AgentCore
Production-ready orchestration framework for agentic AI systems implementing Google's A2A (Agent-to-Agent) protocol v0.2.
Features
A2A Protocol Implementation
- JSON-RPC 2.0 compliant API for standardized agent communication
- Agent discovery via
/.well-known/agent.jsonendpoints - Task coordination with full lifecycle management
- Real-time messaging via WebSocket and Server-Sent Events (SSE)
- Distributed tracing with A2A context propagation
Agent Runtime
- Chain-of-Thought (CoT) reasoning engine
- ReAct pattern for iterative reasoning and action
- Autonomous execution mode for complex multi-step tasks
- Multi-tool integration with API connectors, code execution, and file operations
- Plugin system with version management and validation
- State persistence with backup and recovery
- Sandbox execution with security profiles
Memory Service
- Hierarchical memory with working, episodic, and semantic layers
- Entity-Centric Learning (ECL) pipeline for knowledge extraction
- Graph-based memory with Neo4j integration
- Hybrid search combining vector similarity and graph traversal
- MEMify optimization for memory consolidation and pruning
- Context compression and expansion for efficient token usage
LLM Client Service
- Multi-provider support: OpenAI, Anthropic, Google Gemini
- Intelligent model selection based on task requirements
- Automatic failover with provider health monitoring
- Cost tracking and budget management
- Response caching for optimization
ACE (Adaptive Capability Engine)
- Performance monitoring with baseline tracking
- Capability evaluation and fitness scoring
- Intervention engine with trigger-based decisions
- Playbook management for automated responses
- Delta generation for capability improvements
DSPy Optimization
- MIPROv2 and GEPA optimization algorithms
- MLflow integration for experiment tracking
- A/B testing framework for prompt variants
- Continuous learning with drift detection
- GPU acceleration support
Coordination Service
- Multi-agent coordination with consensus protocols
- Workflow orchestration with graph-based execution
- CQRS pattern with event sourcing
- Saga pattern for distributed transactions
- Parallel execution with dependency resolution
Integration Layer
- Cloud storage: S3, GCS, Azure Blob
- Database connectors: PostgreSQL with async support
- Webhook management with delivery tracking
- Resilience patterns: Circuit breaker, bulkhead, timeout
- Security: Credential management, compliance scanning
Training System
- GRPO (Group Relative Policy Optimization)
- Trajectory recording and replay
- Custom reward registry
- Credit assignment algorithms
- Job management with budget controls
CLI
- 4-layer architecture: CLI, Service, Protocol, Transport
- Agent management: register, list, info, remove
- Task operations: create, list, status tracking
- Session management: create, list, pause
- Workflow control: start, monitor, manage
- Configuration: show, set, validate
Quick Start
Prerequisites
- Python 3.12+
- PostgreSQL 14+
- Redis 7+
- Neo4j 5+ (optional, for graph memory)
Installation
# Clone the repository
git clone https://github.com/Mathews-Tom/AgentCore.git
cd AgentCore
# Install dependencies using uv
uv sync
# Set up environment
cp .env.test.template .env
Running with Docker Compose
# Start all services
docker compose -f docker-compose.dev.yml up
# API available at http://localhost:8001
# API docs at http://localhost:8001/docs
Running Locally
# Start database and Redis
docker compose -f docker-compose.dev.yml up postgres redis
# Apply migrations
uv run alembic upgrade head
# Start development server
uv run uvicorn agentcore.a2a_protocol.main:app --host 0.0.0.0 --port 8001 --reload
CLI Usage
# Register an agent
agentcore agent register --name "my-agent" --capabilities "text_generation,analysis"
# List agents
agentcore agent list
# Create a task
agentcore task create --description "Analyze customer feedback"
# Start a session
agentcore session create --name "analysis-session"
Architecture
src/agentcore/
├── a2a_protocol/ # A2A protocol implementation
│ ├── models/ # Pydantic models (AgentCard, Task, Event)
│ ├── services/ # Business logic and JSON-RPC handlers
│ ├── routers/ # FastAPI endpoints
│ └── database/ # PostgreSQL models and repositories
│
├── agent_runtime/ # Agent execution engine
│ ├── engines/ # CoT, ReAct, Autonomous engines
│ ├── services/ # Lifecycle, tools, state management
│ └── tools/ # Built-in and custom tool support
│
├── ace/ # Adaptive Capability Engine
│ ├── capability/ # Evaluation and scoring
│ ├── intervention/ # Trigger-based decisions
│ └── monitors/ # Performance tracking
│
├── dspy_optimization/ # DSPy optimization framework
│ ├── algorithms/ # MIPROv2, GEPA
│ ├── learning/ # Continuous learning, drift detection
│ └── tracking/ # MLflow integration
│
├── gateway/ # API gateway services
├── integration/ # External service integrations
├── orchestration/ # Workflow orchestration (CQRS, Saga)
├── reasoning/ # Reasoning strategies
└── training/ # RL training system
src/agentcore_cli/ # Command-line interface
├── commands/ # CLI command handlers
├── services/ # Service layer
├── protocol/ # JSON-RPC client
└── transport/ # HTTP transport
Testing
# Run all tests
uv run pytest
# Run with coverage report
uv run pytest --cov=src --cov-report=html
# Run specific test categories
uv run pytest tests/unit/
uv run pytest tests/integration/
uv run pytest tests/cli/
# Load testing
uv run locust -f tests/load/locustfile.py
Development
# Install dev dependencies
uv sync --dev
# Run linter
uv run ruff check src/ tests/
# Auto-fix issues
uv run ruff check --fix src/ tests/
# Type checking
uv run mypy src/
Database Migrations
# Create migration
uv run alembic revision --autogenerate -m "description"
# Apply migrations
uv run alembic upgrade head
# Rollback
uv run alembic downgrade -1
Configuration
All settings via environment variables or .env:
# Database
DATABASE_URL=postgresql+asyncpg://user:pass@host:5432/agentcore
# Redis
REDIS_URL=redis://localhost:6379/0
# Security
JWT_SECRET_KEY=your-secret-key
JWT_ALGORITHM=HS256
# LLM Providers
OPENAI_API_KEY=sk-...
ANTHROPIC_API_KEY=sk-ant-...
GOOGLE_API_KEY=AI...
# Monitoring
ENABLE_METRICS=true
LOG_LEVEL=INFO
Documentation
- API Reference - JSON-RPC method specifications
- Architecture - System design documents
- CLI Reference - Command-line interface guide
- Deployment - Production deployment guides
- Security - Security audit and compliance
License
AGPL-3.0 License - see LICENSE for details.
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