ACE-Agents: Agentic Context Engineering Framework

A Python implementation of the ACE (Agentic Context Engineering) framework for adaptive context optimization using LLM agents.

Overview

ACE-Agents is a framework that enables LLMs to learn and maintain context through an adaptive playbook system. Instead of using static prompts or brief instructions, ACE builds a growing collection of strategies, rules, and insights that evolve through experience.

Key Features

• Three Specialized Agents:

  • Generator: Produces reasoning trajectories using the context playbook
  • Reflector: Analyzes outputs and extracts insights from successes and failures
  • Curator: Manages playbook updates with semantic deduplication

• Adaptive Learning:

  • Offline Adaptation: Learn from labeled training data across multiple epochs
  • Online Adaptation: Update context in real-time during inference

• Flexible LLM Integration:

  • Uses direct HTTP requests for maximum flexibility
  • Easy integration with OpenRouter, OpenAI, Anthropic, and other providers

• Semantic Deduplication:

  • Automatic removal of redundant context using sentence embeddings
  • Preserves high-value insights while preventing context bloat

Installation

# Clone the repository
git clone https://github.com/yourusername/ace-agents.git
cd ace-agents

# Install dependencies
uv sync

# Or install with development dependencies
uv sync --extra dev

Quick Start

Basic Usage

from ace_agents import AceFramework

# Initialize the framework
# Playbook will be automatically saved to data/playbook/playbook.json
ace = AceFramework(
    provider="openrouter",
    api_key="your-api-key",
    model="anthropic/claude-3.5-sonnet"
)

# Generate a response
response = ace.generate("How do I validate an email address?")
print(response)

Custom Playbook Location

# Use a custom directory for playbooks
ace = AceFramework(
    provider="openrouter",
    api_key="your-api-key",
    model="anthropic/claude-3.5-sonnet",
    playbook_dir="my_project/playbooks",
    playbook_name="security_playbook.json"
)

Offline Adaptation

# Prepare training data
training_data = [
    {
        "query": "How do I validate an email?",
        "ground_truth": "Use regex pattern ^[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\\.[a-zA-Z]{2,}$"
    },
    {
        "query": "How do I hash passwords securely?",
        "ground_truth": "Use bcrypt or argon2 with sufficient work factor"
    }
]

# Train the playbook (auto-saved to data/playbook/playbook.json)
stats = ace.offline_adapt(
    training_data=training_data,
    epochs=3
)

print(f"Training complete: {stats}")

Online Adaptation

# The framework automatically loads existing playbook from data/playbook/playbook.json
ace = AceFramework(
    provider="openrouter",
    api_key="your-api-key",
    model="anthropic/claude-3.5-sonnet"
)

# Generate with real-time adaptation (auto-saved after update)
response = ace.online_adapt(
    query="How do I secure API keys?",
    ground_truth="Store in environment variables or vault service"
)

Manual Playbook Management

from ace_agents import ContextPlaybook, Bullet

# Create a playbook
playbook = ContextPlaybook()

# Add bullets
bullet = Bullet(
    id=Bullet.generate_id(),
    content="Always validate user input before processing",
    section="strategies_and_hard_rules"
)
playbook.add_bullet(bullet)

# Save playbook
playbook.save("my_playbook.json")

# Load playbook
loaded = ContextPlaybook.load("my_playbook.json")

Architecture

Context Playbook Structure

The playbook consists of "bullets" - individual pieces of context organized into sections:

{
    "bullets": [
        {
            "id": "ctx-a1b2c3d4",
            "content": "Always validate email with regex before processing",
            "section": "strategies_and_hard_rules",
            "helpful_count": 5,
            "harmful_count": 0,
            "created_at": "2025-01-15T10:30:00",
            "updated_at": "2025-01-15T10:30:00",
            "metadata": {}
        }
    ]
}

Agent Workflow

1. Generator receives a query and uses the playbook to generate a response
2. Reflector analyzes the response against ground truth and extracts insights
3. Curator converts insights into playbook updates (ADD/UPDATE/REMOVE operations)
4. Semantic deduplication removes redundant bullets

Configuration

API Keys

All API keys and configuration must be passed directly to the AceFramework constructor. The framework does not automatically load environment variables.

If you prefer to use environment variables, you can load them yourself:

from dotenv import load_dotenv
import os

# Load environment variables from .env file
load_dotenv()

# Pass to AceFramework
ace = AceFramework(
    provider="openrouter",
    api_key=os.getenv("API_KEY"),
    model=os.getenv("ACE_MODEL", "anthropic/claude-3.5-sonnet"),
    temperature=float(os.getenv("ACE_TEMPERATURE", "0.7")),
    max_tokens=int(os.getenv("ACE_MAX_TOKENS", "2048"))
)

Example .env file

OPENROUTER_API_KEY=your-key-here
ACE_MODEL=anthropic/claude-3.5-sonnet
ACE_TEMPERATURE=0.7
ACE_MAX_TOKENS=2048

Playbook Sections

• strategies_and_hard_rules: Core strategies and mandatory rules
• troubleshooting: Common issues and solutions
• general: General tips and guidelines

Examples

See the examples/ directory for more detailed usage examples:

• basic_usage.py: Complete examples of all framework features
• with_env_vars.py: How to use environment variables with python-dotenv

Using Environment Variables

1. Copy .env.example to .env:

   cp .env.example .env

2. Edit .env and fill in your API keys:

   API_KEY=your-actual-api-key

3. Run the example:

   python examples/with_env_vars.py

Project Structure

ace-agents/
├── src/ace_agents/
│   ├── __init__.py
│   ├── ace_framework.py      # Main framework orchestrator
│   ├── agents.py              # Generator, Reflector, Curator agents
│   ├── context.py             # Bullet and ContextPlaybook classes
│   ├── llm_client.py          # LLM API client
│   └── utils.py               # Utility functions (semantic similarity)
├── test/
│   └── test_context.py        # Unit tests
├── examples/
│   ├── basic_usage.py         # Usage examples
│   └── with_env_vars.py       # Environment variables example
├── docs/
│   └── ticket/                # Implementation tickets
├── .env.example               # Environment variables template
├── pyproject.toml             # Project configuration
└── README.md                  # This file

Performance

Based on the original ACE paper:

• 10.6% average improvement on AppWorld agent tasks
• 86.9% reduction in adaptation latency
• 83.6% reduction in token costs
• 75.1% fewer rollouts needed for adaptation

References

This implementation is based on the paper:

"Agentic Context Engineering: Evolving Contexts for Self-Improving Language Models" arXiv:2510.04618v1

License

MIT License

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

Citation

If you use this framework in your research, please cite:

@article{ace2025,
  title={Agentic Context Engineering: Evolving Contexts for Self-Improving Language Model},
  journal={arXiv preprint arXiv:2510.04618},
  year={2025}
}

Support

For issues, questions, or contributions, please open an issue on GitHub.