Manus AI, Claude Agent SDK, OpenAI Swarm, Google ADK, n8n — The 2026 Framework Deep-Dive

Published 2026-06-03 · 24 min read · By Nikolay Sapunov, CEO at Fora Soft

Why This Matters

The agent-framework decision lesson gave you the three names that dominate the conversation, but a real 2026 proposal will also mention Manus, the model-maker SDKs, Google's kit, or a low-code tool — and you need to know what each one actually is before you can judge the choice. These newer tools change the build-versus-buy math: with Manus you can have a working autonomous agent in an afternoon and no engineers, while with the Claude or OpenAI SDK your team builds exactly the agent you want and owns every line. If you are scoping an agent feature for conferencing, surveillance, OTT, e-learning, or telemedicine, picking the wrong point on that spectrum wastes money in two directions — over-buying a black-box product you can't customize, or over-building a custom agent when a low-code workflow would have done. By the end you will be able to read any "let's just use Manus" or "we'll build on the OpenAI Agents SDK" proposal and check that it fits the task, the team, and the budget.

Where This Lesson Sits

Start with one map. In the agent framework decision lesson we covered the three frameworks that own the search traffic — LangGraph, CrewAI, and AutoGen (now Microsoft Agent Framework). Those are general-purpose, code-first libraries: your engineers wire up the three agent primitives — tool use, memory, and planning — into whatever shape the task needs. This lesson covers the second wave: tools that arrived or matured in 2025–2026 and that change who builds the agent and how much is pre-built for you.

The cleanest way to hold the six tools in your head is a single spectrum, from buying a finished agent to building one from raw parts. On one end is a product you subscribe to and point at a task. On the other end is a toolkit your engineers assemble. The closer a tool sits to the "buy" end, the faster you ship and the less you control; the closer to the "build" end, the more control you get and the more engineering it takes. Every tool in this lesson lives somewhere on that line, and naming its position is the first step to choosing well.

Figure 1. The 2026 agent-tooling spectrum. Manus is a finished product you buy; n8n and Zapier are low-code; Google ADK plus Vertex AI Agent Builder is cloud-native; the Claude and OpenAI SDKs are build-from-primitives toolkits. Speed falls and control rises as you move right.

Four shapes describe the whole field. A managed autonomous agent is a finished product — you describe a goal and it runs, with no code (Manus). A low-code platform lets a non-engineer assemble an agent by connecting boxes on a canvas (n8n, Zapier). A model-maker SDK is a code toolkit from the company that builds the model, tuned to that model's strengths (Claude Agent SDK, OpenAI Agents SDK). A cloud-native kit is a code toolkit plus a managed runtime from a cloud provider, aimed at teams already on that cloud (Google ADK with Vertex AI Agent Builder). Keep those four shapes in mind and each tool below slots into place.

Manus AI — The Autonomous Agent You Buy, Not Build

Manus AI is the tool that made "autonomous agent" a mainstream phrase in 2025. It is not a library your engineers import; it is a finished product you subscribe to. You give Manus a goal in plain language — "research these five competitors and build me a comparison spreadsheet," or "turn this folder of clips into a tagged highlight reel" — and it plans the steps, does the work, and hands back a finished result, with no code from you.

The idea that makes Manus work is worth naming, because the rest of this lesson returns to it: Manus gives the agent a full virtual computer. Behind the scenes the agent gets its own web browser, its own command-line terminal, and its own file system, and it uses them the way a human assistant with a laptop would — opening web pages, writing and running code, saving files, and checking its own work. Giving an agent a computer rather than just a chat box is what lets it complete long, multi-step jobs instead of only answering questions.

Manus came out of Beijing Butterfly Effect Technology and launched its agent publicly in March 2025. It raised a reported $75 million from the venture firm Benchmark at roughly a $500 million valuation in April 2025, and moved its headquarters to Singapore in mid-2025 as it expanded internationally. One corporate fact is in flux and you should treat it as a moving target: Meta reportedly agreed to acquire Manus in late 2025 for around $2 billion, but a Chinese regulator blocked the deal in 2026, leaving ownership unresolved as of mid-2026. The product runs normally throughout; the ownership question is the kind of fact to re-check before you commit a roadmap to it.

The catch with Manus is its pricing model, and it is the same trap we flagged for per-execution platforms in the last lesson. Manus bills in credits, not flat seats. There is a free tier with a daily credit refresh, and paid tiers (Pro and Team) that grant a monthly credit allowance — but a single complex task can consume a large, hard-to-predict number of credits, and there is no upfront estimate before the agent runs. For a one-off research task that unpredictability is tolerable; for a feature that runs the same job thousands of times a month, it makes budgeting genuinely difficult. Manus is the right tool when you want an autonomous result without building anything and the volume is modest. It is the wrong tool when you need to embed agent behavior inside your own product, customize how it reasons, or run it at predictable high volume — that is what the SDKs below are for.

Claude Agent SDK — Give Your Agent A Computer

The Claude Agent SDK is Anthropic's toolkit for building agents on top of its Claude models, and its name has a short history worth knowing because it confuses people. It began as the Claude Code SDK — the engine inside Claude Code, Anthropic's coding assistant — and Anthropic renamed it the Claude Agent SDK on 29 September 2025. The reason for the rename is the whole point of the tool: the engine that powers a coding assistant turned out to power almost any agent, so the name widened to match.

The design principle is the same one that makes Manus work, stated as an engineering goal: give your agent a computer. The SDK hands your agent the same basic tools a person at a keyboard uses — run a command in the terminal, create a file, edit a file, search across files — and that small set is enough to read spreadsheets, search the web, build visualizations, and do most digital work. Anthropic frames the agent's job as a loop that repeats four steps: gather context, take action, verify the work, and repeat until done. That loop is the same reason–act pattern we described in the agent primitives lesson, packaged so your engineers don't rebuild it.

Four capabilities make the Claude Agent SDK strong for production. Subagents let the main agent spin off helpers that each work in their own isolated context and report back only what matters — useful for searching a large archive in parallel without flooding the main agent's memory. Compaction automatically summarizes earlier steps when the context window fills, so a long-running job doesn't run out of room. Agentic search lets the agent use plain commands like grep to find what it needs in a file system, which is often simpler and more transparent than building a vector database. And verification is built into the loop: the agent can check its work against rules, look at a screenshot of its own output, or have a second model judge the result. Like every serious 2026 tool, it connects to outside services through the Model Context Protocol (MCP), the open standard for wiring tools to agents, so it can reach Slack, a database, or an archive without custom integration code.

For a video product, the practical read is this: the Claude Agent SDK suits agents that have to do real digital work over time — sift a video archive, run analysis code, write a report, and check it — rather than only chat. It is a code-first toolkit, so it needs engineers, but it gives them a high ceiling and the same harness Anthropic uses for its own agents.

OpenAI Agents SDK — The Production Successor To Swarm

The OpenAI Agents SDK is OpenAI's lightweight toolkit for building agents, and it carries a name people still search for that no longer points to a live product: Swarm. Swarm was OpenAI's 2024 experiment in multi-agent orchestration — useful for learning, never meant for production. In March 2025 OpenAI replaced it with the Agents SDK, and the old Swarm code now tells you so directly: it is "a production-ready evolution of Swarm." If an engineer proposes "OpenAI Swarm" in 2026, they mean the Agents SDK; building new work on the archived Swarm experiment would be a mistake.

The SDK's whole appeal is that it is small. It has only a few core ideas. Agents are language models given instructions and tools. Handoffs let one agent pass control to another — the way a front-desk agent routes a caller to the right specialist — which is how you build multi-agent systems without heavy machinery. Guardrails run validation checks on what goes into and comes out of an agent, in parallel, and stop a run that fails them. And tracing records every step so your team can see and debug what the agent did. On top of those, the SDK adds sessions (memory across turns), function tools (turn any Python function into a tool), MCP support, and human-in-the-loop controls. It is the quickest path for a team already committed to OpenAI's models that wants minimal scaffolding.

Two 2026 additions make this SDK directly relevant to video. Sandbox agents run a specialist inside a real, isolated workspace with its own files and shell — the same "give the agent a computer" idea, with resumable sessions so a long job survives an interruption. More important for conferencing and telemedicine, the SDK ships realtime voice agents: a built-in pipeline that takes microphone audio, turns it to text, runs the agent, and speaks the reply, plus a low-latency realtime mode with automatic interruption detection. That is exactly the building block a live meeting copilot or a voice assistant in a call needs, and it is the clearest reason a real-time video team would reach for this SDK over a general-purpose framework.

Google ADK And Vertex AI Agent Builder — The Cloud-Native Option

Google's entry has two parts that work together, and keeping them straight avoids confusion. The Agent Development Kit (ADK) is the open-source code toolkit — the part your engineers write agents in. Vertex AI Agent Builder is the managed Google Cloud platform that hosts, scales, and governs those agents in production. You build with ADK; you run on Agent Builder. Google rebranded the wider platform to the "Gemini Enterprise Agent Platform" at Cloud Next 2026, but the two working pieces — the kit and the managed runtime — are what matter for a decision.

ADK is built for teams that think in pipelines and teams of agents. It supports multiple programming languages — Python, Java, Go, and others — and lets engineers define either a fixed, predictable sequence of steps (a workflow) or a more adaptive setup where agents route work between themselves. It natively supports multi-agent designs, so you can compose a small team of specialists, and it is model-agnostic: while it is tuned for Google's Gemini models, it can drive others too. The managed side, Vertex AI Agent Engine, is the runtime that actually executes agents at scale, with persistent memory and the enterprise governance — access control, audit, compliance — that large organizations require. Agent Builder also bundles a low-code visual builder and access to a model garden of 200-plus foundation models, including Anthropic's Claude and Meta's Llama alongside Gemini, billed pay-as-you-go.

The decision rule is the same one we gave for Microsoft Agent Framework and Azure: ADK plus Vertex AI Agent Builder is the natural pick when your organization already runs on Google Cloud. The managed runtime, billing, governance, and Gemini access are already in place there, and the gravity of an existing cloud relationship usually outweighs small feature differences. If you are not on Google Cloud, that gravity is weaker and a model-maker SDK or a low-code tool will often feel more native.

n8n And Zapier — When A Non-Engineer Should Build It

Not every agent needs engineers. Two low-code platforms let a product or operations person assemble an agent by connecting boxes on a visual canvas, trading fine control for speed and accessibility — and they sit at different points even within "low-code."

n8n is the more powerful and the more technical of the two. It is a workflow-automation platform with strong AI features: as of 2026 it ships roughly 70 dedicated AI nodes built on the LangChain toolkit, native MCP support, and 400-plus integrations. Its AI Agent node is the orchestration brain, and you attach sub-nodes to it — a language model, a memory store, and the tools the agent can call — by dragging connections rather than writing code. The defining advantage is that n8n can be self-hosted: you run it on your own server (a small virtual machine can cost roughly $5–$10 a month), which keeps all your data, credentials, and model calls inside your own environment, and means a 20-step workflow costs the same server time as a 2-step one. It supports OpenAI, Anthropic, Google, Mistral, and local models through Ollama, so you are not locked to one provider. n8n is the right pick when a semi-technical team wants control over data and hosting without a full engineering build.

Zapier is the more consumer-friendly and the more locked-in. It is the long-standing no-code automation service connecting thousands of apps, and in 2025 it rebranded around AI, adding three pieces on top of its core automation engine: Copilot (describe an automation in words and it builds it), Agents (autonomous helpers that use your connected apps as tools), and Chatbots (customer-facing support bots). Zapier is the fastest way to wire an agent across apps you already use — Gmail, Slack, a spreadsheet — with no server to manage. The trade-offs are cost and control: Zapier bills per task (one action by one app), and its AI Agents and Chatbots are priced as separate add-ons, so a team using all three can stack well past $150–$200 a month before heavy use, and it runs only as a hosted service in Zapier's cloud. Reach for Zapier when speed and breadth of app connections matter more than data control or per-run cost.

The Head-To-Head Comparison

With each tool defined, here is how the six line up on the dimensions that decide real projects. Read it as a starting filter; the right choice depends on your task, which the next section walks through.

One honest caveat belongs under this table. These categories are blurring. The SDKs added "give the agent a computer" sandboxes; the cloud kit bundles a low-code builder; the low-code tools added agent nodes and MCP. The gaps are narrowing the same way the big-three frameworks converged, which means fewer wrong answers — but the pricing units stay genuinely different, and that is where budgets get hurt.

What It Costs — Mind The Pricing Unit

Frameworks and SDKs that you run yourself are free to install; you pay for the model calls and the servers, which we break down in the cost-of-AI lesson. The money question is the pricing unit of the managed products, because each one counts differently and the demo never matches the bill until you do the arithmetic.

Walk one example. Suppose a surveillance product runs a nightly review agent across 40 cameras, and each camera's nightly run is one unit of work. That is 40 units a night, or 40 × 30 = 1,200 units a month. On Manus, "unit" means a credit-heavy task, and if each nightly review burns, say, 50 credits, that is 1,200 × 50 = 60,000 credits a month — far past any standard plan, with no upfront estimate to size it from. On Zapier, "unit" means tasks, and a single nightly review that touches five apps is five tasks, so 1,200 runs × 5 = 6,000 tasks a month before the AI-agent add-on fees. On the Claude or OpenAI SDK, there is no per-run platform fee at all — you pay only model tokens — so the same 1,200 runs cost whatever the tokens cost, predictable and linear. The lesson is not that one tool is cheapest; it is that you must map your real usage — runs per day times days times sites — onto each tool's pricing unit before you choose, or a credit-based or task-based plan will surprise you at scale.

How To Choose Among The Newcomers

Strip away the brand names and the decision is a short walk down the buy-build spectrum, filtered by who will build the thing and where it runs.

Start with who builds it. If no one on the team can or should write code and you want a finished result, that is Manus (for an autonomous one-off) or Zapier (for cross-app automation). If a semi-technical person can wire a canvas and you care about data control, that is n8n. If you have engineers and need a real custom agent, you are choosing among the SDKs and the cloud kit.

Then filter by where it runs and which model you favor. A team committed to OpenAI's models that wants minimal scaffolding — and especially one building realtime voice into a call — leans to the OpenAI Agents SDK. A team that wants an agent to do heavy digital work with subagents and self-verification leans to the Claude Agent SDK. A team already on Google Cloud that needs enterprise scale and governance leans to Google ADK with Vertex AI Agent Builder, the same way an Azure shop leans to Microsoft Agent Framework.

Finally, weigh control versus speed, the constant trade of this whole field. Buying (Manus, Zapier) gets you a working agent this week and little room to customize; building (the SDKs, ADK) costs engineering time but gives you an agent shaped exactly to the task and a bill you can predict. There is no universally best tool — only the best fit for a task, a team, and a budget. And because the categories are converging, a sensible choice today is rarely a trap tomorrow.

Figure 2. Choosing among the 2026 newcomers. Start with who builds it, then filter the engineering branch by preferred model and cloud. The trade is always control versus speed.

Matching The Newcomers To Video Agent Jobs

The abstract decision lands when you map it onto the video work this curriculum builds toward. Three recurring agent jobs each point to a different newcomer strength.

A meeting copilot for conferencing — covered in its own lesson — has to listen to live audio, reason, and sometimes speak back, all within a tight latency budget. That profile points straight at the OpenAI Agents SDK's realtime voice pipeline, with its built-in speech-to-text, agent step, text-to-speech, and interruption detection. The Claude Agent SDK is a strong alternative when the copilot does heavier document work — pulling context from a knowledge base, drafting and checking a follow-up — rather than only conversing.

A video investigator agent for surveillance — the subject of a later lesson — runs long, branches on what a detector finds, and must sift a large archive without losing its place. The Claude Agent SDK's subagents (parallel search with isolated context) and compaction (surviving a long context) fit that shape; Google ADK with Vertex AI Agent Engine fits it too when the deployment must run at enterprise scale on Google Cloud with full audit and governance.

An async video-review pipeline for an OTT or e-learning archive — the archive pattern lesson — usually runs the same fixed steps over every file: ingest, transcribe, tag, index. The steps don't change, so the work wants a deterministic pipeline, and that is where a low-code tool earns its place — n8n self-hosted for data control, or an ADK workflow on Google Cloud — calling a model only at the one step that needs judgment, to keep the cost down.

Figure 3. The same decision, applied to three recurring video agent jobs. The job's shape — realtime voice, long archive search, or a fixed pipeline — points to the newcomer that fits it.

A Common And Expensive Mistake

The most frequent error with these newer tools is buying an autonomous product when the task needs a deterministic pipeline. Manus is impressive precisely because it decides its own steps — but an archive job that runs the same four steps over every file does not want an agent that re-plans each time; it wants a fixed workflow that calls a model only where judgment is required. Pointing a credit-metered autonomous agent at a high-volume, repetitive job is the fastest way to a surprising bill and a non-reproducible result. The mirror-image mistake is over-building: standing up an SDK, a vector database, and a multi-agent design to do what a five-step n8n workflow or a single Manus task would have handled. As we said in the framework lesson, match the tool to the job — buy when the job is generic and one-off, build when it is core to your product and runs at volume, and pick the simplest tool that covers the real requirement.

Where Fora Soft Fits In

We build video products across conferencing, streaming, OTT, surveillance, e-learning, telemedicine, and AR/VR, and when a feature needs an agent we treat the buy-versus-build choice as a first-class design decision rather than a default. For a realtime conferencing copilot we lean toward the SDK with the strongest voice pipeline; for a surveillance investigator that searches a large archive we lean toward subagents and durable context; for a fixed archive-review pipeline we lean toward a low-code or workflow tool that calls a model only where it earns its cost. Where a client has no engineering appetite and a modest, one-off need, we will say so and point them at a managed product instead of billing for a custom build. The categories are converging, so we optimize for what the client's team can run and afford after we hand the system over — an agent feature nobody can maintain or budget for is a liability, not an asset.

What To Read Next

• LangGraph vs CrewAI vs AutoGen — the agent framework decision
• Tool use, memory, planning — the agent primitives
• Agent eval, safety, cost, observability — AgentOps

Talk To Us · See Our Work · Download

• Talk to a video AI engineer — get a buy-versus-build recommendation matched to your agent feature, team, and budget: /services/llm-agent-development
• See our case studies — surveillance, conferencing, OTT, and telemedicine work: /portfolio
• Download the 2026 agent-tooling cheat sheet — the six tools, the buy-build spectrum, pricing units, and the decision rules on one page: Download the cheat sheet

References

1. Anthropic / Claude — "Building agents with the Claude Agent SDK" (29 September 2025) — https://claude.com/blog/building-agents-with-the-claude-agent-sdk — tier 3 (framework author). Source of truth for the Claude Code SDK → Claude Agent SDK rename (2025-09-29), the "give your agent a computer" design principle, the gather-context → take-action → verify loop, and the subagents, compaction, agentic search, and verification capabilities.
2. Claude API Docs — "Agent SDK overview" and migration guide — https://platform.claude.com/docs/en/agent-sdk/overview — tier 3 (author/de-facto spec). Package and type renames (@anthropic-ai/claude-agent-sdk, ClaudeAgentOptions), subagents, and MCP integration. De-facto spec for the SDK's surface.
3. OpenAI — "OpenAI Agents SDK" official documentation — https://openai.github.io/openai-agents-python/ — tier 3 (author/de-facto spec). Source of truth for "a production-ready upgrade of Swarm," the core primitives (Agents, Handoffs, Guardrails, Tracing), sessions, sandbox agents, MCP, and the realtime/voice agent pipeline (gpt-realtime-2, STT → agent → TTS).
4. OpenAI — Swarm repository (archived; redirects to the Agents SDK) — https://github.com/openai/swarm — tier 3 (author). Confirms Swarm is an educational experiment replaced by the Agents SDK; grounds the "if someone says Swarm in 2026 they mean the Agents SDK" claim.
5. Google Cloud — "Agent Development Kit (ADK)" and Vertex AI Agent Builder / Agent Engine documentation — https://docs.cloud.google.com/agent-builder/agent-development-kit/overview — tier 3 (author). ADK as the open-source multi-language kit, multi-agent and workflow orchestration, model-agnostic design, Agent Engine managed runtime, the Cloud Next 2026 "Gemini Enterprise Agent Platform" rebrand, and the 200+ model garden including Claude and Llama.
6. n8n — official repository and AI documentation — https://github.com/n8n-io/n8n and https://n8n.io/ai-agents/ — tier 3 (author). Fair-code license, 400+ integrations, self-hosting, ~70 LangChain-based AI nodes, native MCP, the AI Agent root/sub-node model, and multi-provider model support (OpenAI, Anthropic, Google, Mistral, Ollama local).
7. Zapier — AI features and pricing documentation — https://zapier.com/ai and https://help.zapier.com — tier 4 (vendor). The 2025 "AI orchestration" rebrand, Copilot / Agents / Chatbots, the per-task pricing model, AI add-on pricing, and the 8,000+ app catalog. Pricing figures are early-2026 and change often.
8. TechCrunch — "Chinese AI startup Manus reportedly gets funding from Benchmark at $500M valuation" (25 April 2025) — https://techcrunch.com/2025/04/25/chinese-ai-startup-manus-reportedly-gets-funding-from-benchmark-at-500m-valuation/ — tier 4 (trade press). The $75M Benchmark round at ~$500M valuation; Beijing Butterfly Effect origin; the March 2025 public launch. Cross-checked against the Manus pricing page and Wikipedia for the Singapore relocation and credit-based pricing.
9. Manus — official plans and pricing page — https://manus.im/pricing — tier 4 (vendor). The credit-based pricing model (free daily refresh; Pro and Team monthly credit allowances) and the "agent with a virtual computer" product description. The unpredictability of credit consumption is the budgeting caveat flagged in the body.
10. Yao et al. — "ReAct: Synergizing Reasoning and Acting in Language Models," arXiv:2210.03629 (Princeton University & Google, 2022) — https://arxiv.org/abs/2210.03629 — tier 5 (primary). The reason–act loop every tool in this lesson implements; grounds the "gather context, take action, verify" framing.