Integrations
DigitalFate is designed to work seamlessly with the tools, services, and platforms you already use. Whether you're embedding it into SaaS applications, scaling on the cloud, or extending it with custom tools, integration is at the heart of the DigitalFate architecture. It's built to adapt, extend, and connect not to lock you in.
⚙️ Supported LLM Providers
DigitalFate provides out-of-the-box compatibility with multiple foundational models and providers:
OpenAI
GPT-4o, GPT-4, GPT-3.5
OPENAI_API_KEY
Anthropic
Claude 3.5 Sonnet, Claude 2.1
ANTHROPIC_API_KEY
DeepSeek
DeepSeek Chat
DEEPSEEK_API_KEY
Azure OpenAI
GPT series with Azure endpoints
AZURE_API_KEY
, endpoint, version
AWS Bedrock
Claude 3.5 via Amazon Bedrock
AWS_ACCESS_KEY_ID
, AWS_SECRET_ACCESS_KEY
, AWS_REGION
You can switch models per task or globally per client instance.
☁️ Cloud Platform Integration
DigitalFate is cloud-ready and works on:
AWS: Deploy agents or entire MCP clusters with Bedrock and Lambda support
GCP: Use container-based workflows and run inference via Vertex AI
Azure: Integrate with OpenAI models via Microsoft’s secure endpoints
Production-ready deployments are available using:
Docker
Kubernetes
Serverless functions
📚 Tool Ecosystem
Tools are modular Python-based wrappers that allow agents to perform real-world tasks:
Included Tools:
Search
: Web searchBrowser
: Simulate UI interactionsPDFReader
: Extract content from PDFsCSVTool
: Read structured dataCodeInterpreter
: Run Python code in a sandboxReviewTool
: Evaluate task outputs
Add Custom Tools:
Custom tools integrate seamlessly with task execution and agent reasoning.
🔐 Secure API Integrations
DigitalFate supports API-level authentication and secure key management for external services. Tools can call internal APIs, webhook-based services, or third-party APIs with encrypted credentials.
OAuth support (planned)
Environment variable key loading
Encrypted config file support
📥 Knowledge Base Sources
You can inject context from:
PDF files
URLs (public or gated)
Web scraping (via
Browser
)Company documents (via API or upload)
Internal tools (via custom adapters)
This allows agents to reason using custom company data or proprietary research.
🧠 Memory & Long-Term Context (Advanced)
DigitalFate’s memory system can be connected to:
Redis (in-memory persistence)
Vector DBs (future feature)
File-based or encrypted local caches
This allows agents to remember:
Past tasks
User preferences
Ongoing goals
Memory is scoped per agent and configurable for secure, production use.
🔁 SaaS & API Integration
You can embed DigitalFate into your app or stack using:
RESTful endpoints (via
client.call()
and server APIs)Webhooks
Scheduled tasks / CRON
Task queues (RabbitMQ, Celery, etc. integration-ready)
Examples:
Trigger an agent workflow via form submission
Schedule reports via agent execution
Automate ticket classification via LLM response
🔄 CI/CD and DevOps Friendly
Use DigitalFate inside your pipelines:
GitHub Actions
GitLab CI
Jenkins jobs
Airflow DAGs
Perfect for:
Testing AI pipelines
Auto-generating docs from source
ML evaluation reports
🔧 Developer-Friendly SDK
The SDK is written in Python and structured for quick extension. Every major function (agents, tools, tasks, servers) can be subclassed and overridden.
Supports:
Async execution
Server-client split
Logging & tracing hooks
Custom agent reasoning loops
🧪 Future Integrations (Coming Soon)
Zapier + Make workflows
Slack / Discord bots
LangChain-compatible tools
RAG pipelines with Pinecone & Weaviate
Frontend embedding with React + Next.js
OpenAPI spec-based tool auto-importing
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