Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add agents/smart-ai-memory/attune-ai/notegit clone --depth 1 https://github.com/Smart-AI-Memory/attune-aiWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00002 | $0.01262 |
| Opus 5 | $0.00001 | $0.00631 |
| Sonnet 5 | $0.00000 | $0.00252 |
| Haiku 4.5 | $0.00000 | $0.00126 |
Grade A, and why
agents-note scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 108 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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:
- the Python API —
from attune.agent_factory import AgentFactory, Framework(the primary surface, documented throughout); - the
/agentskill, inside a Claude Code conversation — create and manage custom agents and teams.
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. |
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago First seen · 108 lines · 2 tokens per session scan A 9bc135a2171f
agents-note is an agent published in the GitHub repository Smart-AI-Memory/attune-ai (10 stars, last pushed 2d ago), licensed Apache-2.0. It adds 2 tokens to every session and 1,262 once invoked, about $0.0000 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other agents, from other repositories
aiwg-model-efficiency-worker
Model-pinned AIWG subagent wrapper for discovery, inventory, focused edits, and other bounded low-cost work.
social-content-generator
Transform blog articles into platform-optimized social media content for all major platforms.
poteto-agent
Compatibility target for imported poteto-mode delegation. Runs one bounded implementation brief with the active playbook and no child delegation.
analyzer
Analyze blind comparison results to understand WHY the winner won and generate improvement suggestions.
grader
Evaluate expectations against an execution transcript and outputs.
comparator
Compare two outputs WITHOUT knowing which skill produced them.