Welcome to Flexo

The Flexo Agent Library is a powerful and flexible codebase that enables users to configure, customize, and deploy a generative AI agent. Designed for adaptability, the library can be tailored to a wide range of use cases, from conversational AI to specialized automation.

Why Flexo?

Simplified Deployment: Deploy anywhere with comprehensive platform guides
Production Ready: Built for scalability and reliability
Extensible: Add custom tools and capabilities
Well Documented: Clear guides for every step

Key Features

Configurable Agent: YAML-based configuration for custom behaviors
Tool Integration: Execute Python functions and REST API calls
Streaming Support: Real-time streaming with pattern detection
Production Ready: Containerized deployment support with logging
FastAPI Backend: Modern async API with comprehensive docs

Supported LLM Providers

☁️ Cloud Providers

OpenAI

GPT-powered models

watsonx.ai

Enterprise AI solutions

Anthropic

Claude family models

xAI

Grok and beyond

Mistral AI

Efficient open models

🖥️ Local & Self-Hosted Options

High-throughput serving

Ollama

Easy local LLMs

Optimized C++ runtime

LM Studio

User-friendly interface

LocalAI

Self-hosted versatility

⚙️ Unified Configuration Interface

Switch providers effortlessly with Flexo's adapter layer. Customize your LLM settings in one place:

gpt-4o:
  provider: "openai"  # Choose your provider
  model: "gpt-4o"     # Select specific model
  temperature: 0.7
  max_tokens: 4000    # Additional model-specific parameters

Need more details? Check our comprehensive Model Configuration Guide for provider-specific settings and optimization tips.

Quick Start Guide

1. Local Development

Start developing with Flexo locally:

2. Production Deployment

Deploy Flexo to your preferred platform:

Platform	Best For	Guide
IBM Code Engine	Serverless, pay-per-use	Deploy →
AWS Fargate	AWS integration	Deploy →
OpenShift	Enterprise, hybrid cloud	Deploy →
Kubernetes	Custom infrastructure	Deploy →

Documentation

Deployment Guides

Code Reference

System Architecture

graph TB
    Client[Client] --> API[FastAPI Server]

    subgraph API["API Layer"]
        Router[Chat Completions Router]
        SSE[SSE Models]
        Validation[Request Validation]
    end

    subgraph Agent["Agent Layer"]
        ChatAgent[Streaming Chat Agent]
        State[State Management]
        Config[Configuration]
    end

    subgraph LLM["LLM Layer"]
        Factory[LLM Factory]
        Detection[Tool Detection]
        Builders[Prompt Builders]

        subgraph Adapters["LLM Adapters"]
            WatsonX
            OpenAI
            Anthropic
            Mistral
        end
    end

    subgraph Tools["Tools Layer"]
        Registry[Tool Registry]
        REST[REST Tools]
        NonREST["Non-REST Tools: Python etc"]
        ExampleTools["Included Example 
        Tools: RAG + Weather"]
    end

    subgraph Database["Database Layer"]
        ES[Elasticsearch]
        Milvus[Milvus]
    end

    API --> Agent
    Agent --> LLM
    Agent --> Tools
    Tools --> Database

    style API stroke:#ff69b4,stroke-width:4px
    style Agent stroke:#4169e1,stroke-width:4px
    style LLM stroke:#228b22,stroke-width:4px
    style Tools stroke:#cd853f,stroke-width:4px
    style Database stroke:#4682b4,stroke-width:4px

    style Router stroke:#ff69b4,stroke-width:2px
    style SSE stroke:#ff69b4,stroke-width:2px
    style Validation stroke:#ff69b4,stroke-width:2px

    style ChatAgent stroke:#4169e1,stroke-width:2px
    style State stroke:#4169e1,stroke-width:2px
    style Config stroke:#4169e1,stroke-width:2px

    style Factory stroke:#228b22,stroke-width:2px
    style Detection stroke:#228b22,stroke-width:2px
    style Builders stroke:#228b22,stroke-width:2px
    style Adapters stroke:#228b22,stroke-width:2px
    style WatsonX stroke:#228b22,stroke-width:2px
    style OpenAI stroke:#228b22,stroke-width:2px
    style Anthropic stroke:#228b22,stroke-width:2px
    style Mistral stroke:#228b22,stroke-width:2px

    style Registry stroke:#cd853f,stroke-width:2px
    style ExampleTools stroke:#cd853f,stroke-width:2px
    style REST stroke:#cd853f,stroke-width:2px
    style NonREST stroke:#cd853f,stroke-width:2px

    style ES stroke:#4682b4,stroke-width:2px
    style Milvus stroke:#4682b4,stroke-width:2px

    style Client stroke:#333,stroke-width:2px

Chat Agent State Flow

stateDiagram-v2
    [*] --> STREAMING: Initialize Agent

    STREAMING --> TOOL_DETECTION: Tool Call Found
    STREAMING --> COMPLETING: Generation Done

    TOOL_DETECTION --> EXECUTING_TOOLS: Process Tool Call
    EXECUTING_TOOLS --> INTERMEDIATE: Tool Execution Complete

    INTERMEDIATE --> STREAMING: Continue Generation
    INTERMEDIATE --> COMPLETING: Max Iterations

    COMPLETING --> [*]: End Response

    note right of STREAMING
        Main generation state
        Handles LLM responses
    end note

    note right of EXECUTING_TOOLS
        Concurrent tool execution
        Error handling
    end note

Contributing

See our Contributing Guide for details.

Security

For security concerns, please review our Security Policy.