AI & LLM Skill
Agent Eval Suite Langsmith
Production agent eval suite LangSmith dataset curation + Promptfoo assertion framework +…
$15
Langchain Architecture
Design LLM applications using LangChain 1.x and LangGraph for agents, memory, and tool…
A production playbook for designing LLM applications with LangChain 1.x and LangGraph: covering agents, typed state, memory, and tool integration. It shows you how to model complex AI workflows as testable StateGraph nodes, wire durable execution with checkpointers, and ship streaming-first, observable applications.
$15 one-time
Add to a kit → Prices include 20% VAT. · Forged on real agency work · one-time, no lock-in
- Type Skill
- Category AI & LLM
- Delivery Email · instant
- License One-time
Inside the run · no black box
See the actual work before you buy it.
Agents without an iteration cap eventually loop forever. Applications are modeled as typed state graphs with checkpointed threads, human approval before side effects, and quality drift caught by a weekly regression set.
- Models the application as a typed state graph: every LLM interaction (retrieve, generate, validate, route) becomes its own node, branching logic lives in conditional edges outside the nodes, and agents get a hard iteration cap to kill infinite loops
- Defines tools as structured schemas with concrete field examples in every description; past roughly 7 tools, routing moves to a supervisor agent because direct selection accuracy collapses
- Wires a checkpointer (in-memory for development, Postgres or Redis for production) so each conversation thread persists its state and a failed run resumes from the last checkpoint instead of restarting
- Inserts human-in-the-loop interrupts before side-effecting nodes such as sending email or writing to a database, where the state is inspected and approved before execution continues
- Streams from the first token: token events render live to the user, tool-start events surface interim status messages, and batch workloads run async in parallel instead of blocking
- Traces every call with token usage, latency and error data in the observability layer, and runs a weekly fixed-question regression set so quality drift is caught by numbers, not complaints
One power source. 6 lines out.
langchain-architecture · core
core active · 6 lines
- ✓ building autonomous agents
Building autonomous agents with tool access
- ✓ orchestrating multi-step…
Orchestrating multi-step LLM workflows that can fail and resume
- ✓ managing conversation me…
Managing conversation memory and persistent state across sessions
- ✓ implementing rag pipelines
Implementing RAG pipelines with retrieve-then-generate graphs
- ✓ designing supervisor-rou…
Designing supervisor-routed multi-agent systems
- ✓ adding langsmith tracing
Adding LangSmith tracing and token-cost observability
Yours to keep.
Drag time forward. Watch what stays.
Forever
That's what owning means.
The rented stack
ai writing tool: subscription
expired · access lostanalytics suite: subscription
expired · access lostdesign platform: subscription
expired · access lost(nothing left)
Your forge
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Debug agents node-by-node instead of staring at a black box
license: perpetual -
Resume long-running workflows from the point of failure, not the start
license: perpetual -
Cut repeat-query cost with caching and smart model routing
license: perpetual -
Lower perceived latency with streaming token and tool events
license: perpetual
subscriptions expire · deeds don't
Everything in the box.
Pick a piece up. Watch it work.
ReAct and multi-agent patterns with create_react_agent
6 parts · one working system · ships instantly by email
This wasn't forged for everyone.
- Not for you if you'd rather rent a tool than own one.
- Not for you if you want someone else to run your stack.
- Not for you if you're happy guessing.
AI and backend engineers building agentic, production-grade LLM applications on the LangChain and LangGraph stack.
then this was forged for you.Works with
Universal by design: these run in any AI. Delivered in the open Agent Skills + MCP format (native in Claude); ChatGPT, Gemini, Cursor and Copilot adapt the same files their own way.
- Claude Native format
- ChatGPT Adapts via open standards
- Gemini Adapts via open standards
- Cursor Adapts via open standards
- Copilot Adapts via open standards
Catch what's on your mind.
the air is clear. nothing between you and the forge.
catch a spark: the forge will answer
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I call the OpenAI SDK directly today. Is this still relevant, or only for existing LangChain users?
It is written for the LangChain 1.x and LangGraph stack, so the patterns assume those primitives: StateGraph nodes, checkpointers, create_react_agent. If you are deciding whether to adopt that stack, the templates show what you would gain; if you plan to stay on raw SDK calls, most of the material will not map over.
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How does it make agent failures debuggable instead of a black box?
By modeling the workflow as typed StateGraph nodes you can inspect step by step, and wiring checkpointers so a failed run resumes from the failing node instead of restarting. Add the LangSmith tracing patterns and you also see token cost per step.
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Is this a hosted service or a library I install?
Neither. It is an architecture playbook with templates: ReAct and multi-agent patterns, typed StateGraph examples for RAG and multi-step workflows, memory options from in-memory to PostgreSQL, and a production deployment checklist. You still write and own the code.
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How is it delivered?
By email right after purchase: ready to run, downloaded instantly, no setup wait.
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One-time or subscription?
A one-time purchase; no subscription or hidden fees. VAT (20%) is included.
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Can I get a refund?
As a digital product, it can’t be refunded once downloaded. That’s why we show exactly what’s inside and who it’s for, right here.