Eidolon AI

Eidolon AI is an open-source framework built by August Data that positions itself as the first AI Agent Server for the enterprise. Released under the Apache License 2.0, it combines a pluggable SDK with a production-ready deployment server, letting developers define, run, and scale agentic applications without building custom infrastructure from scratch.

What It Is

Eidolon provides two core components: an Agent Server (a secure, enterprise-ready deployment runtime for agentic applications) and an Eidolon SDK (a pluggable Python framework for building agents). Agents are defined using declarative YAML configuration and exposed as HTTP services, making them composable, testable, and deployable through standard infrastructure tooling. The project is hosted on GitHub under the eidolon-ai organization and is written primarily in Python.

How the Build-Deploy-Consume Workflow Works

The framework structures development around three stages:

• Build: Developers define agents using YAML manifests or vanilla Python code. Pre-built agent types (chatbot, RAG retriever, multi-agent orchestrator) are available out of the box. Agents can reference each other via agent_refs, enabling hierarchical multi-agent systems.
• Deploy: Because agents are stateless HTTP services, they deploy directly to Kubernetes using Helm charts. The site states agents scale horizontally and can be governed by Kubernetes RBAC policies. Local development is also supported via Docker.
• Consume: Agents expose REST APIs automatically. Eidolon also ships React UI components, a CLI, a Python client (eidolon-ai-client), and a TypeScript client (@eidolon-ai/client) for interacting with deployed agents.

Architecture and Modularity

Eidolon's design centers on avoiding vendor lock-in. The framework's component system lets developers swap LLM backends (GPT-4 Turbo, Mistral Large, Llama 3, Claude Opus/Sonnet, and others are shown in examples), RAG implementations, and tool integrations without rewriting agent logic. Agent-to-agent communication is handled through dynamically generated OpenAPI tool definitions, so agents discover each other's interfaces at runtime. The GitHub README highlights that this modularity is intentional: "developers will not be able to adapt their agents to the rapidly changing AI landscape" without it.

Multi-Model and RAG Support

A single agent definition can expose multiple LLM backends simultaneously, letting end users choose the model at runtime. The YAML examples on the homepage show a single chatbot agent configured with GPT-4 Turbo, Mistral Large, Llama3-8b, Claude Opus, and Claude Sonnet as selectable APUs (Agent Processing Units). RAG is treated as a first-class service: a RetrieverAgent can load documents from GitHub repositories, and any agent can reference it via agent_refs.

Open-Source Status and Community

The repository lives at github.com/eidolon-ai/eidolon, was created in November 2023, and carries 493 stars and 51 forks as of the latest GitHub metadata. The project is actively maintained, with the last push recorded in May 2026. It follows the all-contributors specification and maintains a public list of good-first-issues for new contributors. The team communicates via Discord and publishes content on YouTube and LinkedIn under the August Data company identity. Founding contributors include Dave Brewster (three decades of experience in data analytics and ML architectures) and Luke Lalor, with Ravi Ramachandran leading go-to-market efforts.