The Operating System for AI Agents
FoundationaLLM enables organizations to deploy, scale, and manage AI agents that leverage LLMs from all major vendors, connect to enterprise data, and operate securely with role-based access control and telemetry. It is a complete production platform — not a framework — for enterprise AI.
A standardized, branded chat experience optimized for specific use cases. Secure multi-tenant interface with streaming responses, memory persistence, and Entra ID (Azure AD) SSO. Built with accessibility in mind for large, diverse user populations.
End users can create agents by providing a prompt, reference files, selecting the LLM model and tools, then share with peers. Power users access advanced configuration: data sources, plugins for workflows, tools and data pipelines, API token creation, and publishing.
Agents deliver consistent behavior regardless of the model used. Supports OpenAI, Azure OpenAI, Anthropic, Mistral, Meta Llama, and others interchangeably. A central abstraction layer ensures consistent behavior and telemetry across providers.
Ingestion and embedding services that convert enterprise data into retrievable knowledge. Handle structured and unstructured sources, support hybrid search (semantic + keyword), enforce tenant isolation in vector stores. Manage everything from end-user uploads to large document library indexing in Knowledge Graphs, including document removal for compliance.
Comprehensive solution for measuring agent performance. Create and execute test suites with question/answer pairs and source files. Evaluate multiple agents against each other using LLM-as-judge, static rules, or expected tool use checks. Rich HTML reports summarize performance and timing.
A CLI that automates prompt improvement for agents and their tools. Evaluates prompts, explains improvements, authors improved prompts, and deploys them automatically. Can invoke Agent Evals to measure performance after changes, with human-in-the-loop or fully automated modes.
Granular permission framework across all artifacts: instance level, agents, prompts, models, APIs, and AI-generated artifacts. Comprehensive RBAC, policy-based access control, logging, and tracing. Integrates with Entra ID and Managed Identities. All actions logged for auditing with compliance exports.
Fully integrated with Azure Application Insights for performance tracing, LLM load monitoring, and call failure tracking. Infrastructure logging via Log Analytics enables queryable diagnostics across all platform layers. Tracks latency, token usage, cost allocation, error rates, and evaluation results.
Comprehensive APIs for agent interaction and platform management. .NET SDK (CoreClient): strongly typed, async-ready, integrated with DI. Python SDK: async architecture using aiohttp, pydantic models, and CLI tools. Both SDKs provide APIs for management, orchestration, evaluation, and data access.
Developer frameworks like LangChain, Semantic Kernel, Microsoft Agent Framework, and Crew.AI are useful for building agents in code but do not deliver the complete solution. They do not address:
FoundationaLLM provides the complete toolbox. Agents built with popular frameworks run inside FoundationaLLM and gain centralized management, multi-agent orchestration, evaluation, compliance layers, and comprehensive SDKs and CLIs for integration and automation.
FoundationaLLM uses a simple annual license model based on the number of live agents and end users. There is no monthly subscription fee per user or charge per user action. Total cost of ownership is typically a fraction of comparable SaaS solutions. All users are licensed to create agents with no additional fee to use the full power of the platform.
FoundationaLLM provides the operating system for AI agents. It enables organizations to deploy, scale, and manage agents that leverage LLMs from all major vendors, connect to enterprise data, and operate securely with RBAC and telemetry.
These are developer frameworks for building agents in code. FoundationaLLM is a complete production platform that adds cloud hosting, security, production UIs, no-code agent creation, and enterprise cloud tools. Agents built with these frameworks can run inside FoundationaLLM.
Agents are model-agnostic. They use OpenAI, Azure OpenAI, Anthropic, Mistral, Meta Llama, and others interchangeably through a central abstraction layer that ensures consistent behavior and telemetry.
It integrates with Entra ID and uses Managed Identities. RBAC is enforced at every layer. All actions are logged for auditing with compliance exports available.
Simple annual license based on live agents and end users. No per-user monthly fees or per-action charges. All users can create agents at no additional cost.
Using Azure Developer CLI (azd) targeting Azure Container Apps (development) or Azure Kubernetes Service (production). Automated scripts handle Entra ID app registrations, Managed Identity setup, and role assignments.
Integrated with Azure Application Insights and Log Analytics. Tracks latency, token usage, cost allocation, error rates, and evaluation results with visualization dashboards.
.NET SDK (CoreClient): strongly typed, async-ready, DI-integrated. Python SDK: async with aiohttp, pydantic models, and CLI tools. Both cover management, orchestration, evaluation, and data access.
To learn more or request a demo, visit foundationallm.ai/contact.