agent-ai-lab

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# Agent AI Lab — System Architecture Documentation

A comprehensive, modular, and deeply structured documentation set describing the full architecture of an advanced AI agent system. The goal of the project is to provide a clear, layered, and extensible blueprint for building, evaluating, and deploying intelligent agents capable of reasoning, interacting, learning, and acting safely in complex environments.

The documentation is organized into well‑defined architectural layers, each representing a major subsystem of the agent. Every layer includes conceptual overviews, detailed specifications, observability models, safety considerations, and cross‑layer dependencies.

---

## Status Overview

| Category | Status |
|---------|--------|
| Architecture Docs | Complete (v1 snapshot available) |
| GitHub | Active development |
| Last Commit | See GitHub history |
| Issues | Open for contributions |
| License | MIT |
| Version | v1.0.0 (documentation snapshot) |
| Release | Planned |

---
## 🚀 Overview

`agent-ai-lab` provides a clean, extensible architecture for:

- building deterministic AI agents  
- orchestrating LLM pipelines  
- integrating external tools and APIs  
- running retrieval‑augmented generation (RAG)  
- managing agent memory  
- enabling cryptographic extensions (deterministic signing episodes, hashing, verification)  

The framework is modular, testable, and production‑ready.

---
## Quick Links

- **Architecture Overview** → `ARCHITECTURE.md`  
- **Architecture Diagram** → `ARCHITECTURE_DIAGRAM.md`  
- **Full Documentation Index** → `INDEX.md`  
- **System Design** → `SYSTEM_DESIGN.md`  
- **Glossary** → `GLOSSARY.md`  
- **Governance Model** → `GOVERNANCE_MODEL.md`  
- **Risk Model** → `RISK_MODEL.md`  
- **Security Model** → `SECURITY_MODEL.md`  
- **Release Notes** → `RELEASE_NOTES.md`  
- **Roadmap** → `ROADMAP.md`  
- **Contributing Guide** → `CONTRIBUTING.md`  

---

## 🧱 Architecture Overview

A high-level diagram of the full agent architecture is available in:

ARCHITECTURE_DIAGRAM.md

The system is divided into twelve major layers, each representing a core subsystem of an intelligent agent.

```
──────────────────────────────────────────────┐
│                  API Layer                   │
│              (FastAPI / server/)             │
└──────────────────────────────────────────────┘
│
▼
┌──────────────────────────────────────────────┐
│                Agent Core                    │
│  base_agent.py • memory.py • tools.py        │
│           reasoning.py • planning            │
└──────────────────────────────────────────────┘
│
▼
┌──────────────────────────────────────────────┐
│                Pipelines                     │
│   llm_pipeline.py • retrieval_pipeline.py     │
│   embeddings • vector search • RAG            │
└──────────────────────────────────────────────┘
│
▼
┌──────────────────────────────────────────────┐
│             External Integrations             │
│  LLM providers • vector DBs • tools • APIs    │
└──────────────────────────────────────────────┘
```

---

## 🧠 Agent Core

The Agent Core manages:

- the reasoning loop  
- planning and decision logic  
- memory (short‑term and long‑term)  
- tool execution  
- interaction with LLM pipelines  
- deterministic execution (optional)  

### Core Components

- `base_agent.py` — main agent lifecycle  
- `memory.py` — memory backend  
- `tools.py` — tool registry and execution  
- `reasoning.py` — planning, chain‑of‑thought, decision logic  

---

## 🔌 Pipelines

### LLM Pipeline
Unified interface for:

- OpenAI  
- Anthropic  
- Local models (Ollama, vLLM, LM Studio)  
- Structured outputs  
- Streaming  

### Retrieval Pipeline
Supports:

- embeddings  
- vector search  
- RAG  
- Pinecone / Chroma / pgvector  

---

## 🌐 API Layer

FastAPI backend exposes:

- `POST /agent/run` — run agent with a prompt  
- `GET /agent/tools` — list available tools  
- `GET /health` — health check  

---

## 🧩 Tools

Tools extend agent capabilities in a controlled, auditable way.

Examples:

- system utilities  
- math operations  
- file operations  
- HTTP requests  
- cryptographic operations (planned)  

---

## 🔐 Security & Determinism

The framework is designed to support:

- deterministic planning  
- deterministic tool execution  
- plan hashing  
- execution trace  
- cryptographic signing episodes  
- threshold cryptography (future module)  

These features enable verifiable, trust‑critical AI workflows.

---

## 🧪 Testing

The project includes (or will include):

- unit tests  
- integration tests  
- deterministic execution tests  
- API tests  

---

## 🐳 Deployment

The repository includes:

- Dockerfile  
- docker-compose  
- Makefile  
- CI/CD workflows  

---

## 📚 Documentation

Documentation is located in the `docs/` directory:

- architecture.md  
- whitepaper.md  
- security-model.md  
- roadmap.md  
- crypto-module.md  

---

## 🧭 Roadmap

- deterministic signing episodes  
- crypto module (hashing, Shamir, verification)  
- distributed agent coordination  
- advanced RAG  
- multi-agent workflows  
- plugin system for tools  
- benchmarking suite  

---

## 🏁 Quickstart

```bash
git clone https://github.com/krunixbase/agent-ai-lab.git
cd agent-ai-lab

python -m venv venv
source venv/bin/activate

pip install -r requirements.txt

uvicorn server.main:app --reload
```

---
## 🧑‍💻 Example Usage

```
curl -X POST http://localhost:8000/agent/run \
  -H "Content-Type: application/json" \
  -d '{"prompt": "Hello agent"}'
```

---
## Architecture Map

The architecture is organized into twelve layers:

1. **Interaction Layer** — user communication, intent processing  
2. **Cognitive & Planning Layer** — reasoning, planning, decision-making  
3. **Memory & Knowledge Layer** — episodic, semantic, vector memory  
4. **Tooling & Execution Layer** — tool selection, validation, execution  
5. **Runtime & Orchestration Layer** — execution loop, concurrency, scheduling  
6. **Safety, Ethics & Governance Layer** — safety, compliance, oversight  
7. **Deployment, Reliability & Performance Layer** — scaling, performance  
8. **Evaluation, Testing & Meta-learning Layer** — evaluation, benchmarking  
9. **Multi-agent Layer** — coordination, communication, protocols  
10. **Embodiment & Simulation Layer** — perception, motor control, simulation  
11. **Cross-layer Architecture** — configuration, versioning, observability  
12. **Backup & Migration Logs** — historical documents and migrations  

Each layer has its own folder under:

docs/architecture/


---

## Layer Documentation

- **Interaction Layer**  
  `docs/architecture/interaction/README.md`

- **Cognitive & Planning Layer**  
  `docs/architecture/cognitive-planning/README.md`

- **Memory & Knowledge Layer**  
  `docs/architecture/memory-knowledge/README.md`

- **Tooling & Execution Layer**  
  `docs/architecture/tooling-execution/README.md`

- **Runtime & Orchestration Layer**  
  `docs/architecture/runtime-orchestration/README.md`

- **Safety, Ethics & Governance Layer**  
  `docs/architecture/safety-

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