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Learning track 05

Application Engineering

Turn LLM behavior into maintainable Python services, graph runtimes, APIs, and deployable systems.

Guides
6
Sections
43
Cards
206

Suggested sequence

Follow the track or enter where the problem begins.

Every guide is self-contained. The sequence only preserves prerequisite order when one topic depends on another.

  1. 01
    Vol. 03

    The LLM Framework Ecosystem

    Key abstractions, architectural patterns, pitfalls and selection criteria for the main frameworks of the LLM ecosystem. Each one is disassembled atomically.

    Sections
    8
    Cards
    28
    Open guide
  2. 02
    Vol. 20

    The Python Utility Stack for LLM Pipelines

    LLM applications live not only on models. Around them there is almost always templating, structure validation, string sanitization, JSON parsing, regex, file paths, batching, caching, async I/O and little standard library functions that save hours. This volume is not about “AI”, but about service Python, which makes the LLM pipeline stable and readable.

    Sections
    7
    Cards
    37
    Open guide
  3. 03
    Vol. 22

    Production LangChain + LangGraph: Code Structure & Runtime

    This volume answers a more applied question: how does a real Python project on LangChain + LangGraph spread out into files? Not “which framework is better”, but where exactly “state”, “context”, “tools”, “middleware”, “nodes”, “graph.compile(...)”, “checkpointer”, “store”, HTTP-endpoints, “thread id”, streaming and “resume” live. This is a codebase map, not a concept map.

    Sections
    7
    Cards
    34
    Open guide
  4. 04
    Vol. 24

    Mini-Repository: FastAPI + LangGraph + LangChain as a Service

    This volume does not answer the question “how to write another agent”, but the question “how to make a live external application”. At the center is one solid Python mini-repo: ‘FastAPI’ as a public HTTP/SSE layer, ‘LangGraph’ as runtime, ‘LangChain’ as a layer of models, tools and middleware, external persistence for threads, scaling, auth, health/readiness and a separate place for ‘MCP’ as a tool/data plane.

    Sections
    7
    Cards
    36
    Open guide
  5. 05
    Vol. 25

    Starter Kit: FastAPI + LangGraph + LangChain by File

    It's almost a project template. Not an abstract architecture, but a small, but production-focused starter kit: what files to create, what to put in them, how to assemble a ‘FastAPI’ service, where ‘LangGraph’ and ‘LangChain’ live, how to add durability, streaming, ‘resume’, tests, Docker and an optional ‘MCP’ layer.

    Sections
    7
    Cards
    35
    Open guide
  6. 06
    Vol. 26

    Deployment & DevOps for an LLM Service

    This volume is not about the code inside the agent, but about the operation outside: ingress, TLS, forwarded headers, “root path”, probes, secrets, queues, tracing, metrics, token/cost budgets, autoscaling, rollout/rollback and the place of “MCP” in the production circuit. This is a map of what it takes for an LLM service to not just run, but live and experience real traffic.

    Sections
    7
    Cards
    36
    Open guide
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