDevin comes with built-in tools/environments for the software domain:

It has a code editor interface (likely the agent can open/edit files).
A way to execute code in a sandbox (e.g., run a Python script, capture output and errors).
A web browser the agent can control to search docs or forums (the reference to “browsing the web” suggests integration with something like a headless browser or an API to fetch pages) (Harnessing OpenDevin: A Versatile Platform for AI-Driven Software Development and Multi-Agent Collaboration | by QvickRead | Medium).
Possibly version control (since long-term dev might involve git actions).

These aren’t called “tools” in the MCP sense, but they serve a similar role: actions the agent can perform beyond text generation. OpenDevin likely orchestrates these through either function calls in code or a conversation interface where the agent can issue commands like “open file X” or “run tests”. In effect, the “tools” are tightly integrated into the environment and not generic JSON-RPC calls.

This vertical integration is tailored to coding tasks – which is great for that domain because it can have real-time code execution and fine control. The limitation is that outside of coding, these tools aren’t useful. If a coding agent needed to do something like send an email (maybe as part of deployment or notification), OpenDevin would need a new capability. That’s where something like MCP could theoretically extend it, but again, not a current focus.

Inter-agent Messaging: OpenDevin supports multi-agent delegation, meaning multiple agents can work in parallel or pipeline (Harnessing OpenDevin: A Versatile Platform for AI-Driven Software Development and Multi-Agent Collaboration | by QvickRead | Medium). The nature of their collaboration might be more sequentially organized than free-form chat. For example, they might implement a Manager/Worker pattern: a Planner agent breaks the task into steps, and a Coder agent executes them, then maybe a Tester agent runs tests. They might communicate via the shared environment rather than direct messages (e.g., the Planner writes a task list into a pseudo “spec file” that the Coder reads). The specific inter-agent communication mechanism isn’t detailed, but given the design, it could be more implicit – each agent just reacts to the current project state. For instance, when the Coder agent finishes writing code, it might set a flag or simply commit code to repository; the Tester agent sees new code is present and runs tests. This is akin to a pipeline where each stage triggers the next.

However, OpenDevin could also allow explicit messaging through a console or notes (some agent leaving a message like “I encountered an error I can’t fix, can someone help?”). Since it’s an open platform, one could script agents to talk in natural language as well, but the emphasis seems to be on performing tasks rather than chatting.

Strengths: OpenDevin (OpenHands) is very strong in the software engineering domain. It basically provides an AI with a computer to use. This is extremely powerful for tasks like automated debugging, code refactoring, or even generating entire small programs. It goes beyond what a typical LangChain agent would do by giving the agent the ability to run code and see what happens – a form of self-feedback loop. This execution grounding can reduce hallucination: the agent can test its outputs and correct errors (a concept also seen in frameworks like Microsoft’s AutoGen (with an execution agent) and others, but OpenDevin dedicates a platform to it). Also, by being event-driven and using real tools (shell, browser), it mirrors how a human programmer works, which may structure the agent’s process more effectively than a pure prompt.

Another strength is multi-agent specialization: coding involves many sub-skills (planning, writing, debugging, documenting), and OpenDevin acknowledges that by allowing different agents to handle them, potentially leading to better outcomes (akin to having a team of junior developer + senior architect + QA). The environment and benchmarks (they mention 15 benchmarks to evaluate agents on tasks (Harnessing OpenDevin: A Versatile Platform for AI-Driven Software Development and Multi-Agent Collaboration | by QvickRead | Medium)) provide a way to measure progress, which is great for research/iterative improvement.

Limitations: The specialization to coding tasks means OpenDevin is not a general solution for all agentic problems. It’s heavy-weight: running a sandbox OS and browser for the agent means it’s not trivial to deploy as a lightweight microservice. It likely needs substantial compute (especially if agents are large models). The complexity of the environment (threads of events, sandbox, etc.) also means the failure modes can be complicated. For example, the agent might get stuck on an environment error that it wasn’t trained to handle. Also, building multiple agents that coordinate is hard – the delegation logic might not always work smoothly (e.g., one agent might not know when to hand off to another without some central orchestrator, which OpenDevin presumably has to implement).

In terms of conversation or tool generality, it’s limited: outside the coding context, you wouldn’t use OpenDevin. Even within coding, if the task requires external human input or creative leaps beyond coding (like deciding product requirements), the closed world of OpenDevin’s environment could be a constraint. It also might have a steep learning curve to set up and use.

Comparison to MCP: OpenDevin is a vertically integrated solution, whereas MCP is a horizontal integration layer. In a full AI development assistant product, one could actually imagine combining them: use OpenDevin internally for anything related to coding, but use MCP to interface with other company systems. For example, if after coding, you want the agent to create a JIRA ticket or update a database, an MCP server could handle those external interactions. OpenDevin doesn’t aim to be an integration framework for arbitrary APIs – it’s mostly about local environment actions. Therefore, MCP could complement OpenDevin by providing standardized access to things outside the dev sandbox.

However, in terms of conceptual overlap, there’s not much: OpenDevin’s “tools” are custom and internal. If one wanted to make OpenDevin more interoperable (say allow other AI systems to use its environment), an MCP interface could be layered on it – e.g., an MCP server representing the OpenDevin environment, exposing tools like open_file, run_code to outside clients. That could be interesting: it would let any MCP-compliant agent leverage OpenDevin’s capabilities. But that’s a hypothetical advanced use case.

In summary, OpenDevin is a domain-specific agent platform with deep support for coding tasks. It doesn’t inherently solve general integration, and it’s not directly comparable to frameworks like LangGraph or CrewAI which are more domain-agnostic. MCP doesn’t compete with OpenDevin; rather, if anything, MCP could be an enabler to plug OpenDevin’s capabilities into broader workflows or vice versa (allow coding agents to access external data through MCP). For now, one would choose OpenDevin if the goal is autonomous coding; one would choose MCP if the goal is to connect any agent to various data sources.

AutoGen (Microsoft)

Overview: AutoGen is an open-source framework from Microsoft Research that facilitates building applications with multiple LLM agents conversing with each other and with humans/tools (AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation - Microsoft Research). It provides abstractions for defining different types of agents (LLM-backed agents, tool agents, human agents) and orchestrating their dialogue in a conversation loop. The hallmark of AutoGen is enabling multi-agent conversations – for example, an “Assistant” and a “User” agent (both simulated by LLMs) chatting to solve a problem, or an “Engineer” and a “Critic” agent pair working on code. AutoGen emphasizes customizable conversation patterns, meaning you can script how and when agents interact, mix natural language messages with structured tool calls, etc. (AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation - Microsoft Research). It’s essentially a high-level programming framework for agent interactions, with the conversation as the medium.

MCP Compatibility: AutoGen, being developed in 2023 before MCP, doesn’t include MCP support natively. It has its own way of integrating tools – typically by representing a tool as a special type of agent or by giving an agent an ability to call Python functions. However, one could use MCP within AutoGen by writing a custom tool agent that acts as an MCP client. For instance, you could create an AutoGen agent whose “job” is to handle any function call requests by forwarding them to an MCP server. Another approach: incorporate an MCP server call as part of an agent’s logic (AutoGen allows mixing code and dialogue, so an agent might on its turn execute some Python – that Python code could call an MCP client library to get data, then return it as the agent’s message).

This is certainly feasible because AutoGen is basically Python code orchestrating LLM calls; adding an MCP client call is straightforward if needed. The bigger question is how necessary it is: since AutoGen already can directly integrate Python functions (you might not need MCP if you’re comfortable writing a direct integration). But if we imagine a future where many tools are only available as MCP servers, then an AutoGen agent would benefit from speaking MCP to access them. So, while not out-of-the-box, AutoGen could be extended to leverage MCP without much conceptual conflict.

One compatibility consideration: AutoGen tends to work at the level of messages (Agent says X, other Agent replies Y). MCP is more RPC. So the integration agent might translate a message like “: please get_weather for NYC” into an MCP call and then return the result message. This is analogous to how one might do with LangChain.

Conversation State: AutoGen’s paradigm is literally a conversation among agents (and optional human). The state is the chat transcript plus any additional memory the developer maintains. AutoGen likely stores each turn’s messages and provides them to agents as context for the next turn. Each agent can have a role profile (system prompt) that stays constant, and then the conversation history builds up with each exchange. Because it’s conversation-focused, maintaining relevant history is crucial – AutoGen probably allows specifying how much history to include or when to summarize, though those details would be up to the developer to implement (the framework gives you control, but you must decide the strategy).

Agents in AutoGen are conversable by design (AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation - Microsoft Research), meaning every agent is essentially an LLM that will output a message given the dialogue so far. They operate in various modes – some might be fully automated, others might wait for a human input, etc. The conversation state is naturally the shared knowledge among agents. If one agent learns something (via a tool or reasoning), it has to speak it for others to know (unless you cheat by sharing state via code). Typically, in published AutoGen examples, they show dialogues like:

UserAgent: What is 2+2?
AssistantAgent: I think we should calculate that.
ToolAgent (Calculator): The result of 2+2 is 4.
AssistantAgent: The answer is 4.

This is a contrived example, but it demonstrates the pattern: even tool usage is mediated as messages (the tool might be an agent that responds with an answer). The state (in this case, the fact that 4 is the result) gets communicated through the conversation.

AutoGen doesn’t impose a fixed conversation pattern – you can compose them. For instance, you might have an outer conversation between a user and a system where the system internally spins up a conversation between sub-agents to get an answer. It’s flexible, but at the core, everything is happening via LLM messages that are by default in natural language.

Tool Usage: In AutoGen, tools can be integrated in a few ways. The framework allows defining a special kind of agent that instead of being an LLM, is essentially a function executor. This agent listens for certain “calls” in the conversation and returns results. The lines from Microsoft’s docs hint: “agents… integrate LLMs, tools, and humans via automated agent chat.” (Multi-agent Conversation Framework | AutoGen 0.2). Likely, they provide something like a FunctionCallingAgent that maps a request to a Python function. Alternatively, one agent (LLM) could be dedicated to a tool (like an agent whose role is “I am a Python REPL, give me code and I’ll execute it”). This approach was demonstrated in some research (having a “Python” agent separate from the main reasoning agent).

AutoGen thus supports tools but does so by weaving them into the conversation format rather than calling an API directly from a prompt loop (though internally it is API calls). This design makes it easy to follow the sequence of an agent’s thought process – it’s all visible as if agents were talking. The downside is efficiency; it might be easier to just call a function than to simulate an agent calling it.

Inter-agent Messaging: This is the core of AutoGen: agents conversing. The framework likely handles message passing, ensuring each agent gets the right context for their turn. It’s essentially implementing a turn-based scheduler for the agents. For instance, if you have 3 agents, you may define an order or a condition for who speaks when. AutoGen “conversation patterns” can enforce that (like a round-robin chat or a specific sequence). One can program termination conditions (stop when a certain agent produces a final answer, or after N turns, etc.).

Because it’s conversation-centric, adding a new agent in AutoGen is straightforward (just include it in the loop, with some role) but controlling complexity is challenging if too many agents are present (similar to CrewAI’s complexity of debugging multi-agent chats).

AutoGen explicitly supports human in the loop as an agent as well. That means the conversation state might include times when a human user or moderator agent can intervene or provide feedback. This is powerful for workflows like triaging: agents work, then ask a human if uncertain, then continue.

Strengths: AutoGen’s strengths are flexibility and simplicity in multi-agent orchestration. It provides a clean abstraction where each agent is just a function that takes a list of messages and returns the next message (microsoft/autogen: A programming framework for agentic AI ... - GitHub), and the developer can wire these functions together in creative ways. Some pilot applications have shown that two or three agents can significantly outperform a single agent on tasks like math or code generation by having one check the other (AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation - Microsoft Research). AutoGen makes it relatively easy to set up those interactions without building a whole framework from scratch. It also naturally handles conversation with humans and tools in the same paradigm, which is useful if you want a mix of automation and control.

Another strength is it being open-source and apparently used in research contexts (the COLM 2024 paper (AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation - Microsoft Research) won a best paper in an LLM Agents workshop). This means it has some credibility and likely an active community exploring patterns. It’s more general than CrewAI’s domain-specific roles or OpenDevin’s coding focus – you can use AutoGen for many purposes (the paper lists domains from math and coding to supply-chain optimization and even entertainment) (AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation - Microsoft Research). Essentially, if you can break a problem into roles or subtasks that agents can converse about, AutoGen can help implement that.

Limitations: While flexible, AutoGen may require careful prompting to ensure agents are effective and don’t fall into unproductive loops (like agreeing with each other wrongly). Without a central controller agent (unless you make one), it’s possible for conversations to stagnate or bounce around. The developer might need to hard-code some pattern of interaction that ensures convergence. For example, one might decide “Agent A will always propose a solution, Agent B will always critique, then A will revise once, then we stop” – designing that protocol is on the developer. If not well-designed, agents could also get adversarial or too deferential. Additionally, the overhead of multiple agents still exists. AutoGen presumably provides means to optimize context (like not every message needs to be passed in full every time if agent roles only need certain info), but context length can balloon.

AutoGen’s design, focusing on natural language between agents, might face challenges with factuality or precision – two agents talking doesn’t guarantee they reach the truth if both are misled. For example, for math, they had an agent use a tool to actually calculate to ground the discussion. Without such grounding, it can be two large models confidently agreeing on a wrong answer (the “echo chamber” risk).

Comparison to MCP: AutoGen is more comparable to CrewAI in that both manage multi-agent interactions, but AutoGen is more general (not tied to LangChain) and uses conversation as the backbone. MCP, however, is orthogonal – it could be used within an AutoGen agent to do something, but it doesn’t help orchestrate multiple agents by itself. Where MCP shines (connecting to external tools/data), AutoGen would have to rely on either built-in tool calls or incorporate MCP.

One could imagine a synergy: for instance, use AutoGen to handle a conversation between a “Questioner” agent and a “Researcher” agent, where the Researcher agent, upon receiving a query, uses MCP to fetch information (via tools/resources) and then responds in the conversation with the info. The final result is then formulated by the Questioner agent for the user. Here, AutoGen manages the Q&A interplay, MCP ensures the Researcher gets real data instead of just what’s in model memory.

In terms of building blocks, AutoGen vs MCP can be seen as:

AutoGen: a programming model for multi-agent processes (with direct support for making those agents do tool calls too).
MCP: a connectivity model for hooking up any tool or data source to an agent.

AutoGen could integrate with MCP similarly to how LangChain or others would: by treating MCP servers as callable functions or creating an interface for agents to call MCP. The frameworks like AutoGen could benefit from MCP as it would expand the ease of connecting to new tools (instead of writing custom function for each API, just call the MCP tool). Conversely, an MCP-based system could use AutoGen’s approach if one wanted to have multiple MCP clients (like multiple servers) that need coordination via separate AI agents – though typically a single host orchestrates multiple servers without needing them to be separate “agents” in conversation.

Strengths vs Limitations Recap (for comparison): Summarizing the frameworks in key aspects:

MCP: Strength – interoperability and standardization (acts as a universal adapter) (What is Model Context Protocol (MCP): Explained - Composio) (What is Model Context Protocol (MCP): Explained - Composio); allows composable addition of tools (Architecture – Model Context Protocol Specification). Limitation – doesn’t solve planning or decision logic by itself; it’s low-level (needs to be integrated into an agent system to be useful). Conversation state is host-managed and not part of MCP itself (beyond context exchange).
LangGraph: Strength – fine control, complex workflow support (persistent state, branching) (The Agentic Imperative Series Part 3 — LangChain & LangGraph: Building Dynamic Agentic Workflows | by Adnan Masood, PhD. | Mar, 2025 | Medium) (The Agentic Imperative Series Part 3 — LangChain & LangGraph: Building Dynamic Agentic Workflows | by Adnan Masood, PhD. | Mar, 2025 | Medium); ideal for reliability in production. Limitation – more complex to design, tightly coupled to LangChain ecosystem for tools.
CrewAI: Strength – multi-agent teams with role specialization, allowing collaborative problem solving (What is crewAI? | IBM) (What is crewAI? | IBM); relatively high-level abstraction of agents as team members. Limitation – complexity in debugging, heavy resource usage, currently tied to LangChain (less standardized integration).
OpenDevin: Strength – deep integration for coding (execute code, test, iterate) enabling self-correcting code agents (Harnessing OpenDevin: A Versatile Platform for AI-Driven Software Development and Multi-Agent Collaboration | by QvickRead | Medium) (Harnessing OpenDevin: A Versatile Platform for AI-Driven Software Development and Multi-Agent Collaboration | by QvickRead | Medium); multi-agent possible for complex dev tasks. Limitation – narrow domain, heavy to run, not generic for arbitrary tasks or integrations.
AutoGen: Strength – flexible multi-agent conversations, good for creating dynamic agent interactions and combining strengths of different agents (AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation - Microsoft Research) (AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation - Microsoft Research); direct support for tool and human integration in dialogues. Limitation – free-form interactions can be unpredictable, still requires careful design to ensure convergence; no built-in standard for external integrations (each tool integrated manually, though easier than scratch).

Comparative Summary

To provide a quick comparison, the following table highlights how each framework/system relates to MCP and handles key aspects:

Framework / System	MCP Compatibility & Role in Ecosystem	Conversation State & Memory	Tool Use & External Integration	Inter-agent Messaging & Coordination	Notable Strengths	Key Limitations
MCP (Anthropic)	Interoperability layer, not a full framework. Can be adopted by any host or tool. Provides a standard for connecting agents to data/tools (acts as “plug interface”) (What is Model Context Protocol (MCP): Explained - Composio) (What is Model Context Protocol (MCP): Explained - Composio). Complementary to others (can supply tools to LangGraph, CrewAI, etc.).	Host-managed state; each server sees only what it needs (Architecture – Model Context Protocol Specification). MCP itself doesn’t track dialogue – it’s up to the host. Ensures isolation, so state sharing is deliberate via host.	Standardizes tool/resource access via JSON-RPC. Tools are declared with schemas and called uniformly (Tools – Model Context Protocol Specification) (Tools – Model Context Protocol Specification). High compatibility: any data source can be an MCP server, usable by any compliant agent.	N/A (MCP alone doesn’t coordinate multiple agents) – coordination is done by the host or frameworks on top. MCP does allow a server to request LLM completions (enabling nested agentic calls) (Anthropic Publishes Model Context Protocol Specification for LLM App Integration - InfoQ), but it’s a low-level feature.	Interoperability, composability: one protocol for all integrations (What is Model Context Protocol (MCP): Explained - Composio). Encourages modular “agent plugin” development. Robust schema ensures reliability.	Not an agent framework itself: no planning or multi-agent logic built-in. Depends on adoption – needs clients/servers implemented.
LangGraph	Not inherently MCP-based, but can integrate MCP by treating MCP servers as external tools within a graph ([LangGraph + MCP + Ollama: The Key To Powerful Agentic AI	by Gao Dalie (高達烈)	Data Science Collective	Mar, 2025	Medium](https://medium.com/data-science-collective/langgraph-mcp-ollama-the-key-to-powerful-agentic-ai-e1881f43cf63#:~:text=Said%20John%20Rush)). LangGraph’s focus is internal logic; it can call out to MCP for standardized integrations.	Stateful: Maintains variables & dialogue over multiple steps ([The Agentic Imperative Series Part 3 — LangChain & LangGraph: Building Dynamic Agentic Workflows
CrewAI	No native MCP support yet. Potential to use MCP tools by plugging them in as LangChain tools, but not built-in. Primarily focuses on multi-agent orchestration on top of LangChain, so integration relies on LangChain’s methods.	Multi-agent state: Tracks separate dialogues per agent plus a shared context via a manager ([What is crewAI?	IBM](https://www.ibm.com/think/topics/crew-ai#:~:text=crewAI%27s%20inherent%20delegation%20and%20communication,delegate%20work%20or%20ask%20questions)). Each agent has memory of its exchanges; manager coordinates global task state. Uses roles/backstories to initialize agent state ([What is crewAI?	IBM](https://www.ibm.com/think/topics/crew-ai#:~:text=match%20at%20L207%20The%20agent%E2%80%99s,attributes%20role%2C%20goal%20and%20backstory)).	Inherits LangChain’s tool integration – can use any tool from LC (search, calculators, etc.) ([What is crewAI?	IBM](https://www.ibm.com/think/topics/crew-ai#:~:text=match%20at%20L226%20can%20leverage,crewAI%20Toolkit%20and%20LangChain%20Tools)). Agents invoke tools through the manager agent. No unique standard – integration is manual per tool (MCP could be added to provide more tools uniformly, but not native).
OpenDevin	No direct MCP integration. Focused on coding domain with built-in environment. Could integrate MCP if needed to access external non-dev tools, but generally self-contained.	Event-based state: tracks code files, terminal outputs, browser pages, etc., rather than a linear convo ([Harnessing OpenDevin: A Versatile Platform for AI-Driven Software Development and Multi-Agent Collaboration	by QvickRead	Medium](https://readqvick.medium.com/harnessing-opendevin-a-versatile-platform-for-ai-driven-software-development-and-multi-agent-1a0d83184e5c#:~:text=InteractionMechanism%3A%20Platform%20features%20a%20flexible,work%20on%20specialized%20tasks%20simultaneously)). Agents “see” the evolving software state. Multi-agent: shared project state is the medium for collaboration.	Built-in dev tools: file editor, code executor, web browser ([Harnessing OpenDevin: A Versatile Platform for AI-Driven Software Development and Multi-Agent Collaboration	by QvickRead
AutoGen	No native MCP, but straightforward to integrate by creating tool agents or making agents call MCP APIs. AutoGen itself handles conversation; developers can insert MCP calls in agent logic as needed.	Conversation transcript is core state (AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation - Microsoft Research). All agents share a chat history (or layered histories for sub-conversations). Each agent has its own system prompt (role) and sees the evolving dialogue. State can include intermediate results if shared via messages.	Tools can be represented as function-call agents. AutoGen supports mixing code execution into conversation flow ([Multi-agent Conversation Framework	AutoGen 0.2](https://microsoft.github.io/autogen/0.2/docs/Use-Cases/agent_chat/#:~:text=Multi,humans%20via%20automated%20agent%20chat)). However, each new tool requires coding it in (no universal protocol like MCP by default). If integrated with MCP, one agent could serve as a bridge to many tools.	Flexible multi-agent chat orchestration: you define which agents speak when. Agents communicate in natural language by default (AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation - Microsoft Research). Patterns can be 2-agent (QA pair) or more complex (multiple specialists debating). Supports human agent involvement too.	Versatility in agent interactions: easy to set up different “teams” of agents for different tasks (AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation - Microsoft Research). Can leverage the synergy of multiple perspectives (e.g., solver & checker agents). Developer can script conversation patterns for reliability.

Table: Comparison of MCP and various agentic frameworks on compatibility, state handling, tool integration, multi-agent coordination, strengths and limitations. Sources: Anthropic MCP spec/docs (Architecture – Model Context Protocol Specification) (What is Model Context Protocol (MCP): Explained - Composio), LangGraph info (The Agentic Imperative Series Part 3 — LangChain & LangGraph: Building Dynamic Agentic Workflows | by Adnan Masood, PhD. | Mar, 2025 | Medium) (The Agentic Imperative Series Part 3 — LangChain & LangGraph: Building Dynamic Agentic Workflows | by Adnan Masood, PhD. | Mar, 2025 | Medium), CrewAI description (What is crewAI? | IBM) (What is crewAI? | IBM), OpenDevin summary (Harnessing OpenDevin: A Versatile Platform for AI-Driven Software Development and Multi-Agent Collaboration | by QvickRead | Medium) (Harnessing OpenDevin: A Versatile Platform for AI-Driven Software Development and Multi-Agent Collaboration | by QvickRead | Medium), AutoGen paper (AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation - Microsoft Research) (AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation - Microsoft Research).

Conclusion

The Model-Context-Protocol brings a crucial piece to the puzzle of building advanced agentic AI systems: a common language for tools and data. By separating the protocol (MCP) from the policy (how agents reason and coordinate), we gain the freedom to mix and match. A senior engineer can now imagine designing an AI assistant where the high-level reasoning might be handled by a LangGraph workflow (for reliability) or a CrewAI team of agents (for breadth of skills), while all the low-level integrations – whether accessing databases, files, or external APIs – are handled through MCP servers that can be reused across projects. This not only reduces development effort but also fosters an ecosystem where improvements in one area (say, a better MCP server for web browsing) benefit many different agent frameworks uniformly.

Each of the compared frameworks has a unique focus: LangGraph on structured control, CrewAI on multi-agent collaboration, OpenDevin on code-centric autonomy, AutoGen on conversational multi-agent patterns. MCP does not replace any of these – rather, it can enhance them by providing the “USB-C” connectivity for any external capability (What is Model Context Protocol (MCP): Explained - Composio). Conversely, these frameworks can complement MCP by providing higher-level orchestration that MCP alone doesn’t dictate (MCP assumes the existence of a host strategy to decide when to call what).

As of early 2025, MCP is still gaining adoption, but it has significant mindshare as a potential standard (polls show a plurality of developers believe MCP could become the future standard for agent-tool interactions (LangGraph + MCP + Ollama: The Key To Powerful Agentic AI | by Gao Dalie (高達烈) | Data Science Collective | Mar, 2025 | Medium)). Anthropic’s open-sourcing of the spec and SDKs (Introducing the Model Context Protocol \ Anthropic) (Anthropic Publishes Model Context Protocol Specification for LLM App Integration - InfoQ), and community enthusiasm, suggest a growing ecosystem of MCP-compatible tools. Ensuring interoperability was a main design goal – and if successful, we might see frameworks like LangChain/LangGraph, Semantic Kernel, etc., natively support MCP in the future, much like IDEs support Language Server Protocol.

In designing MCP-compatible agentic workflows, the key is to leverage MCP for what it’s good at (uniform tool access, security isolation, composability (What is Model Context Protocol (MCP): Explained - Composio)) while using an appropriate agent orchestration approach for the decision-making. A complex enterprise AI solution might use multiple layers: e.g., an AutoGen multi-agent brainstorm to decide a plan, then a LangGraph deterministic graph to execute the plan step by step using MCP tools at each step, and CrewAI-like delegation for any parallelizable sub-tasks, with OpenDevin employed specifically if code needs to be written. This kind of hybrid is complex but illustrates the ultimate promise: an AI agent ecosystem where components interoperate seamlessly. MCP is the enabling standard that could make such an ecosystem cohesive rather than fragmented.

In summary, MCP is a foundational technology that, combined with the strengths of agentic frameworks and careful orchestration design, paves the way for more powerful, extensible, and manageable AI agents. It addresses the integration pain point, allowing researchers and developers to focus on improving reasoning strategies and cooperation methods, knowing that any new tool or data source they need can be “plugged in” rather than custom-wired each time. As the agentic AI field matures, we can expect MCP (or protocols inspired by it) to play a central role in achieving interoperability and scalability across diverse AI systems (Anthropic Publishes Model Context Protocol Specification for LLM App Integration - InfoQ) (What is Model Context Protocol (MCP): Explained - Composio).

Sources:

Anthropic, “Introducing the Model Context Protocol” – Announcement of MCP and its goals (Introducing the Model Context Protocol \ Anthropic) (Introducing the Model Context Protocol \ Anthropic).
Anthropic, Model Context Protocol Specification (2024-11-05) – Official spec defining MCP architecture and primitives (Architecture – Model Context Protocol Specification) (Anthropic Publishes Model Context Protocol Specification for LLM App Integration - InfoQ).
Anthropic, Model Context Protocol – Quickstart Documentation – Examples of building and using MCP servers (Anthropic Publishes Model Context Protocol Specification for LLM App Integration - InfoQ).
InfoQ, “Anthropic Publishes Model Context Protocol Spec for LLM Integration” – Summary of MCP and its primitives (Anthropic Publishes Model Context Protocol Specification for LLM App Integration - InfoQ) (Anthropic Publishes Model Context Protocol Specification for LLM App Integration - InfoQ).
Composio.dev, “What is MCP: Explained” – Explanation of MCP’s purpose and benefits (What is Model Context Protocol (MCP): Explained - Composio) (What is Model Context Protocol (MCP): Explained - Composio).
Medium (Adnan Masood), “Agentic Imperative Part 3 – LangChain & LangGraph” – Discussion of LangGraph vs LangChain, with LangGraph features (The Agentic Imperative Series Part 3 — LangChain & LangGraph: Building Dynamic Agentic Workflows | by Adnan Masood, PhD. | Mar, 2025 | Medium) (The Agentic Imperative Series Part 3 — LangChain & LangGraph: Building Dynamic Agentic Workflows | by Adnan Masood, PhD. | Mar, 2025 | Medium).
IBM Blog, “What is crewAI?” – Overview of CrewAI framework and its multi-agent approach (What is crewAI? | IBM) (What is crewAI? | IBM).
Medium (QvickRead), “Harnessing OpenDevin” – TL;DR of OpenDevin’s architecture and capabilities for coding agents (Harnessing OpenDevin: A Versatile Platform for AI-Driven Software Development and Multi-Agent Collaboration | by QvickRead | Medium) (Harnessing OpenDevin: A Versatile Platform for AI-Driven Software Development and Multi-Agent Collaboration | by QvickRead | Medium).
Microsoft Research, “AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation” – Paper describing AutoGen framework (AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation - Microsoft Research) (AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation - Microsoft Research).
Medium (Gao Dalie), “LangGraph + MCP + Ollama: Powerful Agentic AI” – Tutorial on using LangGraph with MCP, indicating community interest in combining them (LangGraph + MCP + Ollama: The Key To Powerful Agentic AI | by Gao Dalie (高達烈) | Data Science Collective | Mar, 2025 | Medium) (LangGraph + MCP + Ollama: The Key To Powerful Agentic AI | by Gao Dalie (高達烈) | Data Science Collective | Mar, 2025 | Medium).

Skill Area / Competency	Essential (Must-Have)	Desirable (Nice-to-Have)	Bonus (Extra Credit)
MCP Protocol & Agentic Workflow Design	• Deep understanding of Anthropic’s MCP (JSON-RPC, capability negotiation, schema validation) and experience designing agentic workflows.	• Prior work integrating or prototyping with similar protocols (e.g., LangChain, AutoGen).	• Contributions to open standards or involvement in the MCP community.
GitHub Platform Integration & API Expertise	• Extensive experience with GitHub APIs (REST/GraphQL), GitHub Actions, and embedding into GitHub’s issue, milestone, and triage workflows.	• Proven track record building GitHub Apps and custom integrations with GitHub’s web UI.	• Prior contributions to GitHub’s ecosystem or recognized open source GitHub integrations.
System Architecture & Distributed Systems	• Proven experience designing and building scalable, distributed systems and microservices architectures with asynchronous messaging and orchestration.	• Hands-on background with cloud-native and container orchestration (e.g., Kubernetes) and event-driven systems.	• Experience with multi-agent system orchestration in production settings.
Software Engineering Best Practices & Agile Development	• Strong proficiency in test-driven development (TDD), CI/CD, Git flow, and agile methodologies, with a history of managing complex product cycles.	• Experience architecting “software factory” environments or automating full-cycle development (e.g., automated triaging, bug reports, roadmap management).	• Prior leadership as a product owner or engineering manager in fast-moving startups.
Programming Languages & Frameworks	• Expert-level proficiency in core languages (Python, TypeScript, and/or Node.js) that underpin orchestration and integration engines.	• Familiarity with modern web frameworks, and experience with AI orchestration frameworks (e.g., LangChain, AutoGen).	• Knowledge of functional or reactive programming paradigms; experience with GraphQL or serverless architectures.
AI/ML & LLM Integration	• Solid understanding of integrating large language models (LLMs) into production workflows and designing policy-driven agent systems.	• Hands-on experience with Anthropic’s Claude, OpenAI, or similar LLM systems; familiarity with agent orchestration in AI contexts.	• Research or publication background in AI agent design and open source contributions in the AI orchestration space.
Security & Compliance	• Experience designing secure, distributed systems with robust permission models, data isolation, and error handling for remote procedure calls.	• Familiarity with industry compliance frameworks and secure coding practices in multi-agent environments.	• Expertise in implementing pluggable policy packs or security policies in regulated industries.
UX / Product Interface Design (Headless Integration)	• Ability to design natural language interfaces and headless workflows that empower non-developers (e.g., product owners) to manage projects through GitHub’s UI.	• Experience in UX design or product design for developer tools and/or natural language UIs.	• Proven record of designing conversational interfaces or integrating chatbot-style interactions within enterprise platforms.
Leadership, Communication & Team Collaboration	• Demonstrated track record in leading technical projects, mentoring junior talent, and translating complex technical ideas for non-technical stakeholders.	• Experience managing or working within very small, cross-functional startup teams.	• Prior roles as a VP of Engineering, Principal Engineer, or Product Owner with transformative product leadership.

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