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Production AI Agents with LangChain + LangGraph [2026]
Master RAG, Multi-Agent Systems, LangGraph and FastAPI -- Build and Deploy Real-World AI Agent Projects in Python. this is applicable to AI Agents Udemy discount offers.
by Paolo Dichone · ⭐ 4.5 via Udemy · 3,728 students · English
$12.99$199.9994% OFF
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✓ 94% discount manually re-verified, not an estimate.
✓ Certificate + lifetime access included by Udemy.
⚠ Redemption-limited — can run out without notice.
Price history First-party data
Tracked since March 11, 2026 to June 29, 2026. Red dot = current price ($12.99) — CoursesWyn records the price on each sync.
Current price
$12.99
Original price
$199.99
Tracked since
March 11, 2026
Last checked
Jun 29
Price and coupon status are recorded from a periodic sync of Udemy’s public catalog. This snapshot was taken on June 29, 2026. Prices are always confirmed at checkout on the provider’s site.
Deal score Computed, methodology below
8.8
/ 10
Discount depth
94%
Udemy rating
4.5 / 5
Popularity
3,728 students
Tracking length
150 days
Score = weighted average of discount depth, how often CoursesWyn re-checks this code, historical price volatility, and how fast past redemptions were claimed. Not an editorial opinion on the course itself.
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Mar 11, 2026Listed by CoursesWyn — price recorded at $12.99Listed
Jun 29, 2026Coupon checked — checkout price $12.99 · listed until Jul 2Working
Course details Synced from Udemy
Fields below are pulled from Udemy’s public catalog and recorded when CoursesWyn syncs — they are catalog data, not editorial description.
| Last checked | June 29, 2026 |
| Level | All Levels |
| Content | 17 hours |
| Language | English |
| Requirements | Python intermediate level — comfortable with functions, classes, decorators, and type hints · Basic command line familiarity — creating directories, running scripts, installing packages |
| Certificate | Yes, on completion |
| Access | Lifetime, mobile & TV |
What you'll learn From the course description
Build composable LLM chains using LangChain v.1's LCEL with structured output, streaming, batch processing, and multi-provider switching
Implement production RAG pipelines with intelligent chunking, vector stores, and 4 advanced retrieval patterns: Multi-Query, Contextual Compression, Hybrid Sear
Design stateful AI agents with LangGraph state machines, conditional routing, self-correcting loops, and human-in-the-loop approval workflows
Orchestrate multi-agent systems using supervisor patterns, agent handoffs, parallel execution, and hierarchical team structures
Secure LLM applications against prompt injection, PII leakage, and output manipulation with production-grade defense layers
Test and evaluate LLM systems using unit tests, integration tests, and semantic evaluation across correctness, relevance, and coherence
Deploy production APIs with FastAPI, rate limiting, response caching, structured logging, metrics, LangSmith tracing, and Docker
Build 3 real-world applications: Customer Support Agent, Multi-Agent Research System, and Code Review Agent, each with measurable business ROI
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