Functions, Tools and Agents with LangChain
by Harrison Chase · DeepLearning.AI
Our Verdict
Worth it — with caveatsFunctions, Tools and Agents with LangChain is a free, roughly 1-hour-45-minute DeepLearning.AI short course taught by Harrison Chase, co-founder and CEO of LangChain, and it is a worthwhile, hands-on primer for developers who want to use OpenAI function calling and LangChain to build tool-using agents. Across six short notebook lessons (OpenAI Functions, LangChain Expression Language, Function Calling, Tagging & Extraction, Tools/Routing/APIs, and a Functional Conversation agent), it focuses on generating structured output and composing chains with LCEL rather than on deep theory. The Coursera guided-project mirror of the course holds a real 4.7/5 rating from 120 ratings, with about 79% of reviewers giving 5 stars. The main caveat is recency: the course was last updated in mid-2024 and teaches LCEL plus AgentExecutor for agent construction, whereas LangChain has since steered serious agent work toward LangGraph. It remains an efficient, code-first introduction to function calling, but treat the agent-orchestration patterns as a foundation to build on rather than the current best practice.
Strong, free, code-first introduction to OpenAI function calling and LCEL from the person who built LangChain, but the mid-2024 content predates the LangGraph era of agent building, so it is best taken as a fundamentals primer rather than an up-to-date agent-architecture course.
Best for: Developers and data professionals comfortable with Python who already understand basic LLM prompting and want a fast, practical introduction to OpenAI function calling, structured output, and composing chains/agents with LangChain Expression Language (LCEL). It is ideal as a follow-on to an introductory LangChain or prompt-engineering course and for engineers who learn best by running Jupyter notebooks.
Skip if: Complete beginners with no Python or LLM experience (it is labeled intermediate and moves quickly), people who want a certificate of completion (none is offered on the free DeepLearning.AI version), and developers who specifically need current best-practice agent architecture, since the course teaches LCEL and AgentExecutor rather than LangGraph, which LangChain now recommends for complex/stateful agents.
About This Course
Use OpenAI function calling and LangChain tools to build agents that can interact with APIs and databases.
What You'll Learn
Curriculum
Introduction to OpenAI's function-calling capability and how an LLM can return structured function-call output instead of free text.
The new declarative syntax for composing chains and runnables, which underpins how chains and agents are customized throughout the course.
Using function calling inside LangChain to produce reliable structured outputs from models.
Applying function calling to classify/tag inputs and extract structured data from unstructured text.
Defining tools, routing between them based on context, and calling external APIs from an LLM application.
Putting the pieces together to build a tool-using conversational agent.
Prerequisites
- Working knowledge of Python (intermediate level)
- Basic familiarity with LLMs and prompting (e.g. having completed ChatGPT Prompt Engineering for Developers or LangChain for LLM Application Development helps)
- An OpenAI API understanding/key to follow along outside the in-browser notebooks
Instructor
Harrison Chase
Instructor · DeepLearning.AI
Pros & Cons
Pros
- Taught by Harrison Chase, LangChain's co-founder and CEO, so the function-calling and LCEL material comes straight from the framework's author
- Completely free on DeepLearning.AI with hands-on, runnable Jupyter notebooks for every lesson (also mirrored as a free Coursera guided project)
- Tightly scoped and efficient: a clear, code-first path through function calling, structured output, tagging/extraction, tool routing, and agents
- Strong real-world reception, with a 4.7/5 rating from 120 ratings on the Coursera mirror and reviewers praising how easy it is to code along
Cons
- Content was last updated in mid-2024 and teaches LCEL plus AgentExecutor, while LangChain has since shifted recommended agent building toward LangGraph for dynamic routing and state management
- Despite the compact ~1h45m runtime, multiple learners note the hands-on coding realistically takes considerably longer once you run and experiment with every notebook
- No certificate of completion on the free version, and the intermediate pace assumes prior Python and LLM familiarity
- Narrow in scope: it covers function calling and basic agents but not production concerns like evaluation, deployment, memory at scale, or multi-agent systems
Alternatives To Consider
Frequently Asked Questions
Is Functions, Tools and Agents with LangChain free?
Yes — Functions, Tools and Agents with LangChain is free to access. Free to audit on DeepLearning.AI (no certificate). The same material is available as a free guided project on Coursera. No paid tier is required for the core content; you only need your own OpenAI API access if running the notebooks outside the provided environment.
Who is Functions, Tools and Agents with LangChain for?
Developers and data professionals comfortable with Python who already understand basic LLM prompting and want a fast, practical introduction to OpenAI function calling, structured output, and composing chains/agents with LangChain Expression Language (LCEL). It is ideal as a follow-on to an introductory LangChain or prompt-engineering course and for engineers who learn best by running Jupyter notebooks.
What will you learn in Functions, Tools and Agents with LangChain?
Generate structured output and call functions from LLMs using OpenAI's function calling capability; Use LangChain Expression Language (LCEL) to compose and customize chains and agents; Apply function calling to practical tasks like tagging and data extraction; Select and route between tools, and connect LLMs to external APIs.
What are the prerequisites for Functions, Tools and Agents with LangChain?
Working knowledge of Python (intermediate level); Basic familiarity with LLMs and prompting (e.g. having completed ChatGPT Prompt Engineering for Developers or LangChain for LLM Application Development helps); An OpenAI API understanding/key to follow along outside the in-browser notebooks.
Is Functions, Tools and Agents with LangChain worth it?
Strong, free, code-first introduction to OpenAI function calling and LCEL from the person who built LangChain, but the mid-2024 content predates the LangGraph era of agent building, so it is best taken as a fundamentals primer rather than an up-to-date agent-architecture course.
How we reviewed this course
This is an independent editorial assessment by Cursarium, based on DeepLearning.AI's published course materials and aggregated public learner feedback (last reviewed 2026-06). We have not independently completed the course. Links to providers are standard references, not paid placements.
Sources
- Official course page - Functions, Tools and Agents with LangChain (DeepLearning.AI)
- Coursera guided project (rating 4.7/5, 120 ratings)
- Class Central listing (provider, duration, free, reviewer notes)
- Course notebooks repo confirming the six lesson titles (Ryota-Kawamura)
- Analysis of LCEL and AgentExecutor limitations / shift to LangGraph (DEV Community)