AI Coding Tools Orchestrator and Agentic Team Runtime

---

Overview

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 — 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 — 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.

Agentic Infrastructure

We provide a rich agentic infrastructure that both the Orchestrator and Agentic Team utilize to empower their AI agents with specialized knowledge, reusable skills, powerful tools, and persistent context memory. This infrastructure is designed to be modular and extensible, allowing for easy addition of new agents, skills, tools, and context nodes as the system evolves.

graph TB
    subgraph "🧠 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 | RELATEDTO, CAUSEDBY, FIXEDBY, SIMILARTO, DEPENDS_ON, etc. |

[!IMPORTANT]

📚 Click for Full Agentic Infrastructure Documentation

Context System

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/ — Node & edge schemas]             OS[store/ — Graph persistence]             OX[search/ — BM25 + semantic + hybrid]             OO[ops/ — Analytics, export, pruning, versioning]         end
subgraph TEAM_CTX["Agentic Team Context<br/>~/.agentic-team/context.db"]             TM[models/ — Node & edge schemas]             TS[store/ — Graph persistence]             TX[search/ — BM25 + semantic + hybrid]             TO[ops/ — Analytics, export, pruning, versioning]         end
subgraph DASH["Context Dashboard :5003"]             APP[app.py — Flask aggregator]             VIZ[templates/ — Interactive visualization]         end
subgraph OBS_OUT["Obsidian Export"]             OBS_V["Obsidian Vault<br/>[[wikilinks]] + graph.json"]         end     end
ORCH_CTX --> DASH     TEAM_CTX --> DASH     ORCHCTX --> OBSOUT     TEAMCTX --> OBSOUT
style ORCH_CTX fill:#1a365d,color:#fff     style TEAM_CTX fill:#1a365d,color:#fff     style DASH fill:#2d3748,color:#fff     style OBS_OUT fill:#7C3AED,color:#fff

Node Types — 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 — 12 relationship types connecting nodes:

| Edge Type | Description | |-----------|-------------| | RELATED_TO | General semantic relationship | | CAUSED_BY | Causal chain (mistake → root cause) | | FIXED_BY | Resolution link (mistake → 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 → conversation) | | USED_IN | Usage relationship (pattern → task) | | REFERENCES | Cross-reference between nodes | | DERIVED_FROM | Derived knowledge (snippet → pattern) | | EVOLVED_INTO | Evolution tracking (v1 pattern → v2) |

Hybrid Search combines three retrieval strategies via Reciprocal Rank Fusion (RRF):

• BM25 — Keyword-based search using term frequency–inverse document frequency
• Semantic — Embedding-based similarity using vector cosine distance
• Hybrid — Fused ranking of BM25 + semantic results using RRF for best-of-both-worlds retrieval

Project-Scoped Context Graphs

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_id is a SHA-256 prefix of the normalized absolute path — 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) — shared across all projects
• Automatic scanning: ProjectScanner detects languages, frameworks, file structure, and config patterns
• Portability: Set PROJECTPATH environment variable or settings.projectpath in config YAML — the system handles the rest
• Incremental updates: rescanproject() rebuilds the graph atomically; deleteproject_graph() cleanly removes all project nodes

[!TIP]

Auto-seeding: Run scripts/seedcontextgraphs.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 — only universally applicable best practices.

[!NOTE]

Context Dashboard: Launch with python -m contextdashboard (port 5003) to visualize both context graphs, inspect nodes/edges, and search across all stored knowledge. See contextdashboard/README.md for details.

[!TIP]

Obsidian Export: Export any graph as an Obsidian vault for offline interactive exploration. Graphify: graphify export obsidian <path>. Context systems: ContextExporter.exportobsidian(). Each vault includes [[wikilinks]], typed folders, YAML frontmatter, and pre-configured .obsidian/graph.json color groups. See GRAPHIFY.md, ORCHESTRATOR.md, and AGENTIC_TEAM.md.

Skills Library & Agent Definitions

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 — 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 — 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 — 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 — 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 — 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.

Configuration Files

| File | Purpose | |------|---------| | .claude/CLAUDE.md | Main instructions for Claude Code — 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 |

Architecture

High-Level Overview

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
OCLI --> OCORE     OUI --> OCORE     OCORE --> OADAPT     OCORE --> ORESIL     OCORE --> OOBS     OCORE --> OSEC     OCORE --> OINFRA     O_ADAPT --> ExtCloud["Cloud CLIs<br/>claude | codex | gemini | copilot"]     O_ADAPT --> ExtLocal["Local Backends<br/>Ollama | llama.cpp"]
ACLI --> AENGINE     AUI --> AENGINE     AENGINE --> AADAPT     AENGINE --> AFALLBACK     A_ADAPT --> ExtCloud     A_ADAPT --> ExtLocal
style Orchestrator fill:#1a1a2e,stroke:#16213e,color:#e0e0e0     style AgenticTeam fill:#1a2e1a,stroke:#162e16,color:#e0e0e0

Orchestrator Workflow Execution

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
    participant FB as Fallback Manager
User->>CLI: Submit task     CLI->>Engine: execute(task, workflow="default")     Engine->>WF: load workflow steps
WF->>Codex: Step 1 -- implement     alt Codex unavailable         Codex-->>FB: error         FB->>FB: route to local-code     end     Codex-->>WF: implementation
WF->>Gemini: Step 2 -- review     Gemini-->>WF: review feedback
WF->>Claude: Step 3 -- refine     Claude-->>WF: refined code
WF-->>Engine: final result     Engine-->>CLI: display output     CLI-->>User: code + report

Agentic Team Communication Flow

The Agentic Team uses free role-to-role communication. Agents speak in turns, address each other by role, and the team lead decides when the task is complete.

sequenceDiagram
    participant User
    participant PM as Project Manager (Lead)
    participant Arch as Software Architect
    participant Dev as Software Developer
    participant QA as QA Engineer
    participant DevOps as DevOps Engineer
User->>PM: "Build a REST API with auth"     PM->>Arch: Define architecture and constraints     Arch->>Dev: Provide interface specs     Dev->>Dev: Implement code     Dev->>QA: Request quality review     QA->>Dev: Report edge cases     Dev->>Dev: Fix issues     Dev->>DevOps: Request deployment review     DevOps->>PM: Confirm operational readiness     PM->>User: Final consolidated response

Adapter Resolution Flow

Both systems resolve which AI backend to use at runtime. The adapter layer abstracts cloud CLIs and local model servers behind a common interface.

flowchart TD
    REQ[Incoming Task Step] --> CHECK{Agent Enabled?}
    CHECK -->|Yes| HEALTH{Health Check}
    CHECK -->|No| SKIP[Skip Agent]
HEALTH -->|Healthy| EXEC[Execute via Adapter]     HEALTH -->|Unhealthy| FB{Fallback Configured?}
FB -->|Yes| LOCAL[Route to Local Adapter<br/>Ollama / llama.cpp]     FB -->|No| ERR[Raise AgentUnavailableError]
EXEC --> PARSE[Parse CLI Output]     LOCAL --> PARSE     PARSE --> RESULT[Return AgentResponse]
style EXEC fill:#2b6cb0,stroke:#2c5282,color:#fff     style LOCAL fill:#276749,stroke:#22543d,color:#fff     style ERR fill:#9b2c2c,stroke:#742a2a,color:#fff

Local model execution semantics and limitation

Local models are deeply integrated for routing, offline mode, and fallback, but they are not direct workspace editors in the current implementation.

| Path | How it runs | Direct file edits | |---|---|---| | Cloud CLI adapters (Codex/Claude/Gemini/Copilot) | CLI process + workspace execution path | Yes (tool-dependent) | | Local adapters (Ollama/llama.cpp/OpenAI-compatible) | HTTP prompt-completion (/api/generate or /v1/completions) | No (text output only) |

Best use for local models: offline drafting, review feedback, and cloud-to-local fallback continuity.

[!CAUTION]

While it is possible to make local LLMs directly edit files (e.g., via a file-editor tool), this approach is currently disabled to prevent unintended destructive changes. Local adapters are advisory — they provide text output that the Orchestrator can use to inform the next steps, but they do not have direct write access to the workspace. This design choice prioritizes safety and predictability while still leveraging local models for their strengths in drafting and feedback. The hard part is not feasibility, it’s safety and reliability: permissions, diff constraints, validation/tests before write, rollback, and preventing bad edits.

Technology Stack Overview

graph LR
    subgraph Backend
        PY[Python 3.8+]
        FL[Flask 3.x]
        SIO[Socket.IO 4.x]
        PD[Pydantic 2.x]
        CL[Click 8.x]
    end
subgraph Frontend         VUE[Vue 3]         NUXT[Nuxt 3]         TW[Tailwind CSS 3.x]         MON[Monaco Editor]         PIN[Pinia]     end
subgraph Observability         PROM[Prometheus]         GRAF[Grafana]         SL[structlog]     end
subgraph Infrastructure         DOCK[Docker]         K8S[Kubernetes]         TF[Terraform]     end
PY --> FL --> SIO     PY --> PD     PY --> CL     VUE --> NUXT --> TW     VUE --> MON     VUE --> PIN     PROM --> GRAF     DOCK --> K8S
style Backend fill:#1a1a2e,stroke:#16213e,color:#e0e0e0     style Frontend fill:#1a2e1a,stroke:#162e16,color:#e0e0e0     style Observability fill:#2e1a1a,stroke:#2e1616,color:#e0e0e0     style Infrastructure fill:#1a1a2e,stroke:#16213e,color:#e0e0e0

System Comparison

The two systems serve different collaboration models. Choose based on your use case.

| Dimension | Orchestrator (orchestrator/) | Agentic Team (agentic_team/) | |---|---|---| | Collaboration model | Step-based pipeline (sequential) | Free role-to-role communication (turns) | | Agent identity | Tool names (codex, gemini, claude) | Roles (PM, Architect, Developer, QA, DevOps) | | Control flow | Dynamic metrics-based planner or workflow YAML step order | Team lead (PM) gates completion dynamically | | When to use | Dynamic planning, or repeatable pipelines: implement, review, refine | Open-ended tasks needing discussion and consensus | | CLI entry point | ai-orchestrator shell | ai-orchestrator agentic-shell | | Web UI port | :5001 | :5002 | | Config file | orchestrator/config/agents.yaml | agentic_team/config/agents.yaml | | Built-in workflows | Dynamic planner (metrics-based routing), plus 7 static (default, quick, thorough, review-only, document, offline-default, hybrid) | N/A (turn-based, no fixed pipeline) | | Fallback strategy | Per-step cloud-to-local routing | Independent fallback manager | | Observability | Prometheus metrics, structured logging, health probes, report generation | Health and readiness probes | | Security module | Input validation, rate limiting, audit logging | N/A (inherits from adapter layer) | | Shared code | None | None |

Feature Highlights

Orchestrator (orchestrator/)

| Category | Features | |---|---| | Workflows | 7 built-in workflows (default, quick, thorough, review-only, document, offline-default, hybrid); define custom ones in YAML | | Agents | Claude, Codex, Gemini, Copilot (cloud); Ollama, llama.cpp (local) | | CLI | Interactive REPL shell, one-shot commands, context-aware follow-ups, readline support | | Web UI | Nuxt 3 + Vue 3 frontend, Flask + Socket.IO backend, Monaco code editor, Pinia state management | | Local model behavior | Local adapters are advisory (text output); direct file edits come from CLI-backed agents | | Resilience | Retry with exponential backoff, circuit breakers, cloud-to-local fallback, offline detection | | Observability | Prometheus metrics, structured logging via structlog, health and readiness probes | | Reports | Execution summaries, agent performance, workflow analytics, config audits, HTML dashboard with Chart.js charts | | Security | Input validation, rate limiting, secret management, audit logging | | Infra | Async executor, response caching, connection pooling, config manager |

Agentic Team (agentic_team/)

| Category | Features | |---|---| | Runtime | Free role-to-role communication, configurable turn limits, lead-gated final responses | | Roles | Project Manager, Software Architect, Software Developer, QA Engineer, DevOps Engineer | | CLI | Dedicated REPL (agentic-shell) with --max-turns and --offline flags | | Web UI | Dedicated Nuxt 3 + Flask UI with Config Studio, real-time turn streaming, team communication view | | Local model behavior | Local adapters contribute role outputs and fallback text, but do not directly write files | | Fallback | Independent fallback manager and offline detector | | Configuration | Separate agents.yaml with agentic_team.roles section for role-to-agent mapping |

Graphify (graphify/)

| Category | Features | |---|---| | Analysis | Python AST (classes, functions, imports, calls, decorators, docstrings), JS/TS, Go, Rust, Java, C++, Markdown, YAML/JSON/TOML | | Graph | SQLite + FTS5, WAL mode, schema migrations (v1→v2→v3), thread-local connections, context manager | | Search | Full-text search, node explanation, path finding (BFS), community detection, god node analysis, complexity hotspots | | Cache | SHA-256 content-addressable cache, incremental scans (only changed files) | | Export | JSON, DOT (Graphviz), Markdown, GraphML, interactive HTML (vis.js), Obsidian vault | | API | Flask REST API with CORS, structured error handling, metrics/snapshot/diff endpoints | | Operations | File watching (watchdog + polling), graph snapshots & diffing, scan metrics collection | | Security | Path traversal protection, input sanitization, bounded parameters, no debug mode |

Quick Start

Prerequisites

• Operating System: Linux, macOS, or Windows (WSL recommended)
• Python: 3.8 or higher
• Node.js: 20+ (for Web UI)
• Memory: Minimum 4GB RAM
• Disk Space: 1GB for installation + workspace
• Network: Required for AI CLI tools and updates
• Claude Code: Installed, setup, and signed in on your machine (Required for any workflows using Claude Code - if you run claude in terminal and it works, you're good)
• OpenAI Codex: Installed and authenticated (if using Codex agent, try running codex and see if it responds)
• Google Gemini CLI: Installed and authenticated (if using Gemini agent, try gemini --version to verify)
• GitHub Copilot CLI: Installed and authenticated (if using Copilot agent, try copilot --version to verify)
• Llama.cpp or Ollama: If using local LLM agents, ensure they are installed and configured properly (try running ollama list or llamacpp --help to verify)
• Optional: Docker and Docker Compose for containerized setup

Install

git clone https://github.com/hoangsonww/AI-Agents-Orchestrator.git
cd AI-Agents-Orchestrator
python3 -m venv venv source venv/bin/activate   # Windows: venv\Scripts\activate
pip install -r requirements.txt chmod +x ai-orchestrator

Run the Orchestrator

# Interactive shell
./ai-orchestrator shell
One-shot task
./ai-orchestrator run "Create a Python REST API" --workflow default
Start the Web UI
make run-ui
Then open http://localhost:5001

Run the Agentic Team

# Interactive REPL
./ai-orchestrator agentic-shell
With options
./ai-orchestrator agentic-shell --max-turns 16 --offline
Start the Web UI
make run-agentic-ui
Then open http://localhost:5002

Verify Installation

./ai-orchestrator --help        # Show all commands
./ai-orchestrator agents        # List available agents
./ai-orchestrator workflows     # List available workflows
./ai-orchestrator validate      # Validate configuration

Project Structure

AI-Coding-Tools/
|
|-- .claude/                         # Claude Code agentic infrastructure
|   |-- CLAUDE.md                    # Main Claude instructions (imports AGENTS.md)
|   |-- settings.json                # Project settings and permissions
|   |-- agents/                      # 11 specialized agent definitions
|   |   |-- web-frontend.md
|   |   |-- backend-api.md
|   |   |-- security-specialist.md
|   |   |-- devops-infrastructure.md
|   |   |-- ai-ml-engineer.md
|   |   |-- database-architect.md
|   |   |-- mobile-developer.md
|   |   |-- performance-engineer.md
|   |   |-- documentation-writer.md
|   |   |-- code-reviewer.md
|   |   +-- test-runner.md
|   |-- skills/                      # 23 reusable skill templates
|   |   |-- development/             # 6 skills (react, REST, async, DB, GraphQL, errors)
|   |   |-- testing/                 # 4 skills (unit, integration, TDD, perf)
|   |   |-- security/                # 4 skills (validation, auth, secure-coding, vuln)
|   |   |-- devops/                  # 3 skills (Docker, CI/CD, K8s)
|   |   |-- ai-ml/                   # 3 skills (embeddings, LLM, RAG)
|   |   |-- documentation/           # 3 skills (API docs, arch docs, code docs)
|   |   |-- generate-reports/        # Standalone: report generation
|   |   |-- health-check/            # Standalone: system health checks
|   |   |-- run-tests/               # Standalone: test suite execution
|   |   +-- context-graph-builder/   # Standalone: context graph operations
|   +-- rules/                       # 11 domain rule files
|       |-- adapters.md
|       |-- api-design.md
|       |-- testing.md
|       |-- performance.md
|       |-- config.md
|       |-- ai-ml.md
|       |-- observability.md
|       |-- frontend.md
|       |-- ci-cd.md
|       |-- security.md
|       +-- database.md
|
|-- .codex/                          # Codex agentic infrastructure
|   |-- config.toml                  # Codex project configuration
|   |-- agents/                      # 13 specialized agent definitions (.toml)
|   |   |-- code-reviewer.toml
|   |   |-- explorer.toml
|   |   |-- security-specialist.toml
|   |   |-- web-frontend.toml
|   |   |-- devops-infrastructure.toml
|   |   |-- implementer.toml
|   |   |-- database-architect.toml
|   |   |-- performance-engineer.toml
|   |   |-- test-runner.toml
|   |   |-- ai-ml-engineer.toml
|   |   |-- backend-api.toml
|   |   |-- documentation-writer.toml
|   |   +-- mobile-developer.toml
|   |-- hooks/                       # Git hook integrations
|   +-- rules/                       # Codex-specific rules
|
|-- mcp_server/                      # MCP server (FastMCP 3.x) — 34+ tools
|   |-- server.py                    # Server entry point + core tools
|   |-- engines.py                   # Engine adapters for both systems
|   |-- repl.py                      # Interactive REPL mode
|   |-- tools/                       # Tool modules by category
|   |   |-- orchestrator_tools.py    # Orchestrator execution tools
|   |   |-- agenticteamtools.py    # Agentic team execution tools
|   |   |-- shared_tools.py          # Shared utility tools
|   |   |-- code_analysis.py         # 4 code analysis tools
|   |   |-- security_tools.py        # 4 security scanning tools
|   |   |-- testing_tools.py         # 4 testing tools
|   |   |-- devops_tools.py          # 5 DevOps tools
|   |   +-- context_tools.py         # 7 context memory tools
|   +-- resources/                   # MCP resource definitions
|
|-- orchestrator/                    # Self-contained orchestrator system
|   |-- init.py
|   |-- adapters/                    # AI agent adapters
|   |   |-- base.py                  # Abstract base adapter
|   |   |-- claude_adapter.py        # Claude Code CLI
|   |   |-- codex_adapter.py         # OpenAI Codex CLI
|   |   |-- gemini_adapter.py        # Google Gemini CLI
|   |   |-- copilot_adapter.py       # GitHub Copilot CLI
|   |   |-- ollama_adapter.py        # Ollama local models
|   |   |-- llamacppadapter.py     # llama.cpp / OpenAI-compatible
|   |   +-- cli_communicator.py      # Robust CLI subprocess handling
|   |-- core/                        # Core orchestration logic
|   |   |-- engine.py                # Main orchestration engine
|   |   |-- workflow.py              # Workflow definitions and runner
|   |   |-- task_manager.py          # Task lifecycle management
|   |   +-- exceptions.py            # Custom exception hierarchy
|   |-- resilience/                  # Fault tolerance
|   |   |-- retry.py                 # Retry with exponential backoff
|   |   |-- fallback.py              # Cloud-to-local fallback routing
|   |   +-- offline.py               # Offline detection
|   |-- observability/               # Monitoring, logging, and reports
|   |   |-- metrics.py               # Prometheus metrics
|   |   |-- logging_config.py        # Structured logging setup
|   |   |-- health.py                # Health and readiness probes
|   |   +-- report_generator.py      # Execution, performance, and HTML reports
|   |-- security_module/             # Security layer
|   |   +-- security.py              # Validation, rate limiting, audit
|   |-- infra/                       # Infrastructure utilities
|   |   |-- cache.py                 # Response caching
|   |   |-- async_executor.py        # Async task execution
|   |   +-- config_manager.py        # Configuration loading
|   |-- cli/                         # CLI interface
|   |   +-- shell.py                 # Interactive REPL shell
|   |-- context/                     # Graph-based context memory
|   |   |-- memory_manager.py        # High-level memory API
|   |   |-- models/                  # Node and edge schemas
|   |   |   +-- schemas.py
|   |   |-- store/                   # Graph persistence layer
|   |   |   +-- graph_store.py
|   |   |-- search/                  # Search engines
|   |   |   |-- bm25_index.py        # BM25 keyword search
|   |   |   |-- embeddings.py        # Embedding generation
|   |   |   |-- hybrid_search.py     # Hybrid BM25 + semantic
|   |   |   +-- advanced_search.py   # Advanced query support
|   |   +-- ops/                     # Operational utilities
|   |       |-- analytics.py         # Graph analytics
|   |       |-- export.py            # Data export
|   |       |-- pruning.py           # Node/edge pruning
|   |       |-- versioning.py        # Version tracking
|   |       +-- project_scanner.py   # Project directory scanning
|   |-- config/
|   |   +-- agents.yaml              # Agents, workflows, settings
|   |-- ui/                          # Web UI
|   |   |-- app.py                   # Flask + Socket.IO backend
|   |   |-- frontend/                # Nuxt 3 + Vue 3 + Tailwind
|   |   |-- static/
|   |   +-- templates/
|   +-- README.md                    # Orchestrator-specific docs
|
|-- agentic_team/                    # Self-contained agentic team system
|   |-- init.py
|   |-- engine.py                    # Role-based communication engine
|   |-- shell.py                     # Agentic team REPL
|   |-- decision_parser.py           # Turn decision parsing
|   |-- config_utils.py              # Config loading utilities
|   |-- constants.py                 # Shared constants
|   |-- fallback.py                  # Independent fallback manager
|   |-- offline.py                   # Independent offline detector
|   |-- adapters/                    # Own copy of AI agent adapters
|   |   |-- base.py
|   |   |-- claude_adapter.py
|   |   |-- codex_adapter.py
|   |   |-- gemini_adapter.py
|   |   |-- copilot_adapter.py
|   |   |-- ollama_adapter.py
|   |   |-- llamacppadapter.py
|   |   +-- cli_communicator.py
|   |-- context/                     # Independent graph-based context memory
|   |   |-- memory_manager.py        # High-level memory API
|   |   |-- models/                  # Node and edge schemas
|   |   |-- store/                   # Graph persistence layer
|   |   |-- search/                  # BM25 + FTS5 hybrid search
|   |   +-- ops/                     # Analytics, export, pruning, project scanning
|   |-- config/
|   |   +-- agents.yaml              # Agents, roles, team settings
|   |-- ui/                          # Dedicated Web UI
|   |   |-- app.py                   # Flask + Socket.IO backend
|   |   |-- frontend/                # Nuxt 3 + Vue 3 + Tailwind
|   |   |-- static/
|   |   +-- templates/
|   +-- README.md                    # Agentic team-specific docs
|
|-- tests/                           # Unified test suite
|   |-- conftest.py
|   |-- test_orchestrator.py
|   |-- test_adapters.py
|   |-- testadapterexecution.py
|   |-- testagenticteam_engine.py
|   |-- testagenticui_backend.py
|   |-- test_integration.py
|   |-- testfunctionale2e.py
|   |-- testenterprisehardening.py
|   |-- testproductionhardening.py
|   +-- ...
|
|-- reports/                         # Generated reports (JSON + HTML dashboard)
|   |-- INDEX.json                   # Report catalog
|   |-- exec_*.json                  # Per-task execution summaries
|   |-- perf_*.json                  # Agent performance analytics
|   |-- workflow_*.json              # Workflow-level analytics
|   |-- health_*.json                # System health snapshots
|   |-- config_*.json                # Configuration audits
|   +-- dashboard_*.html             # Interactive HTML dashboard with charts
|
|-- deployment/                      # Deployment configurations
|   |-- kubernetes/
|   |-- azure/
|   |-- systemd/
|   |-- load-balancer/
|   +-- scripts/
|
|-- docs/                            # Documentation
|   |-- images/                      # Screenshots
|   |-- orchestrator-architecture.md
|   |-- orchestrator-api-reference.md
|   |-- agentic-team-architecture.md
|   |-- agentic-team-api-reference.md
|   |-- configuration-guide.md
|   |-- testing-guide.md
|   |-- security.md
|   +-- offline-mode.md
|
|-- examples/                        # Usage examples
|   |-- orchestrator/
|   +-- agentic_team/
|
|-- context_dashboard/               # Unified context visualization (port 5003)
|   |-- app.py                       # Flask app aggregating both context stores
|   |-- templates/
|   |   +-- dashboard.html           # Interactive graph visualization
|   +-- README.md                    # Dashboard-specific docs
|
|-- scripts/                         # Helper scripts
|   |-- install.sh
|   |-- start-ui.sh
|   |-- start-agentic-ui.sh
|   |-- start-mcp-server.sh
|   |-- start-all.sh
|   |-- seedcontextgraphs.py
|   |-- health-check.sh
|   |-- format.sh
|   |-- lint.sh
|   +-- test.sh
|
|-- ai-orchestrator                  # Main CLI entry point
|-- Dockerfile                       # Multi-stage production image
|-- docker-compose.yml               # Both UIs + monitoring stack
|-- Makefile                         # Development commands
|-- pyproject.toml                   # Project metadata and tool config
|-- requirements.txt                 # Python dependencies
|-- AGENTS.md                        # Shared instructions for all AI coding agents
|-- AGENTIC_INFRA.md                 # Full agentic infrastructure documentation
|-- SETUP.md                         # Installation and setup guide
|-- ARCHITECTURE.md
|-- FEATURES.md
|-- AGENTIC_TEAM.md
|-- OFFLINE_MODE.md
|-- DEPLOYMENT.md
+-- LICENSE

[!IMPORTANT]

Key design decision: orchestrator/ and agentic_team/ are fully self-contained. Each carries its own adapters/, config/, and ui/ directories. There are no shared root-level adapters/, ui/, or config/ directories. The two systems share zero code and zero imports.

Configuration

Both systems read their configuration from their own orchestrator/config/agents.yaml file. The files follow the same schema but are independent.

graph LR
    subgraph Orchestrator Config
        OC["orchestrator/config/agents.yaml"]
        OC --> OA["agents: codex, gemini, claude, ..."]
        OC --> OW["workflows: default, quick, thorough, ..."]
        OC --> OS["settings: maxiterations, outputdir, ..."]
        OC --> OAT["agentic_team: roles (shared schema)"]
    end
subgraph Agentic Team Config         AC["agentic_team/config/agents.yaml"]         AC --> AA["agents: codex, gemini, claude, ..."]         AC --> AW["workflows: (same schema)"]         AC --> AS["settings: (same schema)"]         AC --> AAT["agenticteam: leadrole, max_turns, roles"]     end
style OC fill:#2d3748,stroke:#4a5568,color:#e2e8f0     style AC fill:#22543d,stroke:#276749,color:#e2e8f0

Available Workflows

| Workflow | Pipeline | Use Case | |---|---|---| | default | Codex --> Gemini --> Claude | Production-quality code with full review | | quick | Codex only | Fast prototyping | | thorough | Codex --> Copilot --> Gemini --> Claude --> Gemini | Mission-critical code | | review-only | Gemini --> Claude | Analyzing existing code | | document | Claude --> Gemini | Documentation generation | | offline-default | local-code --> local-instruct | Local-only, no cloud dependency | | hybrid | local-code --> Claude (fallback: local-instruct) | Local drafts with cloud review |

Deployment

Docker Compose (Recommended)

Both systems are packaged in a single multi-stage Docker image. The docker-compose.yml runs each as a separate service.

graph TD
    subgraph Docker Compose
        direction TB
        OUI["orchestrator-ui<br/>:5001"]
        AUI["agentic-team-ui<br/>:5002"]
        PROM["prometheus<br/>:9091<br/>(monitoring profile)"]
        GRAF["grafana<br/>:3000<br/>(monitoring profile)"]
    end
OUI --> SHARED_VOL["Shared Volumes<br/>output/ workspace/ logs/ sessions/"]     AUI --> SHARED_VOL     PROM --> OUI     PROM --> AUI     GRAF --> PROM
style OUI fill:#2b6cb0,stroke:#2c5282,color:#fff     style AUI fill:#276749,stroke:#22543d,color:#fff     style PROM fill:#c05621,stroke:#9c4221,color:#fff     style GRAF fill:#6b46c1,stroke:#553c9a,color:#fff

# Start both UIs
docker compose up --build -d
Start with monitoring (Prometheus + Grafana)
docker compose --profile monitoring up --build -d
Stop everything
docker compose down

Kubernetes

kubectl create namespace ai-coding-tools
kubectl apply -f deployment/kubernetes/
kubectl get pods -n ai-coding-tools

See DEPLOYMENT.md for systemd, Azure, load balancer, and production hardening guides.

Testing

The unified test suite (386 tests across 60+ modules) covers both systems independently.

flowchart LR
    subgraph "make all"
        FMT[format<br/>black + isort] --> LINT[lint<br/>flake8 + pylint]
        LINT --> TYPE[type-check<br/>mypy]
        TYPE --> TEST[test<br/>314 tests]
        TEST --> SEC[security<br/>bandit + safety]
    end
subgraph "Test Targets"         TEST --> T_ORCH[test-orchestrator]         TEST --> T_AGENT[test-agentic]         TEST --> T_UNIT[test-unit]         TEST --> T_INT[test-integration]         TEST --> T_E2E[test-e2e]     end
style FMT fill:#2b6cb0,stroke:#2c5282,color:#fff     style TEST fill:#276749,stroke:#22543d,color:#fff     style SEC fill:#9b2c2c,stroke:#742a2a,color:#fff

# Run all tests
make test
Orchestrator tests only
make test-orchestrator
Agentic team tests only
make test-agentic
Unit tests only
make test-unit
Integration tests only
make test-integration
Tests with coverage report
make test-coverage

Code Quality

The codebase maintains a perfect 10.00/10 pylint score with zero warnings across the entire project, enforced by 15 pre-commit hooks (black, isort, flake8, mypy, bandit, pyupgrade, and more).

| Metric | Value | |--------|-------| | Pylint Score | 10.00 / 10 | | Warnings | 0 | | Tests Passing | 386 | | Pre-commit Hooks | 15 / 15 passing |

make lint              # Lint all Python source (flake8 + pylint)
make format            # Format with black + isort
make type-check        # Run mypy on both subsystems
make security          # Run bandit + safety
make all               # Run everything (format, lint, type-check, test, security)

Monitoring

Prometheus metrics are exposed by the orchestrator UI backend on port 9090.

| Metric | Description | |---|---| | orchestratortaskstotal | Total tasks executed | | orchestratortaskduration_seconds | Task execution time | | orchestratoragentcalls_total | Agent invocations | | orchestratoragenterrors_total | Agent error count | | orchestratorcachehits_total | Cache performance |

Reports

The orchestrator automatically generates reports in reports/ when create_reports: true is set in agents.yaml. Reports include:

| Report Type | Description | |---|---| | Execution Summary | Per-task results: steps, agents used, fallbacks, suggestions, duration | | Agent Performance | Aggregated success rates, call counts, task type distribution | | Workflow Analytics | Per-workflow run counts, success rates, average iterations | | System Health | Health check results, disk/memory, Python version, platform | | Config Audit | Agent availability, workflow structure, settings snapshot | | HTML Dashboard | Interactive Chart.js dashboard with KPI cards, bar/line/doughnut charts |

Reports are also generated programmatically via:

from orchestrator.observability import ReportGenerator gen = ReportGenerator(reports_dir="./reports") gen.seed_reports(config=config)  # Generate all report types with sample data

Health checks:

• Orchestrator: http://localhost:5001/health, http://localhost:5001/ready
• Agentic Team: http://localhost:5002/health, http://localhost:5002/ready

Documentation

| Document | Description | |------------------------------------------------------|------------------------------------------------------------------| | SETUP.md | Prerequisites, installation, environment setup, troubleshooting | | ARCHITECTURE.md | System architecture and design patterns | | FEATURES.md | Comprehensive feature documentation | | AGENTIC_TEAM.md | Agentic team runtime details | | OFFLINE_MODE.md | Offline mode and local model guide | | DEPLOYMENT.md | Docker, Kubernetes, systemd, Azure deployment | | ADD_AGENTS.md | Guide for adding new AI agents | | orchestrator/README.md | Orchestrator subsystem documentation | | agentic_team/README.md | Agentic team subsystem documentation | | docs/ | API references, architecture deep-dives, testing guide, security |

Screenshots

Some screenshots of the Web UIs and CLI interfaces:

Orchestrator Web UI — real-time task execution dashboard with agent status and workflow controls

Agentic Team Web UI — role-based multi-agent collaboration with live communication view

Context Graph Dashboard — interactive knowledge graph visualization with node inspection and hybrid search

Orchestrator CLI — command-line interface for task execution and agent management

Agentic Shell REPL — interactive shell for direct agent communication and debugging

MCP Tools REPL — interactive console for exploring and testing 34+ MCP tools across both engines

MCP Server (Optional -- Model Context Protocol)

Both systems are optionally exposed via a FastMCP server (mcpserver/, port 8000), letting any MCP-compatible client (Claude Desktop, other LLM agents, or custom Python scripts) drive task execution programmatically.

# Start MCP server (stdio -- for Claude Desktop integration)
python -m mcp_server.server
Start MCP server (HTTP -- for remote clients)
python -m mcp_server.server --transport http --port 8000
Interactive REPL mode -- explore and test tools from the terminal
python -m mcp_server repl
Launch the Context Dashboard (port 5003)
python -m context_dashboard

graph LR
    subgraph "MCP Clients"
        CD[Claude Desktop]
        LA[LLM Agent]
        PY[Python Client]
        REPL[REPL Mode]
    end
subgraph "MCP Server :8000 — 34+ Tools"         direction TB         CORE["Core (10)<br/>orchestratorexecute, agenticteamexecute,<br/>listengines, health, config, validate"]         CODE["Code Analysis (4)<br/>codecomplexity, findpatterns,<br/>analyzedeps, codesummary"]         SEC["Security (4)<br/>secretsscan, securityheaderscheck,<br/>dependencyaudit, injection_scan"]         TEST["Testing (4)<br/>suggesttests, testcoverageanalysis,<br/>parsetests, createteststub"]         DEVOPS["DevOps (5)<br/>dockerfileanalysis, composeanalysis,<br/>ciconfigcheck, deploychecklist, envconfig_analysis"]         CTX["Context (7)<br/>contextstoreconversation, contextstoretask,<br/>contextlogmistake, contextstorepattern,<br/>contextsearch, contextgetrelevant, contextstats"]     end
subgraph "Engines"         O[Orchestrator :5001]         A[Agentic Team :5002]         D[Context Dashboard :5003]     end
CD & LA & PY & REPL -->|MCP Protocol| CORE & CODE & SEC & TEST & DEVOPS & CTX     CORE --> O & A     CTX --> D

34+ MCP tools organized across 6 categories:

| Category | Count | Tools | |----------|-------|-------| | Core Orchestration | 10 | orchestratorexecute, agenticteamexecute, listengines, orchestratorhealth, agenticteamhealth, listworkflows, listagents, validateconfig, teamconfig, agenticteam_validate | | Code Analysis | 4 | codecomplexity, findpatterns, analyzedeps, codesummary | | Security | 4 | secretsscan, securityheaderscheck, dependencyaudit, injection_scan | | Testing | 4 | suggesttests, testcoverageanalysis, parsetests, createteststub | | DevOps | 5 | dockerfileanalysis, composeanalysis, ciconfigcheck, deploychecklist, envconfig_analysis | | Context Memory | 7 | contextstoreconversation, contextstoretask, contextlogmistake, contextstorepattern, contextsearch, contextgetrelevant, contextstats |

See mcp_server/tools/ for the full tool implementations.

[!TIP]

The MCP server is entirely optional. Both the Orchestrator and Agentic Team work fully via their own CLIs and Web UIs without it. Use python -m mcp_server repl for an interactive REPL to explore and test tools from the terminal.

Contributing

Contributions are welcome. Please see CONTRIBUTING.md for guidelines.

• Fork the repository.
• Create a feature branch: git checkout -b feature/your-feature
• Make your changes and add tests.
• Run checks: make all
• Commit using conventional commits: git commit -m "feat: add amazing feature"
• Push and open a Pull Request.

Security

For security issues, please email hoangson091104@gmail.com. Do not open public issues for security vulnerabilities. See SECURITY.md for the full security policy.

License

This project is licensed under the MIT License. See LICENSE for details.

Support

• Maintainer: @ho

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