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doc_version: "1.0.0" last_updated: "2026-06-18" status: "Active"

Bast AI Platform Documentation

Version: Current (as of June 2026) Build System: Pants 2.24.2 Python: 3.10-3.12

Overview

Bast AI is a comprehensive AI platform built as a Python monorepo, providing tools for conversational AI, knowledge management, document processing, and multi-agent systems. The platform follows a microservices architecture with shared core libraries.

Project Statistics

Category Count Description
Python Libraries 33 Core services and utilities
Pipelines 18 Data processing pipelines
APIs 9 REST API services
MCP Servers 3 Model Context Protocol servers
Models 13 Conversational AI models
Agents 4 Agent framework implementations

Total Projects: 80

Architecture

Dependency Hierarchy

Level 1 (Foundation)
└── base (bast-ai-base v0.6.4)
    └── Core utilities, FileIO, TextUtils, Crypto, ObjectEnforcer

Level 2 (Core)
└── config (bast-ai-config v0.6.3)
    └── Configuration management with Hydra

Level 3 (Infrastructure)
├── mongo (bast-ai-mongo v0.1.15) - MongoDB integration
├── milvus (bast-ai-milvus v0.1.4) - Vector database
├── aws (bast-ai-aws v0.1.5) - AWS S3 operations
├── azure_index (bast-ai-azure-index v0.1.1) - Azure Cognitive Search
├── neo4j (bast-ai-neo4j v0.1.1) - Graph database
├── oracle (bast-ai-oracle v0.1.4) - Oracle DB/OCI
├── mssql (bast-ai-mssql v0.1.2) - MS SQL Server
└── openai (bast-ai-openai v0.1.6) - OpenAI API

Level 4 (Services)
├── agno (bast-ai-agno v0.3.2) - Agent framework
├── classifier (bast-ai-classifier v0.1.4) - Text classification
├── graphrag (bast-ai-graphrag v0.1.3) - Graph-based RAG
├── llm_evaluation (bast-ai-llm-evaluation v0.1.0) - LLM evaluation
└── model_base (bast-ai-model-base v0.2.2) - Model management

Level 5 (Applications)
├── APIs - REST services
├── Pipelines - Data processing
├── Models - Conversational models
└── Agents - Multi-agent systems

Core Libraries

Key Services

Data Processing

APIs

  • CAT API - Chat processing (v0.2.11)
  • Security API - Authentication (v0.1.21)
  • Analytics API - Metrics and analytics (v0.1.15)

Key Technologies

Category Technologies
Databases MongoDB, Milvus, Neo4j, Oracle, MS SQL, PostgreSQL
Cloud AWS (boto3), Azure, Oracle Cloud Infrastructure
AI/ML OpenAI, LangChain, Agno, RAGAS, spaCy, NLTK
Web FastAPI, Uvicorn
Messaging Slack SDK, MS Graph
Data pandas, DVC, pydantic, rdflib

Getting Started

IDE Integration Setup

# Full setup
make install

# OR manually:
pants generate-lockfiles --resolve=python-default
pants export --py-resolve-format=symlinked_immutable_virtualenv --resolve=python-default

Running Tests

# Test single service with coverage
pants test --output=all --use-coverage --coverage-py-filter="['bast_ai.base']" src/python/base:: -- -s --no-header

# Test specific service
pants test --output=all src/python/agno:: -- -s --no-header

# Run integration test
pants run src/python/agno/integration/add_knowledge.py

Code Quality

pants fmt ::   # Format code
pants lint ::  # Lint code
pants check :: # Type check

Documentation Structure

docs/
├── README.md              # This file
├── python/                # Python library documentation
│   ├── README.md          # Overview of all Python libraries
│   ├── base.md            # Base library
│   ├── agno.md            # Agno agent framework
│   └── ...
├── pipelines/             # Pipeline documentation
│   ├── README.md          # Overview of all pipelines
│   ├── process_pdf.md     # PDF processing pipeline
│   └── ...
├── apis/                  # API documentation
│   ├── README.md          # Overview of all APIs
│   ├── cat.md             # CAT API
│   └── ...
├── mcp/                   # MCP server documentation
│   ├── README.md          # Overview of MCP servers
│   └── ...
├── models/                # Model documentation
│   ├── README.md          # Overview of all models
│   └── ...
└── agent/                 # Agent documentation
    ├── README.md          # Overview of agent frameworks
    └── ...

Maintainers

Bast AI Team

License

Proprietary - Bast AI