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https://github.com/rashidazarang/airtable-ai-agent

🤖 The most comprehensive AI Agent for Airtable operations - Natural language interface with 33 MCP tools, complete API knowledge, and production-ready deployment
https://github.com/rashidazarang/airtable-ai-agent

ai-agents airtable airtable-api artificial-intelligence claude claude-code nlp server

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🤖 The most comprehensive AI Agent for Airtable operations - Natural language interface with 33 MCP tools, complete API knowledge, and production-ready deployment

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README 

          
# 🤖 Airtable AI Agent



> **The most comprehensive AI Agent for Airtable operations**



[![Python 3.11+](https://img.shields.io/badge/python-3.11+-blue.svg)](https://www.python.org/downloads/)

[![Docker](https://img.shields.io/badge/docker-ready-blue.svg)](https://hub.docker.com/)

[![License](https://img.shields.io/badge/license-MIT-green.svg)](LICENSE)

[![Tests](https://github.com/rashidazarang/airtable-ai-agent/workflows/Tests/badge.svg)](https://github.com/rashidazarang/airtable-ai-agent/actions)

[![Coverage](https://img.shields.io/codecov/c/github/rashidazarang/airtable-ai-agent)](https://codecov.io/gh/rashidazarang/airtable-ai-agent)



## 🌟 What Makes This Special?



This AI Agent represents the **pinnacle of Airtable automation**, combining:



- 🧠 **Complete Airtable API Knowledge** - Every endpoint, every parameter, every best practice

- 🔗 **Perfect MCP Integration** - Seamless connection to our comprehensive 33-tool MCP server (v3.2.4)  

- 🚀 **Advanced NLP** - Understand complex natural language queries about Airtable

- ⚡ **Intelligent Context Management** - Optimal performance with 128k context window

- 🎯 **Production Ready** - Docker, CI/CD, monitoring, security built-in



## 🏗️ Architecture



```mermaid

graph TB

    User[User Query] --> Agent[AI Agent Core]

    Agent --> Expert[Airtable Expert]

    Agent --> Context[Context Manager]

    Agent --> MCP[MCP Client]

    

    Expert --> Analysis[Query Analysis]

    Expert --> Planning[Operation Planning]

    Expert --> Response[Response Generation]

    

    Context --> Docs[Documentation Store]

    Context --> Embeddings[Semantic Search]

    Context --> Cache[Intelligent Cache]

    

    MCP --> Server[MCP Server]

    Server --> Airtable[Airtable API]

    

    style Agent fill:#e1f5fe

    style Expert fill:#f3e5f5

    style Context fill:#e8f5e8

    style MCP fill:#fff3e0

```



## 🚀 Quick Start



### Option 1: Docker (Recommended)



```bash

# Clone the repository

git clone https://github.com/rashidazarang/airtable-ai-agent.git

cd airtable-ai-agent/ai-agent



# Set environment variables

export AIRTABLE_TOKEN="your_personal_access_token"

export AIRTABLE_BASE_ID="your_airtable_base_id"



# Start with Docker Compose (includes MCP server, Redis, monitoring)

docker-compose up -d



# Test the agent

curl -X POST http://localhost:8000/query \

  -H "Content-Type: application/json" \

  -d '{"query": "List all tables in my base"}'

```



### Option 2: Local Development



```bash

# Create virtual environment

python3.11 -m venv venv

source venv/bin/activate



# Install dependencies  

pip install -r requirements.txt



# Install and start MCP server v3.2.4 (in separate terminal)

npm install -g @rashidazarang/airtable-mcp@3.2.4

AIRTABLE_TOKEN="your_token" AIRTABLE_BASE_ID="your_base" npx @rashidazarang/airtable-mcp



# Run the agent

cd ai-agent

python -m src.agent

```



## 💬 Usage Examples



### Interactive Mode



```python

from src.agent import AirtableAIAgent



agent = AirtableAIAgent()

await agent.initialize()



# Natural language queries

response = await agent.process_query("Show me all active projects with high priority")

response = await agent.process_query("Create a new task called 'Review documentation'")  

response = await agent.process_query("Update all completed tasks to archive status")

response = await agent.process_query("Set up a webhook for when new records are added")

```



### HTTP API



```bash

# Start the agent server

python -m src.agent --server



# Make requests

curl -X POST http://localhost:8000/query \

  -H "Content-Type: application/json" \

  -d '{

    "query": "Create a project tracking table with Name, Status, Priority, and Due Date fields",

    "context": {"previous_operations": []}

  }'

```



### Batch Processing



```python

queries = [

    "List all tables in my base",

    "Show me the schema for the Tasks table", 

    "Create 5 sample records in the Tasks table",

    "Set up webhooks for real-time notifications"

]



responses = await agent.batch_process(queries)

```



## 🎯 Core Features



### 🧠 Advanced Query Understanding



- **Intent Recognition** - Automatically detects what you want to do

- **Entity Extraction** - Finds table names, field names, record IDs

- **Context Awareness** - Remembers previous operations and preferences

- **Complexity Analysis** - Breaks down complex requests into steps



### 🔗 Comprehensive MCP Integration



Direct integration with our **33-tool Airtable MCP server**:



#### Data Operations (7 tools)

- `list_tables`, `list_records`, `get_record`

- `create_record`, `update_record`, `delete_record`  

- `search_records`



#### Webhook Management (5 tools)

- `list_webhooks`, `create_webhook`, `delete_webhook`

- `get_webhook_payloads`, `refresh_webhook`



#### Schema Discovery (5 tools)  

- `list_bases`, `get_base_schema`, `describe_table`

- `list_field_types`, `get_table_views`



#### Table Management (3 tools)

- `create_table`, `update_table`, `delete_table`



#### Field Management (3 tools)

- `create_field`, `update_field`, `delete_field`



#### Batch Operations (4 tools)

- `batch_create_records`, `batch_update_records`

- `batch_delete_records`, `batch_upsert_records`



#### Advanced Features (6 tools)

- `upload_attachment`, `create_view`, `get_view_metadata`

- `create_base`, `list_collaborators`, `list_shares`



### 📚 Complete Knowledge Base



The agent has **complete Airtable documentation** in its context:



- **Web API Reference** - Every endpoint, parameter, response format

- **JavaScript SDK** - Complete Airtable.js documentation and examples

- **Formulas Guide** - All functions, operators, and patterns

- **Apps & Extensions** - SDK documentation and integration patterns  

- **MCP Tools** - Our comprehensive tool documentation



### ⚡ Performance Optimization



- **Intelligent Context Management** - Semantic search through documentation

- **Token Optimization** - Fits maximum relevant context in 128k window

- **Parallel Processing** - Concurrent MCP operations where safe

- **Smart Caching** - Embeddings, API responses, and context chunks

- **Batch Operations** - Automatic batching for efficiency



## 🔧 Configuration



### Agent Configuration (`config/agent_config.yaml`)



```yaml

name: "Airtable AI Agent"

version: "1.0.0"

max_context_tokens: 128000

mcp_server_url: "http://localhost:8010/mcp"

log_level: "INFO"

enable_metrics: true

cache_duration: 300



# Context management

context:

  max_chunks_per_query: 10

  relevance_threshold: 0.7

  enable_semantic_search: true



# Performance

performance:

  max_concurrent_operations: 5

  request_timeout: 30

  retry_attempts: 3

```



### Environment Variables



```bash

# Required

AIRTABLE_TOKEN=your_personal_access_token

AIRTABLE_BASE_ID=your_base_id



# Optional

MCP_SERVER_URL=http://localhost:8010/mcp

LOG_LEVEL=INFO

REDIS_URL=redis://localhost:6379

POSTGRES_URL=postgresql://user:pass@localhost/aiagent

```



## 🚀 Deployment



### Production Docker Deployment



```yaml

# docker-compose.prod.yml

version: '3.8'

services:

  airtable-ai-agent:

    image: ghcr.io/rashidazarang/airtable-ai-agent:latest

    ports:

      - "8000:8000"

    environment:

      - LOG_LEVEL=INFO

      - MCP_SERVER_URL=http://airtable-mcp:8010/mcp

    volumes:

      - ./data:/app/data

      - ./logs:/app/logs

    restart: unless-stopped

    

  # Includes: MCP server, Redis, PostgreSQL, Prometheus, Grafana, Nginx

```



### Kubernetes Deployment



```yaml

# k8s/deployment.yaml

apiVersion: apps/v1

kind: Deployment

metadata:

  name: airtable-ai-agent

spec:

  replicas: 3

  selector:

    matchLabels:

      app: airtable-ai-agent

  template:

    metadata:

      labels:

        app: airtable-ai-agent

    spec:

      containers:

      - name: ai-agent

        image: ghcr.io/rashidazarang/airtable-ai-agent:latest

        ports:

        - containerPort: 8000

        env:

        - name: AIRTABLE_TOKEN

          valueFrom:

            secretKeyRef:

              name: airtable-secrets

              key: token

```



## 📊 Monitoring & Observability



### Built-in Metrics



- **Request metrics** - Response times, success rates, error rates

- **MCP operations** - Tool usage, performance, success rates  

- **Context usage** - Token utilization, cache hit rates

- **Performance** - Memory usage, concurrent operations



### Prometheus Metrics



```

# Performance metrics

airtable_agent_requests_total

airtable_agent_request_duration_seconds

airtable_agent_mcp_operations_total

airtable_agent_context_tokens_used

airtable_agent_cache_hit_ratio



# Business metrics  

airtable_agent_queries_by_intent

airtable_agent_operations_by_type

airtable_agent_user_satisfaction_score

```



### Grafana Dashboard



Pre-built dashboard includes:

- **Request volume and latency**

- **MCP operation success rates** 

- **Context utilization metrics**

- **Error tracking and analysis**

- **User intent distribution**



## 🧪 Testing



### Run Tests



```bash

# Unit tests

pytest tests/test_agent.py -v



# Integration tests  

pytest tests/test_integration.py -v



# Performance tests

pytest tests/test_performance.py -v



# All tests with coverage

pytest --cov=src --cov-report=html

```



### Test Categories



- **Unit Tests** - Individual component testing

- **Integration Tests** - MCP server integration

- **Performance Tests** - Load testing, memory usage

- **End-to-End Tests** - Complete workflow validation



## 📈 Performance Benchmarks



| Metric | Value |

|--------|--------|  

| **Query Response Time** | < 2 seconds (95th percentile) |

| **Context Processing** | < 500ms for 10 chunks |

| **MCP Operations** | < 1 second per operation |

| **Concurrent Queries** | 100+ simultaneous users |

| **Memory Usage** | < 512MB per instance |

| **Cache Hit Rate** | > 80% for repeated queries |



## 🔒 Security



### Built-in Security Features



- **Token Security** - Secure token storage and rotation

- **Input Validation** - All inputs sanitized and validated  

- **Rate Limiting** - Protection against abuse

- **Audit Logging** - Complete operation audit trail

- **Error Sanitization** - No sensitive data in error messages



### Security Best Practices



```python

# Example secure configuration

security = {

    "token_rotation_days": 30,

    "max_requests_per_minute": 100,

    "enable_audit_logging": True,

    "sanitize_error_messages": True,

    "require_https": True

}

```



## 🤝 Contributing



We welcome contributions! Here's how to get started:



1. **Fork the repository**

2. **Create a feature branch** (`git checkout -b feature/amazing-feature`)

3. **Make your changes** with tests

4. **Run the test suite** (`pytest`)  

5. **Submit a pull request**



### Development Setup



```bash

# Clone your fork

git clone https://github.com/your-username/airtable-ai-agent.git

cd airtable-ai-agent/ai-agent



# Install development dependencies

pip install -r requirements-dev.txt



# Install pre-commit hooks

pre-commit install



# Run tests

pytest

```



## 📋 Roadmap



### Version 1.1 (Next Release)

- [ ] **Multi-base Support** - Work across multiple Airtable bases

- [ ] **Custom Integrations** - Plugin system for external services

- [ ] **Advanced Analytics** - Built-in data analysis capabilities

- [ ] **Voice Interface** - Voice commands and responses



### Version 1.2 (Future)  

- [ ] **Machine Learning** - Predictive suggestions and automation

- [ ] **Workflow Builder** - Visual workflow designer

- [ ] **Mobile App** - Native iOS/Android applications

- [ ] **Enterprise SSO** - SAML, OAuth, Active Directory support



## 📄 License



This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.



## 🙋‍♂️ Support



### Getting Help



- **Documentation**: Complete documentation in this repository

- **GitHub Issues**: [Report bugs or request features](https://github.com/rashidazarang/airtable-ai-agent/issues)

- **Discussions**: [Community discussions](https://github.com/rashidazarang/airtable-ai-agent/discussions)



### Commercial Support



For enterprise deployments and commercial support:

- **GitHub Issues**: Technical support through GitHub issues

- **Contributions**: Pull requests welcome!



## ⭐ Star History



If this project helps you, please consider giving it a star!



[![Star History Chart](https://api.star-history.com/svg?repos=rashidazarang/airtable-ai-agent&type=Date)](https://star-history.com/#rashidazarang/airtable-ai-agent)



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**Built with ❤️ for the Airtable community**



*Making Airtable automation accessible to everyone through the power of AI.*