AgentX Documentation
AgentX is a self-hosted, glassbox AI agent platform: persistent multi-type memory, Agent Teams that delegate, six thinking patterns, MCP tools, and your choice of model — all behind a Django REST API you run yourself, with every step of the agent loop observable.
Current release: v0.21.222 (the v0.20 milestone — “Mobile-Ready Alpha”).
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 · OpenRouter · Vercel]
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 | Thinking patterns in chat + an offline CoT/ToT/ReAct/Reflection kit, plus multi-model drafting | Reasoning |
| MCP Client | Connect to external tool servers via stdio, SSE, or HTTP | MCP |
| Agent Teams | A lead delegates subtasks to specialist teammates | Agent Teams |
| Providers | Unified interface for LM Studio, Anthropic, OpenAI, OpenRouter, Vercel | 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 · sentence-transformers | Translation (NLLB-200) + local embeddings |
| 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, 17):
- Django API with 70+ REST endpoints (chat, agent, memory, MCP, providers, prompts, profiles, jobs, auth, multi-agent, config)
- Tauri desktop app: multi-page layout, browser-style conversation tabs, drawer panels, agent profiles
- Mobile-ready: Tauri v2 Android target (v0.20.0 — “Mobile-Ready Alpha”)
- 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, OpenRouter, Vercel AI Gateway)
- 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)
- Server management (Phase 17): optional session auth, Docker production stack, multi-cluster deploy, client/API version matching
- 190+ backend tests
In Progress:
- Phase 15: Plan execution (Core Complete — executor, Redis-tracked state, streamed progress, cancellation)
- Phase 16: Multi-agent — Agent Teams v1 + routing, ad-hoc delegation, and @-mention routing shipped (~65%)
- Phase 18: UX improvements + memory tuning (~93%)
See the Roadmap for detailed phase history.
License
This project is licensed under the MIT License.