lar-engine 0.6.6

Lár: The PyTorch for Agents. A 'define-by-run' agentic framework.

Lár: The Pytorch for Agents

Lár: The PyTorch for Agents.

Lár by SnathAI™ is an open source "define-by-run" agentic framework for building auditable, multi-provider and reliable AI systems.

Lár (Irish for "core" or "center") is engineered as a robust alternative to static, "black box" frameworks, which obscure logic, inhibit debugging, and fail unpredictably. Lár implements a "glass box" architecture, inspired by the dynamic graphs of PyTorch, where every step of an agent's reasoning process is explicit, inspectable, and logged by default.

This framework provides a deterministic, stateful, and node-based system for orchestrating complex agentic behavior, including self-correction, dynamic branching, and tool-use loops.

---

Full Documentation https://docs.snath.ai

Core Philosophy: "Glass Box" vs. "Black Box"

The primary challenge in production-grade AI is a lack of traceability. When a multi-step agent fails, it's often impossible to determine why.

• The "Black Box" (Other Frameworks): Relies on a "magic" AgentExecutor that tries to do everything at once. When this magic fails, it's a complex black box that is nearly impossible to debug.

• The "Glass Box" (Lár): Lár is, by design, a simple, explicit loop. The GraphExecutor runs one node at a time, logs the exact state change, and then pauses. It supports all major LLMs (OpenAI, Anthropic, Gemini, etc.) via a unified adapter.

This "define-by-run" approach transforms debugging from an art into a science. You can visually trace the execution, inspect the diff of the state at every transition, and pinpoint the exact node where logic failed. Lár's "Flight Log" (history) isn't an add-on; it's the core output of the engine.

Why Lár is Better: The "Glass Box" Advantage

The Problem	"Black Box" Frameworks (e.g., LangChain)	Lár (The "Glass Box" Engine)
Debugging	A Nightmare. When an agent fails, you get a 100-line stack trace from inside the framework's "magic" AgentExecutor. You have to guess what went wrong.	Instant & Precise. Your history log is the debugger. You see the exact node that failed (e.g., ToolNode), You see the exact error (APIConnectionError), and the exact state that caused it.
Auditability	External & Paid. "What happened?" is a mystery. You need an external, paid tool like LangSmith to add a "flight recorder" to your "black box."	Built-in & Free. The "Flight Log" (history log) is the core, default, open-source output of the GraphExecutor. You built this from day one.
Multi-Agent Collaboration	Chaotic "Chat Room." Agents are put in a room to "talk" to each other. It's "magic," but it's uncontrollable. You can't be sure who will talk next or if they'll get stuck in a loop.	Deterministic "Assembly Line." You are the architect. You define the exact path of collaboration using RouterNode and ToolNode.
Deterministic Control	None. You can't guarantee execution order. The "Tweeter" agent might run before the "Researcher" agent is finished.	Full Control. The "Tweeter" (LLMNode) cannot run until the "RAG Agent" (ToolNode) has successfully finished and saved its result to the state.
Data Flow	Implicit & Messy. Agents pass data by "chatting." The ToolNode's output might be polluted by another agent's "thoughts."	Explicit & Hard-Coded. The data flow is defined by you: RAG Output -> Tweet Input. The "Tweeter" only sees the data it's supposed to.
Resilience & Cost	Wasteful & Brittle. If the RAG agent fails, the Tweeter agent might still run with no data, wasting API calls and money. A loop of 5 agents all chatting can hit rate limits fast.	Efficient & Resilient. If the RAG agent fails, the Tweeter never runs. Your graph stops, saving you money and preventing a bad output. Your LLMNode's built-in retry handles transient errors silently.
Core Philosophy	Sells "Magic."	Sells "Trust."

Key Features

• Define-by-Run Architecture: The execution graph is created dynamically, step-by-step. This naturally enables complex, stateful logic like loops and self-correction.

• Total Auditability: The GraphExecutor produces a complete, step-by-step history of every node executed, the state before the run, and the state after.

• Deterministic Logic: Replace "prompt-chaining" with explicit, testable Python code. Use the RouterNode for clear, auditable "if/else" branching.

• Testable Units: Every node is a standalone class. You can unit test your ToolNode (your "hands") and your RouterNode (your "logic") completely independently of an LLM call.

---

A Simple Self-Correcting Loop

graph TD
    A[Start] --> B[Step 0: PlannerNode - Writer]
    B --> C1[Step 1: ToolNode - Tester]
    C1 --> D{Step 2: RouteNode - Judge}

    %% Success path
    subgraph Success_Path
        direction TB
        G[Step 5: AddValueNode - Finalize]
    end

    %% Correction loop
    subgraph Correction_Loop
        direction TB
        E[Step 3: LLMNode - Corrector]
        F[Step 4: ClearErrorNode - Cleanup]
    end

    D -- Success --> G
    D -- Failure --> E
    E --> F
    F --> C1
    G --> H[End]


    classDef default stroke:#8FA3B0, color:#FFFFFF, fill:#1E293B;
    classDef decision stroke:#8FA3B0, color:#FFFFFF, fill:#1E293B;
    classDef startend stroke:#8FA3B0, color:#FFFFFF, fill:#1E293B;

    class A,H startend;
    class B,C1,E,F,G default;
    class D decision;

---

The Lár Architecture: Core Primitives

You can build any agent with four core components:

1. GraphState: A simple, unified object that holds the "memory" of the agent. It is passed to every node, allowing one node to write data (state.set(...)) and the next to read it (state.get(...)).

2. BaseNode: The abstract class (the "contract") for all executable units. It enforces a single method: execute(self, state). The execute method's sole responsibility is to perform its logic and return the next BaseNode to run, or None to terminate the graph.

3. GraphExecutor: The "engine" that runs the graph. It is a Python generator that runs one node, yields the execution log for that step, and then pauses, waiting for the next call.

4. Node Implementations: The "building blocks" of your agent.

   • LLMNode: The "Thinker." Calls any major LLM API (e.g., Gemini, GPT-4, Claude) to generate text... to generate text, modify plans, or correct code. Now supports generation_config for controlling creativity (temperature, top_p).
   • ToolNode: The "Actor." Executes any deterministic Python function (e.g., run code, search a database, call an API). It supports separate routing for success and error.
   • RouterNode: The "Choice." Executes a simple Python function to inspect the state and returns a string key, which deterministically routes execution to the next node. This is your "if/else" statement.
   • ClearErrorNode: A utility node that cleans up state (e.g., removes last_error) to prevent infinite loops.

---

Example: The "Glass Box" Audit Trail

You don't need to guess why an agent failed. lar is a "glass box" that provides a complete, auditable log for every run, especially failures.

This is a real execution log from a lar-built agent. The agent's job was to run a "Planner" and then a "Synthesizer" (both LLMNodes). The GraphExecutor caught a fatal error, gracefully stopped the agent, and produced this perfect audit trail.

Execution Summary (Run ID: a1b2c3d4-...)

Step	Node	Outcome	Key Changes
0	LLMNode	success	+ ADDED: 'search_query'
1	ToolNode	success	+ ADDED: 'retrieved_context'
2	LLMNode	success	+ ADDED: 'draft_answer'
3	LLMNode	error	+ ADDED: 'error': "APIConnectionError"

This is the lar difference. You know the exact node (LLMNode), the exact step (3), and the exact reason (APIConnectionError) for the failure. You can't debug a "black box," but you can always fix a "glass box."

Installation

This project is managed with Poetry.

1. Clone the repository:

   git clone https://github.com/snath-ai/lar.git
   cd lar
   

2. Set Up Environment Variables Lár uses a unified adapter (LiteLLM). Depending on the models you run, you must set the corresponding API keys in your .env file:

Create a .env file:

# Required for running Gemini models:
GEMINI_API_KEY="YOUR_GEMINI_KEY_HERE" 
# Required for running OpenAI models (e.g., gpt-4o):
OPENAI_API_KEY="YOUR_OPENAI_KEY_HERE"
# Required for running Anthropic models (e.g., Claude):
ANTHROPIC_API_KEY="YOUR_ANTHROPIC_KEY_HERE"

3. Install dependencies: This command creates a virtual environment and installs all packages from pyproject.toml.

   poetry install
   

---

The Lár Primitives (The "Lego Bricks")

You can build any agent with 6 core components. lar is a "glass box" because you see exactly how these components connect.

• GraphState: The "Memory." A simple Python object that is passed to every node, allowing them to share data.
• GraphExecutor: The "Engine." A simple generator that runs one node at a time and yields the history log.
• LLMNode: The "Brain." It calls the Gemini API to think, write, or critique. It's resilient and auto-retries on rate limits.
• ToolNode: The "Hands." It runs any Python function (like run_code or search_web) and has separate success and error paths.
• RouterNode: The "Choice." Your if/else statement. It runs a simple Python function to decide which node to run next.
• AddValueNode / ClearErrorNode: "Utility" nodes that clean up the state, copy values, and keep your graph running smoothly.

---

Example: Multi-Agent Orchestration (A Customer Support Agent)

The real power of lar is not just loops, but multi-agent orchestration.

Other frameworks use a "chaotic chat room" model, where agents talk to each other and you hope for a good result. lar is a deterministic "assembly line." You are the architect. You build a "glass box" graph that routes a task to specialized agents, guaranteeing order and auditing every step.

1. The "Glass Box" Flowchart

This is the simple, powerful "Customer Support" agent we'll build. It's a "Master Agent" that routes tasks to specialists.

graph TD
    A[Start] --> B(LLMNode<br/>'Agent 1: Triage');
    B --> C(LLMNode<br/>'Agent 2: Planner');
    C --> D(ToolNode<br/>'Retriever');
    
    %% This is the "hub" node
    D --> E{RouterNode<br/>'Manager: Route By Category'};
    
    %% Define the three parallel paths
    E -- "BILLING_AGENT" --> F;
    E -- "TECH_AGENT" --> G;
    E -- "GENERAL_AGENT" --> H;

    %% Define what's INSIDE the subgraphs
    subgraph "Billing Department"
        F(LLMNode<br/>'Agent 3: Billing Specialist');
    end

    subgraph "Tech Support Department"
        G(LLMNode<br/>'Agent 4: Tech Specialist');
    end
    
    subgraph "General"
        H(LLMNode<br/>'Agent 5: Generalist');
    end

    %% Define the "join" point
    F --> I[AddValueNode<br/>'Final Answer'];
    G --> I;
    H --> I;
    I --> J[END];

The "Lego Bricks" in Action (The Code)

This is the full logic from support_app.py. It's just a clean, explicit Python script.

from lar import *
from lar.utils import compute_state_diff # (Used by executor)

# 1. Define the "choice" logic for our Router
def triage_router_function(state: GraphState) -> str:
    """Reads the 'category' from the state and returns a route key."""
    category = state.get("category", "GENERAL").strip().upper()
    
    if "BILLING" in category:
        return "BILLING_AGENT"
    elif "TECH_SUPPORT" in category:
        return "TECH_AGENT"
    else:
        return "GENERAL_AGENT"

# 2. Define the agent's nodes (the "bricks")
# We build from the end to the start.

# --- The End Nodes (the destinations) ---
final_node = AddValueNode(key="final_response", value="{agent_answer}", next_node=None)
critical_fail_node = AddValueNode(key="final_status", value="CRITICAL_FAILURE", next_node=None)

# --- The "Specialist" Agents ---
billing_agent = LLMNode(
    model_name="gemini-2.5-pro",
    prompt_template="You are a BILLING expert. Answer '{task}' using ONLY this context: {retrieved_context}",
    output_key="agent_answer",
    next_node=final_node
)
tech_agent = LLMNode(
    model_name="gemini-2.5-pro",
    prompt_template="You are a TECH SUPPORT expert. Answer '{task}' using ONLY this context: {retrieved_context}",
    output_key="agent_answer",
    next_node=final_node
)
general_agent = LLMNode(
    model_name="gemini-2.5-pro",
    prompt_template="You are a GENERAL assistant. Answer '{task}' using ONLY this context: {retrieved_context}",
    output_key="agent_answer",
    next_node=final_node
)
    
# --- The "Manager" (Router) ---
specialist_router = RouterNode(
    decision_function=triage_router_function,
    path_map={
        "BILLING_AGENT": billing_agent,
        "TECH_AGENT": tech_agent,
        "GENERAL_AGENT": general_agent
    },
    default_node=general_agent
)
    
# --- The "Retriever" (Tool) ---
retrieve_node = ToolNode(
    tool_function=retrieve_relevant_chunks, # This is our local FAISS search
    input_keys=["search_query"],
    output_key="retrieved_context",
    next_node=specialist_router, 
    error_node=critical_fail_node
)
    
# --- The "Planner" (LLM) ---
planner_node = LLMNode(
    model_name="gemini-2.5-pro",
    prompt_template="You are a search query machine. Convert this task to a search query: {task}. Respond with ONLY the query.",
    output_key="search_query",
    next_node=retrieve_node
)
    
# --- The "Triage" Node (The *real* start) ---
triage_node = LLMNode(
    model_name="gemini-2.5-pro",
    prompt_template="You are a triage bot. Classify this task: \"{task}\". Respond ONLY with: BILLING, TECH_SUPPORT, or GENERAL.",
    output_key="category",
    next_node=planner_node
)

# 3. Run the Agent
executor = GraphExecutor()
initial_state = {"task": "How do I reset my password?"}
result_log = 
    list(executor.run_step_by_step(
        start_node=triage_node, 
        initial_state=initial_state
))
'''
The "glass box" log for Step 0 will show:
"state_diff": {"added": {"category": "TECH_SUPPORT"}}

The log for Step 1 will show:
"Routing to LLMNode" (the tech_support_agent)
'''

---

Ready to Build a Real Agent?

We have built two "killer demos" that prove this "glass box" model. You can clone, build, and run them today.

• snath-ai/rag-demo: A complete, self-correcting RAG agent that uses a local vector database.

• snath-ai/support-demo:The Customer Support agent described above.

Contributing

We welcome contributions to Làr. Please open an issue or submit a pull request for any bugs, features, or documentation improvements.

License

Làr is licensed under the Apache License 2.0

This means:

• You are free to use Làr in personal, academic, or commercial projects.
• You may modify and distribute the code.
• You MUST retain the LICENSE and the NOTICE file.
• If you distribute a modified version, you must document what you changed.
• You receive a patent license for contributions made to the project.

Apache 2.0 protects the original author (SnathAI™) while encouraging broad adoption and community collaboration.

For developers building on Làr: Please ensure that the LICENSE and NOTICE files remain intact to preserve full legal compatibility with the Apache 2.0 terms.