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Connect LLMs to major chat platforms so teams can build, deploy, and operate multi-platform AI chatbots and agents. Provides multi-platform adapters, a plugin marketplace, an MCP server and built-in RAG plus production features like access control, rate limiting and monitoring.
Lets you write compositional Python programs that compile into self‑improving LLM pipelines — replacing brittle prompt engineering with a declarative, programmatic approach and built‑in algorithms to optimize prompts and weights for RAG, multi‑stage pipelines, and agent loops.
Runs an agentic RAG loop over scientific papers: searches literature, gathers and re-ranks evidence chunks, then answers with in-text citations. Adds metadata-aware embeddings, retraction checks, and contradiction detection across full PDFs.
Maps your existing C#, Python, or Java functions into a form AI models can invoke, then translates model requests into real function calls and feeds results back. Model-agnostic middleware: swap in newer models without rewriting your app.
Builds production RAG systems around deep document understanding, explainable chunking, hybrid retrieval, citations, and agent workflows for messy enterprise documents.
Pulls context from your whole codebase via Sourcegraph's search API to power chat, autocomplete, and edits across VS Code, JetBrains, and the CLI. Now ships only inside Sourcegraph Enterprise; the free and Pro tiers are retired.
Run prompts against OpenAI, Claude, Gemini, and dozens of local or remote models from one terminal command, logging every prompt and response to SQLite. Plugins add new providers, tools, and embeddings; supports schema extraction and function calling.
Build LLM apps by chaining nodes on a visual canvas — prompts, branching, RAG, agents, tools — and ship the same graph as an API or hosted app. Bundles a plugin marketplace, model routing across hosted and local providers, and built-in observability.
Translates plain-English questions into pandas/SQL code over CSV, Parquet, and SQL databases, returning tables and charts. Combines LLMs with RAG and a semantic layer so non-coders query data; a Docker sandbox isolates generated code.
Self-hostable chat UI that connects to any LLM and adds Agents, Web Search, RAG, connectors, code execution and image generation. Ships connectors to 40+ sources and deployment guides for Docker/K8s. Best for teams needing private, extensible chat platforms.
Declarative CLI and library to evaluate and red-team LLM apps: run test cases against prompts and models, compare providers side-by-side, and scan for jailbreaks, prompt injection, and data leaks — with CI/CD and pull-request code scanning built in.