codelion/optillm

Features

• Cost-Quality Model Routing - Directs traffic to specific model providers based on a cost-quality evaluation to optimize operational efficiency.
• Model Routing Gateways - Acts as a gateway that routes requests to different AI models based on cost and performance requirements.
• Agentic Reasoning Loops - Uses iterative orchestration patterns including self-reflection and multi-agent interactions to improve the accuracy of model responses.
• Model Request Routing - Implements routing logic to direct API requests to different LLM backends based on cost and performance.
• LLM Tooling Integrations - Connects language models to external resources such as web content, code interpreters, and protocol servers.
• External Tool Integrations - Connects the inference pipeline to external utilities such as protocol servers and code interpreters for real-time data retrieval.
• Reasoning Optimizations - Improves response accuracy using inference-time logic techniques like self-reflection and multi-agent loops.
• LLM Gateways - Functions as a routing layer that directs prompts to specific language models based on cost and provider health.
• LLM Integration Layers - Provides a middleware layer connecting language models to external protocol servers and code interpreters.
• Inference API Proxies - Provides a gateway proxy to optimize requests to language models through routing and context management.
• Structured Output Generators - Ensures language model responses follow machine-readable formats via a schema-driven interface.
• Reasoning Orchestrators - Improves output accuracy using inference-time loops, self-reflection, and multi-agent interaction chains.
• Inference-Time Reasoning Loops - Applies inference-time logic and self-reflection cycles to increase precision on complex tasks.
• LLM Schema Outputs - Generates validated structured data from model responses using predefined schema models for software integration.
• Structured JSON Generation - Enforces JSON schema adherence during the generation process to create consistent machine-readable outputs.
• Schema-Constrained Outputs - Forces model responses to conform to provided JSON schemas, ensuring machine-readable and consistent outputs.
• Data Anonymization - Intercepts requests to remove personally identifiable information before it reaches the model and restores it upon response.
• PII Data Leakage Prevention - Strips personally identifiable information from requests before they reach a language model.
• Memory Storage and Retrieval Systems - Manages short-term conversation history to handle long interactions without exceeding model context limits.
• AI Code Interpreters - Provides an environment where AI models can generate and execute code to validate computations.
• Context Memory Management - Implements systems for maintaining conversation history within LLM context windows to ensure coherence.
• MCP Protocol Integrations - Connects to external services and tools through the Model Context Protocol for standardized resource access.
• Web Content Fetching - Fetches and converts web content from specified URLs into a format optimized for LLM context injection.
• High Availability Deployments - Ensures service uptime by distributing requests across multiple model providers with health monitoring.
• Traffic Load Balancers - Distributes inference requests across multiple service providers using health monitoring to ensure high availability.
• Upstream Endpoint Load Balancing - Distributes inference requests across multiple upstream AI endpoints using real-time health monitoring to ensure high availability.
• Intent-Based Routing - Directs incoming requests to the most effective processing approach based on the intent of the user prompt.
• Domain-Based Routing Proxies - Proxies incoming inference requests to specific models or providers based on defined performance and cost logic.
• Request Interception Middleware - Employs request interception middleware to anonymize private data and structure outputs before delivery to the client.
• Inference and Serving - Optimizing inference proxy for improved performance.
• Inference Engines - Optimizing inference proxy for improved model performance.

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