How much does AI consulting cost?

BlokAI's engagements range from $5,000 to $75,000 depending on scope. Simple automations and LLM integrations start around $5K–$15K. Custom AI agent systems and full data infrastructure projects run $30K–$75K. We scope every project before committing to a price.

What does BlokAI build?

BlokAI builds AI agents, LLM applications, RAG systems, automation pipelines, and data infrastructure for startups and SMBs. We take projects from architecture to production — not demos, not prototypes.

Is BlokAI based in Japan?

Yes. BlokAI is a solo studio run by Kent Tran, based in Tokyo, Japan. We work with clients globally in English.

What is RAG and why does my business need it?

RAG (Retrieval-Augmented Generation) lets AI systems search your own documents, databases, or knowledge bases before answering — instead of relying only on training data. It powers AI customer support, internal search tools, and sales enablement systems that are accurate and up-to-date.

AI Agent Frameworks in 2025: LangGraph, CrewAI, AutoGen — Which One? — BlokAI Blog

"The AI agent framework ecosystem has reached critical mass: LangGraph crossed 1 million monthly downloads, CrewAI surpassed 30,000 GitHub stars, and enterprise adoption of multi-agent systems tripled in Q4 2024." — GitHub / LangChain Blog, February 2025

The AI agent framework space has exploded in the past twelve months. Every few weeks, a new framework appears claiming to solve the agent reliability problem. At the same time, the established frameworks have matured significantly.

If you're building agents in 2025 and wondering which framework to use, this post is the honest comparison I wish existed when we started evaluating them.

The Landscape (As of Early 2025)

The main contenders:

LangGraph — Built by the LangChain team. Graph-based state machine approach to agent workflows. Production-grade, battle-tested, actively developed. CrewAI — Multi-agent framework where you define "crews" of specialised AI agents working together. Higher-level abstraction, faster to get started with for multi-agent scenarios. AutoGen — Microsoft Research's framework for multi-agent conversation. Research-oriented origins, increasingly production-capable. LangChain Agents (legacy) — The original LangChain agent abstraction. Still functional, but largely superseded by LangGraph for complex workflows. Vercel AI SDK — Primarily frontend-focused but increasingly has agent primitives. Best for Next.js/TypeScript stacks where the AI is part of a web application. Custom / No Framework — Direct API calls with your own orchestration logic. More control, more work, but sometimes the right answer.

LangGraph: The Production Choice for Complex Agents

LangGraph uses a directed graph model for agent workflows: nodes are functions (model calls, tool calls, conditional logic), and edges are transitions between them. You define explicit control flow.

Why it wins for production complex agents:

Explicit state management — you define exactly what state the agent carries through its workflow, which makes debugging tractable
Built-in support for human-in-the-loop — pausing for human approval, collecting input mid-workflow
Checkpointing and resumability — workflows can be interrupted and resumed, critical for long-running tasks
First-class streaming support
Strong integration with LangSmith for observability

The trade-off: More verbose and requires more upfront design thinking. The graph model requires you to think carefully about your workflow structure before writing code. This is actually a feature — it forces clarity — but it's more initial work than some alternatives. Best for: Complex, multi-step agent workflows where reliability is critical. Any production system where you need to understand what the agent is doing at each step. We use LangGraph for: Research agents, data pipeline agents, any multi-step workflow that runs autonomously and needs to be debugged in production.

CrewAI: The Fast Path for Multi-Agent Systems

CrewAI offers a higher-level abstraction: you define agents by role ("Researcher", "Writer", "Reviewer") with specific goals and capabilities, then define tasks and let the framework manage the coordination.

Why it's compelling:

Fastest path to a working multi-agent demo — often a few dozen lines of code
Natural mental model for role-based collaboration
Good documentation and active community
Works well for workflows where multiple specialised agents need to collaborate

The trade-offs: Less control over exactly what happens at each step. The framework makes decisions about agent coordination that can be hard to reason about when things go wrong. Debugging failed workflows is harder than in LangGraph. Less production-battle-tested than LangGraph. Best for: Rapid prototyping. Workflows where the multi-agent collaboration model naturally fits the problem. Teams that want to get something working quickly and iterate. We use CrewAI for: POCs and rapid evaluation of multi-agent approaches. Sometimes as a first pass before refactoring to LangGraph if the use case goes to production.

AutoGen: The Research-Heritage Option

AutoGen comes from Microsoft Research and has a strong theoretical foundation in multi-agent systems. It's been used in a significant amount of AI research.

Strengths:

Strong for code-centric agents (unsurprising given its research context)
Good for complex multi-agent coordination scenarios
Microsoft investment means it's going to be maintained

Trade-offs: The API has gone through significant changes and at times feels less ergonomic than alternatives. The learning curve is steeper. It's more commonly used in research contexts than pure production business applications. Best for: Teams with technical depth who specifically want the AutoGen model. Code-centric agent applications. Organisations already invested in Microsoft's AI ecosystem. We use AutoGen for: Rarely, in specific contexts where the code execution model fits the use case precisely.

The "No Framework" Option

Sometimes the right answer is to not use a framework at all.

For simple, single-step "agent" workflows — a model call that uses one or two tools and returns a result — the added complexity of an agent framework isn't worth it. Direct API calls with function calling, your own orchestration logic, and careful error handling is often cleaner and more maintainable.

The "framework tax" — the abstraction layer between your code and the model API — is worth paying when your workflow is genuinely complex (multiple agents, significant state management, complex control flow). For simple cases, it adds indirection without adding value.

We go frameworkless for: Single-model workflows with straightforward tool use, high-performance applications where framework overhead matters, and teams with strong Python skills who prefer explicit code over framework magic.

The Decision Framework

Use LangGraph when:

Building for production (not POC)
The workflow has complex state management
You need human-in-the-loop capabilities
Debugging and observability are critical
The workflow has conditional branching and error recovery paths

Use CrewAI when:

Building a POC or evaluating an approach quickly
The multi-agent collaboration model naturally fits your use case
You want the fastest path to a demo

Use AutoGen when:

Code-centric agents are central to your use case
You're already in the Microsoft ecosystem
Your team has the technical depth to work with its API model

Go frameworkless when:

Simple, single-step tool use
Performance is critical
The workflow doesn't warrant orchestration complexity

The meta-advice: Framework selection matters less than workflow design. A poorly designed workflow will fail regardless of framework. A well-designed workflow with clear state management, error handling, and human oversight points will work in any of these frameworks.

Don't spend more time picking the framework than designing the workflow.

Want to implement AI in your business? Book a 30-minute architecture call at blok-ai.com.