swarmclaw

name	swarmclaw
description	AI agent runtime and multi-agent orchestration platform. Teaches agents how to use SwarmClaw's 6 primitive tools, persistent memory, dreaming, delegation, connectors, credentials, and the skill system. Use when an agent is running on SwarmClaw and needs to understand the platform's capabilities.
metadata	{"openclaw":{"emoji":"🐝","privacyPolicy":"All data stays on the local SwarmClaw instance. Memory, workspace files, and session data are stored on the host machine. No data is sent to external services unless the agent explicitly calls an external API.","dataHandling":"Agent memory is stored locally in the SwarmClaw data directory. Workspace files are scoped per agent. Credentials are injected as environment variables and automatically redacted from tool output."}}
version	2.4.1
author	swarmclawai
homepage	https://swarmclaw.ai
tags	["agents","orchestration","multi-agent","runtime","memory","delegation","skills","connectors","dreaming"]

SwarmClaw Platform

SwarmClaw is an AI agent runtime and multi-agent orchestration platform. It gives agents a uniform set of tools, persistent memory, connector integrations, and the ability to delegate work to other agents.

Website: https://swarmclaw.ai Docs: https://swarmclaw.ai/docs GitHub: https://github.com/swarmclawai/swarmclaw npm: npm install -g swarmclaw

The 6 Primitive Tools

Every agent has access to these core tools. They cover the full range of agent capabilities.

Tool	Purpose	When to Use
files	Read, write, edit, list, search files	Any file operation on the workspace filesystem
execute	Run bash scripts (sandboxed or host)	Shell commands, curl, data processing, package management
memory	Store and retrieve persistent knowledge	Facts, preferences, decisions that should survive across sessions
platform	Tasks, communication, delegation, projects	Coordinating with humans and other agents
browser	Control a headless browser	Interactive web pages, JavaScript-rendered content
skills	Discover and load skill documentation	Learning how to use tools, APIs, or workflows

Tool Selection Guide

Task	Tool
Edit a source file	files (edit action)
Run tests	execute
Call a REST API (JSON)	execute (curl)
Scrape a dynamic web page	browser
Remember a user preference	memory
Ask the user a question	platform (communicate.ask_human)
Send a Slack message	platform (communicate.send_message)
Hand off work to another agent	platform (communicate.delegate)
Find out how a tool works	skills (read action)

Credentials

Credentials are configured per agent in the SwarmClaw UI. They are:

• Injected as environment variables into execute tool runs (e.g., $OPENAI_API_KEY, $GITHUB_TOKEN)
• Automatically redacted from all tool output -- secrets never appear in chat history
• Named by convention: <PROVIDER>_API_KEY or custom names set in the credential config

You never need to ask the user for API keys directly. If a credential is configured, it's available as an env var. If it's not configured, tell the user which credential to add in the agent settings.

The Skill System

Skills are markdown files that teach agents how to use tools, APIs, and workflows. They are documentation, not executable code.

Loading Skills

{ "tool": "skills", "action": "list" }
{ "tool": "skills", "action": "read", "name": "tools/files" }
{ "tool": "skills", "action": "search", "query": "github pr" }

Skill Locations

• skills/ -- built-in skills shipped with SwarmClaw
• data/skills/ -- user-created skills added at runtime

When to Load Skills

• Before using a tool you're unfamiliar with
• When a task involves an API or workflow you haven't used before
• When the user asks you to do something and you're unsure of the best approach

Agent Capabilities

Memory

Agents have persistent memory across sessions:

• Working memory (session-scoped): scratch notes, intermediate results
• Durable memory (cross-session): user preferences, project facts, decisions
• Memories are automatically surfaced in context when relevant
• Store important learnings proactively -- don't wait to be asked

Dreaming

Agents with dreaming enabled automatically consolidate memories during idle periods. You can also trigger a dream manually:

Check dream status

{ "tool": "memory", "action": "list", "category": "dream_reflection" }

Manual dream trigger

Use the platform API to trigger a dream cycle:

{ "tool": "execute", "command": "curl -s -X POST http://localhost:3456/api/memory/dream -H 'Content-Type: application/json' -d '{\"agentId\":\"YOUR_AGENT_ID\"}'" }

Dream cycles produce dream_reflection and consolidated_insight memories that help maintain a clean, coherent memory store over time.

Delegation

Agents can delegate work to other agents:

• delegate: route a task to a specific agent and wait for the result
• spawn: create a subagent that runs independently (fire-and-forget or session-based)
• Use agents.list to discover available agents and their specializations

Connectors

Agents can communicate through external platforms:

• Discord, Slack, Telegram, and custom webhooks
• Messages sent via platform tool with communicate.send_message
• Inbound messages from connectors trigger agent sessions automatically

MCP Servers

Agents can also use tools served by external Model Context Protocol servers:

• Register MCP servers under MCP Servers in the UI (stdio / sse / streamable-http transports supported).
• Quick-setup presets include SwarmVault (local-first knowledge vault) and SwarmDock (agent marketplace — browse tasks, bid, submit work, earn USDC). The SwarmDock preset is pre-filled for the hosted endpoint at https://swarmdock-api.onrender.com/mcp and just needs the Bearer header (generate a key and register an agent at swarmdock.ai/mcp/connect). See docs/mcp-servers.md for the full workflow.
• Once attached to an agent, MCP tools appear alongside the built-in tools at execution time.

Workspace Conventions

• The workspace root is the agent's working directory
• File paths in tool calls are relative to the workspace root
• /workspace/... paths are resolved to the workspace root automatically
• The $WORKSPACE env var points to the workspace root in execute tool runs

Best Practices

1. Load skills before unfamiliar operations. A 30-second skill read prevents minutes of trial and error.

2. Use the right tool for the job. Don't use execute with echo > file.txt when files write action is cleaner. Don't use browser when curl in execute suffices.

3. Store important context in memory. If you learn something that would help in future sessions (user preference, project convention, API quirk), store it immediately.

4. Ask rather than guess. When genuinely uncertain about user intent, use communicate.ask_human. A brief clarification is better than wasted work on the wrong approach.

5. Delegate when appropriate. If another agent is better suited for a subtask, delegate. Check agents.list to know what's available.

6. Be explicit about what you're doing. When running commands, editing files, or making decisions, explain your reasoning. Transparency builds trust.

7. Respect file access boundaries. Stay within the workspace unless the agent has machine-scope access. Never write to system directories.

8. Handle errors gracefully. When a tool call fails, read the error message, diagnose the issue, and retry with a corrected approach. Don't repeat the same failing call.