agnoclaw 0.7.0

A hackable, model-agnostic agent harness built on Agno — with Claude Code's prompt wisdom, OpenClaw's UX patterns, and full Python extensibility

agnoclaw

A hackable, model-agnostic agent harness built on Agno.

Distills the best ideas from Claude Code's system prompt architecture, OpenClaw's UX patterns (heartbeat, workspace, SKILL.md), and LangChain DeepAgents' middleware insights — running them on Agno's production-ready, model-agnostic engine. Built to be owned, forked, and extended.

---

Why agnoclaw?

	Claude Code harness	OpenClaw	agnoclaw
Model support	Anthropic only	Any	Any (30+ via Agno)
Embeddable as library	No (CLI app)	No (standalone app)	Yes (pip install agnoclaw)
Hackable agent loop	No	No	Yes (Agno pre/post hooks, guardrails)
Multi-agent	Task tool only	No	Native (coordinate, route, broadcast)
SKILL.md system	Yes	Yes	Yes (compatible with both)
Heartbeat + Cron	No	Yes	Yes (interval strings + cron expressions)
Workspace	CLAUDE.md only	Full workspace	Full workspace
Self-improving	No	Community skill	Bundled self-improving-agent skill
Atomic multi-edit	Yes (MultiEdit)	No	Yes (multi_edit_file)
Service install	No	Yes (launchd/systemd)	Yes (install-service)
Python-native	No (TypeScript)	No	Yes
Textual TUI	No	No	Yes (streaming, notifications, skill picker)
Async REPL	No	No	Yes (heartbeat alerts while typing)
Production patterns	N/A	N/A	HITL, streaming, tracing, eval

TL;DR: Claude Code is a consumer product. OpenClaw is a standalone app. agnoclaw is a library — embed it in anything, hack it for everything.

HarnessAgent is available as a backward-compatible alias for AgentHarness.

---

Installation

# Core library only (for embedding — zero CLI/TUI deps)
pip install agnoclaw

# With CLI (async REPL, heartbeat notifications)
pip install "agnoclaw[cli]"

# With full Textual TUI
pip install "agnoclaw[tui]"

# Personal-assistant setup (TUI + cron)
pip install "agnoclaw[full]"

# With local Ollama support (no API key needed)
pip install "agnoclaw[local]"

# With Postgres support
pip install "agnoclaw[postgres]"

# With all model providers
pip install "agnoclaw[all-models]"

# With uv (recommended)
uv add agnoclaw
uv add "agnoclaw[tui]"   # for TUI

---

Quick Start

from agnoclaw import AgentHarness

agent = AgentHarness()
agent.print_response("Summarize the files in this directory", stream=True)

With a different model — "provider:model_id" format

agent = AgentHarness("anthropic:claude-sonnet-4-6")
agent = AgentHarness("openai:gpt-4o")
agent = AgentHarness("google:gemini-2.0-flash")
agent = AgentHarness("groq:llama-3.3-70b-versatile")
agent = AgentHarness("ollama:qwen3:8b")   # local, no API key

The model string "provider:model_id" is parsed natively by Agno — no separate provider= needed. Legacy model_id= + provider= kwargs still work.

Try it now (zero config)

from agnoclaw import AgentHarness

# Works out of the box with any Agno-supported model
agent = AgentHarness("ollama:qwen3:8b")  # local, no API key needed
agent.print_response("What files are in this directory?")

With a skill

agent.print_response(
    "Research the state of fusion energy in 2026",
    skill="deep-research",
)

Use a SkillHub / community skill

Skills from ClawHub or any AgentSkills-compatible repo work out of the box — just drop the skill directory in your skills folder:

# Install a community skill (e.g. from SkillHub)
git clone https://github.com/clawhub/skills-collection /tmp/skills-collection
cp -r /tmp/skills-collection/summarize-pr ~/.agnoclaw/skills/summarize-pr

from agnoclaw import AgentHarness

agent = AgentHarness()
agent.print_response(
    "Summarize PR #42 in the agno-agi/agno repo",
    skill="summarize-pr",
)

Or register a skills directory programmatically:

agent = AgentHarness()
agent.skills.add_directory("/path/to/clawhub-skills", trust="community")
agent.print_response("Run the summarize-pr skill", skill="summarize-pr")

Multi-agent team

from agnoclaw.teams import research_team, code_team

team = research_team()
team.print_response("Compare the top AI agent frameworks in 2026", stream=True)

Local inference with Ollama (no API key)

agent = AgentHarness("ollama:qwen3:8b")
agent.print_response("Explain async/await in Python", stream=True)

Async + streaming events

import asyncio
from agno.run.agent import RunEvent

async def main():
    agent = AgentHarness()
    async for event in agent.arun("Analyze this codebase", stream=True, stream_events=True):
        if event.event == RunEvent.run_content:
            print(event.content, end="", flush=True)

asyncio.run(main())

---

Tools

agnoclaw ships a Claude Code-compatible tool set. Key tools:

Tool	Method	Notes
Read	read_file()	Line offset/limit support
Write	write_file()	Creates parent dirs
Edit	edit_file()	Unique string replacement
MultiEdit	multi_edit_file()	Atomic multi-replacement — validates all before applying
Glob	glob_files()	Pattern matching, sorted by mtime
Grep	grep_files()	Regex search with context lines
LS	list_dir()	Directory listing with sizes
Bash	bash()	Shell execution with timeout
WebSearch	web_search()	Auto-detects best backend: Tavily → Exa → Brave → DDGS
WebFetch	web_fetch()	URL fetch + HTML-to-text extraction
TodoWrite/Read	create_todo() / list_todos()	Task management
Task	spawn_subagent()	Subagent spawning

For the unified Claude Code + OpenClaw gap status (implemented/remaining roadmap), see docs/harness-gap-analysis.md.

---

Sandboxed Backends

The built-in workspace tool family is now backend-swappable. agnoclaw still owns prompts, sessions, hooks, policy, permissions, and guardrails, but you can replace the runtime plane behind:

• bash / bash_start / bash_output / bash_kill
• read_file / write_file / edit_file / multi_edit_file / glob_files / grep_files / list_dir

Inject custom backends with AgentHarness(command_executor=..., workspace_adapter=...) or get_default_tools(...). The same backend pair is also propagated into built-in subagents and team presets so tool behavior stays coherent across nested runs.

from agnoclaw import AgentHarness

agent = AgentHarness(
    workspace_dir="/srv/workspace",
    command_executor=my_sandbox_executor,
    workspace_adapter=my_sandbox_workspace,
)

More detail: docs/embedding/workspace-backends.md

---

CLI

# First-run onboarding wizard (persona, user identity, default model)
agnoclaw init

# Interactive chat (async REPL with heartbeat notifications)
agnoclaw chat

# Legacy blocking REPL
agnoclaw chat --sync

# Full Textual TUI (requires agnoclaw[tui])
agnoclaw tui

# One-shot task
agnoclaw run "Find and fix the bug in src/auth.py"

# With model override
agnoclaw run "Summarize the README" --model gpt-4o --provider openai

# With Ollama (local, no API key)
agnoclaw run "Summarize the README" --model qwen3:8b --provider ollama

# With skill
agnoclaw run "Research quantum computing trends" --skill deep-research

# List available skills
agnoclaw skill list

# Inspect a skill
agnoclaw skill inspect deep-research

# Install a skill from a local directory
agnoclaw skill install path/to/my-skill/

# Initialize workspace (non-interactive)
agnoclaw workspace init

# Show workspace files
agnoclaw workspace show

# Start heartbeat daemon (runs until Ctrl+C)
agnoclaw heartbeat start

# Start with custom interval
agnoclaw heartbeat start --interval 15

# Trigger one heartbeat check immediately
agnoclaw heartbeat trigger

# Install as a persistent service (starts on login, survives terminal close)
# macOS → launchd LaunchAgent; Linux → systemd user service
agnoclaw heartbeat install-service

# Uninstall the service
agnoclaw heartbeat install-service --uninstall

---

Workspace

The workspace is your agent's home directory. Default: ~/.agnoclaw/workspace/

~/.agnoclaw/workspace/
├── AGENTS.md       ← behavioral guidelines
├── SOUL.md         ← persona and tone
├── IDENTITY.md     ← capabilities, limitations, specializations
├── USER.md         ← user preferences (create this yourself)
├── MEMORY.md       ← long-term memory (agent-maintained, first 200 lines loaded)
├── TOOLS.md        ← tool policy (allowed commands, restrictions)
├── HEARTBEAT.md    ← heartbeat checklist
├── BOOT.md         ← startup sequence (run on session start)
├── skills/         ← workspace-level skill overrides (highest priority)
└── memory/         ← daily session logs (YYYY-MM-DD.md)

All workspace files are plain Markdown — grep-able, git-backup-friendly, fully transparent.

Populate them with agnoclaw init (interactive wizard) or write them directly.

Example SOUL.md

# Soul

You are a direct, technically precise assistant.
You prefer Python. You work in the ML/AI space.
Never use emojis. Keep responses concise.

Example USER.md

# User

Name: Alice
Timezone: US/Pacific
Preferred language: Python (not JavaScript)
Communication style: Direct, no fluff
Current focus: Building ML infrastructure

Example HEARTBEAT.md

# Heartbeat Checklist

- Check for unreviewed GitHub PRs
- Alert if disk usage > 80%
- Remind about daily standup if it's before 10am

If nothing needs attention, reply HEARTBEAT_OK.

---

Skills

Skills extend the agent with domain-specific instructions. Compatible with the AgentSkills standard and the Claude Code / OpenClaw SKILL.md format.

Built-in skills

Skill	Description
deep-research	Multi-source research with structured findings
code-review	P0/P1/P2/P3 priority code review
git-workflow	Safe git operations with guardrails
daily-standup	Generate standup from git history and todos
memory-manage	Read/update/summarize long-term memory
self-improving-agent	Record corrections/errors/feature-requests in .learnings/; promote stable patterns to workspace files

Using skills

# Via API
agent.print_response("Review src/auth.py", skill="code-review")

# Via CLI
agnoclaw run "Review src/auth.py" --skill code-review

# Via chat slash command
/skill code-review
Review the authentication module.

Writing a skill

Create ~/.agnoclaw/workspace/skills/my-skill/SKILL.md:

---
name: my-skill
description: What this skill does and when to use it
user-invocable: true
allowed-tools: bash, web_search
argument-hint: "[optional arg hint]"
---

# My Skill

Instructions for the agent when this skill is active.

Use $ARGUMENTS to access what the user passed.
Use !`git status` to inject dynamic context.

Skill precedence (highest → lowest):

1. ~/.agnoclaw/workspace/skills/ — workspace overrides
2. ~/.agnoclaw/skills/ — user-level skills
3. Built-in skills (shipped with agnoclaw)

Skill security

Skills are classified by trust level based on their source:

Level	Source	Inline exec (!cmd``)	Install specs
builtin	Shipped with agnoclaw	Allowed	Auto-approved
local	Workspace or ~/.agnoclaw/skills/	Allowed	User approval required
community	External (ClawHub, git clone)	Blocked	User approval + validation

Package names in install specs are validated against shell metacharacters, URL-based installs, and path traversal before any command runs. See docs/skills.md for details.

---

Heartbeat and Cron

The heartbeat runs the agent on a schedule to surface items needing attention. Cron jobs extend this with precise expression-based scheduling — inspired by OpenClaw's CronManager.

Heartbeat runs on the main agent's session (full workspace context). Cron jobs can run in the main session or in an isolated session (fresh, no prior context).

import asyncio
from agnoclaw import AgentHarness
from agnoclaw.heartbeat import HeartbeatDaemon, CronJob

agent = AgentHarness()

def on_alert(message: str):
    # Send to Slack, email, desktop notification, etc.
    print(f"ALERT: {message}")

daemon = HeartbeatDaemon(agent, on_alert=on_alert)

# Add a cron job: daily standup at 9am Mon-Fri (isolated session)
daemon.add_cron_job(CronJob(
    name="daily-standup",
    schedule="0 9 * * 1-5",         # cron expression
    prompt="Run the daily standup.",
    skill="daily-standup",
    isolated=True,                   # fresh session, no conversation history
))

# Or use interval syntax: "30m", "1h", "2h30m"
daemon.add_cron_job(CronJob(
    name="disk-check",
    schedule="1h",
    prompt="Check if disk usage exceeds 80% and alert if so.",
))

asyncio.run(daemon.run_forever())  # blocks until Ctrl+C

For always-on operation (survives terminal close), install as a system service:

# macOS → launchd LaunchAgent; Linux → systemd user service
agnoclaw heartbeat install-service --interval 30

Configuration (.agnoclaw.toml or env vars):

[heartbeat]
enabled = true
interval_minutes = 30
active_hours_start = "08:00"
active_hours_end = "22:00"
model = "claude-haiku-4-5-20251001"  # cheaper model for heartbeat
ok_threshold_chars = 300

---

Configuration

Priority order (highest → lowest):

1. Environment variables (AGNOCLAW_*)
2. ~/.agnoclaw/config.toml (user-level)
3. .agnoclaw.toml in current directory (project-level)
4. Defaults

Example .agnoclaw.toml

See .agnoclaw.toml.example for the full annotated template with all options documented.

Quick start — copy and customize:

cp .agnoclaw.toml.example .agnoclaw.toml
# or for user-level config:
cp .agnoclaw.toml.example ~/.agnoclaw/config.toml

Minimal config (most fields have sensible defaults):

default_model = "claude-sonnet-4-6"
default_provider = "anthropic"

[storage]
backend = "sqlite"

[heartbeat]
enabled = false

Key env vars

# Model
AGNOCLAW_DEFAULT_MODEL=claude-sonnet-4-6
AGNOCLAW_DEFAULT_PROVIDER=anthropic

# Storage
AGNOCLAW_STORAGE__BACKEND=postgres
AGNOCLAW_STORAGE__POSTGRES_URL=postgresql://user:pass@localhost/agnoclaw

# Learning + context
AGNOCLAW_ENABLE_LEARNING=true
AGNOCLAW_LEARNING_MODE=agentic
AGNOCLAW_ENABLE_COMPRESSION=true
AGNOCLAW_ENABLE_SESSION_SUMMARY=true

# Heartbeat
AGNOCLAW_HB_ENABLED=true
AGNOCLAW_HB_INTERVAL_MINUTES=30
AGNOCLAW_HB_MODEL=claude-haiku-4-5-20251001

# Runtime governance
AGNOCLAW_PERMISSION_MODE=default
AGNOCLAW_GUARDRAILS_ENABLED=true

# TUI
AGNOCLAW_THEME=textual-dark

# Debug
AGNOCLAW_DEBUG=true

---

Advanced: Custom tools and extensions

from agno.tools import tool
from agnoclaw import AgentHarness

@tool(description="Query our internal API")
def query_internal_api(endpoint: str, params: dict = {}) -> str:
    """Query the internal analytics API."""
    # your implementation
    return "{...}"

agent = AgentHarness(tools=[query_internal_api])

Custom system prompt section

from agnoclaw import AgentHarness
from agnoclaw.prompts import SystemPromptBuilder

agent = AgentHarness()
agent._prompt_builder.add_section("""
# Company Context

You are assisting engineers at Acme Corp.
Internal Jira: jira.acme.internal
Internal docs: confluence.acme.internal
Follow the Acme style guide for all code changes.
""")

Using Agno's HITL approval

from agno.tools import tool
from agnoclaw import AgentHarness

@tool(requires_confirmation=True)
def deploy_to_production(service: str, version: str) -> str:
    """Deploy a service to production — requires human approval."""
    # deployment logic
    return f"Deployed {service}:{version}"

agent = AgentHarness(tools=[deploy_to_production])
response = agent.run("Deploy the auth service version 2.1.0 to production")

# Check if paused for approval
if response.active_requirements:
    for req in response.active_requirements:
        print(f"Approve: {req.tool_execution.tool_name}({req.tool_execution.tool_args})")
        if input("y/n: ") == "y":
            req.confirm()
        else:
            req.reject()

    # Resume after approval (use _agent directly for Agno continue_run)
    final = agent._agent.continue_run(
        run_id=response.run_id,
        requirements=response.requirements,
    )

---

Multi-agent patterns

Research → Code pipeline

from agnoclaw.teams import research_team, code_team

# Phase 1: Research the solution space
research = research_team()
findings = research.run("What's the best approach for rate limiting in FastAPI in 2026?")

# Phase 2: Implement the chosen approach
code = code_team()
code.print_response(
    f"Implement rate limiting in FastAPI based on these findings:\n{findings.content}",
    stream=True,
)

Custom team

from agno.agent import Agent
from agno.team import Team, TeamMode
from agno.models.anthropic import Claude
from agnoclaw.tools import FilesToolkit, WebToolkit

security_reviewer = Agent(
    name="SecurityReviewer",
    role="Review code for OWASP Top 10 vulnerabilities and security anti-patterns",
    model=Claude(id="claude-sonnet-4-6"),
    tools=[FilesToolkit()],
)

perf_reviewer = Agent(
    name="PerfReviewer",
    role="Review code for performance issues: N+1 queries, memory leaks, unnecessary O(n²)",
    model=Claude(id="claude-sonnet-4-6"),
    tools=[FilesToolkit()],
)

review_team = Team(
    name="Review Team",
    mode=TeamMode.broadcast,  # both review in parallel
    members=[security_reviewer, perf_reviewer],
    model=Claude(id="claude-sonnet-4-6"),
)

review_team.print_response("Review src/database.py", stream=True)

---

Multi-session project tracking (ProgressToolkit)

For complex projects that span multiple sessions or context windows:

import json
from agnoclaw.tools.tasks import ProgressToolkit

toolkit = ProgressToolkit(project_dir=".")

# Define requirements upfront (all start as failing)
toolkit.write_features(json.dumps([
    {"id": "auth-01", "description": "Users can register"},
    {"id": "api-01",  "description": "GET /users/{id} returns profile"},
]))

# Mark features passing as you implement them
toolkit.update_feature_status("auth-01", "passing")

# Save progress before a session ends / context compacts
toolkit.write_progress(
    summary="Auth complete. Starting API layer.",
    next_steps="1. Implement GET /users/{id}\n2. Write tests",
    context="JWT secret in JWT_SECRET env var",
)

# Resume at the start of the next session
print(toolkit.read_progress())
print(toolkit.read_features())

---

Context management for long-running sessions

AgentHarness includes built-in support for managing context in long-running or multi-session agents. These features use Agno's native context management layer.

Tool result compression

For sessions with many tool calls (file reads, bash output, web fetches), tool results can dominate the context window. Enable compression to automatically summarize older tool outputs:

agent = AgentHarness(
    enable_compression=True,
    compress_token_limit=4000,  # compress when tool results exceed this
)

Session summaries (cross-session continuity)

Session summaries capture the key decisions and state at the end of each run. On the next run, the summary is injected into context so the agent picks up where it left off:

agent = AgentHarness(enable_session_summary=True)

# First session
agent.run("Implement user auth with JWT")

# Later session — the agent automatically gets the previous summary
agent.run("Continue: add refresh tokens")

Memory hierarchy

Layer	What	Scope	Storage
Workspace files	MEMORY.md, AGENTS.md, SOUL.md	Per-workspace	Markdown files
LearningMachine (per-user)	User profile + observations	Per-user	SQL
LearningMachine (institutional)	Patterns, entities, decisions	Cross-user	SQL

All SQL-backed memory goes through Agno's LearningMachine — a unified system that handles both per-user facts and institutional knowledge in a single pass.

# Full memory stack
agent = AgentHarness(
    user_id="alice",
    enable_user_memory=True,       # per-user profile + observations
    enable_learning=True,          # institutional knowledge
    learning_mode="agentic",       # agent decides when to learn
    enable_compression=True,       # keep context window manageable
    enable_session_summary=True,   # continuity across sessions
)

Workspace bootstrap limits

Workspace files are capped to prevent context bloat:

• MEMORY.md: first 200 lines only (keep it as an index)
• Per file: 20,000 chars max
• Total: 150,000 chars max across all workspace files

Environment variables

AGNOCLAW_ENABLE_COMPRESSION=true
AGNOCLAW_COMPRESS_TOKEN_LIMIT=4000
AGNOCLAW_ENABLE_SESSION_SUMMARY=true
AGNOCLAW_ENABLE_LEARNING=true
AGNOCLAW_LEARNING_MODE=agentic

---

Architecture

agnoclaw/
├── src/agnoclaw/
│   ├── agent.py           # AgentHarness — main class (HarnessAgent = alias)
│   ├── workspace.py       # Workspace (~/.agnoclaw/workspace/)
│   ├── memory.py          # Memory management utilities
│   ├── config.py          # Settings (TOML + env vars)
│   ├── teams.py           # Pre-built team configurations
│   ├── prompts/
│   │   ├── sections.py    # Prompt sections (identity, tone, tasks, tools, security, git)
│   │   └── system.py      # System prompt assembler
│   ├── tools/
│   │   ├── bash.py        # Shell execution
│   │   ├── files.py       # Read/Write/Edit/MultiEdit/Glob/Grep/LS
│   │   ├── web.py         # WebSearch/WebFetch
│   │   └── tasks.py       # TodoToolkit + ProgressToolkit + SubagentTool
│   ├── skills/
│   │   ├── loader.py      # SKILL.md parser (AgentSkills + OpenClaw frontmatter)
│   │   └── registry.py    # Discovery + selective injection + gate checks
│   ├── heartbeat/
│   │   └── daemon.py      # HeartbeatDaemon + CronJob (interval + cron expressions)
│   ├── runtime/           # v0.2 runtime contracts
│   │   ├── hooks.py       # PreRunHook, PostRunHook
│   │   ├── policy.py      # PolicyEngine, PolicyDecision
│   │   ├── events.py      # EventSink (observability)
│   │   ├── guardrails.py  # Input/output guardrails
│   │   └── permissions.py # Permission modes (bypass, accept_edits, plan)
│   ├── cli/
│   │   ├── main.py        # Click CLI (init, chat, run, tui, skill, heartbeat, workspace)
│   │   └── async_repl.py  # Async REPL with heartbeat notifications
│   └── tui/               # v0.3 Textual TUI (requires agnoclaw[tui])
│       ├── app.py         # AgnoClawApp — main Textual application
│       ├── driver.py      # AgentDriver — async streaming + heartbeat bridge
│       ├── events.py      # Custom Textual Messages
│       ├── screens.py     # Modal screens (skill picker, help)
│       └── widgets/       # ChatLog, InputBar, NotificationPanel, StatusBar, HeaderBar
├── skills/                # Built-in skills
│   ├── deep-research/
│   ├── code-review/
│   ├── git-workflow/
│   ├── daily-standup/
│   ├── memory-manage/
│   └── self-improving-agent/  # Capture corrections/errors → .learnings/
├── docs/
│   └── harness-gap-analysis.md # Unified Claude Code + OpenClaw harness gap status
└── examples/              # 20 runnable examples
    ├── ollama_local.py    # Local inference (no API key)
    ├── openclaw_style.py  # Full OpenClaw-style setup
    ├── openclaw_skills.py # Skill hub creation and usage
    ├── progress_tracking.py   # ProgressToolkit lifecycle
    ├── cron_jobs.py           # CronJob scheduler + service install
    ├── self_improving_agent.py # .learnings/ capture + promotion pattern
    ├── claude_code_tools.py   # CC gap analysis + MultiEdit demo
    └── ...

---

v0.2 Runtime Contracts

AgentHarness v0.2 adds a runtime governance layer between your code and the LLM. Every run() / arun() call passes through this pipeline:

User message
  → PreRunHooks (transform input)
  → PolicyEngine.before_run (allow/deny/redact)
  → PolicyEngine.before_skill_load (gate skills)
  → PolicyEngine.before_prompt_send (inspect/redact prompts)
  → Model call (Agno Agent)
  → PostRunHooks (transform output)
  → EventSink (audit trail)

EventSink (observability)

from agnoclaw.runtime import EventSink

class MyEventSink(EventSink):
    async def emit(self, event):
        # Send to your logging/tracing/analytics system
        print(f"[{event['event_type']}] {event.get('payload', {})}")

agent = AgentHarness(event_sink=MyEventSink())

PolicyEngine (governance)

from agnoclaw.runtime import PolicyEngine, PolicyDecision

class CompliancePolicy(PolicyEngine):
    def before_run(self, run_input, context):
        if "DELETE FROM" in run_input.message.upper():
            return PolicyDecision.deny("SQL DELETE statements are blocked")
        return PolicyDecision.allow()

agent = AgentHarness(policy_engine=CompliancePolicy())

Hooks (middleware)

def log_inputs(run_input, context):
    print(f"User: {run_input.message[:100]}")
    return run_input  # pass through (or return modified)

def log_outputs(run_input, result, context):
    print(f"Output: {result.content[:100]}")
    return result

agent = AgentHarness(
    pre_run_hooks=[log_inputs],
    post_run_hooks=[log_outputs],
)

Permission modes

# Bypass all checks (development)
agent = AgentHarness(permission_mode="bypass")

# Require approval for edits (staging)
agent = AgentHarness(permission_mode="accept_edits")

# Plan-only mode (no writes, no shell)
agent = AgentHarness(permission_mode="plan")

---

Usage Patterns

1. Personal assistant (OpenClaw-style, local)

Use agnoclaw as a personal agent that runs on your machine, connects to messaging, monitors your workspace, and has cron-scheduled tasks:

import asyncio
from agnoclaw import AgentHarness
from agnoclaw.heartbeat import HeartbeatDaemon, CronJob

agent = AgentHarness(
    "anthropic:claude-sonnet-4-6",
    enable_learning=True,
    enable_user_memory=True,
    user_id="me",
)

daemon = HeartbeatDaemon(agent, on_alert=send_to_slack)
daemon.add_cron_job(CronJob(
    name="daily-standup",
    schedule="0 9 * * 1-5",
    prompt="Run daily standup.",
    skill="daily-standup",
    isolated=True,
))
asyncio.run(daemon.run_forever())

For WhatsApp/Telegram/Slack integration, build a thin webhook handler that calls agent.arun(message) and sends the response back. The messaging layer is a platform concern — agnoclaw is the agent backend.

# Install as always-on service
agnoclaw heartbeat install-service

2. Embed in a SaaS product

Use agnoclaw as the AI backend in your existing product. The messaging layer (WebSocket, REST API, etc.) is your platform code — agnoclaw provides the agent harness:

from agnoclaw import AgentHarness
from agnoclaw.runtime import PolicyEngine, PolicyDecision, EventSink

class TenantPolicy(PolicyEngine):
    def before_run(self, run_input, context):
        if context.tenant_id not in allowed_tenants:
            return PolicyDecision.deny("Tenant not authorized")
        return PolicyDecision.allow()

# One harness per tenant (or shared with tenant_id in context)
agent = AgentHarness(
    "anthropic:claude-sonnet-4-6",
    policy_engine=TenantPolicy(),
    event_sink=MyAnalyticsSink(),
    tenant_id="acme-corp",
    enable_learning=True,
    learning_namespace="acme",
)

# Your API endpoint calls this:
result = await agent.arun(user_message, user_id=user_id, session_id=session_id)

3. TUI mode (personal assistant)

The Textual TUI provides a full-featured terminal interface with streaming, heartbeat notifications, skill picker, and a debug log viewer:

pip install "agnoclaw[tui]"
agnoclaw tui

Features: live token streaming with Markdown rendering, notification panel (heartbeat/cron alerts), slash commands (/skill, /clear, /help), Ctrl+N toggle notifications, Ctrl+S skill picker, Ctrl+L debug log.

4. CLI-only (no messaging layer)

The CLI works standalone — it IS the messaging layer. Use this for development, debugging, and personal productivity:

agnoclaw chat                                    # async REPL (default)
agnoclaw chat --sync                             # legacy blocking REPL
agnoclaw run "Fix the bug in src/auth.py"        # one-shot
agnoclaw run "Review src/" --skill code-review   # with skill

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Testing

# Unit tests (no API key needed, ~0.8s)
uv run pytest tests/ -m "not integration" -q

# Integration tests with Ollama (local, no API key)
uv run pytest tests/test_integration.py -v

# Integration tests with a larger local model
AGNOCLAW_TEST_MODEL=qwen3:8b uv run pytest tests/test_integration.py -v

# Integration tests with Anthropic
AGNOCLAW_TEST_PROVIDER=anthropic ANTHROPIC_API_KEY=sk-... pytest tests/test_integration.py

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License

MIT — fork it, hack it, ship it.

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Built with Agno. Inspired by Claude Code, OpenClaw, and LangChain DeepAgents.