Gravitee AI Agent Management

aid: gravitee:gravitee-ai-agent-management
name: Gravitee AI Agent Management
description: Gravitee AI Agent Management brings the API management discipline to agentic AI ecosystems.
  It is delivered as an Enterprise add-on package and includes an LLM Proxy (model routing, prompt-token
  tracking, prompt guard-rails, RAG), an MCP Proxy and MCP Tool Server, an A2A Proxy for agent-to-agent
  flows, an Agent Catalog, and AI-aware Identity and Authorization (MCP resource server, OpenFGA permission
  engine, AuthZen PDP).
humanURL: https://www.gravitee.io/platform/ai-agent-management
baseURL: https://documentation.gravitee.io/apim
tags:
- AI Gateway
- MCP
- LLM Proxy
- Agentic
- A2A
- RAG
- Enterprise
properties:
- type: Documentation
  url: https://documentation.gravitee.io/apim
- type: GitHubRepository
  url: https://github.com/gravitee-io/gravitee-apim-mcp-server
- type: GitHubRepository
  url: https://github.com/gravitee-io/gravitee-inference
- type: GitHubRepository
  url: https://github.com/gravitee-io/gravitee-inference-service
- type: GitHubRepository
  url: https://github.com/gravitee-io/gravitee-policy-ai-prompt-token-tracking
- type: GitHubRepository
  url: https://github.com/gravitee-io/gravitee-policy-ai-prompt-guard-rails
- type: GitHubRepository
  url: https://github.com/gravitee-io/gravitee-policy-ai-retrieval-augmented-generation
- type: GitHubRepository
  url: https://github.com/gravitee-io/gravitee-resource-ai-model-api
- type: GitHubRepository
  url: https://github.com/gravitee-io/gravitee-resource-ai-vector-store-api
- type: GitHubRepository
  url: https://github.com/gravitee-io/gravitee-resource-ai-model-text-classification
- type: GitHubRepository
  url: https://github.com/gravitee-io/llamaj.cpp
- type: Capabilities
  url: capabilities/mcp-publishing.yaml
features:
- name: LLM Proxy
  description: Multi-provider proxy for OpenAI, Anthropic, Azure OpenAI, Bedrock, Mistral, Hugging Face,
    and self-hosted models.
- name: Prompt Token Tracking
  description: Meter input / output tokens per consumer, per model, per route.
- name: Prompt Guard-Rails
  description: Apply allow / deny / classify rules to user prompts before they reach the model.
- name: Retrieval-Augmented Generation
  description: RAG policy that performs vector-store lookups and injects context into prompts.
- name: AI Vector Store Resource
  description: Pluggable vector-store resource API for embedding-based retrieval.
- name: MCP Proxy
  description: Govern Model Context Protocol traffic with authentication, audit, and quota.
- name: MCP Tool Server
  description: Expose existing REST and event APIs as MCP tools to AI agents.
- name: A2A Proxy
  description: Agent-to-agent transport with identity propagation and policy enforcement.
- name: Agent Catalog
  description: Discoverable directory of agents, tools, and MCP servers.
- name: AI Identity and Authorization
  description: OpenFGA permission engine and AuthZen PDP for agent-aware authorization.
- name: ML Inference Service
  description: Run text-classification and embedding models in the gateway via the inference service plugin.
useCases:
- name: AI Cost Governance
  description: Cap and meter LLM spend by team, by route, by tenant.
- name: Safe AI Onramp
  description: Apply prompt guard-rails, content classification, and audit on AI workloads.
- name: MCP-First APIs
  description: Publish enterprise APIs as MCP tools for agents and copilots.
- name: RAG-as-a-Service
  description: Stand up retrieval-augmented endpoints powered by managed vector stores.
integrations:
- name: OpenAI
- name: Anthropic Claude
- name: Azure OpenAI
- name: AWS Bedrock
- name: Hugging Face
- name: Mistral
- name: Ollama
- name: Pinecone
- name: Weaviate
- name: Qdrant
- name: Model Context Protocol
authentication:
- type: OAuth2
- type: API Key
- type: Bearer Token
- type: mTLS