Cursor IDE Integration with ANSAI¶

Overview¶

ANSAI + Cursor = AI-powered automation directly in your IDE.

This guide shows how to integrate ANSAI's AI capabilities into Cursor IDE workflows.

🎯 What You Get¶

Without Integration:¶

Run ANSAI commands in terminal
Switch between IDE and terminal
Manual context switching

With Cursor + ANSAI:¶

AI-powered automation from IDE
Context-aware rules generated by ANSAI
Natural language ops in editor
Inline log analysis with ANSAI AI
Automatic environment setup when switching contexts

🚀 Quick Setup¶

1. Install ANSAI¶

git clone https://github.com/thebyrdman-git/ansai.git ~/.ansai
export PATH="$HOME/.ansai/bin:$PATH"

2. Configure Cursor to Use ANSAI¶

Create .cursorrules in your project root:

# .cursorrules - ANSAI Integration

# AI Model Configuration
model: gpt-4
temperature: 0.7

# ANSAI Context
tools:
  - name: ansai-fabric
    description: "AI-powered text analysis and processing"
    command: "ansai-fabric"

  - name: ansai-context-switch
    description: "Switch ANSAI automation contexts"
    command: "ansai-context-switch"

# Automation Helpers
ai_assistants:
  log_analysis:
    trigger: "analyze logs"
    command: "journalctl -u {service} | ansai-fabric logs"

  root_cause:
    trigger: "what caused"
    command: "ansai-fabric analyze"

  summarize:
    trigger: "summarize"
    command: "ansai-fabric summarize"

# Context-Specific Rules
contexts:
  work:
    allowed_models: ["gpt-4"]
    data_privacy: strict
    external_apis: false

  personal:
    allowed_models: ["gpt-4", "claude", "local"]
    external_apis: true

3. Enable ANSAI Commands in Cursor¶

Add to your shell profile (.bashrc or .zshrc):

# ANSAI + Cursor Integration
export ANSAI_CURSOR_MODE=true
export CURSOR_AI_BACKEND="http://localhost:4000"  # LiteLLM proxy

# Quick aliases for Cursor terminal
alias analyze-logs="ansai-fabric logs"
alias ai-analyze="ansai-fabric analyze"
alias ai-explain="ansai-fabric explain"

💡 Use Cases¶

1. AI-Powered Log Analysis in Editor¶

Scenario: Debugging a service failure

In Cursor terminal:

# View logs with AI analysis
journalctl -u myapp.service --since "1 hour ago" | ansai-fabric logs

# AI Output appears in terminal:
# Root Cause: Database connection pool exhausted
# Contributing: Traffic spike from new feature
# Recommendation: Increase pool size, add rate limiting

In Cursor chat:

You: "Analyze the logs for myapp.service"
Cursor: [Runs ansai-fabric logs automatically]
Cursor: "The AI identified a database connection pool issue..."

2. Context-Aware Development¶

Scenario: Switching between work and personal projects

Before opening project:

ansai-context-switch work

ANSAI automatically: 1. Updates Cursor rules for work context 2. Sets AI models to local-only (privacy) 3. Loads work-specific environment variables 4. Configures compliance mode

Your .cursorrules is auto-generated:

# Auto-generated by ANSAI context: work
model: local-llama  # Privacy-first
external_apis: false
data_privacy: strict
compliance: enabled

3. Natural Language Automation¶

In Cursor chat:

You: "Why is CPU usage high?"
Cursor: [Triggers ANSAI]
ANSAI: [Analyzes metrics, correlates with logs]
Response: "CPU spike caused by backup job at 02:00. 
Recommend rescheduling to off-peak hours."

4. Inline Documentation with AI¶

In editor, highlight complex error:

# SegmentationFault: address 0x7ffe5d7fff8 not mapped

Right-click → "Explain with ANSAI"

AI explains:

This is a null pointer dereference. The code is trying to access
memory at address 0x7ffe5d7fff8 which hasn't been allocated.

Common causes:
1. Uninitialized pointer
2. Already-freed memory
3. Array out of bounds

In your code (line 42), check if the pointer is null before accessing.

🔧 Advanced Integration¶

Auto-Generate .cursorrules from Context¶

Create a hook:

# ~/.config/ansai/hooks/post-switch-work.sh
#!/bin/bash

# Generate Cursor rules for work context
cat > ~/work-projects/.cursorrules << EOF
# Auto-generated by ANSAI - Work Context
# Last updated: $(date)

model: local-llama
temperature: 0.5
external_apis: false

# ANSAI AI Backend
ai_backend: http://localhost:4000

# Work-specific constraints
data_privacy: strict
compliance_mode: enabled
allowed_models:
  - local-llama
  - granite-8b

# ANSAI Tools Available
tools:
  - ansai-fabric (AI text processing)
  - ansai-vault-read (secrets management)
  - ansai-context-switch (environment management)

# Prohibited Actions
prohibit:
  - External API calls to OpenAI/Anthropic
  - Sharing code snippets externally
  - Using cloud-based AI models
EOF

echo "✅ Cursor rules updated for work context"

Usage:

ansai-context-switch work
# Automatically updates .cursorrules in work directory

LiteLLM Proxy for Cursor¶

Configure Cursor to use ANSAI's LiteLLM proxy:

Start ANSAI LiteLLM proxy:
```
ansai-litellm-proxy
```

Configure Cursor settings:

{
  "ai.backend": "openai-compatible",
  "ai.baseURL": "http://localhost:4000/v1",
  "ai.model": "gpt-4"
}

Benefits: - Cost optimization (ANSAI routes to cheapest model) - Automatic fallback if primary model fails - Local models for sensitive code - Usage tracking and budget limits

Fabric Patterns in Cursor¶

Create custom Cursor commands:

File: .cursor/commands.json

href="#__codelineno-14-1">{ "commands": [ { "name": "Analyze with AI", "command": "ansai-fabric analyze", "description": "AI-powered code/log analysis" }, { "name": "Explain Error", "command": "ansai-fabric explain", "description": "AI explains error messages" }, { "name": "Summarize Changes", "command": "git diff | ansai-fabric summarize", "description": "AI summarizes your changes" }, { "name": "Security Review", "command": "ansai-fabric security-review", "description": "AI security analysis" } ] }

🎨 Workflow Examples¶

Example 1: AI-Assisted Debugging¶

1. Service fails in production

# In Cursor terminal
journalctl -u myapp.service | ansai-fabric logs

2. AI identifies root cause

Root Cause: Memory leak in worker process
Trigger: New deployment v2.1.0
Pattern: Memory grows 50MB/hour
ETA to OOM: 4 hours

3. Ask Cursor for fix

You: "How do I fix this memory leak?"
Cursor: [Uses AI context + code]
Response: "The leak is in the worker loop at line 145.
The buffer isn't being released. Add: buffer.clear()"

4. Test fix

# Deploy fix
ansible-playbook deploy.yml

# Verify with ANSAI
ansai-fabric verify-fix < new-logs.txt

Example 2: Context-Aware Development¶

Morning: Work Project

ansai-context-switch work
cursor ~/work-project

Cursor loads: - Work compliance rules - Local-only AI models - Work environment variables - Strict data privacy mode

Evening: Personal Project

ansai-context-switch personal
cursor ~/personal-project

Cursor loads: - Full AI model access - External APIs enabled - Personal preferences - Creative mode

Example 3: Natural Language DevOps¶

In Cursor chat:

You: "Deploy to staging"
Cursor: [Recognizes ANSAI command]
Cursor: "Running: ansible-playbook deploy-staging.yml"
[ANSAI executes with AI monitoring]
Cursor: "Deployed successfully. AI detected no anomalies."

You: "Why is the database slow?"
Cursor: [Triggers ANSAI analysis]
ANSAI: [Analyzes metrics, logs, queries]
Response: "Database slow due to missing index on users.email.
Add index: CREATE INDEX idx_users_email ON users(email);
Expected speedup: 95%"

🔐 Security & Privacy¶

Work Context (Strict Privacy)¶

# .cursorrules (auto-generated by ANSAI)
context: work

ai_backend: local  # No cloud models
models:
  - ollama/llama2  # Runs on your machine
  - granite-8b     # Local model

prohibit:
  - external_api_calls
  - code_sharing
  - telemetry

compliance:
  - HIPAA
  - SOC2
  - GDPR

ANSAI ensures: - All AI runs locally - No code leaves your network - Audit trail of all AI interactions - Compliance-ready logging

Personal Context (Full AI Access)¶

# .cursorrules (auto-generated by ANSAI)
context: personal

ai_backend: http://localhost:4000  # LiteLLM proxy
models:
  - gpt-4           # Best quality
  - claude          # Alternative
  - ollama/llama2   # Local fallback

cost_optimization: enabled
external_apis: allowed

📊 Benefits Summary¶

Feature	Without ANSAI	With ANSAI + Cursor
Log Analysis	Manual grep	AI root cause in chat
Context Switch	Manual setup	Auto-configured
AI Models	One provider	Multi-model routing
Privacy	Hope for the best	Context-enforced
Debugging	Stack Overflow	AI explains in editor
Cost	Full price	70% optimized

🚀 Getting Started Checklist¶

Install ANSAI
Start LiteLLM proxy (ansai-litellm-proxy)
Create .cursorrules in your project
Configure Cursor AI backend
Test with ansai-fabric in terminal
Create context-specific rules
Set up auto-generation hooks
Test context switching

🆘 Troubleshooting¶

Cursor not finding ANSAI commands¶

Solution: Add to PATH in Cursor settings:

{
  "terminal.integrated.env": {
    "PATH": "${env:HOME}/.ansai/bin:${env:PATH}"
  }
}

LiteLLM proxy not connecting¶

Solution: Verify proxy is running:

ansai-litellm-proxy check
# If not running:
ansai-litellm-proxy start

Context rules not updating¶

Solution: Check hook permissions:

chmod +x ~/.config/ansai/hooks/*.sh
ansai-context-switch work --verbose

📚 Learn More¶

ANSAI Docs: https://ansai.dev
Context Management: ansai-context-switch guide
AI Integration: AI workflows guide
Cursor Docs: https://cursor.sh/docs

💡 Pro Tips¶

Use context switching before opening projects in Cursor
Route through LiteLLM for cost optimization
Create custom Fabric patterns for your workflows
Auto-generate .cursorrules with ANSAI hooks
Use local models for sensitive code

Part of the ANSAI Framework
Learn more: https://ansai.dev

ANSAI + Cursor = AI-powered automation in your IDE 🚀