The 6 Lár Primitives (The "Lego Bricks")

Lár is not a heavy, complex framework. It is a tiny, powerful engine with 6 core "primitives." You can combine these "Lego bricks" to build any agent, from a simple chatbot to a complex, multi-agent orchestrator.

• The GraphExecutor

What it is: The "Engine" or "Conveyor Belt."

Job: A simple generator that runs one node at a time. It's the "dumb" loop that powers the "smart" nodes. It is also the "flight data recorder" that yields the audit log for each step.

• The GraphState

What it is: The "Memory" or "Clipboard."

Job: A simple Python object (a dictionary wrapper) that is passed to every node. It's how nodes share information. LLMNode writes to it, ToolNode reads from it, and RouterNode makes decisions with it.

• The LLMNode

What it is: The "Brain" or "Thinker."

Job: This node calls a generative model (like Gemini). It's where reasoning, writing, or classification happens. Our LLMNode is resilient—it has built-in exponential backoff to automatically retry on 429 rate-limit errors. It also logs token usage for cost auditing.

• The ToolNode

What it is: The "Engine" or "Conveyor Belt."

Job: This node runs any simple Python function. This is how your agent interacts with the world: running code, searching a database, calling an API, or even running a local FAISS search. It has separate next_node and error_node paths, making it robust.

• The RouterNode

What it is: The "Choice" or "Manager."

Job: This is your if/else statement. It runs a simple, deterministic Python function (a decision_function) that reads the GraphState and returns a string (e.g., "success"). The RouterNode then uses this string to pick the next node from its path_map.

• The "Utility" Nodes

What they are: Simple "helper" bricks.

AddValueNode: Adds/copies data to the state. It's the perfect "final" node to copy {draft_answer} to {final_answer}.

ClearErrorNode: A "janitor" node. Its only job is to set last_error to None, which is critical for building self-correcting loops.