ship-langchain 1.0.0

LangChain integration for SHIP Protocol - AI coding reliability metrics

ship-langchain

LangChain Integration for SHIP Protocol - AI Coding Reliability Tools

Know if your AI coding task will succeed before you run it.

Why SHIP + LangChain?

70% of AI coding tasks fail. SHIP tells your agent the probability of success before execution.

from ship_langchain import SHIPAssessTool
from langchain.agents import create_react_agent

# Add SHIP assessment to your agent's toolkit
tools = [SHIPAssessTool(), ...other_tools]

# Now your agent can check reliability before modifying code
# Agent: "Let me assess this task first..."
# SHIP: "Score: 85 (A) - High confidence this will work"

Installation

pip install ship-langchain

Quick Start

As a Tool

from ship_langchain import SHIPAssessTool, SHIPFeedbackTool
from langchain.agents import AgentExecutor, create_react_agent
from langchain_openai import ChatOpenAI

# Create tools
ship_assess = SHIPAssessTool()
ship_feedback = SHIPFeedbackTool()

# Add to your agent
tools = [ship_assess, ship_feedback]

# The agent can now:
# 1. Assess code before modification
# 2. Submit feedback after task completion

As a Chain

from ship_langchain import SHIPAssessmentChain

chain = SHIPAssessmentChain()

result = await chain.invoke({
    "code": "def hello(): print('world')",
    "prompt": "Add type hints and docstring",
    "language": "python",
})

print(f"Score: {result.score} ({result.grade})")
print(f"Confidence: {result.confidence}")
print(f"Recommendations: {result.recommendations}")

With Callbacks

from ship_langchain import SHIPCallbackHandler

# Track all SHIP metrics automatically
handler = SHIPCallbackHandler()

agent_executor = AgentExecutor(
    agent=agent,
    tools=tools,
    callbacks=[handler],
)

# Run your agent
result = agent_executor.invoke({"input": "Refactor this code..."})

# Get SHIP summary
summary = handler.get_summary()
print(f"Average SHIP Score: {summary['average_score']}")
print(f"Assessments: {summary['total_assessments']}")

Tools Reference

SHIPAssessTool

Assess code reliability before AI modification.

Input:

• code: The code to assess
• prompt: Task description
• language: Programming language (default: "python")
• file_path: Virtual file path (default: "main.py")

Output:

• ship_score: 0-100 reliability score
• grade: Letter grade (A+ to F)
• confidence, focus, context, efficiency: Component scores
• recommendations: Top improvement suggestions

SHIPFeedbackTool

Submit feedback on task outcomes to improve future predictions.

Input:

• request_id: From assessment response
• task_completed: Whether task succeeded
• first_attempt_success: Success on first try
• total_attempts: Number of attempts needed

SHIPQuickAssessTool

Fast assessment with minimal input.

Input:

• code: Code snippet
• prompt: Task description

Output:

• Simple "SHIP Score: X (Grade)" string

SHIPHealthTool

Check API availability before batch operations.

Philosophy

This integration follows Talebian/Antifragile principles:

1. Never Crash: Tools return degraded responses instead of exceptions
2. Self-Learning: Feedback loop improves predictions over time
3. Observable: Callback handler tracks all metrics
4. Composable: Works with any LLM and agent setup

Grade Interpretation

Grade	Score	What It Means
A+	95-100	95%+ success rate - Ship with confidence
A	85-94	85%+ success rate - Reliable
B	70-84	70%+ success rate - Good, minor risks
C	50-69	50%+ success rate - Proceed with caution
D	30-49	30%+ success rate - High risk
F	0-29	<30% success rate - Likely to fail

Example Agent Workflow

# 1. Agent receives coding task
"Add error handling to the payment processor"

# 2. Agent uses SHIPAssessTool first
ship_assess.run({
    "code": payment_processor_code,
    "prompt": "Add error handling",
})
# Result: Score 72 (B) - "Missing type information reduces confidence"

# 3. Agent decides based on score
if score >= 70:
    # Proceed with modification
    ...
else:
    # Request more context or simplify task
    ...

# 4. After completion, agent submits feedback
ship_feedback.run({
    "request_id": "req-123",
    "task_completed": True,
    "first_attempt_success": True,
})

Links

• SHIP Protocol: https://vibeatlas.dev/docs/ship
• PyPI Package: https://pypi.org/project/vibeatlas-ship/
• GitHub: https://github.com/vibeatlas/ship-protocol

License

MIT License - see LICENSE for details.

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Built with love by VibeAtlas - Making AI coding reliable.