DreamLog Integrations¶

DreamLog provides multiple integration options to work with various tools and platforms.

Available Integrations¶

MCP Server ¶

Model Context Protocol integration for AI assistants like Claude Desktop.

Tool-based interaction with DreamLog
Knowledge base management
Query execution
Dream cycle operations

REST API ¶

HTTP API server for web applications and services.

RESTful endpoints for all operations
WebSocket support for real-time REPL
JSON request/response format
Authentication and rate limiting

Jupyter Integration (Coming Soon)¶

Magic commands for Jupyter notebooks.

%%dreamlog cell magic
Interactive knowledge exploration
Visualization of query results
Dream cycle analysis

TUI (Terminal User Interface)¶

Interactive terminal interface.

dreamlog or python -m dreamlog.tui - Main TUI
Rich formatting and syntax highlighting
LLM and dreaming commands
Knowledge base management

Quick Start¶

MCP Server¶

# Install MCP server
pip install dreamlog[mcp]

# Start server
python -m dreamlog.integrations.mcp

REST API¶

# Start API server
python -m dreamlog.integrations.api --port 8000

# Make requests
curl -X POST http://localhost:8000/query \
  -H "Content-Type: application/json" \
  -d '{"query": "(parent john X)"}'

Jupyter¶

# Load extension
%load_ext dreamlog.integrations.jupyter

# Use magic command
%%dreamlog
(parent john mary)
(parent mary alice)
(grandparent X Z) :- (parent X Y), (parent Y Z)
query: (grandparent john X)

Integration Architecture¶

┌──────────────────────────────────────┐
│         Client Applications          │
├────────┬────────┬────────┬──────────┤
│  MCP   │  REST  │Jupyter │  VS Code │
│ Server │  API   │ Magic  │Extension │
├────────┴────────┴────────┴──────────┤
│       DreamLog Core Engine          │
├──────────────────────────────────────┤
│    Knowledge Base & LLM Providers    │
└──────────────────────────────────────┘

Common Integration Patterns¶

1. Embedded Integration¶

Embed DreamLog directly in your Python application:

from dreamlog import DreamLog

dl = DreamLog()
dl.add_fact("(parent john mary)")
results = dl.query("(parent john X)")

2. Service Integration¶

Run DreamLog as a service and communicate via API:

import requests

response = requests.post(
    "http://dreamlog-server:8000/query",
    json={"query": "(parent john X)"}
)
results = response.json()

3. Tool Integration¶

Use DreamLog through MCP tools in AI assistants:

{
  "tool": "dreamlog_query",
  "parameters": {
    "query": "(parent john X)",
    "knowledge_base": "family.dl"
  }
}

Configuration¶

Each integration can be configured via:

Environment variables
Configuration files (dreamlog.yaml)
Command-line arguments

Example configuration:

# dreamlog.yaml
integrations:
  api:
    port: 8000
    host: "0.0.0.0"
    cors: true

  mcp:
    max_query_results: 100
    enable_dreaming: true

  jupyter:
    auto_display: true
    syntax_highlight: true

Security Considerations¶

When using integrations:

Authentication: Use API keys for REST endpoints
Rate limiting: Prevent abuse of LLM providers
Input validation: Sanitize all user inputs
Sandboxing: Run untrusted queries in isolation
Audit logging: Track all operations

Performance Tips¶

Connection pooling: Reuse connections for API calls
Caching: Cache query results and LLM responses
Async operations: Use async/await for I/O operations
Batch processing: Group multiple operations
Resource limits: Set memory and time limits

Troubleshooting¶

Common Issues¶

MCP Server not connecting - Check firewall settings - Verify port availability - Review MCP configuration

REST API timeout - Increase timeout settings - Optimize complex queries - Check LLM provider response times

Jupyter kernel crashes - Limit query result size - Check memory usage - Update Jupyter and dependencies

Contributing¶

To add a new integration:

Create module in dreamlog/integrations/
Implement integration protocol
Add documentation
Write tests
Submit pull request

See the GitHub repository for details.