Universal Agent Factory — create, run, and orchestrate AI agents across frameworks

Overview

The agents feature is Attune's Universal Agent Factory — one interface for creating, running, and orchestrating AI agents, backed by your choice of framework (native, LangChain, LangGraph, AutoGen, or Haystack) without rewriting code when you switch frameworks. The entry point is AgentFactory: it picks a framework adapter, and its create_agent / create_workflow methods return BaseAgent / BaseWorkflow objects with a uniform interface.

The agent and workflow run methods (invoke, run, stream) are async — await them.

You reach it two ways:

There is no attune agent CLI command and no MCP tool.

Scope. This feature is the framework-agnostic Agent Factory (src/attune/agent_factory/). The release-readiness agent team (src/attune/agents/release/) is documented under release-prep, and the agent state/recovery store (src/attune/agents/state/) is that team's persistence layer — not part of the Factory's public surface.

Concepts

One factory, many frameworks

AgentFactory(framework=None, provider="anthropic", api_key=None, use_case="general") is the entry point. framework is a Framework enum (or its string) — native (the default when unset), langchain, langgraph, autogen, or haystack. Each non-native framework is an optional dependency loaded lazily; AgentFactory.list_frameworks( installed_only=True) reports what's available and AgentFactory.recommend_framework(use_case) suggests one. Call switch_framework(framework) to move an existing factory to another backend.

Create agents and workflows

Method Returns What it does
create_agent(name, role=AgentRole.CUSTOM, model_tier="capable", ...) BaseAgent Build one agent. Many options — capabilities, tools, system_prompt, temperature, memory_enabled, resilience_enabled, …
create_workflow(name, agents, mode="sequential", ...) BaseWorkflow Coordinate several agents (sequential or other modes).
create_tool(name, description, func, args_schema=None) tool Wrap a Python callable as an agent tool.
create_coordinator / create_researcher / create_writer / create_reviewer / create_debugger BaseAgent Role-preset agent shortcuts.
create_code_review_pipeline() / create_research_pipeline(topic, include_reviewer=True) BaseWorkflow Ready-made multi-agent pipelines.
get_agent(name) / list_agents() BaseAgent | None / list[str] Look up agents the factory has created.

Agents and workflows run async

A BaseAgent exposes async invoke(input_data, context=None) -> dict and an async stream(...) generator, plus add_tool, get_conversation_history, and clear_history. A BaseWorkflow exposes async run(input_data, initial_state=None) -> dict and async stream(...), plus get_agent and get_state. Always await the run methods.

Config and taxonomy

AgentConfig and WorkflowConfig capture an agent's / workflow's settings (the create_* kwargs map onto them). AgentRole enumerates roles (coordinator, researcher, writer, reviewer, editor, executor, debugger, security, architect, tester, documenter, retriever, summarizer, answerer, custom) and AgentCapability enumerates capabilities (code_execution, tool_use, web_search, file_access, memory, retrieval, vision, function_calling).

Adapters implement one protocol

Each framework is wrapped by a BaseAdapter with a uniform surface — create_agent(config), create_workflow(config, agents), create_tool(...), get_model_for_tier(tier, provider), and is_available(). The factory selects the adapter; you normally don't touch adapters directly.

Notes & tips