AI & MCP

Choosing an agent framework: LangChain vs LangGraph vs CrewAI vs PydanticAI vs Mastra vs Vercel AI SDK

Nolan Sullivan

March 4, 2026 - 28 min read

AI & MCP

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Picking the right agent framework is key to efficient development, leading to faster prototyping, cleaner architecture, and a deployment path that fits your runtime from day one. Picking the wrong framework creates technical debt that compounds quietly until it brings your team to a halt.

To help inform your decision, this comparison evaluates seven agent frameworks and two SDKs:

• LangChain
• LangGraph
• CrewAI
• PydanticAI
• OpenAI Agents SDK
• Mastra
• Vercel AI SDK
• n8n
• Vellum

We compare these frameworks using the following five criteria:

• Developer experience (DX)
• Agent capabilities
• Context and memory management
• Deployment and hosting
• Security and compliance

What is an agent?

An agent is a loop. It is an LLM that receives a prompt, decides what to do next (for example, call a tool, ask a clarifying question, or return a final answer), executes that action, observes the result, and repeats. Unlike a single LLM call, an agent takes many steps before it stops.

Four properties define agents:

• Tool use: The agent decides when and how to invoke external functions, such as conducting a search, querying a database, or calling an API.
• Memory: The agent’s state can persist within a session, across sessions, or both.
• Planning: More sophisticated agents decompose goals into subgoals, spawn subagents, or check their own work.
• Autonomy: The agent runs multiple steps without a human approving each one.

Do you need a framework?

An agent framework is a software platform for building, deploying, and managing agents.

First, assess whether you need a framework.

• If your agent calls two or three tools in a linear flow, skip the framework: Use the OpenAI Agents SDK or the Vercel AI SDK’s generateText with maxSteps. Either gives you everything you need. When you have simple requirements, chains, nodes, crews, and runners add unnecessary friction.
• If your agent calls many tools in a complex flow, build your own framework: Unusual orchestration requirements, strict latency budgets, and non-standard memory architectures are usually cleaner with a thin custom layer than with a framework you are fighting.
• If your team and agent fall somewhere in the middle, that’s where a framework earns its keep: Agent frameworks work well when you have human-in-the-loop workflows, multi-agent coordination, or consistent tool schemas across a large codebase, especially if you need durable execution that survives crashes or built-in tracing.

The ecosystem at a glance

The nine options in this guide fall into two categories.

Code-first frameworks

These frameworks, in addition to the OpenAI Agents SDK and Vercel AI SDK, give you libraries and primitives for building agents in your language of choice:

• LangChain
• LangGraph
• CrewAI
• PydanticAI
• Mastra

You control orchestration logic, memory, and deployment.

Hybrid frameworks

These frameworks offer both a visual builder and a real code backend, with parity between the two interfaces:

• n8n
• Vellum

They are strong choices when non-technical stakeholders need to build or maintain workflows.

Gram

Gram  is an open-source platform for building production-ready MCP servers. It helps teams curate focused toolsets, enrich tool descriptions with business context, and compose atomic tools into complete workflow operations so agents stop getting confused by raw API exposure.

Whether you use an SDK, a code-first framework, or a hybrid framework, Gram can help you organize the tools your agent can access.

LangChain

LangChain is a Python agent framework with over 1,000 model and tool integrations. It has the highest download volume in this comparison. The October 2025 v1.0 general availability release  added a simplified create_agent primitive, semantic versioning, and a middleware layer for summarization, PII detection, and human-in-the-loop patterns.

Developer experience

LangChain’s strongest selling point is breadth. It is model-agnostic with over 1,000 integrations, uses the LangChain Expression Language (LCEL) pipe operator for readable chain composition, and provides a stable v1.0 API, making it fast for standard use cases.

If your framework requirements leave the standard path, you encounter its limits.

• Abstraction depth hurts debuggability: When something breaks, you debug LangChain’s internals, not your own logic. Octomind’s engineering team  hit this limit directly, as LangChain’s abstractions made it impossible to write the lower-level code they needed.
• Lowest DX score in the category: In a 90-day Nextbuild benchmark , LangChain scored 5/10 for developer experience, the lowest among five frameworks tested. PydanticAI scored 8/10 in the same benchmark. For teams that prioritize debuggability and type safety, LangChain’s breadth is a cost, not a feature.
• Historical instability leaves residue: Prior to v1.0, breaking changes earned LangChain a reputation for churn. The v1.0 release addressed this, but outdated Stack Overflow answers and fragmented documentation still slow down onboarding.

It’s important to note that LangChain v1.0 is a meaningfully better framework than its reputation suggests, and the ecosystem is unmatched. However, if you prioritize type safety and debuggability over integration coverage, PydanticAI and the OpenAI Agents SDK are sharper tools for the job.

Agent capabilities

LangChain runs a Reasoning and Acting (ReAct)-style tool-calling loop: the LLM picks a tool, executes it, observes the result, and repeats until it produces a final answer.

It is the right choice for:

• Single-agent, linear tool-calling workflows: LangChain has over 300 community integrations that work as ready-made tools.
• Tool variety: It provides MCP server connectivity for tool discovery across most modern APIs.
• Rapid prototyping: The docs cover most common patterns.

It is the wrong choice for:

• Cyclic or branching workflows: It’s better to use LangGraph for anything requiring cycles, persistent state, or durable execution.
• Role-based multi-agent systems: Unlike CrewAI, which ships roles, goals, and delegation as first-class primitives, radically reducing how much code its customers produce , LangChain requires you to write all coordination yourself.
• Crash recovery: LangChain has no native checkpointing. Durable execution requires upgrading to LangGraph or adding Temporal.

Context and memory

LangChain ships eight memory classes:

• ConversationBufferMemory (unbounded)
• Windowed
• Token-limited
• LLM-summarized
• Hybrid
• Knowledge graph
• Vector store
• Entity memory

LangChain v1.0 also adds SummarizationMiddleware for autocompression.

The drawback is that ConversationBufferMemory has no token limit. It requires zero configuration, which makes it the obvious default in development and a silent failure in production. It will overflow the context window on long conversations before it returns an error.

LangChain requires you to pick your memory class deliberately, whereas a framework like Mastra (with its Observational Memory) handles compression automatically at 30,000 tokens with no configuration.

Deployment and hosting

LangServe has been deprecated, so LangGraph Platform is now the recommended path for deployment and hosting. It provides the following across Cloud SaaS, Hybrid, Self-hosted, and Standalone Container tiers:

• Persistence
• Human-in-the-loop pausing
• Cron jobs
• Background processing

However, LangGraph Platform doesn’t support serverless environments, so teams on Vercel or Cloudflare Workers should use Mastra or the Vercel AI SDK instead.

The framework itself is MIT licensed and free. The platform cost scales with usage: the LangSmith  Plus plan runs at $39 per seat per month and LangGraph Platform  adds $0.001 per node, putting a team of five with 500 daily active users at roughly $230 per month before LLM costs.

Security and compliance

Authentication falls outside LangChain’s scope. Implement auth via FastAPI primitives.

LangSmith is SOC 2 Type II-certified, HIPAA-compliant with a BAA on Enterprise plans, and GDPR-compliant with EU data residency. The v1.0 middleware layer adds built-in features like PII detection, summarization, human-in-the-loop approval, and call limits.

Virtual private cloud (VPC) deployment is available on Enterprise plans.

LangGraph

LangGraph is a lower-level runtime that LangChain now runs on internally. If LangChain’s abstractions are too high, LangGraph is the layer below them, not an alternative ecosystem.

Developer experience

In LangGraph, agents are StateGraph objects: they add nodes, add edges, compile, and invoke. State is a TypedDict. The model is explicit and verbose by design.

The LangGraph learning curve is steep, and the documentation you need is fragmented across the LangGraph, LangChain, and LangSmith sites, and its stack traces run deep. If you want convenience, use CrewAI. If you want control, use LangGraph.

Agent capabilities

LangGraph models agents as stateful graphs with typed state, explicit cycles, and durable execution backed by persistent checkpoints at every supra-step.

It is the right choice for:

• Cyclic agent loops: LangGraph can model iteration without a stopping condition.
• Crash-proof execution: LangGraph checkpoints at every node transition, so agents survive server restarts and replay from any prior state.
• Human-in-the-loop workflows: You can pause mid-run via interrupt_before or interrupt_after, then resume with the modified state.
• Time-travel debugging: Replay or fork execution from any prior checkpoint using LangGraph Studio.
• Multi-agent coordination at scale: The subgraphs, langgraph-swarm and RemoteGraph, handle cross-network agent calls.

It is the wrong choice for:

• Role-based agent abstractions: For example, CrewAI ships roles, goals, backstories, and delegation as primitives. LangGraph requires you to model all four explicitly as nodes and states.
• TypeScript teams: Frameworks like Mastra cover most of LangGraph’s feature set with lower setup overhead and serverless-first deployment.
• Serverless environments: LangGraph Platform is not compatible with Vercel or Cloudflare Workers by design, while frameworks like Mastra and the Vercel AI SDK work fine.

Context and memory

State is a TypedDict passed between nodes. The LangGraph MemorySaver automatically checkpoints every transition. For a production-level backend, you can use PostgresSaver, RedisSaver, or the Store interface and LangMem for long-term cross-thread memory.

There are two drawbacks to using LangGraph for production:

• It doesn’t have native token budget management: You need to manage your token budget at the model integration level. Context bloat will silently degrade your agent if you are not deliberate about it.
• Large state objects bloat checkpoints. LangGraph stores a full copy of the state at every supra-step. You need to offload large file contents to external storage, because MemorySaver is RAM-only and won’t survive a restart.

Deployment and hosting

LangGraph Platform manages scaling, persistence, streaming, and cron scheduling. Here are the deployment tiers:

• The free, self-hosted Developer tier lets you use up to 100,000 nodes per month.
• The fully-managed Plus tier costs $0.001 per node.
• The Enterprise tier offers cloud, hybrid, and self-hosted options at custom pricing.

Security and compliance

LangGraph inherits LangSmith’s SOC 2 Type II certification, HIPAA compliance (with a BAA on the Enterprise tier), and GDPR compliance. However, its data residency for the SaaS Plus tier is not documented publicly, so you should verify with the team before committing.

Agent Authorization (beta) is available on all tiers. Enterprise adds single sign-on (SSO) and role-based access control (RBAC).

CrewAI

CrewAI has the broadest enterprise production adoption in this comparison, with PwC, DocuSign, IBM, and PepsiCo  using the framework. This is due to its mental model, which mirrors how humans already organize work.

Developer experience

CrewAI allows you to set up a crew, a group of agents that work together to accomplish tasks, within minutes.

• Each agent has a role, goal, and backstory.
• Each task has a description and expected output.
• The @start, @listen, and @router flows add event-driven orchestration.

Its limitations appear during debugging:

• Black-box abstractions: When something goes wrong, print and log functions do not work inside Task callbacks, and unit testing individual agents is a documented gap.
• Complex conditional logic. Flows help, but for non-linear branching, LangGraph’s graph-based approach is more flexible. If flow control is your primary issue, CrewAI is not the right tool.

CrewAI offers the fastest path from idea to working multi-agent prototype, but it isn’t the fastest path from prototype to debuggable production system.

Agent capabilities

With CrewAI, you assign each agent a role and a goal, and the agents autonomously delegate tasks to one another.

It is the right choice for:

• Role-based multi-agent systems: You define agents as first-class primitives and wire them into sequential or hierarchical crews with a single configuration block.
• Deterministic control when needed: Flows add event-driven orchestration when autonomous delegation is too unpredictable.
• Broad integration coverage: CrewAI supports over 20 LLM providers, more than 30 built-in tools, and MCP servers via MCPServerAdapter.

It is the wrong choice for:

• Hierarchical crews at scale: Delegation loops are a known reliability issue with no built-in safeguard.
• Deep debugging: CrewAI’s abstractions make it hard to trace what has happened when an agent produces the wrong output.
• Cost-sensitive workloads: CrewAI doesn’t have a built-in token budget limiter. It has been documented that uncapped loops have reached $414 in a single run .

Context and memory

CrewAI ships a unified Memory class with four types: short-term (ChromaDB with RAG), long-term (SQLite3), entity (RAG-based), and contextual (which integrates the other three types).

Similar to LangGraph, CrewAI doesn’t have a built-in token budget cap, making token cost your most serious production risk, especially as running three or four agents in sequence multiplies the number of LLM calls per turn. To avoid incidents like the following, set max_iters before deploying and verify costs against your LLM provider’s dashboard instead of CrewAI’s internal reporting, which doesn’t always match provider billing:

• A known Anthropic-specific bug caused stop sequences to fail , inflating costs tenfold, from $0.10 to $1.00 per call.
• A separate two-agent experiment on Gemini reached $414 in a single run .

Deployment and hosting

CrewAI Agent Management Platform (AMP) offers two hosted paths:

• AMP Cloud (managed SaaS)
• AMP Factory (your infrastructure, on-prem or VPC on AWS, Azure, or GCP via Kubernetes).

Alternatively, you can self-host the open-source library, which has AWS-documented Lambda deployment patterns.

The framework is MIT-licensed and free. The real cost is platform fees and LLM API spend. AMP pricing starts at $25 per month for the Professional plan and scales to $6,000–$120,000 per year for Enterprise plans.

We recommend modeling token costs before committing, because using multiple agents in sequence multiplies call counts, memory embeds, and overhead.

Security and compliance

CrewAI is SOC 2 Type II and HIPAA compliant. It provides PII detection, audit logs, and secret manager integrations. For additional data privacy, AMP Factory keeps data within your infrastructure.

CrewAI also provides SSO, SAML/LDAP, and RBAC authentication.

PydanticAI

PydanticAI, described as the “FastAPI feeling applied to GenAI,” is a Python agent framework from the Pydantic team, with full type safety and automatic LLM output validation against Pydantic models. The September 2025 v1.0 release  added an API stability commitment, making it a production-ready alternative to LangChain for teams that prioritize type safety over ecosystem breadth.

Developer experience

A basic agent is five lines: its tools are decorated Python functions, and the dependencies are injected via a dataclass. In the Nextbuild  90-day benchmark, PydanticAI scored 8/10 for developer experience, the highest among five frameworks tested. By comparison, LangChain scored 5/10. The type system caught 23 bugs during development that would have reached production in LangChain.

The tradeoff is ecosystem size. The PydanticAI ecosystem is roughly 15 times smaller than LangChain’s. Third-party integrations and community resources are thin, and you will hit undocumented edges that LangChain users solved two years ago.

Agent capabilities

PydanticAI runs a single-agent model. Each agent.run() call is isolated by default, and it coordinates multiple agents by registering one agent’s run method as a callable tool inside another.

It is the right choice for:

• Type-safe single-agent workflows: It automatically validates LLM outputs against Pydantic models. The Nextbuild benchmark  found that this caught 23 production bugs that, for example, LangChain missed.
• Token budget control: Configurable Usage Limits  covering request tokens, response tokens, total tokens, and tool calls are an important agent configuration.
• Standard interoperability: PydanticAI has built-in A2A protocol and MCP server support. You can use pydantic-graph or finite state machine (FSM)-style control flows.

It is the wrong choice for:

• Role-based multi-agent systems: Where CrewAI ships roles, goals, and autonomous delegation as primitives and LangGraph wires via graph nodes, PydanticAI offers neither pattern.
• Agent handoffs: PydanticAI doesn’t yet have built-in handoff lists between agents. If your architecture depends on OpenAI-style handoffs, this is currently a limitation.

Context and memory

PydanticAI is stateless by default. Each agent.run() is isolated unless you pass message_history explicitly, and there is no built-in persistence layer. Developers serialize state via ModelMessagesTypeAdapter and store it in their own database.

Where PydanticAI really stands out is in its inclusion of Usage Limits, configurable caps on request tokens, response tokens, total tokens, and tool calls. These bake budget controls into the agent configuration.

For durable execution, the framework officially documents integration with Temporal, DBOS, and Prefect.

Deployment and hosting

PydanticAI is a standard Python library: it runs anywhere and is serverless-compatible with Lambda, GCF, and Azure Functions. There is no PydanticAI-specific managed hosting, though AWS teams can use Amazon Bedrock AgentCore.

The Nextbuild  90-day benchmark found PydanticAI licensing and infrastructure costs were $390 in total, with zero licensing costs, $240 in infrastructure, and $150 for Temporal Cloud. It is more efficient than CrewAI, which cost $1,088. It also has a low lock-in risk: PydanticAI is MIT licensed, model-agnostic across over 20 providers, and built on OTel standards throughout.

Security and compliance

PydanticAI has no built-in auth, RBAC, prompt injection detection, or guardrails. Instead, security is enforced through code:

• Type safety prevents data integrity bugs
• Dependency injection secures secrets
• Output validation blocks malformed data from triggering downstream actions
• Human-in-the-loop tool approval prevents dangerous operations

If you need SOC 2 Type II or HIPAA compliance at the framework layer, use LangSmith or Vellum instead.

OpenAI Agents SDK

OpenAI shipped the Agents SDK  in early 2025 as the minimal Python abstraction, with Agent and Runner classes, tools, handoffs, guardrails, and sessions. At v0.10.2 with over 18,900 GitHub stars  and a TypeScript port, it has earned genuine adoption.

Developer experience

A working agent is only four lines, and you can learn the entire API in an afternoon. Tracing is enabled by default and displayed in the OpenAI Dashboard, making the Agents SDK faster to debug out-of-the-box than a framework like LangChain, which requires LangSmith configuration. The ceiling is provider lock-in: hosted tools are zero-infrastructure and excellent, but bound to OpenAI models.

Agent capabilities

The SDK runs an imperative Runner loop where handoffs are a first-class primitive. You define handoffs=[agent_b, agent_c], and the LLM decides when to delegate.

The framework can help you:

• Build handoff chains with minimal wiring: agent.as_tool() wraps any agent as a callable tool, and handoffs=[] is the entire multi-agent configuration.
• Access tools without setup overhead: The Agents SDK provides access to the best zero-infrastructure hosted tools in this comparison (such as WebSearchTool, FileSearchTool, ComputerTool, and Code Interpreter) with no setup overhead.
• Debug from day one: Tracing is enabled by default and viewable in the OpenAI Dashboard at no extra cost.
• Integrate MCP servers as native tool sources: MCP server integration doesn’t require additional adapters.

However, the framework can’t help you:

• Build graph-based orchestration: LangGraph and Mastra’s .branch() handle complex state machines; this SDK is designed for handoff chains.
• Run agents in parallel: The SDK doesn’t let you do this natively.
• Model role-based agent systems: Unlike the OpenAI Agents SDK, CrewAI’s roles, goals, and delegation are purpose-built for team-style coordination.
• Use hosted tools on non-OpenAI models: WebSearchTool, FileSearchTool, and Code Interpreter are OpenAI-only.

Context and memory

Sessions are opt-in by design: no session parameter means no memory, which forces explicit state decisions, and built-in backends (SQLiteSession, RedisSession, SQLAlchemySession, OpenAIConversationsSession) cover most persistence needs. For long-running agents, Temporal is the documented durable execution path. Unlike LangGraph’s native checkpointing, crash recovery is not built in.

Deployment and hosting

The SDK runs as a standard Python library, serverless-compatible with Lambda, Azure Functions, and GCP Cloud Functions, with no managed hosting from OpenAI and no built-in crash recovery without Temporal.

It is MIT-licensed with no SDK surcharge. You pay standard OpenAI per-token pricing plus additional costs per hosted tool call. Your lock-in risk is low at the SDK level and moderate if you rely on hosted tools or the Conversations API.

Security and compliance

The Agents SDK includes the EncryptedSession wrapper, which handles encrypted storage, and openai-guardrails-python, which covers PII redaction and jailbreak detection.

The OpenAI platform itself handles most security concerns. It is SOC 2 Type II, ISO 27001, HIPAA (BAA available), GDPR, and CCPA compliant, and has data residency across nine regions.

Mastra

Mastra is a TypeScript-native agent framework built by the cofounders of Gatsby and Netlify, with Tony Kovanen (a Next.js cocreator) as founding engineer. At $13,000,000 raised (YC W25 ), over 21,100 GitHub stars , and over 300,000 weekly npm downloads, Mastra has production adoption at Replit, PayPal, Sanity, and Brex.

Developer experience

If you know TypeScript, you will be familiar with 90% of Mastra. Agents, tools, and workflows are plain TypeScript with Zod validation, backed by over 40 providers and over 600 models via the Vercel AI SDK. Running mastra dev spins up a local playground, Swagger UI, and autogenerated OpenAPI docs out-of-the-box, providing a developer experience LangGraph cannot match.

The trade-offs are:

• Vercel platform coupling via the AI SDK dependency
• Clunky branching logic with non-LLM agents
• Auth packages still carry the experimental_ prefix

Agent capabilities

Mastra combines ReAct-style autonomous agents with a graph-based workflow engine and an LLM-routed Agent Networks layer, purpose-built for TypeScript teams.

The framework can help you:

• Build graph-based TypeScript workflows: Use the .then(), .branch(), and .parallel() operators, which are the TypeScript equivalent of LangGraph’s stateful orchestration.
• Coordinate agents via Agent Networks: Use .network(), and an LLM router decides dynamically which agent handles each input. No manual routing logic is required.
• Deploy to serverless environments natively: Mastra officially supports @mastra/deployer-vercel, Cloudflare Workers, and Netlify, unlike LangGraph which cannot scale to zero.
• Get automatic context compression: Mastra’s Observational Memory compresses conversations 5-40 times, scores approximately 95% on the LongMemEval benchmark, and triggers automatically at 30,000 tokens.

However, the framework can’t help you:

• Get role-based abstractions out of the box: CrewAI ships roles, goals, and delegation as first-class primitives; Mastra’s Agent Networks require explicit coordination code.
• Debug with time-travel: Mastra doesn’t have replays, while tools like LangGraph let you replay from any prior checkpoint.

Context and memory

Mastra’s memory system is the most sophisticated in this comparison, with the following four types:

• Message history: In a configurable window
• Working memory: Uses Zod-validated JSON or markdown
• Semantic recall: RAG via vector similarity
• Observational Memory: Doesn’t require any manual configuration

Mastra’s default storage is LibSQL, with PostgreSQL, MongoDB, and Upstash for production. However, LibSQL doesn’t work with file URLs in serverless environments, so teams on Vercel or Cloudflare Workers must configure an external storage backend.

Deployment and hosting

With its support for @mastra/deployer-vercel, Cloudflare Workers, and Netlify, Mastra is serverless-first. Its self-hosted options include Express, Hono, and Fastify. Mastra Cloud (beta) is in waitlist stage and doesn’t have public pricing yet.

Mastra is Apache 2.0-licensed and free. However, the hidden cost is Observational Memory, which runs background LLM calls using Gemini 2.5 Flash by default, and those compression costs don’t appear in your agent’s token usage.

Security and compliance

Although Mastra supports auth packages (@mastra/auth for JWT, Clerk, and Better Auth) and MCP OAuth, they all carry the experimental_auth prefix and are actively maturing.

It has built-in guardrails that cover prompt injection detection and PII redaction, but as of March 2026, Mastra holds no SOC 2 certification. Enterprise buyers with SOC 2 requirements should use LangSmith, Vellum, or n8n instead.

Vercel AI SDK

The Vercel AI SDK is the most widely adopted AI SDK in the JavaScript ecosystem. It’s built for web engineers shipping AI-powered features in Next.js or React. The AI SDK 6  added a proper Agent interface, DurableAgent for resumable workflow steps, full MCP support, and a DevTools panel. It has over 22,200 GitHub stars  and over 20,000,000 monthly npm downloads.

Developer experience

The developer experience is excellent for its target audience: useChat and useCompletion handle streaming UI state automatically across over 25 providers, and Thomson Reuters built CoCounsel  with only three developers in two months. Three hard limits apply outside the happy path: the 186kB core package strains edge runtimes, function timeouts cap long-running agents at 300 seconds on Pro and 800 on Enterprise, and features like DurableAgent and AI Gateway require Vercel infrastructure to reach their potential.

Agent capabilities

The Vercel AI SDK runs a tool loop, in which stopWhen, stepCountIs, and prepareStep control multi-step execution, and ToolLoopAgent handles production-ready agent abstraction.

The framework can help you:

• Build streaming AI UIs in React and Next.js with minimal code: The useChat and useCompletion hooks automatically stream state across over 25 LLM providers.
• Run multi-step agents: Use ToolLoopAgent with the prepareStep parameter for dynamic per-step control of the model, system prompt, and active toolset.
• Integrate MCP servers for tool discovery: The SDK supports native MCP server integration.
• Deploy and monitor agents: Deploy to Vercel with zero configuration and access the AI Gateway and DevTools panel for monitoring.

However, the framework can’t help you:

• Run long-horizon agents: Vercel function timeouts create a hard ceiling of 300 seconds on the Pro tier and 800 seconds on the Enterprise tier. Mastra and LangGraph have no equivalent limit.
• Build graph-based orchestration or complex multi-agent coordination: For example, LangGraph and Mastra’s .branch() are purpose-built for this; the tool loop model does not scale to complex routing.
• Get durable execution without external tooling: There is no native crash recovery or checkpointing; Inngest is the recommended integration.

Context and memory

Without automatic token counting, summarization, or a persistence layer, the Vercel AI SDK’s context management is entirely manual:

• You can access the history in the useChat React state.
• To configure server-side persistence, pass the messages array and save it in the onFinish callback.

The AI SDK is well suited to projects that have simple memory requirements or already have session management. If you need automatic compression at scale, Mastra’s Observational Memory is a stronger choice.

Deployment and hosting

The AI SDK supports Vercel-first, zero-config, Next.js deployment, but the SDK itself runs on Express, Hono, Fastify, Deno, Bun, and Cloudflare Workers.

Timeout limits are a real constraint:

• The Hobby plan has a 60-second timeout
• The Pro plan has a 300-second timeout
• Enterprise tiers can have a timeout of up to 800 seconds

The SDK is Apache 2.0-licensed and free. Vercel Pro costs $20 per user per month, with hidden costs for function duration (billed by GB hour), streaming compute time, and bandwidth beyond the included limits. It has a low lock-in risk at the SDK level, but it increases to medium-high for teams using Vercel’s full AI stack.

Security and compliance

The SDK has no built-in auth, input sanitization, or prompt-injection defenses: security is a platform concern, not an SDK concern. Setting needsApproval: true on tools provides human-in-the-loop gates, and AI Gateway Zero Data Retention mode handles sensitive workloads. Vercel’s platform carries SOC 2 Type II, ISO 27001, HIPAA (BAA available), PCI DSS, and GDPR compliance, with Secure Compute available on Enterprise plans.

n8n

n8n is a visual workflow automation platform with built-in AI agent capabilities, not a code-first agent framework. At over 177,000 GitHub stars  (#1 JavaScript Rising Stars 2025 ), a $2,500,000,000 valuation  with NVIDIA as an investor, and production customers that include Delivery Hero, Wayfair, Vodafone, and Microsoft, it is the right tool for connecting APIs and exposing AI workflows to non-technical teams.

Developer experience

The visual drag-and-drop editor with over 400 prebuilt nodes is excellent for its target: connecting APIs, building business automations, and exposing AI workflows to non-technical teams without code. The ceiling appears when workflows require dynamic branching on unpredictable runtime data: LangGraph’s conditional edges handle this natively, while n8n’s IF/Switch nodes are more static and the canvas becomes a liability.

Agent capabilities

n8n runs AI agents as nodes in a visual workflow canvas, using three agent types (Tools Agent, ReAct Agent, AI Agent Tool) backed by LangChain.js, where any of the over 400 built-in integrations can serve as an agent tool.

The framework can help you:

• Connect AI reasoning to real-world APIs without writing integration code: The over 400 prebuilt nodes make n8n faster than any code-first framework for hybrid automations combining deterministic logic with AI reasoning.
• Build multi-agent delegation: Use the AI Agent Tool type to create workflows where one workflow agent hands off to another.
• Expose AI workflows to non-technical teams: The visual canvas builder and form triggers are ideal for teams without developer experience.
• Integrate MCP servers for tool discovery: n8n has dedicated nodes for integrating MCP servers.

However, the framework can’t help you:

• Build complex agent collaboration: LangGraph and CrewAI are architecturally designed for this; n8n’s canvas becomes unwieldy for dynamic branching on unpredictable runtime data.
• Persist agent state natively across executions: n8n’s Simple Memory is lost on workflow restart, and production requires an external Postgres or Redis backend with explicit session ID management.
• Track token-level costs per agent run: n8n has no built-in token tracking, whereas LangSmith and Langfuse offer it natively.

Context and memory

The Simple Memory tool (formerly Window Buffer Memory) works in development but the conversation history it stores is lost on workflow restart, making it unreliable for production. Persistent memory requires an external backend, such as Postgres, Redis, or MongoDB Chat Memory, or vector stores, like Qdrant or Pinecone. When setting up an external backend, take care not to hardcode session IDs. Hardcoded session IDs cause all users to share memory, which is an easy configuration mistake to make but hard to debug.

Deployment and hosting

You can deploy to n8n Cloud, which is hosted on Azure Frankfurt, or you can self-host a Docker deployment. Using n8n Cloud requires a Starter plan (with 2,500 executions per month and up to five concurrent executions) or a custom Enterprise plan. Self-hosted Docker deployments are unlimited but require PostgreSQL and Redis for production at $50-$500 per month. Billing is per workflow execution rather than per step, making complex multi-step automation 10-20 times cheaper than Zapier.

n8n uses a Fair Code license (Sustainable Use License, not OSI-approved), which permits internal use and self-hosting but doesn’t allow you to resell n8n or build a product for which n8n provides the core value. Cloud pricing runs at €20–€667 per month, and the Community Edition is free with no execution limits.

Security and compliance

n8n Cloud is SOC 2 Type II-certified and GDPR compliant (with data in Frankfurt). The n8n Business and Enterprise tiers provide AES-256 encrypted credentials (FIPS 140-2), RBAC, and SSO via SAML and LDAP. However, n8n isn’t HIPAA-certified and doesn’t offer a BAA, so teams with healthcare compliance requirements should use Vellum or LangSmith instead.

Vellum

Vellum is the most complete platform for teams that need both a visual workflow builder and a real code backend. It offers three modes:

• The agent builder lets you create agents using natural language
• The visual workflow IDE includes drag-and-drop functionality
• The Python SDK has bidirectional CLI sync

Vellum has raised $25,500,000 and has over 150 production customers , including Drata, Redfin, and Headspace. It is the only framework in this comparison that keeps the code and UI in sync bidirectionally.

Developer experience

Its bidirectional sync is Vellum’s defining feature. You can edit a workflow in the UI and pull it to code, or push code changes back to the UI. The Vellum agent builder bootstraps workflows from natural language but requires manual refinement to achieve production quality. The Free-to-Pro feature jump is steep, and Vellum’s community size and GitHub stars are not publicly available.

Agent capabilities

Vellum runs graph-based workflows with loops, parallel branches, conditional edges, and nested subworkflows, built from a visual IDE with bidirectional sync to a Python SDK.

The framework can help you:

• Build and iterate on agent workflows both visually and using code: You can pull UI changes to code and push code back to the UI.
• Evaluate production traffic directly: You can run online evaluations (with configurable sample rates) against live agent outputs. Vellum has the strongest evaluation pipeline in this comparison.
• Refine your prompting: You can manage prompts with side-by-side model comparison, versioning, and human review queues built into the platform.
• Build multi-agent patterns: Use the subworkflow composition and supervisor nodes, and use MCP integration for tool discovery.

However, the framework can’t help you:

• Debug with native checkpointing or time-travel: LangGraph checkpoints every state transition and lets you replay or fork from any point; Vellum has no equivalent feature.
• Support A2A (Agent-to-agent) protocol: If you require agent-to-agent communication across external systems, Vellum is not the right choice.
• Handle high concurrency on lower tiers: The Vellum Free tier allows one concurrent execution and the Pro tier allows four. If you need to run with higher concurrency, use the Business tier or above.

Context and memory

Vellum stores chat history in chat_history workflow variables, and the Set State node handles Set and Append operations within sessions. Cross-session persistence requires external databases or RAG/Search nodes; Vellum doesn’t offer a built-in long-term memory store, automatic token truncation, or summarization.

Vellum’s per-step token count and cost-tracking feature partially compensate for the lack of built-in options by making memory costs visible at the workflow level.

Deployment and hosting

Vellum offers one-click deployment from sandbox to production API, and on the Pro tier and above, includes versioned deployments, rollback, and staging.

Execution limits are a hard constraint on lower payment tiers:

• The Free plan only allows one execution at a time for a maximum of three minutes
• At $25 per month, the Pro plan allows four concurrent executions for a maximum of 30 minutes
• At $50 per month, the Business plan allows 12 concurrent executions, also for a maximum of 30 minutes
• On-prem and VPC deployment are available on custom Enterprise plans

Your lock-in risk is moderate-to-high: the platform is closed-source except for the SDK, and workflows are tied to Vellum’s format.

Security and compliance

Vellum never uses customer data to train external LLMs. It is SOC 2 Type I and Type II-certified and HIPAA-compliant (with a BAA) on Enterprise plans. It uses AES-256 GCM encryption at rest and TLS in transit. RBAC and SSO authentication is also available on Enterprise plans.

Vellum’s public documentation doesn’t explicitly confirm GDPR compliance, so we recommend checking with Vellum’s team before committing if that’s a requirement.

Wrapping up

No framework perfectly meets all five criteria. Use the following three questions to determine the best choice for your use case:

• What is your primary language and runtime?

  • Python teams building stateful production agents should use LangGraph.
  • Teams that prioritize type safety and developer experience should use PydanticAI.
  • TypeScript teams get the broadest feature set from Mastra (or the Vercel AI SDK if their primary use case is streaming web UI).

• How complex is your orchestration?

  • Simple tool loops on OpenAI belong in the OpenAI Agents SDK.
  • Role-based multi-agent collaboration is fastest to prototype in CrewAI.
  • Multi-step stateful workflows in Python belong in LangGraph, and in TypeScript, Mastra.
  • Visual workflows for non-technical teams belong in n8n or Vellum, with Vellum being the stronger choice when evaluation pipelines and staging environments matter.

• Who builds and maintains the agents?

  • Mixed technical and non-technical teams should use n8n or Vellum for the non-technical layer and a code-first framework for complex orchestration.
  • Teams building MCP servers to expose internal tools and APIs to their agents should evaluate Gram alongside their framework of choice.

The following table recaps the frameworks we discussed across all categories: