egregore 0.1.5a0

Tools for creating llm applications

Egregore v0.1.2a0

AI Agent Framework Built on PACT Architecture

Egregore is a powerful Python framework for building AI agents with structured, manageable context. Unlike traditional frameworks that treat context as simple chat logs, Egregore uses the PACT (Positioned Adaptive Context Tree) architecture to give you precise control over what the LLM sees and when.

Think of it as a virtual DOM for AI context - you can insert, update, delete, and manage individual components with temporal lifecycle control (TTL), precise positioning, and automatic cleanup.

Why Egregore?

Traditional Problem: Context is an opaque black box. You dump messages in and hope the LLM does the right thing.

Egregore's Solution: Context is a queryable, manipulable tree structure where:

• Every component has a precise position and lifecycle
• You control what persists, expires, or moves through the context
• Components can be temporary, sticky, cyclic, or permanent
• Real-time debugging shows you exactly what the LLM sees at each turn

🚀 Quick Start

from egregore.core.agent import Agent

# Create an agent with tools
def calculator(operation: str, a: float, b: float) -> float:
    """Perform basic math operations."""
    ops = {"add": a + b, "subtract": a - b, "multiply": a * b, "divide": a / b}
    return ops.get(operation, 0)

agent = Agent(
    provider="openai:gpt-4o",
    tools=[calculator],
    instructions="You are a helpful math assistant."
)

# Call the agent
response = agent("What's 25 * 4?")
print(response)  # Uses calculator tool and returns result

🏗️ Core Architecture

PACT: Positioned Adaptive Context Tree

Egregore's context system uses PACT coordinates for universal addressability:

from egregore.core.context_management import Context
from egregore.core.context_management.pact.components.core import TextContent

context = Context()

# Add messages with precise positioning
context.add_user("What is 2+2?")                    # d0,0,0 - active message
context.pact_insert("d0,0,1", note_component)       # d0,0,1 - annotation at offset 1
context.pact_insert("d0,0,-1", reminder_component)  # d0,0,-1 - pre-context at offset -1

# Components with lifecycle control
temporary = TextContent(content="Expires after 3 turns", ttl=3)
sticky = TextContent(content="Always at same relative position", ttl=1, cadence=1)
permanent = TextContent(content="Never expires", ttl=None)

Component Lifecycle System

4 Component Types:

• Permanent (ttl=None): Never expire
• Temporary (ttl=N): Expire after N turns, gone forever
• Sticky (ttl=1, cadence=1): Expire and reappear each turn at same relative position
• Cyclic (ttl=N, cadence=M): Expire after N turns, reappear every M turns

Render Lifecycle System: Components can move through multiple positions based on lifecycle stages:

from egregore.core.context_management.pact.context.position import Pos

component = TextContent(content="Task alert")
component.render_lifecycle([
    Pos("d0, 0, 1", ttl=2),     # Stage 1: offset 1, for 2 turns
    Pos("d0, 0, -1", ttl=3),    # Stage 2: offset -1, for 3 turns
    Pos("d0, 0, -2")            # Stage 3: offset -2 (permanent)
])

context.pact_insert("d0,0,1", component)
# Component automatically transitions through stages as TTL expires

✨ Key Features

🎯 PACT Context Management

• Universal Coordinates: Address any component with (depth, position, offset) tuples
• Temporal Management: TTL+Cadence lifecycle with 4 component types
• ODI System: Overlap Demotion Invariant for automatic depth shifting
• Render Lifecycle: Components can move through positions based on TTL stages
• PACT v0.1 Canonical Compliance: Full specification compliance with automatic serialization

🛠️ Advanced Tool System

• @tool Decorator: Automatic parameter extraction and validation
• @operation Decorator: Scaffold methods become tools automatically
• Concurrent Execution: Parallel tool calls with result aggregation
• Tool Pair Tracking: Registry maintains call→result relationships

🧠 Context Scaffolds

• Persistent Memory: Agent state that survives across sessions
• IPC System: Inter-scaffold communication with source tracking
• Reactive Rendering: Automatic re-rendering on context changes
• @operation Methods: Scaffold methods exposed as tools

🔧 Agent Hook System

Decorator-based lifecycle hooks for observing and modifying behavior:

@agent.hooks.tool.pre_call
def before_tool(ctx: ToolExecContext):
    print(f"Calling tool: {ctx.tool_name}")

@agent.hooks.context.before_change
def validate_change(ctx: ContextExecContext):
    # Cancel operation by raising exception
    if ctx.operation_type == "delete" and ctx.component.critical:
        raise ValueError("Cannot delete critical component")

@agent.hooks.streaming.on_chunk
def process_chunk(ctx: StreamExecContext):
    print(f"Chunk: {ctx.chunk_data}")

🌊 Unified Streaming

• Consistent streaming interface across all providers
• Real-time token processing and tool call accumulation
• Streaming hooks for chunk-level control

📊 Analytics & Debugging

• ContextExplorer: Interactive REPL-like debugger for step-by-step execution
• ContextViewer: Static snapshots with tree, text, XML, and provider-specific views
• Episode Management: Proper TTL testing with explorer.step("render")

🔗 Workflow System

• Node-based Execution: Declarative workflow composition
• Native Agent Discovery: Automatic agent detection in workflows
• Parallel Processing: Dictionary-based result aggregation from terminal nodes
• Parameter Mapping: Intelligent parameter flow between nodes

🔌 Universal Provider Interface

• 30+ Providers: OpenAI, Anthropic, Google, and more
• Standardized API: Consistent interface across all providers
• Universal Token Counting: Multi-tokenizer support
• OAuth Interceptors: Premium model access

📦 Installation

# Clone repository
git clone https://github.com/yourusername/egregore-v2.git
cd egregore-v2

# Install with Poetry
poetry install

# Or with pip
pip install -e .

🎯 Real-World Examples

Example 1: Context Debugging with ContextExplorer

from egregore.analytics.context_explorer import ContextExplorer
from egregore.core.agent import Agent

def my_tool(text: str) -> str:
    return f"Processed: {text}"

agent = Agent(provider="openai", tools=[my_tool])
explorer = ContextExplorer(agent)

# Step through conversation
explorer.step("user_message", "Test message")
explorer.print("tree")  # See context structure

# Simulate LLM tool call
from egregore.core.messaging import ProviderResponse, ProviderToolCall
explorer.step("seal", ProviderResponse(content=[
    ProviderToolCall(
        tool_name="my_tool",
        tool_call_id="call_123",
        parameters={"text": "test"}
    )
]))

# Execute tools
explorer.step("execute_tools")

# Verify execution
tool_results = explorer.context.select(".tool_result")
print(f"Results: {tool_results[0].content}")

Example 2: TTL Component Lifecycle

from egregore.analytics.context_explorer import ContextExplorer
from egregore.core.context_management.pact.components.core import TextContent

explorer = ContextExplorer()
explorer.step("user_message", "Test")

# Component that expires after 2 turns
component = TextContent(content="Important note", ttl=2)
explorer.context.pact_insert("d0,0,1", component)

# Advance episodes
explorer.step("render")  # TTL=2, age=1
explorer.step("render")  # TTL=2, age=2, component expires

# Verify removal
assert explorer.context[0, 0, 1] is None

Example 3: Agent with Context Hooks

from egregore.core.agent import Agent

agent = Agent(provider="openai")

@agent.hooks.context.before_change
def log_changes(ctx):
    print(f"About to {ctx.operation_type} at {ctx.selector}")

@agent.hooks.context.after_change
def track_changes(ctx):
    print(f"Modified: {ctx.component}")

response = agent("Analyze this data")
# Hooks fire on every context operation

Example 4: Scaffold with Operations

from egregore.core.context_scaffolds import BaseContextScaffold
from egregore.core.context_scaffolds.decorators import operation

class TaskManager(BaseContextScaffold):
    def __init__(self):
        super().__init__()
        self.tasks = []

    @operation
    def add_task(self, description: str, priority: str = "medium") -> str:
        """Add a new task to the task list."""
        self.tasks.append({"description": description, "priority": priority})
        return f"Added task: {description}"

    @operation
    def list_tasks(self) -> list:
        """List all current tasks."""
        return self.tasks

# Operations automatically become tools the agent can use
agent = Agent(provider="openai", scaffolds=[TaskManager()])
agent("Add a high priority task to implement authentication")

📚 Documentation

Comprehensive documentation in the docs/ directory:

Architecture

• PACT Overview - Core PACT architecture and concepts
• Context Management - PACT context tree system
• Context Lifecycle - TTL, cadence, and ODI
• Scaffolds - Persistent agent capabilities

Guides

• Analytics Module - ContextExplorer and ContextViewer
• Agent Hooks System - Lifecycle hooks
• Workflow System - Node-based orchestration
• Modifying Tool Outputs - Tool result transformation
• Component Triggers - Reactive context management

Testing & Contributing

• Testing Guide - Universal testing strategy
• Provider Development - Adding new providers

Internal (Development)

• Plans - Feature roadmap and implementation plans
• Reports - Analysis and audit reports

🧪 Testing

# Run all tests
poetry run pytest

# Run specific test categories
poetry run pytest tests/core/context_management/
poetry run pytest tests/analytics/

# With coverage
poetry run pytest --cov=egregore --cov-report=html

🔑 Key Concepts

PACT Coordinates

• Depth (d): Message turn position (0=active, 1+=history)
• Position (p): Container within depth (0=message, 1+=components)
• Offset (o): Relative positioning within container
• Examples: d0,0,0 (active message), d0,0,1 (component at offset 1), d1,0,0 (previous turn)

PACT Selectors

CSS-like syntax for querying context:

context.select("+tool_call")           # All tool calls (by tag)
context.select(".tool_result")         # All tool results (by type)
context.select("scaffold[memory]")     # Memory scaffolds
context.select("*[ttl<5]")            # Components expiring soon

MessageContainer Pattern

Clean separation of message content from context components. Messages live at dN,0, components attach at dN,1+.

ODI (Overlap Demotion Invariant)

Automatic depth shifting when message count changes:

• Message count increases → increment depths by difference
• Message count decreases → decrement depths by difference
• Maintains relative positions of permanent/sticky components

🛣️ Roadmap

Current focus areas:

• ✅ PACT v0.1 Canonical Compliance - Full specification compliance
• ✅ Render Lifecycle System - Dynamic component positioning
• ✅ Agent Hook System - Comprehensive lifecycle hooks
• ✅ Context Explorer - Interactive debugging tool
• 🔄 Observability CLI - Production debugging and monitoring (planned)
• 🔄 Context History Snapshots - Session persistence and replay (planned)

See internal/plans/index.md for detailed roadmap.

🤝 Contributing

We welcome contributions! Key areas:

1. Testing: Expand test coverage for edge cases
2. Providers: Add new LLM provider integrations
3. Documentation: Improve examples and guides
4. Features: Implement planned features from roadmap

See docs/contributing/index.md for details.

📄 License

MIT License - see LICENSE file for details.

🙏 Acknowledgments

• Built on the PACT specification for structured AI context
• Inspired by the need for predictable, debuggable AI agents
• Thanks to all contributors making this framework possible

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Current Version: v0.1.2a0 Last Updated: October 2025 Status: Alpha - Active Development