agents-builder 1.2.0

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agents-builder

agents-builder is a small Python framework for building retrieval-aware agents on top of LangGraph, LangChain, and MCP tools.

The package gives you a few focused primitives:

• A base Agent that owns graph construction, LLM factories, and MCP tool loading.
• Config-driven runtime objects via Pydantic settings models.
• Reusable Prompt, LLMNode, ToolBasedNode, and RouterNode building blocks.
• Lightweight support for multiple LLM backends such as Ollama and OpenRouter.

The design is intentionally narrow: compose agents from small graph nodes, keep configuration explicit, and avoid framework-heavy abstractions that hide behavior.

What This Project Contains

Core package layout:

src/agents_builder/
├── __init__.py          # Base Agent
├── constants.py         # Shared typing aliases and config path
├── exceptions.py        # Domain-specific exceptions
├── mixins.py            # Config loading helpers
├── settings.py          # Pydantic settings models
├── utils.py             # Dynamic loading, MCP caching, message helpers
├── llm/
│   ├── __init__.py
│   ├── factory.py       # LLMFactory and role-based model lookup
│   └── llm.py           # OllamaLLM / OpenRouterLLM implementations
└── langgraph/
    ├── __init__.py      # Prompt, Node, LLMNode, DeletionStrategy
    ├── nodes.py         # Ready-made retrieval/answering node types
    └── states.py        # Typed graph state

Tests are split by intent:

• tests/unit: isolated behavior for factories, nodes, mixins, utils, and errors.
• tests/integration: config loading and caching behavior.
• tests/e2e: a full retrieval-answer graph wired together end to end.

Installation

This project uses uv and Python 3.12.

uv sync --group dev --all-extras

Common local commands:

make lint
make type-check
make test
make test-cov
make ci

Core Concepts

1. Agent

Subclass agents_builder.Agent and implement build_graph().

The base class already handles:

• config storage
• lazy graph compilation
• LLM factory initialization
• MCP tool loading with caching
• graph export via push()

2. Settings-first runtime objects

Most runtime classes follow the same pattern:

• define a Pydantic settings model
• inherit from FromConfigMixin[...]
• construct the runtime object from validated config

This keeps runtime behavior strongly tied to validated configuration instead of ad hoc dictionaries.

3. Role-based LLM selection

LLMFactory maps roles like query, grader, or answer to specific model configs.

That makes it easy to:

• use different models for different steps
• swap providers without changing graph code
• keep model selection in config rather than inside node logic

4. LangGraph node building blocks

The package exposes low-level but reusable graph parts:

• Prompt: render a system prompt from state
• SchemaBasedPrompt: prompt plus structured output schema
• LLMNode: invoke an LLM against rendered messages
• ToolBasedNode: base for MCP-backed tool nodes
• RouterNode: return route keys for conditional edges
• DeletionStrategy: trim message history in a controlled way

Configuration

Configuration is built around Pydantic settings models in src/agents_builder/settings.py.

Important settings types:

• AgentSettings
• LLMFactorySettings
• OllamaLLMSettings
• OpenRouterLLMSettings
• MCPServerSettings

The package-level default YAML path is:

/config/config.yaml

You can also construct objects directly from Python with .from_config(...) or load from YAML with .from_yaml(...).

Development Workflow

Quality checks already configured in the project:

• ruff for linting and formatting
• ty for type checking
• pytest for unit, integration, and e2e tests
• bandit for security checks

Recommended local loop:

make lint
make type-check
make test

Before merging broader changes:

make ci

Design Principles

This repository works best when changes stay aligned with a few constraints:

• Prefer explicit graph nodes over magic orchestration layers.
• Keep configuration typed and validated.
• Use small runtime classes with narrow responsibilities.
• Let tests describe behavior at the node and graph level.

If you are contributing code or using AI coding agents in this repository, read AGENTS.md next.