agents-builder 2.0.0

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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.
• An agent evaluation API for running scenarios, scoring trajectories, and annotating traces.
• An agent evaluation API for running scenarios, scoring trajectories, and annotating traces.
• 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
├── eval/
│   ├── __init__.py      # EvalRunner, TraceAnnotator, assertions, scoring
│   ├── annotator.py     # Phoenix trace annotation integration
│   ├── assertion.py     # Reusable trajectory assertions
│   └── trajectory.py    # EvalScenario and AgentTrajectory models
├── eval/
│   ├── __init__.py      # EvalRunner, TraceAnnotator, assertions, scoring
│   ├── annotator.py     # Phoenix trace annotation integration
│   ├── assertion.py     # Reusable trajectory assertions
│   └── trajectory.py    # EvalScenario and AgentTrajectory models
├── 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

5. Agent evaluation API

Use agents_builder.eval to run repeatable evaluation scenarios against a compiled agent graph.

The core flow is:

• define an EvalScenario with the user query and metadata
• run the graph with EvalRunner
• convert graph messages into an AgentTrajectory
• score the trajectory with SuccessAssertion and EfficiencyAssertion checks
• emit trace annotations through a TraceAnnotator

from agents_builder.eval import (
    AgentTrajectory,
    EfficiencyAssertion,
    EvalRunner,
    EvalScenario,
    SuccessAssertion,
    TraceAnnotator,
    TrajectoryScorer,
)


class RequiresAnswer(SuccessAssertion):
    def check(self, trajectory: AgentTrajectory) -> bool:
        passed = bool(trajectory.steps) and bool(trajectory.steps[-1].action.content)
        self.logger.annotate(
            annotation_name="answered",
            annotator_kind="CODE",
            label="pass" if passed else "fail",
            score=1.0 if passed else 0.0,
            explanation=trajectory.serialize(),
        )
        if not passed:
            raise AssertionError("agent did not produce a final answer")
        return passed


class LimitsStepCount(EfficiencyAssertion):
    def __init__(self, logger: TraceAnnotator, max_steps: int) -> None:
        super().__init__(logger)
        self.max_steps = max_steps

    def check(self, trajectory: AgentTrajectory) -> bool:
        passed = len(trajectory.steps) <= self.max_steps
        self.logger.annotate(
            annotation_name="step_count",
            annotator_kind="CODE",
            label="pass" if passed else "fail",
            score=1.0 if passed else 0.0,
            metadata={"max_steps": self.max_steps, "actual_steps": len(trajectory.steps)},
        )
        return passed


scenario = EvalScenario(
    name="contract lookup",
    query="Find the renewal terms",
    category="retrieval",
    tier="smoke",
    metadata={"dataset": "contracts"},
)

logger = ...
runner = EvalRunner(graph=agent.graph, logger=logger)
scorer = TrajectoryScorer().success(RequiresAnswer(logger)).expect(LimitsStepCount(logger, max_steps=3))
trajectory = await runner.run(scenario, scorer)

For LLM-as-judge checks, use LLMJudgeAssertion from agents_builder.eval.assertion. Its prompt must contain exactly {query} and {trajectory} placeholders, and each criterion is emitted as an LLM trace annotation.

Phoenix-backed annotation is available through PhoenixTraceAnnotator with PhoenixTraceAnnotatorSettings:

from agents_builder.eval.annotator import PhoenixTraceAnnotator
from agents_builder.settings import PhoenixTraceAnnotatorSettings

logger = PhoenixTraceAnnotator(
    PhoenixTraceAnnotatorSettings(
        module_path="agents_builder.eval.annotator.PhoenixTraceAnnotator",
        base_url="http://localhost:6006",
        project_name="agents-builder",
    )
)

Configuration

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

Important settings types:

• AgentSettings
• LLMFactorySettings
• OllamaLLMSettings
• OpenRouterLLMSettings
• MCPServerSettings
• TraceAnnotatorSettings
• PhoenixTraceAnnotatorSettings
• TraceAnnotatorSettings
• PhoenixTraceAnnotatorSettings

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.