naas-abi

Multi-agent orchestrator and knowledge graph management system for AI orchestration, providing comprehensive coordination of specialized AI agents and semantic data management capabilities.

Overview

naas-abi is the central coordination hub for the ABI (Agentic Brain Infrastructure) ecosystem. It provides:

Multi-Agent Orchestration: Central coordinator managing specialized AI agents (ChatGPT, Claude, Mistral, Gemini, Grok, Llama, Perplexity, Qwen, DeepSeek, Gemma)
Knowledge Graph Operations: Complete CRUD operations for semantic data management
Ontology Engineering: BFO-compliant entity extraction and SPARQL generation
Intelligent Routing: Weighted decision hierarchy with context preservation
Multilingual Support: Native French/English interactions with cultural awareness
Production Integration: Event-driven triggers and YAML ontology publishing

Installation

pip install naas-abi

Core Components

ABIModule

The ABIModule is the main module that orchestrates the entire ABI system. It automatically loads marketplace modules (AI agents, applications, domain experts) and provides the core infrastructure.

Configuration:

modules:
  - module: naas_abi
    enabled: true
    config:
      datastore_path: "abi"
      workspace_id: "{{ secret.WORKSPACE_ID }}"
      storage_name: "{{ secret.STORAGE_NAME }}"

Dependencies:

The module automatically loads marketplace modules as soft dependencies (optional):

AI Agents: ChatGPT, Claude, Mistral, Gemini, Grok, Llama, Perplexity, Qwen, DeepSeek, Gemma
Applications: GitHub, LinkedIn, Google services, and 50+ other integrations
Domain Experts: Support, Software Engineering, Data Analysis, and more

Required Services:

Secret: For credential management
TripleStoreService: For knowledge graph operations
ObjectStorageService: For file storage

Agents

AbiAgent

The main multi-agent orchestrator that coordinates specialized agents.

Features:

Intelligent routing based on request type and context
Context preservation across conversations
Multilingual support (French/English)
Weighted decision hierarchy for optimal agent selection
Strategic advisory capabilities

Usage:

from naas_abi.agents.AbiAgent import create_agent

agent = create_agent()
response = agent.invoke("Route this to the best AI for code generation")

Routing Priorities:

Context Preservation (0.99): Maintains active conversations
Identity/Strategic (0.95): Direct Abi responses
Web Search (0.90): Routes to Perplexity/ChatGPT
Creative/Multimodal (0.85): Routes to Gemini
Truth Seeking (0.80): Routes to Grok
Advanced Reasoning (0.75): Routes to Claude
Code & Math (0.70): Routes to Mistral
Knowledge Graph (0.68): Opens KG Explorer
Internal Knowledge (0.65): Uses ontology agent
Platform Operations (0.45): Routes to Naas agent
Issue Management (0.25): Routes to Support agent

EntitytoSPARQLAgent

Extracts entities from natural language and generates SPARQL queries.

Capabilities:

BFO-compliant entity extraction
Automatic SPARQL query generation
Entity relationship mapping
Ontology-aware query construction

Usage:

from naas_abi.agents.EntitytoSPARQLAgent import create_agent

agent = create_agent()
response = agent.invoke(
    "Extract entities from: 'John works at Microsoft as a software engineer'"
)

KnowledgeGraphBuilderAgent

Provides complete CRUD operations for the knowledge graph.

Capabilities:

Add individuals (entities) to the knowledge graph
Update properties and relationships
Remove individuals
Merge duplicate entities
Query and explore the graph

Usage:

from naas_abi.agents.KnowledgeGraphBuilderAgent import create_agent

agent = create_agent()
response = agent.invoke(
    "Add a new organization called 'NaasAI' to the knowledge graph"
)

OntologyEngineerAgent

Specialized agent for BFO ontology engineering and management.

Capabilities:

Ontology creation and modification
BFO-compliant structure validation
Class and property definition
Ontology publishing to YAML

Usage:

from naas_abi.agents.OntologyEngineerAgent import create_agent

agent = create_agent()
response = agent.invoke(
    "Create a new ontology class for 'SoftwareProject' with properties"
)

Workflows

AgentRecommendationWorkflow

Recommends the best AI agent for a given intent using SPARQL queries.

Features:

Intent-to-query matching
SPARQL template parameterization
Weighted recommendation scoring
Provider preference support

Usage:

from naas_abi.workflows.AgentRecommendationWorkflow import (
    AgentRecommendationWorkflow,
    AgentRecommendationConfiguration,
    AgentRecommendationParameters
)

workflow = AgentRecommendationWorkflow(
    AgentRecommendationConfiguration(queries_file_path="path/to/queries.ttl")
)

result = workflow.run(AgentRecommendationParameters(
    intent_description="I need help with code generation",
    provider_preference="openai"
))

ArtificialAnalysisWorkflow

Fetches and stores AI model data from the Artificial Analysis API.

Features:

Fetches model performance data
Filters for modules with active agents
Saves timestamped JSON files
Supports multiple endpoints (models, providers, categories)

Usage:

from naas_abi.workflows.ArtificialAnalysisWorkflow import (
    ArtificialAnalysisWorkflow,
    ArtificialAnalysisWorkflowConfiguration,
    ArtificialAnalysisWorkflowParameters
)

workflow = ArtificialAnalysisWorkflow(
    ArtificialAnalysisWorkflowConfiguration(
        api_key="your_api_key",
        base_url="https://artificialanalysis.ai/api/v2"
    )
)

result = workflow.run(ArtificialAnalysisWorkflowParameters(
    endpoint="models",
    validate_agents_only=True
))

SearchIndividualWorkflow

Searches for individuals (entities) in the knowledge graph.

Features:

Semantic search across entities
Fuzzy matching support
Property-based filtering
Result ranking

GetSubjectGraphWorkflow

Retrieves the graph structure for a specific subject (entity).

Features:

Entity relationship exploration
Configurable depth traversal
Graph visualization data
Property and relationship extraction

GetObjectPropertiesFromClassWorkflow

Retrieves object properties for a given ontology class.

Features:

Class property discovery
BFO-compliant property extraction
Relationship type identification
Ontology hierarchy traversal

Pipelines

AddIndividualPipeline

Adds new individuals (entities) to the knowledge graph.

Features:

Duplicate detection
Automatic URI generation
Property assignment
Relationship creation

AIAgentOntologyGenerationPipeline

Generates AI agent ontologies from Artificial Analysis data.

Features:

BFO-structured ontology generation
Model-to-agent mapping
Timestamped audit trails
Automatic deployment to module folders

Execution Steps:

Loads Artificial Analysis data
Groups models by AI agent
Generates ontologies in timestamped folders
Deploys current versions to module folders
Creates audit trail and summary

InsertDataSPARQLPipeline

Inserts data into the knowledge graph using SPARQL INSERT queries.

Features:

SPARQL query execution
Batch insert operations
Validation and error handling
Transaction support

MergeIndividualsPipeline

Merges duplicate individuals in the knowledge graph.

Features:

Duplicate detection
Property merging
Relationship consolidation
Audit logging

RemoveIndividualPipeline

Removes individuals from the knowledge graph.

Features:

Safe deletion with validation
Relationship cleanup
Audit trail creation
Backup generation

Update Pipelines

Specialized pipelines for updating specific entity types:

UpdateDataPropertyPipeline: Updates data properties
UpdatePersonPipeline: Updates person entities
UpdateCommercialOrganizationPipeline: Updates organization entities
UpdateSkillPipeline: Updates skill entities
UpdateLinkedInPagePipeline: Updates LinkedIn page data
UpdateTickerPipeline: Updates stock ticker information
UpdateWebsitePipeline: Updates website information
UpdateLegalNamePipeline: Updates legal names

Ontologies

The module includes a comprehensive ontology structure organized in a 4-level hierarchy:

Top-level: BFO foundational ontologies
Mid-level: Common Core Ontologies (CCO)
Domain-level: Domain-specific ontologies
Application-level: Use-case specific ontologies

Location: naas_abi/ontologies/

Models

The module supports multiple AI model configurations:

Cloud Mode (Default)

Model: gpt-4.1-mini
Provider: OpenAI
Temperature: 0 (precise orchestration)
Requires: OPENAI_API_KEY

Airgap Mode

Qwen3 (default): Temperature 0.7, 8K context
Gemma3 (alternative): Temperature 0.2, 8K context
Requires: Docker Model Runner on localhost:12434

Configuration:

# Set environment variable
AI_MODE=cloud  # or "airgap" or "local"

CLI Tools

The module provides CLI commands for creating new components:

# Create a new module
python -m naas_abi.cli create-module

# Create a new agent
python -m naas_abi.cli create-agent

# Create a new integration
python -m naas_abi.cli create-integration

# Create a new workflow
python -m naas_abi.cli create-workflow

# Create a new pipeline
python -m naas_abi.cli create-pipeline

# Create a new ontology
python -m naas_abi.cli create-ontology

Each command provides an interactive wizard to guide you through the creation process.

Usage Examples

Basic Agent Interaction

from naas_abi_core.engine.Engine import Engine

# Initialize engine
engine = Engine()
engine.load(module_names=["naas_abi"])

# Get AbiAgent
from naas_abi.agents.AbiAgent import create_agent

agent = create_agent()

# Interact with agent
response = agent.invoke("What agents are available?")
print(response)

Knowledge Graph Operations

from naas_abi.agents.KnowledgeGraphBuilderAgent import create_agent

kg_agent = create_agent()

# Add an organization
kg_agent.invoke("Add organization 'NaasAI' with website 'https://naas.ai'")

# Search for entities
from naas_abi.workflows.SearchIndividualWorkflow import (
    SearchIndividualWorkflow,
    SearchIndividualWorkflowConfiguration,
    SearchIndividualWorkflowParameters
)

workflow = SearchIndividualWorkflow(
    SearchIndividualWorkflowConfiguration(
        triple_store=engine.services.triple_store
    )
)

results = workflow.run(SearchIndividualWorkflowParameters(
    search_term="NaasAI"
))

Workflow Execution

from naas_abi.workflows.AgentRecommendationWorkflow import (
    AgentRecommendationWorkflow,
    AgentRecommendationConfiguration,
    AgentRecommendationParameters
)

workflow = AgentRecommendationWorkflow(
    AgentRecommendationConfiguration(
        queries_file_path="path/to/queries.ttl"
    )
)

recommendations = workflow.run(AgentRecommendationParameters(
    intent_description="I need help with data analysis",
    provider_preference="anthropic"
))

Pipeline Execution

from naas_abi.pipelines.AddIndividualPipeline import (
    AddIndividualPipeline,
    AddIndividualPipelineConfiguration,
    AddIndividualPipelineParameters
)

pipeline = AddIndividualPipeline(
    AddIndividualPipelineConfiguration(
        triple_store=engine.services.triple_store,
        search_individual_configuration=SearchIndividualWorkflowConfiguration(
            triple_store=engine.services.triple_store
        )
    )
)

graph = pipeline.run(AddIndividualPipelineParameters(
    individual_label="New Company",
    individual_type="CommercialOrganization"
))

Configuration

Environment Variables

Variable	Values	Default	Description
`AI_MODE`	`cloud` \| `airgap` \| `local`	`cloud`	Model deployment mode
`OPENAI_API_KEY`	API key	Required (cloud)	For cloud models
`NAAS_API_KEY`	API key	Optional	For production triggers
`ENV`	`dev` \| `prod`	`dev`	Environment mode

Module Configuration

modules:
  - module: naas_abi
    enabled: true
    config:
      datastore_path: "abi"
      workspace_id: "{{ secret.WORKSPACE_ID }}"
      storage_name: "{{ secret.STORAGE_NAME }}"

Key Features

🔄 Context-Aware Orchestration

Preserves active conversations while enabling intelligent agent transitions.

🌍 Multilingual Support

Native French/English code-switching with cultural awareness.

🎯 Weighted Decision Routing

Sophisticated hierarchy for optimal agent selection based on request type.

🔍 Knowledge Graph Integration

Direct access to SPARQL querying and semantic data exploration.

🔒 Deployment Flexibility

Choice between cloud (OpenAI) and airgap (Docker Model Runner) models.

📊 Strategic Advisory

Direct consultation capabilities for business and technical guidance.

🛡️ Production Ready

Event-driven triggers, comprehensive testing, and error resilience.

Dependencies

naas-abi-core>=1.0.0: Core ABI framework
naas-abi-marketplace>=1.0.0: Marketplace modules and agents
thefuzz>=0.22.1: Fuzzy string matching

Architecture

Module Structure

naas_abi/
├── agents/              # Agent implementations
│   ├── AbiAgent.py
│   ├── EntitytoSPARQLAgent.py
│   ├── KnowledgeGraphBuilderAgent.py
│   └── OntologyEngineerAgent.py
├── workflows/           # Business logic workflows
│   ├── AgentRecommendationWorkflow.py
│   ├── ArtificialAnalysisWorkflow.py
│   ├── SearchIndividualWorkflow.py
│   ├── GetSubjectGraphWorkflow.py
│   └── GetObjectPropertiesFromClassWorkflow.py
├── pipelines/           # Data processing pipelines
│   ├── AddIndividualPipeline.py
│   ├── AIAgentOntologyGenerationPipeline.py
│   ├── InsertDataSPARQLPipeline.py
│   ├── MergeIndividualsPipeline.py
│   └── Update*Pipeline.py
├── ontologies/          # Ontology definitions
├── models/              # Model configurations
├── cli.py               # CLI commands
└── __init__.py          # Module initialization

Testing

# Run all tests
pytest naas_abi/ -v

# Test specific agent
pytest naas_abi/agents/AbiAgent_test.py -v

# Test workflows
pytest naas_abi/workflows/ -v

# Test pipelines
pytest naas_abi/pipelines/ -v

License

MIT License

Metadata

Release files for naas-abi 2.86.0

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