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LangGraph vs CrewAI for Freshers: Your First Multi-Agent Mini Project

By , trainer LinkedIn Published: Last updated: 5 min read

CrewAI and LangGraph are popular ways to build multi-agent apps (more than one AI role working together). CrewAI Cloud lists a Basic Free tier with about 50 workflow executions/month on its pricing page (23 October 2026); Enterprise is custom. Both ecosystems also offer open-source libraries you can run locally with your own API keys. LangGraph is positioned as a low-level agent runtime from the LangChain family — start free locally, and check LangSmith/LangGraph platform pricing separately if you use their cloud. This guide is a fresher mini-project compare, not a full framework course.

Suppose a GenAI fresher JD mentions LangChain, LangGraph or CrewAI. You do not memorise jargon only. You build a tiny "researcher + writer" flow, explain it in an interview, and keep secrets out of GitHub. That portfolio beat is what this page practices.

Related

For no-code agents see the n8n guides. For Python agent basics see the course agent chapters. This page is CrewAI vs LangGraph for freshers.

Related guide

Want a beginner coding agent with Spec mode? AWS Kiro for Beginners: Spec-First Coding (India Students) covers AWS Kiro Free/Students notes for India.

What you will learn in this guide

  • Multi-agent in plain words
  • CrewAI Free vs OSS notes
  • Researcher + writer in CrewAI style
  • Same flow sketched in LangGraph
  • Map skills to a sample fresher JD
  • Safety: keys in .env only

1. Suppose two AI teammates split one homework brief

Suppose you ask AI to research "UPI safety tips from public RBI pages" and then write a 120-word summary. One agent gathers bullets; another writes. You still open the links. That is multi-agent thinking at beginner size.

2. Words

Word Simple meaning
Agent An AI role with a goal and optional tools
Crew / graph How multiple agents are organised
CrewAI Framework + studio oriented around crews of agents
LangGraph Graph-style control for agent steps and loops
Execution One run of a workflow (cloud Free quotas apply)
Fresher multi-agent path in five steps Steps from top to bottom: 1 Pick one tiny goal, Researcher plus writer; 2 Try CrewAI style roles, Two agents only; 3 Sketch LangGraph nodes, Add a retry loop cap; 4 Keep keys in env, Never commit secrets; 5 Write honest JD bullets, No fake metrics. 1Pick one tiny goalResearcher plus writer 2Try CrewAI style rolesTwo agents only 3Sketch LangGraph nodesAdd a retry loop cap 4Keep keys in envNever commit secrets 5Write honest JD bulletsNo fake metrics

Figure 1. Fresher multi-agent path in five steps: pick one tiny goal, try CrewAI style roles, sketch LangGraph nodes, keep keys in env, then write a JD skills line.

3. Cost notes (checked 23 October 2026)

  1. CrewAI Cloud Basic Free: about 50 workflow executions/month on crewai.com/pricing; Enterprise custom.
  2. CrewAI / LangGraph open-source: free libraries; you pay model APIs (OpenAI, Gemini, etc.).
  3. LangGraph platform / LangSmith: separate cloud products — open their pricing if you leave local-only mode. Do not invent a flat "LangGraph Pro ₹999" fee.
  4. For learning, local OSS + a small Gemini/OpenAI credit is enough.

4. Worked example 1: researcher + writer in CrewAI style

  1. Install the official CrewAI package in a practice venv (follow current docs).
  2. Define two roles in code or Studio: Researcher (find 5 public bullets) and Writer (120-word summary).
  3. Give tools only if needed (web search) and use public URLs.
  4. Run once on topic: "UPI OTP fraud signs for beginners".
  5. Manually open any cited links before you trust the summary.

Pseudo-structure (shape only — check live docs for exact APIs):

Researcher goal: collect 5 bullets from public sources
Writer goal: rewrite bullets in simple English under 120 words
Shared rule: no invented RBI circular numbers

Expected output: a short summary you can paste into a README as a learning sample.

5. Worked example 2: same flow sketched in LangGraph

  1. Draw boxes on paper: START → research_node → write_node → END.
  2. In LangGraph terms, each node is a function; edges decide the next step.
  3. Add a conditional edge: if research bullets are under 3, loop research once more (cap at 2 loops).
  4. Implement later with official LangGraph docs; keep the drawing in your notes.
  5. Interview line: "CrewAI made roles obvious; LangGraph made the loop explicit."

Expected output: a one-page graph sketch you can show on camera.

Ravindra Bagale's Tip

Freshers paste API keys into Discord for "debug help". Rotate the key immediately if you ever did that. Use .env and .gitignore.

6. Worked example 3: map to a sample GenAI fresher JD

Sample JD skills (made-up company, real skill names): "Python, LangChain/LangGraph or CrewAI, RAG basics, prompt design."

  1. Write three bullets for your resume from real practice only:
- Built a two-agent researcher-writer mini project (CrewAI learning path)
- Sketched the same flow as a LangGraph state diagram with a retry loop
- Kept API keys in .env; used public URLs only
  1. Do not invent production metrics ("reduced cost 80%").
  2. Link a public GitHub repo without secrets.
  3. Cross-link interview prep: course chapter on Gen AI interview questions.
  4. No fake salary claims on LinkedIn.

7. Beginner mistakes and quick fixes

Mistake What happens Quick fix
10 agents on day one Confusion Two roles only
No loop cap Infinite spend Max 2 retries
Keys in repo Leak .env + gitignore
Claiming Expert Interview fail Say learning project
Skipping citations Hallucinations Open every link

8. Try at home

Try at home

  1. Build or Studio-run a two-agent summary on a public topic.
  2. Draw the LangGraph-style boxes for the same flow.
  3. Add three honest resume bullets — no invented metrics.

Recap

In short

  1. Multi-agent means roles + a control path.
  2. CrewAI Free cloud has limited executions; OSS is free software.
  3. LangGraph shines when you need explicit graphs and loops.
  4. Two agents + public data is enough for a fresher demo.
  5. Keys stay in .env; resumes stay truthful.

Two honest agents in a tiny project beat ten buzzwords on a slide.

Frequently asked questions

Which should I learn first?

Many freshers feel CrewAI roles faster. Learn LangGraph next when you need explicit loops. Either is fine if you ship one mini project.

Is CrewAI Free enough?

About 50 cloud executions/month on the public Basic Free card — fine for learning. Heavy practice may need local OSS + your API keys.

Do I need LangChain first?

Helpful but not mandatory for a tiny demo. Learn core chat API calls, then add frameworks.

Will this get me a job alone?

No. Combine with Python basics, one RAG mini project, and honest interview practice.