react-agent-compensation 0.2.0

Framework-agnostic compensation/rollback library for ReAct agents

react-agent-compensation

A framework-agnostic compensation/rollback library for ReAct agents. Automatically tracks actions and rolls them back when subsequent operations fail, ensuring data consistency in AI agent workflows.

Features

• Framework-agnostic Core: Works with any agent framework (LangChain, CrewAI, custom)
• LangChain/LangGraph Integration: First-class support with middleware and agent factories
• MCP Integration: Auto-discover compensation pairs from MCP server tool annotations
• Automatic Rollback: When an action fails, all previously completed actions are automatically compensated
• Retry Strategies: Configurable exponential backoff, linear backoff, and fixed delay
• Dependency Tracking: Topological sort ensures correct rollback order
• Multi-Agent Support: Shared transaction logs across multiple agents
• Extraction Strategies: Multiple ways to extract compensation parameters (schema, heuristic, LLM-based)
• Strategic Context Preservation (v0.2.0): Tracks cumulative failures to help LLM make informed decisions about what to try next
• Goal-Aware Recovery (v0.2.0): Reminds LLM of optimization objectives during replanning for holistic decision-making
• Permanent Failure Detection (v0.2.0): Classifies errors as permanent vs transient to guide retry strategies

Installation

# Core only
pip install react-agent-compensation

# With LangChain support
pip install react-agent-compensation[langchain]

# With MCP support (for Model Context Protocol servers)
pip install react-agent-compensation[mcp]

# With LLM-based extraction
pip install react-agent-compensation[llm]

# Everything
pip install react-agent-compensation[all]

Quick Start

Basic Usage with LangChain

from langchain_openai import ChatOpenAI
from langchain_core.tools import tool
from react_agent_compensation.langchain_adaptor import CompensationMiddleware

# Define your tools
@tool
def book_flight(destination: str, date: str) -> dict:
    """Book a flight to a destination."""
    return {"booking_id": "FL123", "destination": destination, "date": date}

@tool
def cancel_flight(booking_id: str) -> dict:
    """Cancel a flight booking."""
    return {"cancelled": True, "booking_id": booking_id}

# Create middleware with compensation pairs
middleware = CompensationMiddleware(
    compensation_pairs={"book_flight": "cancel_flight"},
)

# Wrap your tools
wrapped_tools = middleware.wrap_tools([book_flight, cancel_flight])

# Use with LangGraph
from langgraph.prebuilt import create_react_agent

model = ChatOpenAI(model="gpt-4")
agent = create_react_agent(model, wrapped_tools)

# Run the agent - compensation is automatic on failures
result = agent.invoke({"messages": [("user", "Book a flight to NYC for tomorrow")]})

# Access transaction log
for record_id, record in middleware.log.snapshot().items():
    print(f"{record.action}: {record.status.value}")

# Manual rollback if needed
middleware.rollback()

MCP Integration

Connect to MCP servers with automatic compensation discovery:

from langchain_google_genai import ChatGoogleGenerativeAI
from react_agent_compensation.langchain_adaptor import create_compensated_mcp_agent

# Create agent connected to MCP server
agent, client = await create_compensated_mcp_agent(
    model=ChatGoogleGenerativeAI(model="gemini-2.0-flash"),
    mcp_servers={
        "myserver": {
            "url": "http://localhost:8000/sse",
            "transport": "sse",
        }
    },
    system_prompt="You are a helpful assistant.",
)

# Compensation pairs are auto-discovered from server annotations
pairs = await client.get_compensation_pairs()
print(f"Discovered pairs: {pairs}")
# Output: {'add_item': 'delete_item', 'create_order': 'cancel_order'}

# Run the agent
result = await agent.ainvoke({
    "messages": [("user", "Add an item called Widget")]
})

# Rollback if needed
await client.rollback()

Core Components

from react_agent_compensation.core import RecoveryManager, RetryPolicy

# Create recovery manager
manager = RecoveryManager(
    compensation_pairs={
        "book_flight": "cancel_flight",
        "reserve_hotel": "cancel_hotel",
    },
    retry_policy=RetryPolicy(
        max_retries=3,
        initial_delay=1.0,
        backoff_multiplier=2.0,
    ),
)

# Record actions
record = manager.record_action("book_flight", {"dest": "NYC", "date": "2024-01-15"})

# Mark complete with result
manager.mark_completed(record.id, result={"booking_id": "FL123"})

# On failure, rollback all completed actions
rollback_result = manager.rollback()
print(f"Rolled back: {rollback_result.compensated}")

Goal-Aware Recovery (v0.2.0)

Enable holistic replanning with optimization goals:

from react_agent_compensation.langchain_adaptor import create_compensated_agent

# Create agent with optimization goals
agent = create_compensated_agent(
    model=ChatOpenAI(model="gpt-4"),
    tools=[book_flight, cancel_flight, reserve_hotel, cancel_hotel],
    compensation_pairs={
        "book_flight": "cancel_flight",
        "reserve_hotel": "cancel_hotel",
    },
    goals=["minimize_total_cost", "prefer_direct_flights", "maximize_loyalty_points"],
)

# When failures occur, the LLM receives:
# 1. Cumulative failure context (what was tried and failed)
# 2. List of rolled-back actions that need re-doing
# 3. Reminder of optimization goals for holistic replanning

Strategic Context Preservation (v0.2.0)

Access cumulative failure context programmatically:

from react_agent_compensation.core import RecoveryManager

manager = RecoveryManager(compensation_pairs={"book": "cancel"})

# After failures, get summary for LLM context
failure_summary = manager.get_failure_summary()
# Returns formatted summary like:
# [PREVIOUS FAILED ATTEMPTS]
# book:
#   - Attempt 1: (dest=NYC) [PERMANENT]
#     Error: Flight unavailable due to weather
# Consider using different parameters or approaches.

# Access failure context directly
for attempt in manager.failure_context.attempts:
    print(f"{attempt.action}: {attempt.error} (permanent={attempt.is_permanent})")

MCP Server Setup

Create an MCP server with compensation annotations:

from fastmcp import FastMCP

mcp = FastMCP("My Server")

@mcp.tool(
    annotations={
        "x-compensation-pair": "delete_item",
        "x-action-type": "create",
    }
)
def add_item(name: str) -> dict:
    """Add an item to the database."""
    # Your implementation
    return {"id": "123", "name": name}

@mcp.tool(
    annotations={
        "x-compensation-pair": "add_item",
        "x-action-type": "delete",
    }
)
def delete_item(item_id: str) -> dict:
    """Delete an item from the database."""
    # Your implementation
    return {"deleted": True}

if __name__ == "__main__":
    mcp.run(transport="sse", host="0.0.0.0", port=8000)

Extraction Strategies

Multiple ways to extract parameters for compensation actions:

from react_agent_compensation.langchain_adaptor import CompensationMiddleware

# Schema-based extraction
middleware = CompensationMiddleware(
    compensation_pairs={"book_flight": "cancel_flight"},
    compensation_schemas={
        "book_flight": {
            "cancel_flight": {
                "booking_id": "result.booking_id",
            }
        }
    },
)

# State mapper (custom function)
middleware = CompensationMiddleware(
    compensation_pairs={"book_flight": "cancel_flight"},
    state_mappers={
        "book_flight": lambda input, result: {"booking_id": result["booking_id"]},
    },
)

# Heuristic (auto-detect common ID fields)
from react_agent_compensation.core.extraction import HeuristicExtraction

manager = RecoveryManager(
    compensation_pairs={"book_flight": "cancel_flight"},
    extraction_strategy=HeuristicExtraction(),
)

Examples

See the examples directory for complete working examples:

• LangChain Agent: Basic LangChain integration with LangSmith tracing
• MCP Integration: Full MCP server/client example with MongoDB backend

Documentation

• MCP Integration Guide - Complete guide for MCP server integration

Architecture

┌─────────────────────────────────────────┐
│            Your Agent                    │
│    (LangChain, CrewAI, Custom)          │
└────────────────────┬────────────────────┘
                     │
┌────────────────────▼────────────────────┐
│      CompensationMiddleware              │
│   or CompensatedMCPTool                  │
│  - Intercepts tool calls                 │
│  - Records actions in TransactionLog     │
│  - Triggers rollback on failure          │
└────────────────────┬────────────────────┘
                     │
┌────────────────────▼────────────────────┐
│         RecoveryManager                  │
│  - Manages compensation pairs            │
│  - Handles retry logic                   │
│  - Executes rollback in correct order    │
└────────────────────┬────────────────────┘
                     │
┌────────────────────▼────────────────────┐
│         TransactionLog                   │
│  - Stores action records                 │
│  - Tracks status and dependencies        │
│  - Supports multi-agent scenarios        │
└─────────────────────────────────────────┘

API Reference

Core Classes

Class	Description
RecoveryManager	Main orchestrator for compensation logic
TransactionLog	Stores and manages action records
ActionRecord	Individual action with status and metadata
RetryPolicy	Configuration for retry behavior
FailureContext	Tracks cumulative failures for Strategic Context Preservation
FailedAttempt	Record of a single failed attempt
is_likely_permanent	Heuristic to classify errors as permanent vs transient

LangChain Adaptor

Class/Function	Description
CompensationMiddleware	Wraps tools with compensation tracking
create_compensated_agent	Factory for creating compensated agents
create_multi_agent_log	Shared log for multi-agent scenarios

MCP Integration

Class/Function	Description
MCPCompensationClient	High-level MCP client with compensation
CompensatedMCPTool	Wrapped MCP tool with tracking
create_compensated_mcp_agent	Factory for MCP-based agents

Contributing

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

License

MIT License - see LICENSE for details.