Before writing the first line of Agent code, build a complete cognitive framework. This includes LLM principles, Agent paradigms, key concepts, plus a readable, reproducible, thinkable knowledge system.
See the connections between knowledge nodes at a glance. Choose where to dive based on your progress.
graph TD
Start([π€ Start here]) --> A[π€ What is an LLM?]
A --> B[π Prompt Engineering]
A --> C[π¬ Transformer & Attention]
B --> D[π οΈ Function Calling]
D --> E[π€ Agent Paradigms]
C --> F[π§ Reasoning]
F --> E
E --> G[π ReAct Loop]
E --> H[π Plan-and-Execute]
E --> I[π₯ Multi-Agent]
G --> J[πΎ Memory Systems]
H --> J
I --> J
J --> K[π§° Tools & Orchestration]
K --> L[π RAG]
L --> M[π Project Blueprint]
K --> M
I --> M
M --> N([π― Deliver Large Agent])
classDef critical fill:#7c5cff,stroke:#00d4ff,color:#fff,stroke-width:2px;
classDef important fill:#00d4ff,stroke:#00ffa3,color:#000;
classDef useful fill:#00ffa3,stroke:#7c5cff,color:#000;
classDef final fill:#ff6ec7,stroke:#ffb84d,color:#fff,stroke-width:3px;
class A,B,D,E critical;
class G,H,I,J important;
class C,F,K,L useful;
class M,N final;
From the LLM "brain", to tools, memory, and planning β dissect each key Agent capability.
The Agent's "brain". Based on the Transformer architecture, learns statistical patterns from massive text, generates the next token autoregressively.
Steering LLM output through carefully constructed instructions. The "art" of talking to LLMs.
Lets LLMs break beyond pure text, calling external APIs, querying databases, executing code. The source of Agent "action capability".
More than pattern matching. LLMs "reason" between tokens via CoT, Self-Consistency, Tree-of-Thoughts.
Short-term conversation buffer, long-term fact storage, vector semantic memory β key to Agent continuous evolution.
Decompose complex tasks into executable steps, choose appropriate tools, handle exceptions during execution and dynamically adjust plans.
Understand each paradigm's design motivation, use cases, and typical implementations.
The most classic Agent paradigm. Each loop: the model first thinks (Thought) about current state, decides the next action, executes, observes the result, and enters the next loop until the task is complete.
LLM analyzes current state, decides what to do next. This determines the Agent's "intelligence".
Choose tools, generate parameters, call external systems. The Agent's "action capability".
Receive tool return results as input for the next loop.
If task is complete, output the final answer; otherwise return to Thought to continue.
ReAct's "thought quality" directly determines Agent upper limit. This is why OpenAI o1/o3 reasoning models continue to lead in large tool-call scenarios.
Suitable for complex, decomposable tasks. First use LLM to generate a complete execution plan, then execute step by step. Typical representatives: BabyAGI, AutoGPT, LangGraph planner.
Hand complex tasks to multiple specialized Agents who collaborate, debate, supervise. CrewAI, AutoGen, MetaGPT are representatives.
Multi-Agent isn't always better. It increases token costs and complexity. Single Agent is more efficient when tasks are clear.
LangGraph abstracts Agents as directed graphs (DAG) with nodes as actions and edges as conditions. Naturally supports state management, human intervention, parallel branches.
Complete the checklist below. If anything is uncertain, return to the relevant section.
List complete? Move on to the next phase:
Phase 2 Β· Skills & Tools β