agentao 0.2.7

An Eastern-philosophy-inspired CLI agent harness for personal research and data analysis assistance.

Agentao (Agent + Tao)

   ___                      _
  / _ \ ___ _ ___  ___  ___| |_  ___  ___
 /  _  // _` / -_)| _ \/ _ \  _|/ _` / _ \
/_/ |_| \__, \___||_// \___/\__|\__,_\___/
        |___/        (The Way of Agents)

"Order in Chaos, Path in Intelligence."

Agentao is the running path of the intelligent agent — an Agent Harness inspired by Eastern philosophy, combining rigorous governance with fluid orchestration.

"Tao" (道) represents the underlying Laws, Methods, and Paths that govern all things. In Agentao, it is the invisible structure that keeps autonomous agents safe, connected, and observable.

A powerful CLI agent harness with tools, skills, and MCP support. Built with Python and designed to work with any OpenAI-compatible API.

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Why Agentao?

Most agent frameworks give you power. Agentao gives you power with discipline.

The name itself encodes the design: Agent (capability) + Tao (governance). Every feature is built around three pillars of the Harness Philosophy:

Pillar	What it means	How Agentao implements it
Constraint (约束)	Agents must not act without consent	Tool Confirmation — shell, web, and destructive ops pause for human approval
Connectivity (连接)	Agents must reach the world beyond their training	MCP Protocol — seamlessly connects to any external service via stdio or SSE
Observability (可观测性)	Agents must show their work	Live Thinking display + Complete Logging — every reasoning step and tool call is visible

One-liner demo — try it right after install:

# Ask Agentao to analyze the current directory
agentao -p "List all Python files here and summarize what each one does"

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Core Capabilities

🏛️ Autonomous Governance (自治治理)

A disciplined agent that acts deliberately, not impulsively:

• Multi-turn conversations with persistent context
• Function calling for structured tool usage
• Smart tool selection and execution
• Tool confirmation — user approval required for Shell, Web, and destructive Memory operations
• Reliability principles — system prompt enforces read-before-assert, discrepancy reporting, and fact/inference distinction on every turn
• Operational guidelines — tone & style rules, shell command efficiency patterns, tool parallelism, non-interactive flags, and explain-before-act security rules
• Auto-loading of project instructions from AGENTAO.md at startup
• Current date context — injected as <system-reminder> into each user message rather than the system prompt, keeping the system prompt stable across turns for prompt cache efficiency
• Live thinking display — shows LLM reasoning and tool calls in real time with Rule separators
• Streaming shell output — shell command stdout displayed in real-time as it executes
• Complete logging of all LLM interactions to agentao.log
• Multi-line paste support — paste multi-line text as one unit (prompt_toolkit native; Alt+Enter for manual newline, Enter to submit)
• Slash command Tab completion — type / and press Tab for an autocomplete menu

🧠 Elastic Context Engine (弹性上下文引擎)

Agentao automatically manages long conversations to stay within LLM context limits:

• Three-tier token counting — (1) real prompt_tokens from provider API response after every LLM call; (2) provider count_tokens API (extension point); (3) local estimator: tiktoken for GPT-4/Claude/DeepSeek families, CJK-aware character scan (ASCII = 0.25 tok/char, non-ASCII = 1.3 tok/char, adapted from gemini-cli) as fallback. Install tiktoken with uv sync --extra tokenizer
• Token breakdown — /status shows context split by component: system prompt / conversation messages / tools schema, plus session-total prompt and completion tokens
• Two-tier compression — at 55% usage, microcompaction cheaply truncates old oversized tool results (no LLM call); at 65% usage, full LLM summarization kicks in and replaces early messages with a structured [Conversation Summary] block
• Structured 9-section summary — LLM summarization produces a detailed summary covering: intent, key concepts, files touched, errors & fixes, problem solving, user messages, pending tasks, current work, and next step — preserving full continuity
• Partial compaction — keeps the most recent 20 messages verbatim; split point advances to the next user boundary so tool call sequences are never split mid-flight
• Boundary marker + file hints — compressed history is preceded by a [Compact Boundary] header and a list of recently-read files so the agent knows where to re-read for details
• Pinned messages — messages starting with [PIN] are always kept verbatim and never summarized
• Circuit breaker — after 3 consecutive compression failures, auto-compact is disabled to prevent infinite retry loops (/context shows failure count)
• Tool result truncation — tool outputs larger than 80K characters (~20K tokens) are truncated before being added to messages
• Auto-save summaries — compression summaries are saved to memory with tag conversation_summary for future reference
• Graceful degradation — if compression fails, the original messages are preserved unchanged
• Three-tier overflow recovery — if the API returns a context-too-long error: (1) force-compress and retry; (2) if still too long, keep only the last 2 messages and retry; (3) only surfaces an error to the user if all three tiers fail

Default context limit is 200K tokens. Override with AGENTAO_CONTEXT_TOKENS environment variable.

💾 SQLite Memory (持久记忆)

A SQLite-backed persistent memory system that automatically surfaces relevant context — no vector database required.

Storage: Two SQLite databases, auto-created on first run:

Database	Path	Scope
Project store	.agentao/memory.db	Per-project memories + session summaries
User store	~/.agentao/memory.db	Cross-project user preferences

Three data types:

Type	Where stored	Lifetime
Persistent memories	memories table (soft-delete)	Until explicitly deleted
Session summaries	session_summaries table	Current session; cleared on /memory clear
Recall candidates	In-memory only (never stored)	Single turn

Injection strategy (per-turn, two blocks):

1. <memory-stable> — persistent memories up to a character budget. Summaries from previous sessions are also appended here (pre-reserved so they are never crowded out by fact entries), giving the LLM cross-session continuity after a restart.

2. <memory-context> — top-k recall candidates scored against the current user message using a keyword / Jaccard / tag / recency formula; injected dynamically so the stable prefix stays cache-friendly.

Retrieval performance & CJK quality: the retriever maintains a write_version-gated inverted index (token → record IDs) so each recall call scores only the records that share at least one query token, avoiding a full scan as the memory store grows. CJK text is segmented with jieba word segmentation rather than character bigrams — "版本管理" tokenizes to {"版本", "管理"} instead of {"版本", "本管", "管理"}, eliminating the noise bigrams that polluted ranking. Single-character CJK tokens are filtered out (mirroring the Latin len > 1 rule). Add domain-specific terms — project names, technical jargon, proper nouns — to ~/.agentao/userdict.txt and jieba will pick them up on first recall.

Session summary channels: the current session's summary lives only in self.messages as a [Conversation Summary] block (injecting it into the system prompt too would duplicate context). Previous sessions' summaries have no message-history channel after a restart, so they flow through <memory-stable> instead.

Save a memory:

❯ Remember that this project uses uv for package management
❯ Save my preferred language as Python

Skill Crystallization: /crystallize suggest reads the current session transcript, asks the LLM to identify the most repeatable workflow, and displays a draft SKILL.md. /crystallize create [name] writes it to skills/ (global or project scope) and reloads skills immediately.

💡 Semantic Display Engine

The terminal display provides clean, low-noise tool execution output using Rich formatting:

→ read  src/agent.py             ← fast + silent: header only, no footer

$ pytest tests/ -q
  ...........
  … +42 lines
✓ $ pytest tests/ -q  3.1s      ← slow (≥2s): footer shown

← edit  src/agent.py
  --- a/agent.py
  +++ b/agent.py
  @@ -12,3 +12,4 @@
  -old line
  +new line
✓ edit  src/agent.py  12ms      ← diff shown: footer shown

$ pandoc doc.md -o doc.pdf
  [warning] Missing character: 'X'
  … +14 similar warnings         ← consolidated warnings
✓ $ pandoc …  1.8s

• Semantic tool headers — each tool call renders with a meaningful icon and argument preview: → read ← edit $ shell ✱ search ↗ fetch ◈ remember
• Buffered output — all output (including shell) is buffered and shown at completion; prevents screen flooding and handles \r progress bars, ANSI codes, and \r\n line endings correctly
• Tail-biased truncation — long output shows the last 8 lines with a … +N lines fold indicator; errors and results near the tail are always visible
• Expand / collapse — shell commands show buffered output; read/search/memory tools collapse silently; errors on collapsed tools surface the tail retroactively
• Diff rendering — replace shows a colored unified diff; write_file shows a syntax-highlighted content preview (first 16 lines, lexer auto-detected from extension)
• Tool aggregation — parallel tools in the same LLM turn shown with + header prefix to signal batching
• Live elapsed timer — spinner updates to tool 0.8s for tools running longer than 0.5 s
• Conditional completion footer — shown only when there is output, a diff, an error, or the tool takes ≥ 2 s; fast/silent tools display a single header line only: ✓ read 32ms / ✗ run_shell_command 1.2s Permission denied
• Warning consolidation — consecutive similar warnings in shell output are collapsed to a single summary: … +N similar warnings
• Sub-agent lifecycle — foreground sub-agents wrapped with cyan ▶/◀ rule separators; stats shown on completion
• Thinking display — LLM reasoning shown in dim italic style under a separator
• Structured reasoning — before each set of tool calls the agent prints its Action, Expectation, and If wrong plan — a falsifiable prediction you can verify against the actual tool result

✅ Session Task Tracking

For multi-step tasks, Agentao maintains a live task checklist that the LLM updates as it works:

/todos

Task List (2/4 completed):

  ✓ Read existing code           completed
  ✓ Design new module structure  completed
  ◉ Write new module             in_progress
  ○ Run tests                    pending

• LLM-managed — the agent calls todo_write at the start of complex tasks and updates statuses as each step completes (pending → in_progress → completed)
• Always visible — current task list is injected into the system prompt so the LLM always knows its own progress
• Session-scoped — cleared automatically on /clear or /new; not persisted to disk (unlike memory)
• /status summary — shows Task list: 2/4 completed when tasks are active

🤖 SubAgent System

Agentao can delegate tasks to independent sub-agents, each running its own LLM loop with scoped tools and turn limits. Inspired by Gemini CLI's "agent as tool" pattern.

Built-in agents:

• codebase-investigator — read-only codebase exploration (find files, search patterns, analyze structure)
• generalist — general-purpose agent with access to all tools for complex multi-step tasks

Two trigger paths:

1. LLM-driven — the parent LLM decides to delegate via agent_codebase_investigator / agent_generalist tools; supports optional run_in_background=true for async fire-and-forget
2. User-driven — use /agent <name> <task> to run a sub-agent directly, /agent bg <name> <task> for background, /agents to view the live dashboard

Visual framing — foreground sub-agents are wrapped with cyan rule separators so their output is clearly distinct from the main agent:

──────────── ▶ [generalist]: task description ────────────
  ⚙ [generalist 1/20] read_file (src/main.py)
  ⚙ [generalist 2/20] run_shell_command (pytest)
──────── ◀ [generalist] 3 turns · 8 tool calls · ~4,200 tokens · 12s ────

Confirmation isolation:

• Foreground sub-agents: confirmation dialog shows [agent_name] tool_name so you know which sub-agent is requesting permission
• Background sub-agents: all tools auto-approved (no interactive prompts from background threads, which would corrupt the terminal)

Cancellation propagation — pressing Ctrl+C cleanly stops the current agent and any foreground sub-agent in progress (they share the same CancellationToken). Background agents are unaffected — they run to completion independently.

Background completion push — when a background agent finishes, the parent LLM is automatically notified at the start of the next turn via a <system-reminder> message, without needing to poll check_background_agent.

Parent context injection — sub-agents receive the last 10 parent messages as context so they understand the broader task.

Custom agents: create .agentao/agents/my-agent.md with YAML frontmatter (name, description, tools, max_turns) — auto-discovered at startup.

🔌 MCP (Model Context Protocol) Support

Connect to external MCP tool servers to dynamically extend the agent's capabilities. Agentao acts as the central hub connecting your LLM brain to the outside world:

graph LR
  User((User)) -- CLI --> Agentao[Agentao Harness]
  Agentao -- MCP --> Filesystem[Filesystem Server]
  Agentao -- MCP --> GitHub[GitHub Server]
  Agentao -- MCP --> Custom[Your Custom Server]
  Agentao -- LLM API --> Brain[OpenAI / Gemini / DeepSeek]

• Stdio transport — spawn a local subprocess (e.g. npx @modelcontextprotocol/server-filesystem)
• SSE transport — connect to remote HTTP/SSE endpoints
• Auto-discovery — tools are discovered on startup and registered as mcp_{server}_{tool}
• Confirmation — MCP tools require user confirmation unless the server is marked "trust": true
• Env var expansion — $VAR and ${VAR} syntax in config values
• Two-level config — project .agentao/mcp.json overrides global ~/.agentao/mcp.json

🎯 Dynamic Skills System

Skills are auto-discovered from the skills/ directory. Each subdirectory contains a SKILL.md file with YAML frontmatter. Skills are listed in the system prompt and can be activated with the activate_skill tool.

Add new skills by creating a directory with a SKILL.md file — no code changes needed. Or use Skill Crystallization to generate one from your current session: /crystallize suggest drafts a skill from the session transcript; /crystallize create [name] writes it and reloads skills immediately.

🛠️ Comprehensive Tools

File Operations:

• read_file - Read file contents
• write_file - Write content to files (requires confirmation)
• replace - Edit files by replacing text
• list_directory - List directory contents

Search & Discovery:

• glob - Find files matching patterns (supports ** for recursive search)
• search_file_content - Search text in files with regex support

Shell & Web:

• run_shell_command - Execute shell commands (requires confirmation)
• web_fetch - Fetch and extract content from URLs (requires confirmation); uses Crawl4AI for clean Markdown output if installed, otherwise falls back to plain text extraction
• google_web_search - Search the web via DuckDuckGo (requires confirmation)

Task Tracking:

• todo_write - Update the session task checklist (pending → in_progress → completed); use /todos to view

Agents & Skills:

• agent_codebase_investigator - Delegate read-only codebase exploration to a sub-agent (supports run_in_background)
• agent_generalist - Delegate complex multi-step tasks to a sub-agent (supports run_in_background)
• check_background_agent - Poll the status of a background sub-agent by ID; pass empty string to list all
• activate_skill - Activate specialized skills for specific tasks
• ask_user - Pause and ask the user a clarifying question mid-task

MCP Tools:

• Dynamically discovered from connected MCP servers
• Named mcp_{server}_{tool} (e.g. mcp_filesystem_read_file)
• Require confirmation unless server is trusted

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Design Principles

Agentao is built around three foundational principles:

1. Minimalism (极简) — Zero friction to start. pip install agentao and you're running. No databases, no complex config, no cloud dependencies.

2. Transparency (透明) — No black boxes. The agent's reasoning chain is displayed in real time. Every LLM request, tool call, and token count is logged to agentao.log. You always know what the agent is doing and why.

3. Integrity (完整) — Context is never silently lost. Conversation history is compressed with LLM summarization (not truncated blindly), and memory recall ensures relevant context resurfaces automatically. The agent maintains a coherent world-model across sessions.

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Installation

Prerequisites

• Python 3.10 or higher
• An API key (OpenAI, Anthropic, Gemini, DeepSeek, or any OpenAI-compatible provider)

Install

pip install agentao

Then create a .env file with your API key:

echo "OPENAI_API_KEY=your-api-key-here" > .env

For contributors (source install)

git clone https://github.com/jin-bo/agentao
cd agentao
uv sync
cp .env.example .env

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Minimum Viable Configuration

Everything you need to get Agentao running from scratch.

Supported Python versions

Version	Status
3.10	✅ supported
3.11	✅ supported
3.12	✅ supported
< 3.10	❌ not supported

Verify before installing:

python --version   # must be 3.10 or higher

Required environment variable

Only one variable is mandatory to start Agentao:

Variable	Required	Example
OPENAI_API_KEY	Yes (default provider)	sk-...

All other variables are optional. The absolute minimum .env:

OPENAI_API_KEY=sk-your-key-here

Create it in the directory where you run agentao:

echo "OPENAI_API_KEY=sk-your-key-here" > .env

Note: Agentao loads .env from the current working directory, then falls back to ~/.env. No system-level setup is needed.

Default provider behavior

When no provider is explicitly configured, Agentao uses these defaults:

Setting	Default	Override with
Provider	OPENAI	LLM_PROVIDER=ANTHROPIC
API key	$OPENAI_API_KEY	$<PROVIDER>_API_KEY
Model	gpt-5.4	OPENAI_MODEL=gpt-4o
Base URL	OpenAI public API	OPENAI_BASE_URL=https://...
Temperature	0.2	LLM_TEMPERATURE=0.7

Each provider reads its own <NAME>_API_KEY, <NAME>_BASE_URL, and <NAME>_MODEL:

# Use Anthropic Claude instead of OpenAI
LLM_PROVIDER=ANTHROPIC
ANTHROPIC_API_KEY=sk-ant-...
ANTHROPIC_MODEL=claude-sonnet-4-6
ANTHROPIC_BASE_URL=https://api.anthropic.com/v1

Minimal runnable example

pip install agentao
echo "OPENAI_API_KEY=sk-your-key-here" > .env

# Verify it works without a UI (exits after one response)
agentao -p "Reply with the single word: OK"

Expected output:

OK

If that works, start the interactive session:

agentao

Troubleshooting common startup failures

Symptom	Likely cause	Fix
AuthenticationError	Missing or invalid API key	Ensure OPENAI_API_KEY is set correctly in .env
NotFoundError: model not found	Model name doesn't match provider	Set OPENAI_MODEL=gpt-4o (or the correct model for your provider)
APIConnectionError	Network / firewall / proxy issue	Check your internet connection; set OPENAI_BASE_URL if behind a proxy
command not found: agentao	CLI not on PATH	Confirm install succeeded; add ~/.local/bin (Linux/Mac) or Scripts\ (Windows) to $PATH
Starts but gives wrong-provider errors	LLM_PROVIDER mismatch	Make sure LLM_PROVIDER matches the key you provided (e.g. LLM_PROVIDER=OPENAI with OPENAI_API_KEY)
ModuleNotFoundError on startup	Incomplete install	Re-run pip install agentao; check Python version ≥ 3.10
.env not loaded	File in wrong directory	Run agentao from the directory containing .env, or place it in ~/.env

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Configuration

Edit .env with your settings:

# Required: Your API key
OPENAI_API_KEY=your-api-key-here

# Optional: Base URL for OpenAI-compatible APIs
# OPENAI_BASE_URL=https://api.openai.com/v1

# Optional: Model name
# OPENAI_MODEL=gpt-4-turbo-preview

# Optional: Context window limit in tokens (default: 200000)
# AGENTAO_CONTEXT_TOKENS=200000

# Optional: Maximum tokens the LLM may generate per response (default: 65536)
# LLM_MAX_TOKENS=65536

# Optional: LLM sampling temperature (default: 0.2)
# LLM_TEMPERATURE=0.2

MCP Server Configuration

Create .agentao/mcp.json in your project (or ~/.agentao/mcp.json for global servers):

{
  "mcpServers": {
    "filesystem": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-filesystem", "/path/to/dir"],
      "trust": true
    },
    "github": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-github"],
      "env": { "GITHUB_TOKEN": "$GITHUB_TOKEN" }
    },
    "remote-api": {
      "url": "https://api.example.com/sse",
      "headers": { "Authorization": "Bearer $API_KEY" },
      "timeout": 30
    }
  }
}

Field	Description
command	Executable for stdio transport
args	Command-line arguments
env	Extra environment variables (supports $VAR / ${VAR} expansion)
cwd	Working directory for subprocess
url	SSE endpoint URL
headers	HTTP headers for SSE transport
timeout	Connection timeout in seconds (default: 60)
trust	Skip confirmation for this server's tools (default: false)

MCP servers connect automatically on startup. Use /mcp list to check status.

Using with Different Providers

Agentao supports switching between providers at runtime with /provider. Add credentials for each provider to your .env (or ~/.env) using the naming convention <NAME>_API_KEY, <NAME>_BASE_URL, and <NAME>_MODEL:

# OpenAI (default)
OPENAI_API_KEY=sk-...
OPENAI_MODEL=gpt-4-turbo-preview

# Gemini
GEMINI_API_KEY=...
GEMINI_BASE_URL=https://generativelanguage.googleapis.com/v1beta/openai/
GEMINI_MODEL=gemini-2.0-flash

# DeepSeek
DEEPSEEK_API_KEY=...
DEEPSEEK_BASE_URL=https://api.deepseek.com/v1
DEEPSEEK_MODEL=deepseek-chat

Then switch at runtime:

/provider           # list detected providers
/provider GEMINI    # switch to Gemini
/model              # see available models on the new endpoint

The /provider command detects any *_API_KEY entry already loaded into the environment, so it works with ~/.env and system environment variables — not just a local .env file.

---

Usage

Starting the Agent

agentao

Non-Interactive (Print) Mode

Use -p / --print to send a single prompt, get a plain-text response on stdout, and exit — no UI, no confirmations. Useful for scripting and pipes.

# Basic usage
agentao -p "What is 2+2?"

# Read from stdin
echo "Summarize this: hello world" | agentao -p

# Combine -p argument with stdin (both are joined and sent as one prompt)
echo "Some context" | agentao -p "Summarize the stdin"

# Pipe output to a file
agentao -p "List 3 prime numbers" > output.txt

# Use in a pipeline
agentao -p "Translate to French: Good morning" | pbcopy

In print mode all tools are auto-confirmed (no interactive prompts). The exit code is 0 on success and 1 on error.

Headless / SDK Use

Embed Agentao in your own Python code with no terminal UI:

from agentao import Agentao
from agentao.transport import SdkTransport

events = []
transport = SdkTransport(
    on_event=events.append,           # receive typed AgentEvents
    confirm_tool=lambda n, d, a: True,  # auto-approve all tools
)
agent = Agentao(transport=transport)
response = agent.chat("Summarize the current directory")

SdkTransport accepts four optional callbacks: on_event, confirm_tool, ask_user, on_max_iterations. Omit any you don't need — unset ones fall back to safe defaults (auto-approve, sentinel for ask_user, stop on max iterations).

For fully silent headless use with no callbacks, just Agentao() — it uses NullTransport automatically.

ACP (Agent Client Protocol) Mode

Launch Agentao as an ACP stdio JSON-RPC server so ACP-compatible clients (e.g. Zed) can drive Agentao as their agent runtime:

agentao --acp --stdio
# or, when the console script isn't on PATH:
python -m agentao --acp --stdio

The server reads newline-delimited JSON-RPC 2.0 messages on stdin, writes responses and session/update notifications on stdout, and routes logs (and any stray print) to stderr. Press Ctrl-D or close stdin to shut down cleanly.

Supported methods: initialize, session/new, session/prompt, session/cancel, session/load. Tool confirmations are surfaced via server→client session/request_permission requests with allow_once / allow_always / reject_once / reject_always options. Per-session cwd and mcpServers injection are supported; multi-session isolation (cancel/permission/messages) is enforced.

v1 limits: stdio transport only; text and resource_link content blocks only (image/audio/embedded resource are rejected with INVALID_PARAMS); MCP servers limited to stdio + sse (http capability is false); ACP-level fs/* and terminal/* host capabilities are not proxied — files and shell commands run locally in the session's cwd.

See docs/ACP.md for the full launch flow, supported method table, capability advertisement, annotated NDJSON transcript, event mapping reference, troubleshooting, and contributor notes.

Commands

All commands start with /. Type / and press Tab for autocomplete.

Command	Description
/help	Show help message
/clear	Save current session, clear conversation history and all memories, start fresh
/new	Alias for /clear
/status	Show message count, model, active skills, memory count, context usage
/model	Fetch and list available models from the configured API endpoint
/model <name>	Switch to specified model (e.g., /model gpt-4)
/provider	List available providers (detected from *_API_KEY env vars)
/provider <NAME>	Switch to a different provider (e.g., /provider GEMINI)
/skills	List available and active skills
/memory	List all saved memories
/memory user	Show user-scope memories (~/.agentao/memory.db)
/memory project	Show project-scope memories (.agentao/memory.db)
/memory session	Show current session summary (from session_summaries table)
/memory status	Show memory counts, session size, and recall hit count
/memory search <query>	Search memories (searches keys, tags, and values)
/memory tag <tag>	Filter memories by tag
/memory delete <key>	Delete a specific memory
/memory clear	Clear all memories (with confirmation)
/crystallize suggest	Analyze session transcript and draft a reusable skill
/crystallize create [name]	Write the skill draft to skills/ (prompts for name and scope)
/mcp	List MCP servers with status and tool counts
/mcp add <name> <cmd|url>	Add an MCP server to project config
/mcp remove <name>	Remove an MCP server from project config
/context	Show current context window usage (tokens and %)
/context limit <n>	Set context window limit (e.g., /context limit 100000)
/agent	List available sub-agents
/agent list	Same as /agent
/agent <name> <task>	Run a sub-agent in foreground (with ▶/◀ visual boundary)
/agent bg <name> <task>	Run a sub-agent in background (returns agent ID immediately)
/agent dashboard	Live auto-refreshing dashboard of all background agents
/agent status	Show all background agent tasks (status, elapsed, stats)
/agent status <id>	Show full result or error for a specific background agent
/agents	Shorthand for /agent dashboard
/mode	Show current permission mode
/mode read-only	Block all write and shell tools
/mode workspace-write	Allow file writes and safe read-only shell; ask for web (default)
/mode full-access	Allow all tools without prompting
/plan	Enter plan mode (LLM researches and drafts a plan; no mutations allowed)
/plan show	Display the saved plan file
/plan implement	Exit plan mode, restore prior permissions, display saved plan
/plan clear	Archive and clear the current plan; exit plan mode
/plan history	List recently archived plans
/copy	Copy last agent response to clipboard (Markdown)
/sessions	List saved sessions
/sessions resume <id>	Resume a saved session
/sessions delete <id>	Delete a specific session
/sessions delete all	Delete all saved sessions (with confirmation)
/todos	Show the current session task list with status icons
/tools	List all registered tools with descriptions
/tools <name>	Show parameter schema for a specific tool
/exit or /quit	Exit the program

Permission Modes (Safety Feature)

Agentao controls which tools execute automatically versus which require user confirmation via three named permission modes. Switch with /mode — the choice is persisted to .agentao/settings.json and restored on the next launch.

Mode	Behavior
workspace-write	Default. File writes (write_file, replace) and safe read-only shell commands (git status/log/diff, ls, grep, cat, etc.) execute automatically. Web access (web_fetch, google_web_search) asks. Unknown shell commands ask. Dangerous patterns (rm -rf, sudo) are blocked.
read-only	All write and shell tools are blocked. Only read-only tools (read_file, glob, grep, etc.) are permitted.
full-access	All tools execute without prompting. Useful for trusted, fully automated workflows.

/mode                   (show current mode)
/mode workspace-write   (default — file writes + safe shell allowed)
/mode read-only         (block all writes and shell)
/mode full-access       (allow everything)

Tools that still ask in workspace-write mode:

• web_fetch / google_web_search — network access
• run_shell_command — when the command doesn't match the safe-prefix allowlist
• mcp_* — MCP server tools (unless server has "trust": true)

During a confirmation prompt, if you press 2 (Yes to all) the session escalates to full-access mode in memory — no prompts for the rest of the session, but the saved mode is unchanged so the next launch uses whatever /mode you set last.

Plan Mode

Plan mode is a dedicated workflow for complex tasks where you want the LLM to research and draft a plan first, then execute only after you approve.

/plan                   (enter plan mode — prompt turns [plan])
"Plan how to refactor the logging module"
                        (agent reads files, calls plan_save → gets draft_id)
                        (agent calls plan_finalize(draft_id) when ready)
                        "Execute this plan? [y/N]"
y                       (exit plan mode, restore permissions, agent implements)

What plan mode enforces:

• File writes (write_file, replace) are denied
• Memory writes (save_memory, todo_write) are denied
• Non-allowlisted shell commands are denied (no accidental side effects)
• Safe read-only shell commands (git diff, ls, cat, grep, etc.) are allowed
• Web access (web_fetch, google_web_search) asks as usual
• Skill activation is allowed (skills only modify the system prompt)

Model tools — plan_save(content) and plan_finalize(draft_id) are available to the agent in plan mode. The agent calls plan_save to save a draft and receives a draft_id. It must pass that exact draft_id to plan_finalize to trigger the approval prompt — ensuring you approve the exact draft you reviewed.

Plan mode preset takes precedence over any custom permissions.json rules — a workspace allow for write_file cannot bypass plan mode restrictions.

Workflow:

1. /plan — enter plan mode; prompt indicator turns [plan] (magenta)
2. Ask the agent to plan something — it reads files and writes a structured plan
3. Agent calls plan_save to persist the draft; the approval prompt only appears after plan_finalize
4. Press y at the "Execute?" prompt to implement, or n to continue refining
5. /plan implement — manually exit plan mode and restore prior permissions
6. /plan clear — delete the plan file and exit plan mode

Notes:

• Prior permission mode is saved and restored exactly on /plan implement
• /mode is blocked while in plan mode (use /plan implement to exit first)
• /clear resets plan mode automatically

Confirmation menu keys (no Enter needed):

• 1 — Yes, execute once
• 2 — Yes to all for this session (escalates to full-access in memory)
• 3 or Esc — Cancel

Example Interactions

Reading and analyzing files:

❯ Read the file main.py and explain what it does
❯ Search for all Python files in this directory
❯ Find all TODO comments in the codebase

Working with code:

❯ Create a new Python file called utils.py with helper functions
❯ Replace the old function in utils.py with an improved version
❯ Run the tests using pytest

Web and search:

❯ Fetch the content from https://example.com
❯ Search for Python best practices

Memory:

❯ Remember that I prefer tabs over spaces for indentation
❯ Save this API endpoint URL for future use
❯ What do you remember about my preferences?
/memory status              (see entry counts, session size, recall hits)
/memory user                (browse profile-scope memories)
/memory project             (browse project-scope memories)

Skill crystallization:

/crystallize suggest        (draft a skill from the current session)
/crystallize create         (write the skill to skills/ and reload)

Context management:

❯ /context                     (check current token usage)
❯ /context limit 100000        (set a lower context limit)
❯ /status                      (see memory count and context %)

Using agents:

❯ Analyze the project structure and find all API endpoints
     (LLM may auto-delegate to codebase-investigator)
/agent codebase-investigator find all TODO comments in this project
/agent generalist refactor the logging module to use structured output

/agent bg generalist run the full test suite and summarize failures
/agents                        (live dashboard — auto-refreshes while agents run)
/agent status a1b2c3d4         (get full result of a specific background agent)

Using MCP tools:

/mcp list                   (check connected servers and tools)
/mcp add fs npx -y @modelcontextprotocol/server-filesystem /tmp
❯ List all files in the project     (LLM may use MCP filesystem tools)

Task tracking:

❯ Refactor the logging module to use structured output
     (LLM creates a task list, updates statuses as it works)
/todos                          (view current task list at any time)
/status                         (shows "Task list: 3/5 completed")

Using skills:

❯ Activate the pdf skill to help me merge PDF files
❯ Use the xlsx skill to analyze this spreadsheet

Planning before implementing:

/plan
"Plan how to add a /foo command to the CLI"
                        (agent reads files, calls plan_save, then plan_finalize)
                        "Execute this plan? [y/N]" → y
                        (exits plan mode, agent implements)
/plan implement         (manual exit if you pressed n)
/plan show              (view saved plan at any time)
/plan clear             (discard plan and exit plan mode)

Copying output:

/copy                           (copy last response to clipboard as Markdown)

Inspecting tools:

/tools                          (list all registered tools)
/tools run_shell_command        (show parameter schema)
/tools web_fetch                (check what args it accepts)

---

Project Instructions (AGENTAO.md)

Agentao automatically loads project-specific instructions from AGENTAO.md if it exists in the current working directory. This is the most powerful customization feature — it injects your instructions at the top of the system prompt, making them higher-priority than any built-in agent guidelines.

Use AGENTAO.md to define:

• Code style and conventions
• Project structure and patterns
• Development workflows and testing approaches
• Common commands and best practices
• Reliability rules (e.g. require the agent to cite file and line number when making factual claims)

If the file doesn't exist, the agent works normally with its default instructions. Think of it as a per-project .cursorrules or CLAUDE.md — a lightweight way to give the agent deep project context without touching any code.

---

Project Structure

agentao/
├── main.py                  # Entry point
├── pyproject.toml           # Project configuration
├── .env                     # Configuration (create from .env.example)
├── .env.example             # Configuration template
├── AGENTAO.md             # Project-specific agent instructions
├── README.md                # This file
├── tests/                   # Test files
│   ├── test_context_manager.py      # ContextManager tests (24 tests, mock LLM)
│   ├── test_memory_management.py    # Memory tool tests
│   ├── test_reliability_prompt.py   # Reliability principles in system prompt (6 tests)
│   ├── test_transport.py            # Transport protocol tests (26 tests)
│   └── test_*.py                    # Other feature tests
├── docs/                    # Documentation
│   ├── features/            # Feature documentation
│   └── updates/             # Update logs
└── agentao/
    ├── agent.py             # Core orchestration
    ├── cli.py               # CLI interface (Rich)
    ├── context_manager.py   # Context window management + Agentic RAG
    ├── transport/           # Transport protocol (decouple runtime from UI)
    │   ├── events.py        # AgentEvent + EventType
    │   ├── null.py          # NullTransport (headless / silent)
    │   ├── sdk.py           # SdkTransport + build_compat_transport
    │   └── base.py          # Transport Protocol definition
    ├── llm/
    │   └── client.py        # OpenAI-compatible LLM client
    ├── agents/
    │   ├── __init__.py      # SubAgent exports
    │   ├── manager.py       # AgentManager — loads definitions, creates wrappers
    │   ├── tools.py         # TaskComplete, CompleteTaskTool, AgentToolWrapper
    │   └── definitions/     # Built-in agent definitions (.md with YAML frontmatter)
    │       ├── codebase-investigator.md
    │       └── generalist.md
    ├── mcp/
    │   ├── __init__.py      # MCP package exports
    │   ├── config.py        # Config loading + env var expansion
    │   ├── client.py        # McpClient + McpClientManager
    │   └── tool.py          # McpTool wrapper for Tool interface
    ├── memory/
    │   ├── __init__.py      # Exports MemoryManager, MemoryRetriever, SkillCrystallizer
    │   ├── models.py        # MemoryEntry, IndexEntry, RetrievalHit dataclasses + constants
    │   ├── manager.py       # SQLite memory manager: persistent memories, session summaries, recall
    │   ├── retriever.py     # Index-based dynamic recall (tokenize, score, recall, format)
    │   └── crystallizer.py  # Skill Crystallization (suggest prompt + SKILL.md writer)
    ├── tools/
    │   ├── base.py          # Tool base class + registry
    │   ├── file_ops.py      # Read, write, edit, list
    │   ├── search.py        # Glob, grep
    │   ├── shell.py         # Shell execution
    │   ├── web.py           # Fetch, search
    │   ├── memory.py        # Persistent memory tools (save, search, delete, clear, filter, list)
    │   ├── skill.py         # Skill activation
    │   ├── ask_user.py      # Mid-task user clarification
    │   └── todo.py          # Session task checklist
    └── skills/
        └── manager.py       # Skill loading and management

---

Testing

# Run all tests (requires source checkout)
python -m pytest tests/ -v

# Run specific test files
python -m pytest tests/test_context_manager.py -v
python -m pytest tests/test_memory_management.py -v

Tests use unittest.mock.Mock for the LLM client — no real API calls required.

---

Logging

All LLM interactions are logged to agentao.log:

tail -f agentao.log    # Real-time monitoring
grep "ERROR" agentao.log

Logged data includes: full message content, tool calls with arguments, tool results, token usage, and timestamps.

---

Development

Adding a Tool

1. Create a tool class in agentao/tools/:

from .base import Tool

class MyTool(Tool):
    @property
    def name(self) -> str:
        return "my_tool"

    @property
    def description(self) -> str:
        return "Description for LLM"

    @property
    def parameters(self) -> Dict[str, Any]:
        return {
            "type": "object",
            "properties": {
                "param": {"type": "string", "description": "..."}
            },
            "required": ["param"],
        }

    @property
    def requires_confirmation(self) -> bool:
        return False  # Set True for dangerous operations

    def execute(self, param: str) -> str:
        return f"Result: {param}"

2. Register in agent.py::_register_tools():

tools_to_register.append(MyTool())

Adding an Agent

Create a Markdown file with YAML frontmatter. Built-in agents go in agentao/agents/definitions/, project-level agents go in .agentao/agents/.

---
name: my-agent
description: "When to use this agent (shown to LLM for delegation decisions)"
tools:                    # optional — omit for all tools
  - read_file
  - search_file_content
  - run_shell_command
max_turns: 10             # optional, default 15
---
You are a specialized agent. Instructions for the sub-agent go here.
When finished, call complete_task to return your result.

Restart Agentao — agents are auto-discovered and registered as agent_my_agent tools.

Adding a Skill

Option A: manually

1. Create skills/my-skill/SKILL.md:

---
name: my-skill
description: Use when... (trigger conditions for LLM)
---

# My Skill

Documentation here...

2. Restart Agentao — skills are auto-discovered.

Option B: crystallize from a session

/crystallize suggest   (LLM drafts a skill from the current session transcript)
/crystallize create    (prompts for name + scope, writes SKILL.md, reloads immediately)

Skills created with /crystallize create are written to .agentao/skills/ (project scope) or ~/.agentao/skills/ (global scope) and are available immediately without restarting.

---

Troubleshooting

Model List Not Loading: /model queries the live API endpoint. If it fails (invalid key, unreachable endpoint, no models endpoint), a clear error is shown. Verify your OPENAI_API_KEY and OPENAI_BASE_URL settings.

Provider List Empty: /provider scans the environment for *_API_KEY entries. Make sure your credentials are in ~/.env or exported into the shell — a local .env in the project directory is not required.

API Key Issues: Verify .env exists and contains a valid key with correct permissions.

Context Too Long Errors: Agentao handles these automatically with three-tier recovery (compress → minimal history → error). Common causes: very large tool results (e.g. reading huge files) or extremely long conversations. If errors persist, lower the limit with /context limit <n> or AGENTAO_CONTEXT_TOKENS.

Memory Not Appearing in Responses: Check /memory status — verify entries exist and recall hit count is incrementing. The retriever scores entries against your query using keyword overlap and recency; if your query doesn't share tokens with any entry's title, tags, keywords, or content, nothing will be recalled. Try rephrasing or use /memory user / /memory project to inspect entries directly. Note that the stable block always includes user-scope entries and structural project types (decision, constraint, workflow, preference, profile) regardless of the query — only project_fact and note entries depend on the per-turn recall scoring (with the 3 most-recently-updated also surfaced unconditionally).

MCP Server Not Connecting: Run /mcp list to see status and error messages. Verify the command exists and is executable, or that the SSE URL is reachable. Check agentao.log for detailed connection errors.

Tool Execution Errors: Check file permissions, path correctness, and that shell commands are valid for your OS.

---

Etymology

Agentao = Agent + Tao (道)

道 (Tao/Dào) is a foundational concept in Chinese philosophy, representing the natural order that underlies all things. It carries three intertwined meanings:

• Laws (法则) — the rules that constrain and shape behavior
• Methods (方法) — the paths and techniques for accomplishing goals
• Paths (路径) — the routes through which things flow and connect

In the context of this project, Tao captures what an Agent Harness should be: not just a raw capability engine, but a disciplined path through which intelligent agents can act safely, transparently, and purposefully. An agent without Tao is powerful but unpredictable. Agentao is the structure that makes that power trustworthy.

---

License

This project is open source. Feel free to use and modify as needed.

Acknowledgments

• Built with OpenAI Python SDK
• CLI interface powered by Rich
• Input handling powered by prompt_toolkit
• Optional enhanced web fetching via Crawl4AI
• MCP support via Model Context Protocol SDK
• MCP architecture inspired by Gemini CLI
• Inspired by Claude Code