Agent Framework Comparison for Vibe Coders

All 13 Tools at a Glance

All core libraries are open source and free — costs only appear at the managed-hosting layer. Details, hosted-tier pricing, and license terms for each tool follow below.

Agent Framework Comparison: Why "Which Tool Is Best" Is the Wrong Question

In short: Every comparison article on AI agent frameworks lines up a handful of tools in a table and crowns a winner. The problem: the tools aren't in the same race. Some decide what an agent does. Others make sure that decision still runs on Monday morning when Vercel restarts the process.

Two Layers, One Mix-Up

This article covers 13 tools and sorts them by layer first, fit second.

Ask five vibe coders which agent framework is best and you'll get five different answers — not because the question is hard, but because it's imprecise. Comparing LangGraph to Windmill is like weighing a programming language against a cron scheduler. Both potentially belong in the same stack. Neither replaces the other.

The dividing line:

Reasoning layer — decides what the agent does next. Takes in state, calls a model, picks a tool, updates state. This is the thinking logic.

Orchestration layer — makes sure the run survives. Retries after a crash, schedules, queues, cost control, observability across hundreds of parallel runs. This is the operational logic.

Eight tools sit in the first layer: Pydantic AI, LangGraph, Mastra, Vercel AI SDK, OpenAI Agents SDK, Google ADK, Microsoft Agent Framework, and LlamaIndex. Five sit in the second: Windmill, Trigger.dev, Inngest, Prefect, and DBOS. Taking a vibe-coding project from prototype to something production-ready usually needs one tool from each column, not one tool that "wins." At IJONIS, we see this exact mix-up constantly with solo builders trying to scale their first agent past a chat prototype.

For how a single agent is built internally, How to Build an AI Agent: The 5 Parts is the right starting point. For how ten or twenty agents get governed across a company, Agent Operating System (Agent OS): Orchestrating AI Agents is the next step — it covers the governance layer that sits above the tools discussed here.

Reasoning Frameworks Compared

Eight tools decide what an agent does next. Three are pure agent frameworks: Pydantic AI, LangGraph, Mastra. Two are official vendor SDKs: OpenAI Agents SDK, Google ADK. One sits a layer deeper (Vercel AI SDK), one is the successor to two well-known frameworks (Microsoft Agent Framework), and one comes from the RAG world (LlamaIndex).

Pydantic AI — the type-safe Python approach

Pydantic AI comes from the team behind Pydantic, the validation library already embedded in OpenAI's Software Development Kit (SDK), Google's Agent Development Kit (ADK), and Anthropic's SDK. The framework itself is open source and free, no licensing fees.

Core idea: agent outputs are typed and validated through Pydantic models before they get used downstream. That catches an entire class of errors that would otherwise only surface at runtime with unstructured responses from a large language model (LLM). Pydantic AI is model-agnostic — OpenAI, Anthropic, Gemini, Mistral, Bedrock, Vertex AI, Ollama, and more run through the same interface.

Important for the architecture: Pydantic AI is stateless by default. Every agent.run() call is isolated unless you pass message history explicitly. There's no built-in persistence layer. That's not a gap — it's exactly why Pydantic AI almost always gets paired with an orchestration layer once a run outlives a single request.

LangGraph — the graph approach with built-in crash recovery

LangGraph models agent behavior as a graph of nodes and edges instead of a linear chain — part of the LangChain ecosystem, available for both Python and TypeScript.

The key difference from Pydantic AI: LangGraph ships with native crash recovery. If an agent crashes mid-run, it can resume from the last checkpoint after a restart — inside the LangGraph runtime itself, not through an external queue. That makes LangGraph the natural pick for complex, multi-step workflows with branching and loops.

The open-source library is MIT-licensed and free. For managed hosting, LangSmith offers three tiers: Developer free (up to 5,000 traces/month, no self-hosting), Plus starting at $39 per seat/month (up to 10,000 traces, still cloud-only), Enterprise at custom pricing — self-hosting or a hybrid deployment only becomes available here. Self-hosting isn't extra-cost, it's simply absent from the lower tiers.

Production references show up consistently across multiple 2026 comparisons: Uber, Klarna, LinkedIn, JPMorgan, BlackRock, Cisco, and Replit are frequently cited as LangGraph users — making LangGraph currently the broadest production foundation among the reasoning frameworks in this article.

Mastra — TypeScript-native, built for the Vercel stack

Mastra positions itself explicitly as the TypeScript-native counterpart to the Python-heavy frameworks — built by the Gatsby team, YC W25 batch, backed by $13M in funding. Launched October 2024, hit 1.0 in January 2026, and crossed 22,000+ GitHub stars in 15 months.

The framework itself is Apache 2.0-licensed and free, including agents, workflows, and a built-in memory system. Notably, Mastra deploys directly to serverless platforms like Vercel or Cloudflare Workers — a difference from LangGraph Platform, which doesn't support serverless deployment in its lower tiers.

Mastra Cloud, the managed offering, has three tiers. Starter is free: 100K observability events, then $10 per 100K, 24 CPU hours included, 15-day data retention. Teams starts at $250/month with 1M events, 250 CPU hours, and 6-month retention. Enterprise is custom-priced with Role-Based Access Control (RBAC), Single Sign-On (SSO), and guaranteed data residency in a Virtual Private Cloud (VPC).

If you're already building in Next.js/TypeScript — the classic vibe-coding stack via Cursor, Claude Code, or Vercel v0 — Mastra is often the path of least resistance: no second language, no Python toolchain running in parallel to the existing project. Mastra technically builds on top of the Vercel AI SDK (see next section) but adds graph workflows, checkpointing, and memory the SDK doesn't ship natively.

Vercel AI SDK — the layer underneath, not Mastra's competitor

The Vercel AI SDK isn't an agent framework in the same sense as Mastra or LangGraph — it's the deeper layer Mastra itself is built on. Positioning in their own words: "the TypeScript toolkit to build AI-powered applications and agents with React, Next.js, Vue, Svelte, and Node.js." TypeScript-only, Apache 2.0-licensed, with roughly 25,600 GitHub stars (as of July 16, 2026) — fully free and self-hostable since it's a library, not a hosted service.

The SDK offers three surfaces: AI SDK Core (a unified interface across 25+ model providers), AI SDK UI (framework-agnostic hooks like useChat for streaming into React/Vue/Svelte), and more recently Harnesses for agent loops. What's missing is exactly what Mastra adds: native crash recovery, checkpointing, and built-in memory for long-running agents. Vercel's own docs point to a separate "Workflows" product for agents that need to "suspend, resume, and survive function timeouts" — a signal the base library isn't built for that.

Cost model: the SDK itself is free. Real costs come either directly from your chosen model provider, or through the Vercel AI Gateway, which passes tokens through with no markup — free to start (limited model selection, lower rate limits) and paid via purchased Gateway credits.

OpenAI Agents SDK — minimal wiring, deep in OpenAI's own stack

The OpenAI Agents SDK is the official successor to Swarm, OpenAI's earlier experimentation framework — in their own words: "a production-ready upgrade... in a lightweight, easy-to-use package with very few abstractions." Available as an official Python and TypeScript library, MIT-licensed, with roughly 27,900 GitHub stars for the Python variant (as of July 16, 2026).

The core primitives are deliberately minimal: Agents (model plus instructions plus tools), Handoffs (delegation between agents), and Guardrails (input/output validation). The framework works natively with OpenAI models, but supports other providers through LiteLLM adapters. Costs match standard OpenAI API pricing plus separate fees for hosted tools like web search or Code Interpreter.

The real drawback versus LangGraph or Mastra: per multiple 2026 comparisons, the SDK cannot handle graph-based orchestration or run agents in parallel — good for handoff chains with minimal wiring, a real architectural ceiling for anything more complex. One more thing worth knowing: tracing is enabled by default and sends data to OpenAI's own dashboard unless you explicitly swap in a custom tracing processor — an opt-out pattern, not opt-in.

Google ADK — the youngest of the three big SDKs

The Google Agent Development Kit launched at Google Cloud Next in April 2025, making it the youngest framework in this comparison. Their own positioning: "Build production agents, not prototypes."

Python is the most mature implementation; official TypeScript, Go, Java, and Kotlin versions also exist — broader language coverage than any other tool here, though the Kotlin implementation and parts of Python are still described as beta by third-party comparisons.

Apache 2.0-licensed, with roughly 20,600 GitHub stars for the Python variant (as of July 16, 2026). ADK itself is free; real costs come from Gemini API usage — a free tier exists, and paid usage runs between $0.10 and $15 per million tokens depending on the model.

The strength is native support for the Agent2Agent Protocol (A2A) and one-command deployment to Vertex AI Agent Engine, including managed infrastructure, authentication, and Cloud Trace observability. ADK also supports non-Gemini models through LiteLLM, Ollama, and vLLM integrations. Google uses ADK internally for its own products, Agentspace and Customer Engagement Suite.

The downside: multiple independent 2026 comparisons rank ADK as the least mature of the three big SDKs — smaller community, fewer external production references than LangGraph or the OpenAI Agents SDK. The consistent recommendation: ADK pays off mainly when a project is already committed to Google Cloud or Gemini Enterprise. Without that commitment, several sources describe LangGraph as the safer bet.

Microsoft Agent Framework — the successor to AutoGen and Semantic Kernel

Microsoft Agent Framework reached version 1.0 on April 3, 2026, unifying two older Microsoft frameworks: "combines AutoGen's simple agent abstractions with Semantic Kernel's enterprise features — session-based state management, type safety, middleware, telemetry — and adds graph-based workflows for explicit multi-agent orchestration," per their own documentation.

Python and .NET are fully production-ready (GA); a Go version exists only as a preview. Fully open source under the MIT license, free, with roughly 12,200 GitHub stars currently — meaningfully fewer than AutoGen's historical 59,800 stars, mostly because the unified repository has only existed since April 2026.

Per the official AutoGen repository, AutoGen is "in maintenance mode. It will not receive new features or enhancements and is community managed going forward... New users should start with Microsoft Agent Framework." That's more precise than "discontinued" — AutoGen lives on, just without further development from Microsoft itself. Semantic Kernel carries a similar status since roughly October 2025 per several independent sources, though less explicitly documented than AutoGen's case.

The honest take for vibe coders: Microsoft Agent Framework is architecturally and documentation-wise enterprise-first — governance, observability, typed graph workflows, Azure Foundry integration, explicit legal disclaimers around third-party models. It works outside Azure too (OpenAI, Anthropic, and Ollama are directly supported), but the docs and case studies skew heavily corporate. For a pure TypeScript project in a Next.js stack, it's not a natural fit either way — only 2.5% of the repository is TypeScript.

LlamaIndex — RAG-first, with agent capabilities layered on

LlamaIndex now describes itself as "the leading document agent and OCR platform" — a notably document- and retrieval-centric framing, even for its own agent product, AgentWorkflow. Python is the primary language with roughly 50,900 GitHub stars, MIT-licensed and free for the open-source core.

Important gotcha for TypeScript teams: LlamaIndex.TS was officially discontinued and archived as of April 30, 2026. If you're building in the Next.js/Vercel stack, there's effectively no actively maintained option here anymore — a clear difference from Mastra or the Vercel AI SDK.

LlamaIndex is strongest when retrieval is the central problem — agents that need to search and merge many heterogeneous data sources, not general-purpose agent orchestration. Customers like Rakuten, Salesforce, and Experian use it accordingly for document-heavy use cases. Earlier complaints about frequent breaking changes (version 0.10, 2024) have largely settled per current sources — the API is considered meaningfully more stable in 2026.

Commercial pricing runs through LlamaCloud/LlamaParse, separate from the free open-source core: Free at $0 (10,000 credits, up to 100 users), Starter at $50/month (40,000 credits included), Pro at $500/month (400,000 credits included), Enterprise custom.

Orchestration Infrastructure Compared

Five tools make sure an agent run survives a crash, hits a schedule, and keeps costs under control: Windmill, Trigger.dev, Inngest, Prefect, and DBOS.

Windmill — polyglot, self-hosting-first

Windmill turns scripts into webhooks, workflows, and UIs — with support for Python, TypeScript, Go, Rust, and Bash in the same workflow. That makes Windmill the option when you're not locked into a single language.

The Community Edition is self-hosted, free, with unlimited executions, up to three workspaces, and 50 users — but under AGPLv3 license. That's a copyleft license with its own obligations for derivative works, a detail worth catching during legal review, not after. Self-hosted Enterprise starts at $120/month plus $20 per developer, including a commercial license, unlimited audit logs, and SAML support.

Trigger.dev — TypeScript-focused, permissive license

Trigger.dev is built for TypeScript projects that need long-running tasks, schedules, and event-driven workflows without running their own servers — exactly the setup most vibe-coding projects already run on.

Unlike Windmill, Trigger.dev runs under Apache 2.0 license — permissive, self-hosting is free with no feature restrictions. Managed hosting starts at $0 (20 concurrent runs, $5 compute credit included, 1-day log retention), Hobby at $10/month, Pro at $50/month (200+ concurrent runs, 30-day retention), Enterprise at custom pricing including SSO and SOC 2 reports.

Inngest — event-driven, with granular flow control

Inngest models work as functions that subscribe to events — a different mental model from Trigger.dev, which calls tasks directly, closer to a remote function call. Neither model is objectively better; the fork is about the shape of the work, not quality.

The server and CLI run under the Server Side Public License (SSPL) with delayed publication under Apache 2.0; the SDKs themselves are Apache 2.0-licensed. Self-hosting is documented and supported, though it sits less at the center of the positioning than it does for Trigger.dev.

Pricing per the official page: Hobby free (50K executions/month, 5 concurrent executions, 3 users), Pro starting at $99/month (1M+ executions, 100+ concurrent executions, 15 users, tiered overage pricing across every dimension), Enterprise custom with SAML and audit trails.

The standout feature is a named flow-control toolkit: concurrency, throttling, batching, debounce, priority, and singleton runs — all configurable in one line of code, globally, per function, or per tenant. At roughly 5,600 GitHub stars, Inngest is meaningfully smaller than Windmill or Trigger.dev, but well-funded with a $21M Series A (September 2025, led by Altimeter).

Prefect — Python-native pipeline orchestration

Prefect positions itself as "workflow orchestration for data, ML, and agents" — but competes more directly with Airflow and Dagster in the data-engineering space than with Trigger.dev or Windmill in the agent-backend space. Any Python function becomes a workflow via @flow and @task decorators, with minimal boilerplate.

Apache 2.0-licensed, with roughly 23,400 GitHub stars — noticeably more than Inngest or DBOS, a result of being on the market since 2018. The self-hosted Prefect Server is free with unlimited runs. Prefect Cloud pricing (per independent sources, not directly confirmed on Prefect's own page): Hobby free (2 users, 5 deployments), Starter $100/month, Team starting at $100 per user/month with a 4-user minimum, Enterprise custom.

For a typical vibe-coding project — a SaaS app with AI agents in the backend — Prefect is rarely the obvious pick. The positioning, case studies, and decorator model consistently point toward data and ML pipeline orchestration, not product backends.

DBOS — durable execution without a separate server

DBOS takes a fundamentally different architectural approach from everything else in this section: there's no separate orchestration server. Durability comes from checkpointing execution state as rows in the Postgres database your app already has — no extra cluster, no extra infrastructure.

The roots trace to three years of joint MIT-Stanford research, co-founded by Turing Award laureate Mike Stonebraker and Databricks co-founder Matei Zaharia. The company was founded in 2024 and raised $8.5M in seed funding.

Open-source core is MIT-licensed, free, self-hosted with no limits. Official SDKs exist for Python, TypeScript, Go, and Java. Pricing: Pro at $99/month (2 users, up to 3 apps, 1M checkpoints included), Teams at $499/month (10 users, up to 10 apps, 10M checkpoints, SOC 2 and HIPAA compliance), Enterprise custom with a self-hosted "Conductor" option.

DBOS advertises roughly 10x less code than Temporal for the same durability guarantee — a vendor claim, not independently benchmarked, though a public benchmark repository exists.

The real trade-off: there's no Temporal-comparable web UI for debugging across a full execution history. For most backend services, DBOS is a reasonable lightweight starting point, with Temporal as the fallback once scale genuinely gets extreme.

Two Fallback Options for Extreme Scale

Decision Logic: Two Questions, Not One Ranking

Question 1 — Reasoning: which language, which provider, how much state control?

Match the pick to the language you're already building in first. Then decide whether you're comfortable locking into one model provider, and how much branching your agent actually needs.

• Pydantic AI: Python stack. Maximum type safety. Simple to moderate workflows.
• LangGraph: Python or TypeScript. Complex branching. Built-in crash recovery wanted.
• Mastra: TypeScript stack. Serverless deployment. Existing Next.js/Vercel environment.
• Vercel AI SDK: only need model calls and streaming, not a full agent framework.
• OpenAI Agents SDK: committed to OpenAI models. Simple handoff chains. Minimal wiring wanted.
• Google ADK: already on Google Cloud or Gemini Enterprise.
• Microsoft Agent Framework: .NET stack or committed Azure environment.
• LlamaIndex: retrieval across many data sources is the core problem, not general agent logic.

Question 2 — Orchestration: how much control, which language, which license?

• Windmill: multiple languages in the project. Strong data-residency focus. AGPL acceptable.
• Trigger.dev: pure TypeScript stack. Apache 2.0 preferred. Simple onboarding wanted.
• Inngest: event-driven mental model. Granular flow control (throttling, batching, debounce) matters.
• Prefect: Python-heavy data or ML pipeline, not primarily a product backend.
• DBOS: Postgres already running. No separate orchestration server wanted.
• Temporal: workflows running weeks or months. Scaling well past solo operation.

The combination that works most often in practice: one reasoning framework for the decision logic, orchestrated through Windmill, Trigger.dev, Inngest, or DBOS for everything involving schedules, retries, and cost control.

Teams that pick only the reasoning framework and leave orchestration to chance end up building exactly the problem described in Agent OS: Orchestrating AI Agents — the moment more than one agent hits production.

FAQ: Agent Framework Comparison

What's the difference between LangGraph and Windmill?

LangGraph is a reasoning framework — it decides what an agent does next, based on state and model output. Windmill is an orchestration platform — it makes sure a run survives retries, schedules, and queues. Both potentially belong in the same stack; they don't compete for the same job. In short: LangGraph thinks, Windmill makes sure the thinking still runs tomorrow.

Is Pydantic AI better than LangGraph?

Neither — they solve slightly different problems. Pydantic AI wins on type safety and simplicity for Python teams building shorter, cleanly structured agent runs. LangGraph wins on native crash recovery and graph-based modeling for more complex, branching workflows with many intermediate steps. The choice depends on how complex your agent logic is, not a general quality ranking.

Do I need Mastra if I'm already using Vercel AI SDK?

Vercel AI SDK is built primarily for model calls and streaming, not full agent workflows with memory and multi-step logic. Mastra technically builds on the AI SDK and adds exactly what's missing: graph workflows, checkpointing, and built-in memory. For simple chat interfaces, the AI SDK alone is often enough; for agents with multiple steps and state, Mastra becomes relevant.

Trigger.dev or Inngest — what's the difference?

Both are TypeScript-focused orchestration tools with a similar target audience, but a different mental model. Trigger.dev calls tasks directly, closer to a remote function call. Inngest models work as functions subscribing to events, with a named flow-control toolkit for throttling, batching, and debounce. Trigger.dev runs under the permissive Apache 2.0 license; Inngest under SSPL with delayed Apache 2.0 publication. For most vibe-coding projects, programming style decides more than any hard technical limit.

What does it cost to get started with these 13 frameworks?

All core libraries are open source and free — none of the 13 charges a licensing fee just to use the code. Costs appear at the managed-hosting or model-usage layer: LangSmith Plus starting at $39 per seat, Mastra Cloud Teams starting at $250/month, Windmill Enterprise starting at $120/month, Trigger.dev Pro starting at $50/month, Inngest Pro starting at $99/month, DBOS Pro starting at $99/month, Prefect Starter around $100/month. Self-hosting remains possible for nearly all of them, under different license terms — from AGPLv3 (Windmill Community) to SSPL (Inngest server) to Apache 2.0 and MIT for most of the rest.

Which framework fits a solo vibe-coding project?

For a TypeScript stack with no server ops of your own: Mastra for reasoning, Trigger.dev or Inngest for orchestration — all three serverless-capable, no cluster to run. For a Python project prioritizing type safety: Pydantic AI plus DBOS, if you're already running Postgres and don't want a separate orchestration server. Committed to one model provider and fine with minimal wiring: OpenAI Agents SDK or Google ADK, depending on which cloud ecosystem you're already in.

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Not sure which combination fits your project? IJONIS builds agent architectures from framework selection through production — including for solo builders taking a prototype into real operation. Talk to us about your specific situation, or dig deeper with How to Build an AI Agent: The 5 Parts and Agent OS: Orchestrating AI Agents.