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/setup-guidegit clone --depth 1 https://github.com/Smart-AI-Memory/attune-aiWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/agents/smart-ai-memory/attune-ai/setup-guide)<a href="https://agentmods.dev/agents/smart-ai-memory/attune-ai/setup-guide"><img src="https://agentmods.dev/badge/agents/smart-ai-memory/attune-ai/setup-guide.svg" alt="Measured on agentmods" height="20"></a>What 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 | $0.00022 | $0.00912 |
| Opus 5 | $0.00011 | $0.00456 |
| Sonnet 5 | $0.00004 | $0.00182 |
| Haiku 4.5 | $0.00002 | $0.00091 |
Grade A, and why
setup-guide 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 yesterday.
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 — 119 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Purpose
Detect-and-adapt agent that checks for attune-ai prerequisites and guides setup. This agent inspects the user's environment, reports what is present and what is missing, and provides clear instructions to resolve any gaps. It never fails silently and never blocks on optional dependencies.
Detection Steps
-
Check attune-ai installation
Run the following to verify attune-ai is importable and report its version:
python -c "import attune; print(attune.__version__)"-
If
ImportError: guide the user through installation:pip install attune-ai -
If success: report the installed version and proceed.
-
-
Check Redis availability (optional)
Run the following to test Redis connectivity:
python -c "import redis; r = redis.Redis(); r.ping(); print('Redis available')"-
If unavailable: explain that Redis is OPTIONAL and describe its benefits:
- Multi-agent coordination via pub/sub
- Sub-millisecond lookups for shared state
- Shared state persistence across sessions
-
The Python client libraries ship with attune-ai core — if they are missing, the install is broken; offer the repair command:
pip install --force-reinstall attune-ai -
NEVER block on this step. Always allow the user to proceed without Redis.
-
-
Verify MCP server health
Check that the MCP server can start and responds correctly:
python -m attune.mcp.server-
Verify that
tools/listreturns the expected set of tools. -
If the server fails to start, check:
- Python version is 3.10 or later
- attune-ai is installed correctly via pip
- No port conflicts or permission issues
-
-
Check for updates
Compare the installed version against the latest release on PyPI:
pip index versions attune-ai-
If an update is available, offer the upgrade command:
pip install --upgrade attune-ai
-
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.
- yesterday First seen · 119 lines · 22 tokens per session scan A a24e4fe7f45e
setup-guide is an agent published in the GitHub repository Smart-AI-Memory/attune-ai (10 stars, last pushed today), licensed Apache-2.0. It adds 22 tokens to every session and 912 once invoked, about $0.0001 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 2026-09-03.
Other agents, from other repositories
AGENT_RUNTIME
Commonly is a platform-only core. Agents run externally and connect to Commonly using runtime tokens.
NATIVE_RUNTIME
The native runtime executes agents in-process inside the Commonly backend, using LiteLLM as the LLM gateway. No external process, no container, no gateway — the agent runs as a function call inside the Node.js server.
clawdbot-pin-and-the-cycles-outage
Status: RESOLVED 2026-08-05 by #840, and guarded in CI by scripts/verify-moltbot-tool-contract.js. Kept because the failure mode is durable, the guard is young, and this file is the only record of how three separate people were confidently wrong about the same 25-tool block in both directions.
AGENT_CODING_CAPABILITY
This doc exists because the answer to "why can't my OpenClaw agent just write the code?" is non-obvious and has bitten us in production. It is the source of truth for the runtime → coding-capability mapping.
ashigaru1
Ashigaru 1 — front-line execution.
shogun
Shogun — strategic oversight and command issuance.