mnfst/manifest

Features

• LLM Provider Adapters - Standardizes access to multiple different AI backends through a single unified interface.
• Unified Model Interfaces - Provides a standardized API interface that abstracts diverse AI model providers into a single request format.
• Model Provider Integrations - Offers unified interfaces to connect and configure multiple language model providers through a single gateway.
• Model Request Routing - Directs AI queries to specific backends based on task complexity and cost to optimize performance.
• Fallback Configurations - Implements backup model configurations to automatically reroute queries when a primary provider fails.
• Model Routing - Directs queries to the most cost-effective or capable model based on request complexity and performance.
• API Connection Managers - Manages API keys and subscriptions to establish stable connections with cloud-based and local model servers.
• AI Token Budget Controllers - Enforces real-time token-based spending limits to prevent unexpected operational costs from AI models.
• AI Query Routing - Routes user inputs to different processing pipelines or models based on the nature of the request.
• Multi-Tenant Identity Management - Isolates AI agents into tenant groups with dedicated keys for secure authentication and telemetry.
• Agent Identities - Assigns unique identities and dedicated keys to AI agents to manage authentication and telemetry ingestion.
• AI Agent Tenant Isolation - Isolates AI agents into tenants with dedicated keys for secure authentication and per-tenant telemetry.
• AI Cost Monitoring - Provides utilities to track token usage and model efficiency to monitor and optimize operational AI expenses.
• AI and Agent Observability - Provides specialized instrumentation for tracking token usage, response speed, and logs across multiple AI agents.
• AI Model Telemetry - Provides a centralized dashboard to monitor token usage, costs, and message logs across multiple AI agents.
• AI Spending Quotas - Implements financial controls that cap total expenditure for AI token usage at the gateway level.
• Token Usage Analytics - Tracks token consumption and associated costs per request to provide operational observability.
• Model Pricing Managers - Synchronizes model costs from external sources daily to maintain an accurate database for financial tracking.
• High Availability Infrastructure - Ensures continuous service availability by implementing automatic failover and redundancy across AI model providers.
• Service Call Fallbacks - Implements logic to redirect requests to alternative models when a primary AI provider fails to ensure service continuity.
• Model Routing Configurations - Provides declarative configuration for defining model providers and routing tiers.
• Agent Performance Monitoring - Tracks operational metrics, token usage, and costs to monitor the performance of AI agents.
• AI Observability Tools - Real-time cost observability for AI agents using OTLP-native signals.
• LLM Development Frameworks - LLM router for cost-effective model selection and benchmarking.
• Observability And Monitoring - Provides real-time cost observability for AI agents.
• DevOps and Infrastructure - Backend-as-a-Service for rapid application development.
• Monitoring and Observability - Real-time cost observability and token usage dashboard.
• AI Coding Assistants - Alternative backend platform for AI-powered code editors.

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