3. Data, Science & AI

Automates synchronization of session context, decisions, and patterns to Mem0 for seamless AI agent continuity across sessions.

Enables AI agents to retain and recall semantic context across sessions, maintaining continuity from previous interactions.

Provides testing patterns for LLM-based applications, including mocking responses, testing timeouts, and validating structured outputs.

Enables Redis-based semantic caching for LLM applications, optimizing costs through vector similarity and response caching.

Tracks LLM cost savings from caching via Langfuse, monitoring cache effectiveness and attributing costs to agents in multi-agent AI systems.

Enables LLMs to interact with external tools via function calling, including schema definition and structured output generation.

Optimizes LLM costs by caching prompts for Claude and OpenAI, reducing token usage for repeated system prompts and prefixes.

Enhances RAG systems by implementing hybrid BM25 and vector search to maintain context and reduce retrieval failures in AI models.

Optimizes LLM inference latency and throughput using vLLM, quantization, and speculative decoding for efficient deployment.

Manages golden datasets for AI/ML evaluation, ensuring integrity via backup, restore, and validation to prevent data loss.

Adds and validates documents to a golden dataset for AI/ML testing and training using multi-agent validation to ensure data quality.

Validates golden datasets for AI evaluation through schema checks, duplicate detection, and coverage analysis to ensure data integrity.

Provides agentic workflow patterns for autonomous LLM reasoning, enabling ReAct agents to plan and execute multi-step tasks.

Improves semantic search accuracy using hypothetical document embeddings to resolve vocabulary mismatches in RAG systems.

Retrieves decisions and patterns from knowledge graph for AI-driven pattern recognition and historical decision recall.

Enables image-text retrieval and cross-modal search via multimodal RAG using CLIP, SigLIP 2, and Voyage models.

Provides a functional API for building stateful AI workflows with parallel execution, persistence, and human-in-the-loop capabilities using LangGraph decorators.

Provides advanced LLM guardrails for safety, accuracy, and compliance using NeMo, Guardrails AI, and OpenAI. Features hallucination prevention, fact-checking, and toxicity detection.

Stores user decisions and patterns in a structured knowledge graph for recall of effective approaches and patterns.

Decomposes complex, multi-concept queries into simpler components to improve retrieval accuracy and coverage in AI-driven search systems.

Enables image analysis, document understanding, and visual question answering through advanced vision-language models.

Preloads session context by auto-loading memories from mem0 and graph at session start.

Enables efficient LLM fine-tuning using LoRA, QLoRA, and DPO alignment with synthetic data generation for domain-specific model customization.

Enables local inference of large language models using Ollama, supporting development, CI pipelines, and cost efficiency with model selection and LangChain integration.