Five independent systems β an AI Orchestrator, an Agentic Team runtime, an MCP Server, a Context Dashboard, and Graphify (project-to-graph intelligence engine) β that coordinate cloud and local AI coding assistants (Claude, Codex, Gemini, Copilot, Ollama, llama.cpp) to collaborate on software development tasks. Includes enterprise-grade agentic infrastructure with specialized agents, skills library, 34+ MCP tools, project-scoped graph-based context memory, and Graphify's 22-language code analysis with persistent queryable knowledge graphs, interactive visualization, and REST API.
Overview | Architecture | Agentic Infrastructure | System Comparison | Features | Quick Start | Project Structure | Configuration | Deployment | Testing | MCP Server
AI Coding Tools ships five independent systems in a single repository:
- The Orchestrator runs step-based workflows where AI agents execute tasks in sequence (implement, review, refine).
- The Agentic Team runs a free-communication runtime where role-based agents (Project Manager, Architect, Developer, QA, DevOps) discuss a task in turns until the team lead declares the work complete.
- Graphify turns any project directory into a queryable knowledge graph β classes, functions, imports, call graphs, and design rationale stored in a local SQLite database with FTS5 search.
- The MCP Server bridges both engines to IDE-based AI assistants.
- The Context Dashboard visualizes the graph memory. Each system carries its own adapters, configuration, UI, and CLI β they share zero code and zero imports.
Beyond the core engines, we provide a complete Agentic Infrastructure that empowers AI agents:
- 9 Specialized Agents for web, backend, security, DevOps, AI/ML, database, mobile, performance, and documentation
- 22 Reusable Skills across development, testing, security, DevOps, AI/ML, and documentation
- 34+ MCP Tools for code analysis, security scanning, testing, DevOps, and context memory
- Graph Context System with hybrid search (BM25 + semantic) for persistent memory and learning
- Domain Rules encoding best practices for security, database, API design, performance, and AI/ML
Tip
Quickstart with the Orchestrator for structured workflows, or the Agentic Team for open-ended collaboration. Both systems benefit from the shared agentic infrastructure and context memory. See QUICKSTART.md for quick setup instructions to get started in ~2 minutes. Or, see #quick-start below for a detailed walkthrough.
graph TB
subgraph "π§ Agentic Infrastructure"
direction LR
subgraph AGENTS["Specialized Agents (9)"]
WEB[Web Frontend]
API[Backend API]
SEC[Security]
OPS[DevOps]
ML[AI/ML]
DB[Database]
end
subgraph SKILLS["Skills Library (22)"]
DEV[Development]
TEST[Testing]
SECS[Security]
DEVOPS[DevOps]
AIML[AI/ML]
DOCS[Documentation]
end
subgraph TOOLS["MCP Tools (34+)"]
CODE[Code Analysis]
SCAN[Security Scan]
TTOOLS[Testing]
DTOOLS[DevOps]
CTX[Context Memory]
end
subgraph CONTEXT["Graph Context"]
GRAPH[(Graph Store)]
SEARCH[Hybrid Search]
EMBED[Embeddings]
end
end
AGENTS --> SKILLS
SKILLS --> TOOLS
TOOLS --> CONTEXT
| Component | Count | Description |
|---|---|---|
| Specialized Agents | 9 | Domain experts for web, backend, security, DevOps, AI/ML, database, mobile, performance, documentation |
| Skills | 22 | Reusable task templates across 6 categories |
| MCP Tools | 34+ | Code analysis, security scanning, testing, DevOps, context memory |
| Node Types | 10 | Conversation, Task, Mistake, Pattern, Decision, CodeSnippet, Preference, File, Concept, Project |
| Edge Types | 12 | RELATED_TO, CAUSED_BY, FIXED_BY, SIMILAR_TO, DEPENDS_ON, etc. |
Important
Both the Orchestrator and Agentic Team maintain independent graph-based context databases for persistent memory and cross-session learning. A unified Context Dashboard aggregates both stores for visualization.
graph TB
subgraph "Context System Architecture"
direction TB
subgraph ORCH_CTX["Orchestrator Context<br/>~/.ai-orchestrator/context.db"]
OM[models/ β Node & edge schemas]
OS[store/ β Graph persistence]
OX[search/ β BM25 + semantic + hybrid]
OO[ops/ β Analytics, export, pruning, versioning]
end
subgraph TEAM_CTX["Agentic Team Context<br/>~/.agentic-team/context.db"]
TM[models/ β Node & edge schemas]
TS[store/ β Graph persistence]
TX[search/ β BM25 + semantic + hybrid]
TO[ops/ β Analytics, export, pruning, versioning]
end
subgraph DASH["Context Dashboard :5003"]
APP[app.py β Flask aggregator]
VIZ[templates/ β Interactive visualization]
end
end
ORCH_CTX --> DASH
TEAM_CTX --> DASH
style ORCH_CTX fill:#1a365d,color:#fff
style TEAM_CTX fill:#1a365d,color:#fff
style DASH fill:#2d3748,color:#fff
Node Types β 10 types of knowledge stored in the graph:
| Node Type | Description |
|---|---|
| Conversation | Past chat sessions with full message history |
| Task | Completed tasks with outcomes, agent used, and duration |
| Mistake | Errors with corrections and prevention strategies |
| Pattern | Reusable code patterns and best practices |
| Decision | Architectural decisions with rationale and trade-offs |
| CodeSnippet | Useful code fragments with language and context |
| Preference | Learned user preferences (tools, style, workflows) |
| File | Source files with language, size, and framework metadata |
| Concept | Abstract concepts and domain knowledge |
| Project | Registered project roots with scan metadata |
Edge Types β 12 relationship types connecting nodes:
| Edge Type | Description |
|---|---|
RELATED_TO |
General semantic relationship |
CAUSED_BY |
Causal chain (mistake β root cause) |
FIXED_BY |
Resolution link (mistake β fix) |
SIMILAR_TO |
Similarity link (patterns, tasks) |
DEPENDS_ON |
Dependency relationship |
PRECEDED_BY |
Temporal ordering (earlier event) |
FOLLOWED_BY |
Temporal ordering (later event) |
LEARNED_FROM |
Knowledge derivation (preference β conversation) |
USED_IN |
Usage relationship (pattern β task) |
REFERENCES |
Cross-reference between nodes |
DERIVED_FROM |
Derived knowledge (snippet β pattern) |
EVOLVED_INTO |
Evolution tracking (v1 pattern β v2) |
Hybrid Search combines three retrieval strategies via Reciprocal Rank Fusion (RRF):
- BM25 β Keyword-based search using term frequencyβinverse document frequency
- Semantic β Embedding-based similarity using vector cosine distance
- Hybrid β Fused ranking of BM25 + semantic results using RRF for best-of-both-worlds retrieval
Both systems support project-scoped context graphs for full portability. When a user points the system at their project directory, agents automatically scan and build a rich context graph of the codebase.
graph TB
subgraph "Project Context Scoping"
direction TB
USER[User configures PROJECT_PATH] --> SCAN[ProjectScanner]
SCAN --> PID["project_id = SHA-256[:16] of path"]
subgraph "Isolated Project Graphs"
P1["Project A<br/>pid=a1b2c3..."]
P2["Project B<br/>pid=d4e5f6..."]
P3["Global Scope<br/>pid='' (no project)"]
end
PID --> P1 & P2
SCAN --> FILES[File Nodes]
SCAN --> PATTERNS[Pattern Nodes]
SCAN --> DECISIONS[Decision Nodes]
SCAN --> EDGES[Relationship Edges]
end
style P1 fill:#2b6cb0,stroke:#2c5282,color:#fff
style P2 fill:#276749,stroke:#22543d,color:#fff
style P3 fill:#744210,stroke:#975a16,color:#fff
Key features:
- Deterministic IDs:
project_idis a SHA-256 prefix of the normalized absolute path β idempotent and reproducible - Multi-project isolation: Each project gets its own graph partition; queries filter by
project_id - Global scope: Nodes with
project_id=""are universal (patterns, reference knowledge) β shared across all projects - Automatic scanning:
ProjectScannerdetects languages, frameworks, file structure, and config patterns - Portability: Set
PROJECT_PATHenvironment variable orsettings.project_pathin config YAML β the system handles the rest - Incremental updates:
rescan_project()rebuilds the graph atomically;delete_project_graph()cleanly removes all project nodes
Tip
Auto-seeding: Run scripts/seed_context_graphs.py to populate both context databases with generic reference knowledge (patterns, mistakes, decisions) on first use. Seed data contains no hallucination-prone fake tasks or conversations β only universally applicable best practices.
Note
Context Dashboard: Launch with python -m context_dashboard (port 5003) to visualize both context graphs, inspect nodes/edges, and search across all stored knowledge. See context_dashboard/README.md for details.
AI coding agents are enhanced with specialized role definitions, reusable skills, and domain rules that are automatically loaded based on context.
graph LR
subgraph "Agent Ecosystem"
direction TB
subgraph CLAUDE[".claude/"]
CA["agents/ (11)"]
CS["skills/ (23)"]
CR["rules/ (11)"]
CC[CLAUDE.md]
end
subgraph CODEX[".codex/"]
XA["agents/ (13)"]
XC[config.toml]
XR[rules/]
end
AGENTS_MD[AGENTS.md β Shared instructions]
end
CA --> CS
CA --> CR
AGENTS_MD --> CLAUDE
AGENTS_MD --> CODEX
style CLAUDE fill:#7c3aed,color:#fff
style CODEX fill:#059669,color:#fff
Claude Agents β 11 specialized agents in .claude/agents/:
| Agent | File | Domain |
|---|---|---|
| Web Frontend | web-frontend.md |
React, Vue, CSS, accessibility, responsive design |
| Backend API | backend-api.md |
REST, GraphQL, databases, Flask/FastAPI |
| Security Specialist | security-specialist.md |
OWASP, vulnerability analysis, secure coding |
| DevOps Infrastructure | devops-infrastructure.md |
Docker, Kubernetes, CI/CD, cloud |
| AI/ML Engineer | ai-ml-engineer.md |
ML pipelines, embeddings, LLM integration |
| Database Architect | database-architect.md |
Schema design, query optimization, migrations |
| Mobile Developer | mobile-developer.md |
iOS, Android, React Native, Flutter |
| Performance Engineer | performance-engineer.md |
Profiling, load testing, optimization |
| Documentation Writer | documentation-writer.md |
API docs, architecture, tutorials |
| Code Reviewer | code-reviewer.md |
Code quality, best practices, PR reviews |
| Test Runner | test-runner.md |
Test execution, coverage, failure diagnosis |
Codex Agents β 13 specialized agents in .codex/agents/:
| Agent | File | Domain |
|---|---|---|
| Code Reviewer | code-reviewer.toml |
Code quality and review automation |
| Explorer | explorer.toml |
Codebase exploration and research |
| Security Specialist | security-specialist.toml |
Security auditing and vulnerability scanning |
| Web Frontend | web-frontend.toml |
Frontend development and UI patterns |
| DevOps Infrastructure | devops-infrastructure.toml |
Infrastructure and deployment automation |
| Implementer | implementer.toml |
Feature implementation and coding |
| Database Architect | database-architect.toml |
Database design and optimization |
| Performance Engineer | performance-engineer.toml |
Performance profiling and optimization |
| Test Runner | test-runner.toml |
Test suite execution and diagnosis |
| AI/ML Engineer | ai-ml-engineer.toml |
ML pipelines and AI system design |
| Backend API | backend-api.toml |
Backend services and API development |
| Documentation Writer | documentation-writer.toml |
Technical documentation |
| Mobile Developer | mobile-developer.toml |
Mobile application development |
Skills Library β 24 reusable skill templates in .claude/skills/ across 7 categories:
| Category | Count | Skills |
|---|---|---|
| Development | 6 | react-components, rest-api-design, python-async, database-queries, graphql-development, error-handling |
| Testing | 4 | unit-testing, integration-testing, test-driven-development, performance-testing |
| Security | 4 | input-validation, authentication, secure-coding, vulnerability-assessment |
| DevOps | 3 | docker-containerization, ci-cd-pipelines, kubernetes-deployment |
| AI/ML | 3 | embeddings-retrieval, llm-integration, rag-pipeline |
| Documentation | 3 | api-documentation, architecture-docs, code-documentation |
| Context | 1 | context-graph-builder |
Four additional standalone skills (
context-graph-builder,generate-reports,health-check,run-tests) provide operational task automation.
Domain Rules β 11 rule files in .claude/rules/ encoding best practices:
| Rule | File | Enforces |
|---|---|---|
| Adapters | adapters.md |
Adapter pattern, base class contracts |
| API Design | api-design.md |
RESTful conventions, versioning, error formats |
| Testing | testing.md |
Pytest patterns, coverage requirements, fixtures |
| Performance | performance.md |
Profiling, caching, async patterns |
| Config | config.md |
YAML config, environment variables, validation |
| AI/ML | ai-ml.md |
Model integration, embeddings, prompt patterns |
| Observability | observability.md |
Logging, metrics, health checks |
| Frontend | frontend.md |
Component patterns, accessibility, state management |
| CI/CD | ci-cd.md |
Pipeline design, deployment gates, rollback |
| Security | security.md |
Input validation, auth, secrets management |
| Database | database.md |
Schema design, migrations, query safety |
Note
Agents automatically inherit access to all skills and rules in their scope. When Claude Code is invoked with a specialized agent (e.g., @security-specialist), it loads the agent definition, relevant skills, and applicable domain rules to provide expert-level guidance.
| File | Purpose |
|---|---|
.claude/CLAUDE.md |
Main instructions for Claude Code β imports AGENTS.md and sets project context |
.claude/settings.json |
Claude project settings (permissions, model preferences) |
.codex/config.toml |
Codex project configuration |
.codex/agents/*.toml |
Codex agent role definitions with system prompts |
AGENTS.md |
Shared instructions read by all AI coding agents (Codex, Gemini CLI, etc.) |
AGENTIC_INFRA.md |
Full documentation of the agentic infrastructure |
graph TD
subgraph Repository["AI Coding Tools Repository"]
direction TB
subgraph Orchestrator["orchestrator/"]
O_CLI["CLI Shell"]
O_UI["Web UI<br/>Nuxt 3 + Flask + Socket.IO"]
O_CORE["Core Engine<br/>Workflow Manager<br/>Task Manager"]
O_ADAPT["Adapters<br/>Claude | Codex | Gemini<br/>Copilot | Ollama | llama.cpp"]
O_RESIL["Resilience<br/>Retry | Fallback | Offline"]
O_OBS["Observability<br/>Prometheus | Logging | Health"]
O_SEC["Security Module<br/>Validation | Rate Limiting | Audit"]
O_INFRA["Infra<br/>Cache | Async Executor | Config Manager"]
O_CONF["orchestrator/config/agents.yaml"]
end
subgraph AgenticTeam["agentic_team/"]
A_CLI["CLI REPL"]
A_UI["Web UI<br/>Nuxt 3 + Flask + Socket.IO"]
A_ENGINE["Engine<br/>Free Communication<br/>Lead-Gated Output"]
A_ADAPT["Adapters<br/>Claude | Codex | Gemini<br/>Copilot | Ollama | llama.cpp"]
A_FALLBACK["Fallback + Offline"]
A_CONF["orchestrator/config/agents.yaml"]
end
end
O_CLI --> O_CORE
O_UI --> O_CORE
O_CORE --> O_ADAPT
O_CORE --> O_RESIL
O_CORE --> O_OBS
O_CORE --> O_SEC
O_CORE --> O_INFRA
O_ADAPT --> ExtCloud["Cloud CLIs<br/>claude | codex | gemini | copilot"]
O_ADAPT --> ExtLocal["Local Backends<br/>Ollama | llama.cpp"]
A_CLI --> A_ENGINE
A_UI --> A_ENGINE
A_ENGINE --> A_ADAPT
A_ENGINE --> A_FALLBACK
A_ADAPT --> ExtCloud
A_ADAPT --> ExtLocal
style Orchestrator fill:#1a1a2e,stroke:#16213e,color:#e0e0e0
style AgenticTeam fill:#1a2e1a,stroke:#162e16,color:#e0e0e0
The Orchestrator processes tasks through a configurable pipeline of AI agents. Each step in a workflow maps to a specific agent and role.
sequenceDiagram
participant User
participant CLI as CLI / Web UI
participant Engine as Core Engine
participant WF as Workflow Manager
participant Codex as Codex Adapter
participant Gemini as Gemini Adapter
participant Claude as Claude Adapter
parRelease History
| Version | Changes | Urgency | Date |
|---|---|---|---|
| v1.0.0 | ## What's Changed * introduces comprehensive CI/CD pipelines for CircleCI, GitHub Actions (Azure), and GitLab, enabling automated testing, security scanning, Docker image building, and blue-green deployments for the AI Orchestrator project by @hoangsonww in https://github.com/hoangsonww/AI-Agents-Orchestrator/pull/1 * docs: update documentation for clarity and consistency and add GitHub CI by @hoangsonww in https://github.com/hoangsonww/AI-Agents-Orchestrator/pull/2 ## New Contributors * @ | Low | 11/29/2025 |
