A3S Code

Rust framework for building agentic AI applications — Embed agents that read, write, and execute code into any application. Tool calling, task planning, safety controls, multi-machine distribution — native Node.js and Python bindings included.

Why A3S Code?

Embeddable — Rust library, not a service. Node.js and Python bindings included. CLI for terminal use.
Safe by Default — Permission system, HITL confirmation, skill-based tool restrictions, and error recovery (parse retries, tool timeout, circuit breaker).
Extensible — 19 trait-based extension points, all with working defaults. Slash commands, tool search, and multi-agent teams.
Scalable — Lane-based priority queue with multi-machine task distribution.

Quick Start

1. Install

# Node.js
npm install @a3s-lab/code

# Python
pip install a3s-code

2. Configure

Create agent.hcl:

default_model = "anthropic/claude-sonnet-4-20250514"

providers {
  name    = "anthropic"
  api_key = env("ANTHROPIC_API_KEY")
}

3. Use

TypeScript

import { Agent, DefaultSecurityProvider } from '@a3s-lab/code';

const agent = await Agent.create('agent.hcl');
const session = agent.session('.', {
  securityProvider: new DefaultSecurityProvider(),
  builtinSkills: true,
  planning: true,
});

const result = await session.send('Refactor auth + update tests');
console.log(result.text);

Python

from a3s_code import Agent, SessionOptions, DefaultSecurityProvider

agent = Agent("agent.hcl")
opts = SessionOptions()
opts.security_provider = DefaultSecurityProvider()
opts.builtin_skills = True
session = agent.session(".", opts, planning=True)

result = session.send("Refactor auth + update tests")
print(result.text)

Core Features

🛠️ Built-in Tools (15)

Category	Tools	Description
File Operations	`read`, `write`, `edit`, `patch`	Read/write files, apply diffs
Search	`grep`, `glob`, `ls`	Search content, find files, list directories
Execution	`bash`	Execute shell commands
Web	`web_fetch`, `web_search`	Fetch URLs, search the web
Git	`git_worktree`	Create/list/remove/status git worktrees for parallel work
Subagents	`task`	Delegate to a named agent; blocks until the child agent replies
Parallel subagents	`parallel_task`	Fan-out to multiple named agents concurrently
Team workflow	`run_team`	Lead → Worker → Reviewer team with dynamic decomposition and quality review
Parallel tools	`batch`	Execute multiple tools concurrently in one call

🔒 Security & Safety

Permission System — Allow/Deny/Ask rules per tool with wildcard matching. Configure via PermissionPolicy (Rust) or SessionOptions.permissions (TypeScript/Python).

Default Security Provider — Auto-redact PII (SSN, API keys, emails, credit cards), prompt injection detection, SHA256 hashing. Enable via securityProvider: new DefaultSecurityProvider() (TypeScript) or opts.security_provider = DefaultSecurityProvider() (Python).

HITL Confirmation — Require human approval for sensitive operations. Configure via ConfirmationManager with ConfirmationPolicy.enabled().

Skill-Based Tool Restrictions — Skills define allowed tools via allowed-tools field, enforced at execution time.

🧠 Skills System (Claude Code Compatible)

7 built-in skills (4 code assistance + 3 tool documentation). Custom skills are Markdown files with YAML frontmatter:

---
name: api-design
description: Review API design for RESTful principles
allowed-tools: "read(*), grep(*)"
kind: instruction
tags: [api, design]
version: 1.0.0
---
# API Design Review
Check for RESTful principles, naming conventions, error handling.

Enable via builtinSkills: true (TypeScript) or opts.builtin_skills = True (Python). Load custom skills with skillsDirs: ['./skills'] or opts.skills_dirs = ['./skills'].

🎯 Planning & Goal Tracking

Decompose complex tasks into dependency-aware execution plans with wave-based parallel execution:

Enable via planning: true (TypeScript) or opts.planning = True (Python). Goal tracking monitors progress across multiple turns with goalTracking: true.

The planner creates steps with dependencies. Independent steps execute in parallel waves via tokio::JoinSet. Goal tracking monitors progress across multiple turns.

🖼️ Multi-Modal Support (Vision)

Send image attachments alongside text prompts. Requires a vision-capable model (Claude Sonnet, GPT-4o).

Supported formats: JPEG, PNG, GIF, WebP. Image data is base64-encoded for both Anthropic and OpenAI providers.

⚡ Slash Commands

Interactive session commands dispatched before the LLM. Custom commands via the SlashCommand trait:

Command	Description
`/help`	List available commands
`/compact`	Manually trigger context compaction
`/cost`	Show token usage and estimated cost
`/model`	Show or switch the current model
`/clear`	Clear conversation history
`/history`	Show conversation turn count and token stats
`/tools`	List registered tools
`/mcp`	List connected MCP servers and their tools
`/loop [interval] <prompt>`	Schedule a recurring prompt (e.g.,`/loop 5m check build`)
`/cron-list`	List all scheduled recurring prompts
`/cron-cancel <id>`	Cancel a scheduled task by ID

Programmatic Scheduler API (SDK)

// TypeScript
const commands = session.listCommands();   // CommandInfo[]

const taskId = session.scheduleTask('check deployment status', 30);   // every 30s
const tasks  = session.listScheduledTasks();   // ScheduledTaskInfo[]
const ok     = session.cancelScheduledTask(taskId);   // boolean

// Also via slash commands:
await session.send('/loop 5m summarize recent commits');
await session.send('/cron-list');
await session.send(`/cron-cancel ${taskId}`);

# Python
commands = session.list_commands()   # list[dict] — name, description, usage
session.register_command("status", "Show session status",
    lambda args, ctx: f"Model: {ctx['model']}, History: {ctx['history_len']} msgs")

task_id = session.schedule_task("check deployment status", 30)   # every 30s
tasks   = session.list_scheduled_tasks()   # list[dict] — id, prompt, interval_secs, fire_count, next_fire_in_secs
ok      = session.cancel_scheduled_task(task_id)   # True

# Also via slash commands:
session.send("/loop 5m summarize recent commits")
session.send("/cron-list")
session.send(f"/cron-cancel {task_id}")

🔍 Tool Search (Per-Turn Filtering)

When the MCP ecosystem grows large (100+ tools), injecting all tool descriptions wastes context. Tool Search selects only relevant tools per-turn based on keyword matching. Configure via AgentConfig::tool_index (Rust) — the agent loop extracts the last user message, searches the index, and only sends matching tools to the LLM. Builtin tools are always included.

When configured via AgentConfig::tool_index, the agent loop extracts the last user message, searches the index, and only sends matching tools to the LLM. Builtin tools are always included.

🔌 MCP Integration

Connect external tool servers via the Model Context Protocol. MCP tools are namespaced as mcp__{server}__{tool} to avoid collisions with built-in tools. Both global (config-level) and per-session servers are merged into a single ToolExecutor — the LLM sees all tools in one flat list.

HCL config (global — loaded at agent startup, connected once):

mcp_servers {
  name    = "filesystem"
  command = "npx"
  args    = ["-y", "@modelcontextprotocol/server-filesystem", "/workspace"]
}

mcp_servers {
  name    = "github"
  command = "npx"
  args    = ["-y", "@modelcontextprotocol/server-github"]
  env     = { GITHUB_TOKEN = env("GITHUB_TOKEN") }
}

Dynamic registration (per-session, runtime — no restart needed):

TypeScript

const session = agent.session('.');

// Connect and inject tools
const count = await session.addMcpServer(
  'filesystem', 'stdio', 'npx',
  ['-y', '@modelcontextprotocol/server-filesystem', '/tmp'],
);

// Status + tools
const status = await session.mcpStatus();   // McpServerStatusEntry[]
const tools = session.toolNames();          // string[]

// Remove
await session.removeMcpServer('filesystem');

// Refresh global cache
await agent.refreshMcpTools();

Python

session = agent.session(".")

# Connect and inject tools
count = session.add_mcp_server(
    "filesystem",
    command="npx",
    args=["-y", "@modelcontextprotocol/server-filesystem", "/tmp"],
)

# Status + tools
status = session.mcp_status()   # {name: {connected, tool_count, error}}
tools  = session.tool_names()   # list[str]

# Remove
session.remove_mcp_server("filesystem")

# Refresh global cache
agent.refresh_mcp_tools()

⚡ Direct Queue Submission (Parallel Tasks)

Inject tasks directly into the lane queue from SDK code — bypassing the LLM turn. Useful for proactively scheduling work or fan-out patterns:

TypeScript

// Single task
const result = await session.submit('execute', { command: 'cargo test' });

// Batch (parallel for query lane)
const results = await session.submitBatch('query', [
  { path: 'src/main.rs' },
  { path: 'src/lib.rs' },
]);

Python

# Single task
result = session.submit("execute", {"command": "cargo test"})

# Batch (parallel for query lane)
results = session.submit_batch("query", [
    {"path": "src/main.rs"},
    {"path": "src/lib.rs"},
])

Lane priority: control (P0) > query (P1) > execute (P2) > generate (P3). Query-lane batches execute in parallel; all other lanes are sequential.

👥 Agent Teams (Multi-Agent Coordination)

Automated Lead → Worker → Reviewer workflows with real LLM execution:

How it works:

Lead decomposes the goal into a JSON task list via LLM
Workers concurrently claim and execute tasks (each via its own AgentSession)
Reviewer inspects completed work — APPROVED moves the task to Done, REJECTED re-queues it for retry
Loop continues until all tasks are Done or max_rounds is reached

TypeScript

import { Agent, Team, TeamRunner, TeamConfig } from '@a3s-lab/code';

const agent = await Agent.create('agent.hcl');

const config: TeamConfig = { maxTasks: 50, channelBuffer: 128, maxRounds: 10, pollIntervalMs: 200 };
const team = new Team('refactor-auth', config);
team.addMember('lead', 'lead');
team.addMember('worker-1', 'worker');
team.addMember('reviewer', 'reviewer');

const runner = new TeamRunner(team);

// Option A: bind pre-built sessions
runner.bindSession('lead',     agent.session('.'));
runner.bindSession('worker-1', agent.session('.'));
runner.bindSession('reviewer', agent.session('.'));

// Option B: bind from Agent + agent definition (auto-loads AgentRegistry from agentDirs)
// runner.bindAgent('lead',     agent, '.', 'lead-agent',    ['./agents']);
// runner.bindAgent('worker-1', agent, '.', 'general',       []);
// runner.bindAgent('reviewer', agent, '.', 'code-reviewer', []);

const result = await runner.runUntilDone('Refactor auth module to use JWT');
console.log(`Done: ${result.doneTasks.length} tasks, ${result.rounds} rounds`);
for (const task of result.doneTasks) {
  console.log(`  [${task.id}] ${task.description}\n  → ${task.result}`);
}

Python

from a3s_code import Agent, Team, TeamRunner, TeamConfig

agent = Agent.create("agent.hcl")

config = TeamConfig(max_rounds=10, poll_interval_ms=200)
team = Team("refactor-auth", config)
team.add_member("lead", "lead")
team.add_member("worker-1", "worker")
team.add_member("reviewer", "reviewer")

runner = TeamRunner(team)

# Option A: bind pre-built sessions
runner.bind_session("lead",     agent.session("."))
runner.bind_session("worker-1", agent.session("."))
runner.bind_session("reviewer", agent.session("."))

# Option B: bind from Agent + agent definition (auto-loads AgentRegistry from agent_dirs)
# runner.bind_agent("lead",     agent, ".", "lead-agent",    ["./agents"])
# runner.bind_agent("worker-1", agent, ".", "general",       [])
# runner.bind_agent("reviewer", agent, ".", "code-reviewer", [])

result = runner.run_until_done("Refactor auth module to use JWT")
print(f"Done: {len(result.done_tasks)} tasks, {result.rounds} rounds")
for task in result.done_tasks:
    print(f"  [{task.id}] {task.description}\n  → {task.result}")

Supports Lead/Worker/Reviewer roles, mpsc peer messaging, broadcast, and a full task lifecycle (Open → InProgress → InReview → Done/Rejected).

run_team built-in tool — The LLM can also trigger the same Lead → Worker → Reviewer workflow autonomously at runtime via the run_team tool (no SDK wiring required):

# Python — call directly from SDK code
result = session.tool("run_team", {
    "goal": "Audit the auth module for security issues and produce a remediation plan",
    "max_steps": 10,  # per-member agent; lead/worker/reviewer all default to "general"
})
print(result.output)

// TypeScript — call directly from SDK code
const result = await session.tool('run_team', {
  goal: 'Audit the auth module for security issues and produce a remediation plan',
  maxSteps: 10,
});
console.log(result.output);

The LLM can also call run_team on its own when the delegate-task skill is loaded — it selects it automatically for goals with an unknown number of subtasks or that require reviewer sign-off.

🎛️ Agent Orchestrator (Master-SubAgent Coordination)

Spawn, monitor, and dynamically control multiple SubAgents from a central coordinator with a real-time event bus. Supports External Lane Dispatch — route individual tool calls to remote workers while the orchestrator coordinates SubAgents in parallel.

Event	When
`SubAgentStarted/Completed`	SubAgent lifecycle
`SubAgentProgress`	Each tool-call step
`ToolExecutionStarted/Completed`	Individual tool lifecycle
`ExternalTaskPending`	Tool waiting for external worker
`ExternalTaskCompleted`	External result delivered, SubAgent unblocked
`ControlSignalReceived/Applied`	Pause / resume / cancel

SDK shorthand — Orchestrator.create() + AgentSlot for simpler multi-agent patterns:

TypeScript

import { Agent, Orchestrator, AgentSlot } from '@a3s-lab/code';

const agent = await Agent.create('agent.hcl');
const orch = Orchestrator.create(agent);

// Spawn a single subagent by slot definition
const slot: AgentSlot = {
  agentType: 'general',
  prompt: 'Summarize the authentication module in 3 bullet points.',
  description: 'Auth summarizer',
  permissive: true,
  maxSteps: 5,
};
const handle = orch.spawn(slot);
const result = handle.wait();
console.log(result.output);

// Or run a full Lead → Worker → Reviewer team via AgentSlot array
const slots: AgentSlot[] = [
  { agentType: 'general', role: 'lead',     prompt: '', description: 'Lead',     permissive: true, maxSteps: 5 },
  { agentType: 'general', role: 'worker',   prompt: '', description: 'Worker',   permissive: true, maxSteps: 5 },
  { agentType: 'general', role: 'reviewer', prompt: '', description: 'Reviewer', permissive: true, maxSteps: 3 },
];
const teamResult = await orch.runTeam(
  'Audit the auth module for common security issues',
  '.',
  slots,
);
console.log(`Done: ${teamResult.doneTasks.length} tasks, ${teamResult.rounds} rounds`);

Python

from a3s_code import Agent, Orchestrator, AgentSlot

agent = Agent.create('agent.hcl')
orch = Orchestrator.create(agent)

# Spawn a single subagent
slot = AgentSlot(
    agent_type='general',
    prompt='Summarize the authentication module in 3 bullet points.',
    description='Auth summarizer',
    permissive=True,
    max_steps=5,
)
handle = orch.spawn(slot)
result = handle.wait()
print(result.output)

# Or run a full Lead → Worker → Reviewer team
slots = [
    AgentSlot(agent_type='general', role='lead',     prompt='', description='Lead',     permissive=True, max_steps=5),
    AgentSlot(agent_type='general', role='worker',   prompt='', description='Worker',   permissive=True, max_steps=5),
    AgentSlot(agent_type='general', role='reviewer', prompt='', description='Reviewer', permissive=True, max_steps=3),
]
result = orch.run_team(
    'Audit the auth module for common security issues',
    '.',
    slots,
)
print(f"Done: {len(result.done_tasks)} tasks, {result.rounds} rounds")

🎨 System Prompt Customization (Slot-Based)

Customize the agent's behavior without overriding the core agentic capabilities. The default prompt (tool usage strategy, agentic behavior, completion criteria) is always preserved:

Slot	Position	Behavior
`role`	Before core	Replaces default "You are A3S Code..." identity
`guidelines`	After core	Appended as `## Guidelines` section
`response_style`	Replaces section	Replaces default `## Response Format`
`extra`	End	Freeform instructions (backward-compatible)

TypeScript

const session = agent.session('.', {
  role: 'You are a senior Rust developer',
  guidelines: 'Use clippy. No unwrap(). Prefer Result.',
  responseStyle: 'Be concise. Use bullet points.',
  extra: 'This project uses tokio and axum.',
});

Python

opts = SessionOptions()
opts.role = "You are a senior Rust developer"
opts.guidelines = "Use clippy. No unwrap(). Prefer Result."
opts.response_style = "Be concise. Use bullet points."
opts.extra = "This project uses tokio and axum."
session = agent.session(".", opts)

💻 CLI (Terminal Agent)

Interactive AI coding agent in the terminal:

# Install
cargo install a3s-code-cli

# Interactive REPL
a3s-code

# One-shot mode
a3s-code "Explain the auth module"

# Custom config
a3s-code -c agent.hcl -m openai/gpt-4o "Fix the tests"

Config auto-discovery: -c flag → A3S_CONFIG env → ~/.a3s/config.hcl → ./agent.hcl

🚦 Lane-Based Priority Queue

Tool execution is routed through a priority queue backed by a3s-lane:

Lane	Priority	Tools	Behavior
Control	P0 (highest)	pause, resume, cancel	Sequential
Query	P1	read, glob, grep, ls, web_fetch, web_search	Parallel
Execute	P2	bash, write, edit, delete	Sequential
Generate	P3 (lowest)	LLM calls	Sequential

Higher-priority tasks preempt queued lower-priority tasks. Configure per-lane concurrency via SessionQueueConfig with query_max_concurrency, execute_max_concurrency, enable_metrics, and more.

Advanced features: retry policies, rate limiting, priority boost, pressure monitoring, DLQ.

🔒 Sandbox Execution (A3S Box Integration)

Route bash commands through an A3S Box MicroVM for isolated execution. No Cargo feature flag required — the host application supplies a concrete BashSandbox implementation via with_sandbox_handle().

Transparent routing — configure once via with_sandbox_handle(Arc::new(my_sandbox_impl)), and the bash tool routes through the sandbox automatically. Any type implementing the BashSandbox trait works. SafeClaw ships an A3S Box–backed implementation; supply your own for other environments.

The workspace directory is mounted at /workspace inside the MicroVM. See BashSandbox for the trait definition.

🌐 Multi-Machine Distribution

Offload tool execution to external workers via three handler modes:

Mode	Behavior
Internal (default)	Execute within agent process
External	Send to external workers, wait for completion
Hybrid	Execute internally + notify external observers

Switch a lane to External mode via session.set_lane_handler(SessionLane::Execute, LaneHandlerConfig { mode: TaskHandlerMode::External, .. }). Workers poll session.pending_external_tasks() and call session.complete_external_task() to return results.

🔌 Extensibility (19 Extension Points)

All policies are replaceable via traits with working defaults:

Extension Point	Purpose	Default
SecurityProvider	Input taint, output sanitization	DefaultSecurityProvider
PermissionChecker	Tool access control	PermissionPolicy
ConfirmationProvider	Human confirmation	ConfirmationManager
ContextProvider	RAG retrieval	FileSystemContextProvider
EmbeddingProvider	Vector embeddings for semantic search	OpenAiEmbeddingProvider
VectorStore	Embedding storage and similarity search	InMemoryVectorStore
SessionStore	Session persistence	FileSessionStore
MemoryStore	Long-term memory backend (from `a3s-memory`)	InMemoryStore
Tool	Custom tools	14 built-in tools
Planner	Task decomposition	LlmPlanner
HookHandler	Event handling	HookEngine
HookExecutor	Event execution	HookEngine
McpTransport	MCP protocol	StdioTransport
HttpClient	HTTP requests	ReqwestClient
SessionCommand	Queue tasks	ToolCommand
LlmClient	LLM interface	Anthropic/OpenAI
BashSandbox	Shell execution isolation	LocalBashExecutor
SkillValidator	Skill activation logic	DefaultSkillValidator
SkillScorer	Skill relevance ranking	DefaultSkillScorer

Implement any trait and inject via SessionOptions builder methods (e.g., with_security_provider, with_permission_checker, with_session_store).

Architecture

5 core components (stable, not replaceable) + 19 extension points (replaceable via traits):

Agent (config-driven)
  ├── CommandRegistry (slash commands: /help, /model, /loop, /cron-list, /cron-cancel, ...)
  │     └── CronScheduler (session-scoped recurring prompts, lazy-start background ticker)
  └── AgentSession (workspace-bound)
        ├── AgentLoop (core execution engine)
        │     ├── ToolExecutor (15 built-in tools, batch parallel execution)
        │     ├── ToolIndex (per-turn tool filtering for large MCP sets)
        │     ├── SystemPromptSlots (role, guidelines, response_style, extra)
        │     ├── Planning (task decomposition + wave execution)
        │     └── HITL Confirmation
        ├── SessionLaneQueue (a3s-lane backed)
        │     ├── Control (P0) → Query (P1) → Execute (P2) → Generate (P3)
        │     └── External Task Distribution
        ├── HookEngine (11 lifecycle events)
        ├── Security (PII redaction, injection detection)
        ├── Skills (instruction injection + tool permissions)
        ├── Context (RAG providers: filesystem, vector)
        └── Memory (AgentMemory: working/short-term/long-term via a3s-memory)

AgentTeam (multi-agent coordination)
  ├── TeamTaskBoard (post → claim → complete → review → approve/reject)
  ├── TeamMember[] (Lead, Worker, Reviewer roles)
  └── mpsc channels (peer-to-peer messaging + broadcast)

TeamRunner (LLM-integrated orchestrator)
  ├── Lead  → decomposes goal into JSON task list
  ├── Workers → concurrently claim + execute tasks via AgentSession
  └── Reviewer → approve (Done) or reject (re-queued for retry)

Configuration

A3S Code uses HCL configuration format exclusively.

Minimal

default_model = "anthropic/claude-sonnet-4-20250514"

providers {
  name    = "anthropic"
  api_key = env("ANTHROPIC_API_KEY")
}

Full

default_model = "anthropic/claude-sonnet-4-20250514"

providers {
  name    = "anthropic"
  api_key = env("ANTHROPIC_API_KEY")
}

providers {
  name    = "openai"
  api_key = env("OPENAI_API_KEY")
}

queue {
  query_max_concurrency   = 10
  execute_max_concurrency = 5
  enable_metrics          = true
  enable_dlq              = true

  retry_policy {
    strategy         = "exponential"
    max_retries      = 3
    initial_delay_ms = 100
  }

  rate_limit {
    limit_type     = "per_second"
    max_operations = 100
  }

  priority_boost {
    strategy    = "standard"
    deadline_ms = 300000
  }

  pressure_threshold = 50
}

search {
  timeout = 30
  engine {
    google { enabled = true, weight = 1.5 }
    bing   { enabled = true, weight = 1.0 }
  }
}

storage_backend = "file"
sessions_dir    = "./sessions"
skill_dirs      = ["./skills"]
agent_dirs      = ["./agents"]
max_tool_rounds = 50
thinking_budget = 10000

API Reference

Python SDK — Agent Teams

from a3s_code import Agent, Team, TeamRunner, TeamConfig, TeamTaskBoard

# Build team
config = TeamConfig(max_tasks=50, max_rounds=10, poll_interval_ms=200)
team = Team("my-team", config)
team.add_member("lead", "lead")       # role: "lead" | "worker" | "reviewer"
team.add_member("worker-1", "worker")
team.add_member("reviewer", "reviewer")

# Bind sessions and run
runner = TeamRunner(team)             # consumes the team
runner.bind_session("lead", agent.session("."))
result = runner.run_until_done("Build the feature")

# Inspect results
result.done_tasks      # List[TeamTask]
result.rejected_tasks  # List[TeamTask]  (did not pass review after max_rounds)
result.rounds          # int

# Direct board access
board = runner.task_board()
board.post("Fix lint", "lead")
board.claim("worker-1")              # → TeamTask | None
board.complete(task_id, "Fixed")
board.approve(task_id)
board.reject(task_id)
tasks = board.by_status("done")      # "open"|"in_progress"|"in_review"|"done"|"rejected"
(open, prog, rev, done, rej) = board.stats()

Python SDK

from a3s_code import Agent, SessionOptions, builtin_skills, DefaultSecurityProvider, FileMemoryStore, FileSessionStore, MemorySessionStore

# Create agent
agent = Agent("agent.hcl")

# Create session
opts = SessionOptions()
opts.model = "anthropic/claude-sonnet-4-20250514"
opts.builtin_skills = True
opts.role = "You are a Python expert"
opts.guidelines = "Follow PEP 8. Use type hints."
session = agent.session(".", opts)

# Send / Stream
result = session.send("Explain auth module")
for event in session.stream("Refactor auth"):
    if event.event_type == "text_delta":
        print(event.text, end="")

# Direct tools
content = session.read_file("src/main.py")
output = session.bash("pytest")
files = session.glob("**/*.py")
matches = session.grep("TODO")
result = session.tool("git_worktree", {"command": "list"})

# Memory
session.remember_success("task", ["tool"], "result")
items = session.recall_similar("auth", 5)

# Slash commands
commands = session.list_commands()   # list[dict] — name, description, usage
session.register_command("status", "Show session status",
    lambda args, ctx: f"Model: {ctx['model']}, History: {ctx['history_len']} msgs")
result = session.send("/status")

# Hooks
session.register_hook("audit", "pre_tool_use", handler_fn)

# Scheduled tasks (programmatic)
task_id = session.schedule_task("check deployment status", 30)   # every 30s
tasks   = session.list_scheduled_tasks()   # list[dict] — id, prompt, interval_secs, fire_count, next_fire_in_secs
ok      = session.cancel_scheduled_task(task_id)   # True
# Also via slash commands:
session.send("/loop 5m summarize recent commits")
session.send("/cron-list")
session.send(f"/cron-cancel {task_id}")

# MCP management
count  = session.add_mcp_server("filesystem", command="npx", args=["-y", "@modelcontextprotocol/server-filesystem", "/tmp"])
status = session.mcp_status()        # {name: {connected, tool_count, error}}
tools  = session.tool_names()        # list[str]
session.remove_mcp_server("filesystem")
agent.refresh_mcp_tools()           # refresh global MCP tool cache

# Queue
stats = session.queue_stats()
dead = session.dead_letters()

# Persistence — set ID + auto-save, then resume later
opts2 = SessionOptions()
opts2.session_store = FileSessionStore('./sessions')
opts2.session_id = 'my-session'
opts2.auto_save = True
session2 = agent.session(".", opts2)
resumed = agent.resume_session('my-session', opts2)

Node.js SDK — Agent Teams

import { Agent, Team, TeamRunner, TeamConfig, TeamTaskBoard } from '@a3s-lab/code';

// Build team
const config: TeamConfig = { maxTasks: 50, channelBuffer: 128, maxRounds: 10, pollIntervalMs: 200 };
const team = new Team('my-team', config);
team.addMember('lead', 'lead');       // role: "lead" | "worker" | "reviewer"
team.addMember('worker-1', 'worker');
team.addMember('reviewer', 'reviewer');

// Bind sessions and run
const runner = new TeamRunner(team);  // consumes the team
runner.bindSession('lead', agent.session('.'));
const result = await runner.runUntilDone('Build the feature');

// Inspect results
result.doneTasks      // TeamTask[]
result.rejectedTasks  // TeamTask[]  (did not pass review after maxRounds)
result.rounds         // number

// Direct board access
const board = runner.taskBoard();
board.post('Fix lint', 'lead');
board.claim('worker-1');             // → TeamTask | null
board.complete(taskId, 'Fixed');
board.approve(taskId);
board.reject(taskId);
const tasks = await board.byStatus('done');  // "open"|"in_progress"|"in_review"|"done"|"rejected"
const stats = board.stats();         // { open, inProgress, inReview, done, rejected, total }

Node.js SDK

import { Agent, DefaultSecurityProvider, FileMemoryStore, FileSessionStore, MemorySessionStore } from '@a3s-lab/code';

// Create agent
const agent = await Agent.create('agent.hcl');

// Create session
const session = agent.session('.', {
  model: 'anthropic/claude-sonnet-4-20250514',
  builtinSkills: true,
  role: 'You are a TypeScript expert',
  guidelines: 'Use strict mode. Prefer interfaces over types.',
});

// Send / Stream
const result = await session.send('Explain auth module');
const stream = await session.stream('Refactor auth');
for await (const event of stream) {
  if (event.type === 'text_delta') process.stdout.write(event.text);
}

// Direct tools
const content = await session.readFile('src/main.ts');
const output = await session.bash('npm test');
const files = await session.glob('**/*.ts');
const matches = await session.grep('TODO');
const result = await session.tool('git_worktree', { command: 'list' });

// Memory
await session.rememberSuccess('task', ['tool'], 'result');
const items = await session.recallSimilar('auth', 5);

// Hooks
session.registerHook('audit', 'pre_tool_use', handlerFn);

// Slash commands
const commands = session.listCommands();   // CommandInfo[]

// Scheduled tasks (programmatic)
const taskId = session.scheduleTask('check deployment status', 30);   // every 30s
const tasks  = session.listScheduledTasks();   // ScheduledTaskInfo[]
const ok     = session.cancelScheduledTask(taskId);   // boolean
// Also via slash commands:
await session.send('/loop 5m summarize recent commits');
await session.send('/cron-list');
await session.send(`/cron-cancel ${taskId}`);

// MCP management
const count  = await session.addMcpServer('filesystem', 'stdio', 'npx', ['-y', '@modelcontextprotocol/server-filesystem', '/tmp']);
const status = await session.mcpStatus();   // McpServerStatusEntry[]
const tools  = session.toolNames();         // string[]
await session.removeMcpServer('filesystem');
await agent.refreshMcpTools();             // refresh global MCP tool cache

// Queue
const stats = await session.queueStats();
const dead = await session.deadLetters();

// Persistence — set ID + auto-save, then resume later
const session2 = agent.session('.', {
  sessionStore: new FileSessionStore('./sessions'),
  sessionId: 'my-session',
  autoSave: true,
});
const resumed = agent.resumeSession('my-session', { sessionStore: new FileSessionStore('./sessions') });

Examples

All examples use real LLM configuration from ~/.a3s/config.hcl or $A3S_CONFIG.

Tutorial Series (Rust)

#	Example	Feature
01	`01_basic_send`	Non-streaming prompt execution
02	`02_streaming`	Real-time AgentEvent stream
03	`03_multi_turn`	Context preservation across turns
04	`04_model_switching`	Provider/model override + temperature
05	`05_planning`	Task decomposition + goal tracking
06	`06_skills_security`	Built-in skills + security provider
07	`07_direct_tools`	Bypass LLM, call tools directly
08	`08_hooks`	Lifecycle event interception
09	`09_queue_lanes`	Priority-based tool scheduling
10	`10_resilience`	Auto-compaction, circuit breaker, parse retries

cargo run --example 01_basic_send
cargo run --example 02_streaming
# ... through 10_resilience

Multi-Language SDKs

Language	File	Coverage
Rust	`core/examples/test_git_worktree.rs`	Git worktree tool: direct calls + LLM-driven
Rust	`core/examples/test_prompt_slots.rs`	Prompt slots: role, guidelines, response style, extra
Python	`sdk/python/examples/agentic_loop_demo.py`	Basic send, streaming, multi-turn, planning, skills, security
Python	`sdk/python/examples/advanced_features_demo.py`	Direct tools, hooks, queue/lanes, security, resilience, memory
Python	`sdk/python/examples/test_git_worktree.py`	Git worktree tool: direct calls + LLM-driven
Python	`sdk/python/examples/test_prompt_slots.py`	Prompt slots: role, guidelines, response style, extra
Python	`sdk/python/examples/test_agent_teams.py`	Multi-agent teams: TeamRunner, Lead/Worker/Reviewer workflow
Python	`sdk/python/examples/test_agent_slot_kimi.py`	AgentSlot + Orchestrator.create(): spawn() and wait()
Python	`sdk/python/examples/test_run_team_kimi.py`	Orchestrator.run_team() with AgentSlot array (Lead/Worker/Reviewer)
Python	`sdk/python/examples/test_run_team_tool.py`	`run_team` built-in tool via session.tool() + LLM-driven
Python	`sdk/python/examples/test_run_team_tool_kimi.py`	`run_team` tool smoke test with external LLM
Node.js	`sdk/node/examples/agentic_loop_demo.js`	Basic send, streaming, multi-turn, planning, skills, security
Node.js	`sdk/node/examples/advanced_features_demo.js`	Direct tools, hooks, queue/lanes, security, resilience, memory
Node.js	`sdk/node/examples/test_git_worktree.js`	Git worktree tool: direct calls + LLM-driven
Node.js	`sdk/node/examples/test_prompt_slots.js`	Prompt slots: role, guidelines, response style, extra
Node.js	`sdk/node/examples/test_agent_teams.js`	Multi-agent teams: TeamRunner, Lead/Worker/Reviewer workflow
Node.js	`sdk/node/examples/test_agent_slot_kimi.ts`	AgentSlot + Orchestrator.create(): spawn() and wait()
Node.js	`sdk/node/examples/test_run_team_kimi.ts`	Orchestrator.runTeam() with AgentSlot array (Lead/Worker/Reviewer)
Node.js	`sdk/node/examples/test_run_team_tool.ts`	`run_team` built-in tool via session.tool() + LLM-driven
Node.js	`sdk/node/examples/test_run_team_tool_kimi.ts`	`run_team` tool smoke test with external LLM
Python	`sdk/python/examples/test_loop_commands.py`	Slash commands: /loop, /cron-list, /cron-cancel, list_commands, register_command, schedule_task
Node.js	`sdk/node/examples/test_loop_commands.ts`	Slash commands: /loop, /cron-list, /cron-cancel, listCommands, scheduleTask

Integration & Feature Tests

integration_tests — Complete feature test suite
test_task_priority — Lane-based priority preemption with real LLM
test_external_task_handler — Multi-machine coordinator/worker pattern
test_lane_features — A3S Lane v0.4.0 advanced features
test_builtin_skills — Built-in skills demonstration
test_custom_skills_agents — Custom skills and agent definitions
test_search_config — Web search configuration
test_auto_compact — Context window auto-compaction
test_security — Default and custom SecurityProvider
test_batch_tool — Parallel tool execution via batch
test_vector_rag — Semantic code search with filesystem context
test_hooks — Lifecycle hook handlers (audit, block, transform)
test_parallel_processing — Concurrent multi-session workloads
test_git_worktree — Git worktree tool: create, list, remove, status + LLM-driven
test_prompt_slots — System prompt slots: role, guidelines, response style, extra + tool verification

Testing

cargo test          # All tests
cargo test --lib    # Unit tests only

Test Coverage: 1500 tests, 100% pass rate

Contributing

Follow Rust API guidelines
Write tests for all new code
Use cargo fmt and cargo clippy
Update documentation
Use Conventional Commits

Community

Join us on Discord for questions, discussions, and updates.

License

MIT License - see LICENSE

Star History

Related Projects

A3S Lane — Priority-based task queue with DLQ
A3S Search — Multi-engine web search aggregator
A3S Box — Secure sandbox runtime with TEE support
A3S Event — Event-driven architecture primitives

Built by A3S Lab | Documentation | Discord

Name		Name	Last commit message	Last commit date
Latest commit History 418 Commits
.github		.github
core		core
sdk		sdk
.gitattributes		.gitattributes
.gitignore		.gitignore
Cargo.lock		Cargo.lock
Cargo.toml		Cargo.toml
LICENSE		LICENSE
README.md		README.md
RELEASE_CHECKLIST.md		RELEASE_CHECKLIST.md
agent.example.hcl		agent.example.hcl
check-version.sh		check-version.sh
justfile		justfile
release.sh		release.sh

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A3S Code

Why A3S Code?

Quick Start

1. Install

2. Configure

3. Use

Core Features

🛠️ Built-in Tools (15)

🔒 Security & Safety

🧠 Skills System (Claude Code Compatible)

🎯 Planning & Goal Tracking

🖼️ Multi-Modal Support (Vision)

⚡ Slash Commands

Programmatic Scheduler API (SDK)

🔍 Tool Search (Per-Turn Filtering)

🔌 MCP Integration

⚡ Direct Queue Submission (Parallel Tasks)

👥 Agent Teams (Multi-Agent Coordination)

🎛️ Agent Orchestrator (Master-SubAgent Coordination)

🎨 System Prompt Customization (Slot-Based)

💻 CLI (Terminal Agent)

🚦 Lane-Based Priority Queue

🔒 Sandbox Execution (A3S Box Integration)

🌐 Multi-Machine Distribution

🔌 Extensibility (19 Extension Points)

Architecture

Configuration

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Full

API Reference

Python SDK — Agent Teams

Python SDK

Node.js SDK — Agent Teams

Node.js SDK

Examples

Tutorial Series (Rust)

Multi-Language SDKs

Integration & Feature Tests

Testing

Contributing

Community

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