AgentX Documentation

AgentX is an AI Agent Platform combining MCP client integration, multi-model reasoning, drafting strategies, and a persistent memory system — all backed by a Django REST API.

Architecture at a Glance

graph TB
    Client[Tauri Client<br/>React 19 + Vite]

    subgraph API["Django API (port 12319)"]
        Agent[Agent Core<br/>planner · session · context]
        Reasoning[Reasoning<br/>CoT · ToT · ReAct · Reflection]
        Drafting[Drafting<br/>speculative · pipeline · candidate]
        Providers[Providers<br/>LM Studio · Anthropic · OpenAI]
        MCP[MCP Client<br/>stdio · SSE · HTTP]
        Prompts[Prompt System<br/>profiles · composition]
        Translation[Translation Kit<br/>NLLB-200 · 200+ languages]
        Memory[Agent Memory<br/>episodic · semantic · procedural · working]
    end

    subgraph Data["Data Layer (Docker)"]
        Neo4j[Neo4j<br/>entity graphs]
        Postgres[PostgreSQL + pgvector<br/>vectors · episodic · audit]
        Redis[Redis<br/>working memory cache]
    end

    Client -->|HTTP| API
    Agent --> Reasoning & Drafting & Providers & MCP & Prompts & Memory
    Memory --> Neo4j & Postgres & Redis
    MCP -->|stdio/SSE| ExtServers[External MCP Servers]

Key Features

Feature	Description	Docs
Agent Chat	Conversational AI with streaming, tool use, and session management	Chat
Reasoning	4 strategies (CoT, ToT, ReAct, Reflection) with auto-selection	Reasoning
Drafting	Speculative decoding, multi-stage pipelines, N-best candidates	Drafting
MCP Client	Connect to external tool servers via stdio, SSE, or HTTP	MCP
Providers	Unified interface for LM Studio, Anthropic, and OpenAI	Providers
Prompts	Profile-based prompt composition with global prompt layer	Prompts
Memory	4-type persistent memory with recall, extraction, and consolidation	Memory
Translation	Two-level detection + NLLB-200 translation for 200+ languages	Translation

Quick Links


Quick Start — Install and run AgentX in minutes	API Reference — All REST API endpoints with examples
Architecture — System design, module layout, request lifecycle	Development — Setup, contributing, and testing
Database Stack — Neo4j, PostgreSQL + pgvector, Redis	Roadmap — Development history and future plans

Technology Stack

Layer	Technology	Purpose
Frontend	Tauri v2 + React 19	Desktop application shell
Build	Vite + TypeScript	Fast development and bundling
Backend	Django 5.2	REST API framework
AI/ML	HuggingFace Transformers	Translation models (NLLB-200)
Graph DB	Neo4j 5.15	Entity relationships and knowledge graphs
Vector DB	PostgreSQL + pgvector	Semantic search and episodic memory
Cache	Redis 7	Working memory and session state
Task Runner	Task (Taskfile)	Development automation
Python	uv	Fast dependency management
Client	bun	Client package management

Project Status

Completed (Phases 1-14):

Django API with 54 endpoints across 8 subsystems
Tauri desktop app: 3-page layout, browser-style conversation tabs, drawer panels, agent profiles
Two-level translation system (200+ languages)
Database stack (Neo4j, PostgreSQL + pgvector, Redis)
MCP client with stdio/SSE/HTTP transports
Model provider abstraction (LM Studio, Anthropic, OpenAI)
Drafting framework (speculative, pipeline, candidate)
Reasoning framework (CoT, ToT, ReAct, Reflection)
Agent core with task planning and goal tracking
Memory system: 4 types, recall layer (5 techniques), extraction pipeline, consolidation
Context gating: task-aware compression, intent-based retrieval, trajectory compression
Agent identity: Docker-style IDs, self-memory channels, assistant self-extraction
Three-layer fact verification pipeline (hash → semantic → LLM adjudication)
190+ backend tests

Up Next:

Phase 15: Plan execution + memory tuning
Phase 16: Multi-agent conversations

See the Roadmap for detailed phase history.

License

This project is licensed under the MIT License.