ChainLite streamlines creation of AI agents by abstracting the complexities of LLM orchestration into reusable chain modules. Using simple Python decorators and configuration files, developers define agent behaviors, tool interfaces and memory structures. The framework integrates with popular LLM providers (OpenAI, Cohere, Hugging Face) and external data sources (APIs, databases), allowing agents to fetch real-time information. With a built-in browser-based UI powered by Streamlit, users can inspect token-level conversation history, debug prompts, and visualize chain execution graphs. ChainLite supports multiple deployment targets, from local development to production containers, enabling seamless collaboration between data scientists, engineers, and product teams.

Rags provides a modular pipeline to build retrieval-augmented generative applications. It integrates with popular vector stores (e.g., FAISS, Pinecone), offers configurable prompt templates, and includes memory modules to maintain conversational context. Developers can switch between LLM providers like Llama-2, GPT-4, and Claude2 through a unified API. Rags supports streaming responses, custom preprocessing, and evaluation hooks. Its extensible design enables seamless integration into production services, allowing automated document ingestion, semantic search, and generation tasks for chatbots, knowledge assistants, and document summarization at scale.

Claude-Code-OpenAI transforms Anthropic’s Claude API into a drop-in replacement for OpenAI models in Python applications. After installing via pip and configuring your OPENAI_API_KEY and CLAUDE_API_KEY environment variables, you can use familiar methods like openai.ChatCompletion.create(), openai.Completion.create(), or openai.Embedding.create() with Claude model names (e.g., claude-2, claude-1.3). The library intercepts calls, routes them to the corresponding Claude endpoints, and normalizes responses to match OpenAI’s data structures. It supports real-time streaming, rich parameter mapping, error handling, and prompt templating. This allows teams to experiment with Claude and GPT models interchangeably without refactoring code, enabling rapid prototyping for chatbots, content generation, semantic search, and hybrid LLM workflows.

Cognita offers a modular architecture for building RAG applications: ingest and index documents, select from OpenAI, TrueFoundry or third-party embeddings, and configure retrieval pipelines via YAML or Python DSL. Its integrated frontend UI lets you test queries, tune retrieval parameters, and visualize vector similarity. Once validated, Cognita provides deployment templates for Kubernetes and serverless environments, enabling you to scale knowledge-driven AI assistants in production with observability and security.

Nestor offers a modular architecture to assemble AI agents that maintain conversation state, invoke external tools, and customize processing pipelines. Key features include session-based memory stores, a registry for tool functions or plugins, flexible prompt templating, and unified LLM client interfaces. Agents can execute sequential tasks, perform decision branching, and integrate with REST APIs or local scripts. Nestor is framework-agnostic, enabling users to work with OpenAI, Azure, or self-hosted LLM providers.