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# Getting Started with CleverAgents
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Welcome to CleverAgents! This guide will help you get up and running in about 5 minutes, walk you through your first project, and point you toward deeper learning resources.
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## What is CleverAgents?
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CleverAgents is a Python-first AI agent orchestration platform that lets you build, configure, and run intelligent automation workflows. It provides:
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- **Unified CLI** (`agents` command) for all interactions
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- **Interactive TUI** (Terminal User Interface) for hands-on agent management
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- **Actor System** — composable AI agents with tools and skills
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- **Plan Lifecycle** — structured workflow from strategy to execution to application
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- **Resource Management** — handle files, databases, containers, and more
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- **Multi-Provider Support** — OpenAI, Anthropic, Google, Azure, and others
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## Quick Start (5 Minutes)
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### 1. Clone the Repository
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```bash
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git clone https://git.cleverthis.com/cleveragents/cleveragents-core.git
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cd cleveragents-core
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```
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### 2. Set Up Your Environment
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```bash
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# Create a virtual environment
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python -m venv .venv
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source .venv/bin/activate # On Windows: .venv\Scripts\activate
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# Install CleverAgents with development dependencies
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pip install -e ".[dev,tests,docs]"
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# Set up pre-commit hooks and verify tooling
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bash scripts/setup-dev.sh
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```
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### 3. Verify Installation
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```bash
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# Check the CLI is working
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agents --version
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agents --help
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# Run diagnostics to check LLM provider configuration
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agents diagnostics
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```
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### 4. Configure an LLM Provider
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CleverAgents works with multiple LLM providers. Set up at least one:
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**OpenAI:**
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```bash
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export OPENAI_API_KEY="sk-..."
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```
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**Anthropic:**
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```bash
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export ANTHROPIC_API_KEY="sk-ant-..."
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```
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**Google:**
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```bash
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export GOOGLE_API_KEY="..."
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```
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See [LLM Provider Configuration](#llm-provider-configuration) below for all supported providers.
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### 5. Launch the Interactive TUI
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```bash
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# Install the TUI extra if not already installed
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pip install -e ".[tui]"
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# Launch the interactive terminal UI
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agents tui
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```
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Inside the TUI, you can:
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- Type messages and press `Enter` to chat with the active actor
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- Press `/` to open the slash command overlay
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- Press `@` to insert file/resource references
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- Press `!` to enter shell mode
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- Press `F1` for context-sensitive help
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- Press `Ctrl+Q` to quit
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## Your First Project: Hello World Agent
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Let's create a simple agent that greets you and answers questions.
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### Step 1: Create a Basic Actor Configuration
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Create a file `my-first-actor.yaml`:
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```yaml
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# Simple greeting actor
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name: local/hello-world
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entry_node: greeter
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nodes:
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greeter:
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model: gpt-4o # or your preferred model
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tool_sources: [builtin]
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system_prompt: |
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You are a friendly greeting agent.
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Respond warmly and helpfully to user messages.
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Keep responses concise and friendly.
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```
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### Step 2: Use the Actor in the CLI
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```bash
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# Tell the agent to do something
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agents tell --actor local/hello-world "Say hello and tell me what you can do"
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# Or use the v3 plan workflow
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agents plan use local/hello-world my-project
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agents plan execute <PLAN_ID>
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agents plan apply <PLAN_ID>
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```
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### Step 3: Use the Actor in the TUI
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```bash
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# Launch the TUI
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agents tui
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# Inside the TUI:
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# 1. Press Ctrl+T to cycle through available actors
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# 2. Select "local/hello-world"
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# 3. Type a message and press Enter
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```
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## Basic Concepts
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### Actors
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**Actors** are the execution units of CleverAgents. Each actor:
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- Is defined in YAML with a name, entry node, and node graph
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- Binds an LLM, tools, and optional integrations (LSP, MCP)
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- Can be built-in (e.g., `openai/gpt-4o`) or custom (e.g., `local/my-actor`)
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Example actor structure:
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```yaml
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name: local/my-actor
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entry_node: main
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nodes:
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main:
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model: gpt-4o
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tool_sources: [builtin, mcp://bash-tools]
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```
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See [Actor System](../architecture.md#actor-system) for details.
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### Tools
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**Tools** are atomic capabilities available to actors. They include:
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- Built-in tools (file operations, shell commands)
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- MCP (Model Context Protocol) tools from external servers
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- LSP (Language Server Protocol) tools for code intelligence
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- Custom tools defined in your project
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Tools are registered in the `ToolRegistry` and invoked by actors during execution.
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### Skills
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**Skills** are composable capability bundles that expose one or more tools. They:
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- Load from YAML or AgentSkills.io-compatible directories
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- Support progressive disclosure (discover → activate → deactivate)
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- Are tracked by the `SkillRegistry`
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### Resources
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**Resources** are managed external entities like files, databases, and containers. They:
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- Organize into a DAG (Directed Acyclic Graph) with dependency tracking
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- Support multiple types: `file`, `directory`, `sqlite`, `postgresql`, `container.docker`, etc.
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- Each type has a handler implementing CRUD, checkpoint, and rollback
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### Plan Lifecycle
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The **Plan Lifecycle** is the central workflow abstraction:
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```
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Action → Strategize → Execute → Apply
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↑ ↓
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└──────────────────────┘
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(correction/rollback)
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```
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| Phase | Description |
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|-------|-------------|
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| **Action** | User intent captured as a plan request |
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| **Strategize** | LLM generates a structured plan with operations |
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| **Execute** | Operations executed against resources via tools |
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| **Apply** | Validated changes committed; diff reviewed and approved |
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See [Plan Lifecycle](../architecture.md#plan-lifecycle) for details.
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### Personas
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**Personas** are named identities that bind:
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- An actor (which LLM and tools to use)
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- Argument presets (default parameters)
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- Scope references (which resources are available)
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Personas are persisted in `~/.config/cleveragents/personas/` and can be switched in the TUI with `Ctrl+T`.
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### Sessions
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**Sessions** are conversation histories. You can:
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- Create new sessions: `agents session create --actor openai/gpt-4o`
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- List sessions: `agents session list`
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- Export sessions: `agents session export --session-id <ID> --output session.json`
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- Import sessions: `agents session import --input session.json`
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## LLM Provider Configuration
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CleverAgents automatically discovers and uses configured LLM providers. Set environment variables for the providers you want to use:
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| Provider | Environment Variable | Example |
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|----------|----------------------|---------|
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| OpenAI | `OPENAI_API_KEY` | `sk-...` |
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| Anthropic | `ANTHROPIC_API_KEY` | `sk-ant-...` |
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| Google | `GOOGLE_API_KEY` or `GOOGLE_GENAI_API_KEY` | `AIza...` |
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| Azure OpenAI | `AZURE_OPENAI_API_KEY`, `AZURE_OPENAI_ENDPOINT`, `AZURE_OPENAI_DEPLOYMENT` | See Azure docs |
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| OpenRouter | `OPENROUTER_API_KEY` | `sk-or-...` |
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| Groq | `GROQ_API_KEY` | `gsk_...` |
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| Together | `TOGETHER_API_KEY` | `...` |
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| Cohere | `COHERE_API_KEY` | `...` |
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### Setting a Default Provider
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```bash
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# Pin the global provider
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export CLEVERAGENTS_DEFAULT_PROVIDER=openai
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# Pin a specific model
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export CLEVERAGENTS_DEFAULT_MODEL=gpt-4o
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```
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### Checking Your Configuration
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```bash
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# See which providers are configured and which actor is selected
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agents diagnostics
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```
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## Common First-Time Issues and Solutions
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### Issue: "No LLM provider configured"
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**Symptom:** Error message says no API keys found.
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**Solution:**
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1. Verify you've set an environment variable: `echo $OPENAI_API_KEY`
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2. If empty, set it: `export OPENAI_API_KEY="sk-..."`
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3. Run `agents diagnostics` to verify the provider is detected
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4. Restart your terminal or shell session if you just set the variable
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### Issue: "Actor not found"
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**Symptom:** Error says `local/my-actor` doesn't exist.
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**Solution:**
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1. Check the actor file exists: `ls my-first-actor.yaml`
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2. Verify the file is valid YAML (check indentation)
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3. Use the full path if the file is not in the current directory
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4. Built-in actors use the format `<provider>/<model>` (e.g., `openai/gpt-4o`)
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### Issue: "TUI won't start"
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**Symptom:** `agents tui` fails or shows a blank screen.
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**Solution:**
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1. Ensure the TUI extra is installed: `pip install -e ".[tui]"`
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2. Check your terminal supports 256 colors: `echo $TERM`
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3. Try running with explicit terminal: `TERM=xterm-256color agents tui`
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4. Check for conflicting environment variables: `env | grep -i textual`
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### Issue: "Tool execution fails"
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**Symptom:** Actor tries to use a tool but gets an error.
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**Solution:**
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1. Check the tool is available: `agents tools list` (if implemented)
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2. Verify tool permissions are granted (TUI shows permission overlay)
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3. Check tool configuration in the actor YAML
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4. Review tool documentation: see [Tool System](../architecture.md#tool-system)
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### Issue: "Session not found"
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**Symptom:** Error when trying to export or import a session.
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**Solution:**
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1. List available sessions: `agents session list`
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2. Use the correct session ID from the list
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3. Check the session file exists (for import): `ls session.json`
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4. Verify the JSON is valid: `python -m json.tool session.json`
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### Issue: "Permission denied" errors
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**Symptom:** Actor can't read/write files or access resources.
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**Solution:**
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1. Check file permissions: `ls -la <file>`
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2. Ensure the file is readable/writable by your user
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3. In the TUI, approve permission requests when prompted (press `y`)
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4. Check resource configuration in your project
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## Next Learning Steps
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Now that you're up and running, here's what to explore next:
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### 1. **Understand the Architecture** (30 minutes)
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- Read [Architecture Overview](../architecture.md)
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- Learn about the layered design and key components
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- Understand the Plan Lifecycle in detail
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|
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### 2. **Build Your First Custom Actor** (1 hour)
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- Create a YAML actor configuration
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- Add tools and integrations
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- Test in the TUI and CLI
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- See [Actor System](../architecture.md#actor-system) for details
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|
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### 3. **Work with Resources** (1 hour)
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- Create file and database resources
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- Use resources in your actor
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- Understand the Resource DAG
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- See [Resource System](../architecture.md#resource-system)
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|
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### 4. **Explore Tools and Skills** (1 hour)
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- Discover available tools
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- Create custom tools
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- Load and manage skills
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- See [Tool System](../architecture.md#tool-system) and [Skill System](../architecture.md#skill-system)
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|
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### 5. **Master the Plan Lifecycle** (2 hours)
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- Create and execute plans
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- Understand phase transitions
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- Use correction and rollback
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- See [Plan Lifecycle](../architecture.md#plan-lifecycle)
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|
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### 6. **Integrate with External Services** (2 hours)
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- Set up MCP (Model Context Protocol) servers
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- Configure LSP (Language Server Protocol) integration
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- Use ACMS (Advanced Context Management System)
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- See [MCP Integration](../architecture.md#mcp-integration) and [LSP Integration](../architecture.md#lsp-integration)
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|
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### 7. **Deploy to Production** (2 hours)
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- Use server mode: `agents server connect`
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- Deploy with Kubernetes (see `k8s/`)
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- Configure observability and logging
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- See [Server Architecture](../development/agent-system-specification.md)
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## Key Documentation References
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|
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| Topic | Document |
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|-------|----------|
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| **Architecture** | [Architecture Overview](../architecture.md) |
|
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| **Specification** | [Full Specification](../specification.md) |
|
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| **API Reference** | [API Docs](../api/index.md) |
|
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| **Development** | [Development Guide](../development/agent-system-specification.md) |
|
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| **Testing** | [Testing Guide](../development/testing.md) |
|
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| **FAQ** | [Frequently Asked Questions](../faq.md) |
|
||||
| **Design Decisions** | [Architecture Decision Records (ADRs)](../adr/index.md) |
|
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|
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## Tips for Success
|
||||
|
||||
1. **Start Small** — Create simple actors before complex ones
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2. **Use the TUI** — The interactive interface is great for learning and debugging
|
||||
3. **Read the Specification** — `docs/specification.md` is the authoritative source
|
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4. **Check the Examples** — See `examples/` for real-world configurations
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5. **Run Tests** — Use `nox -s unit_tests` to verify your changes
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6. **Ask for Help** — Check the FAQ and ADRs for common questions
|
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|
||||
## Troubleshooting Commands
|
||||
|
||||
```bash
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# Check your setup
|
||||
agents diagnostics
|
||||
|
||||
# List available actors
|
||||
agents actor list
|
||||
|
||||
# List available tools
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||||
agents tools list # if implemented
|
||||
|
||||
# List sessions
|
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agents session list
|
||||
|
||||
# View help for any command
|
||||
agents <command> --help
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||||
|
||||
# Run tests to verify everything works
|
||||
nox -s unit_tests
|
||||
|
||||
# Check code quality
|
||||
nox -s lint
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nox -s typecheck
|
||||
```
|
||||
|
||||
## What's Next?
|
||||
|
||||
- **Build an actor** — Create a custom actor for your use case
|
||||
- **Explore the TUI** — Spend time in the interactive interface
|
||||
- **Read the architecture** — Understand the design decisions
|
||||
- **Join the community** — Check out discussions and issues
|
||||
- **Contribute** — See [CONTRIBUTING.md](../../CONTRIBUTING.md) for guidelines
|
||||
|
||||
Happy automating! 🚀
|
||||
@@ -0,0 +1,134 @@
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||||
"""Step definitions for TDD Issue #10487 — MigrationRunner SQLite threading error.
|
||||
|
||||
These steps verify that ``MigrationRunner.get_current_revision()`` can be
|
||||
called safely from a background thread without raising a SQLite threading
|
||||
error.
|
||||
|
||||
Currently FAILS because ``get_current_revision()`` creates a SQLite engine
|
||||
without ``connect_args={"check_same_thread": False}``. When a connection
|
||||
created in the main thread is used from a background thread, SQLite raises:
|
||||
|
||||
ProgrammingError: SQLite objects created in a thread can only be used
|
||||
in that same thread.
|
||||
|
||||
Compare with ``init_or_upgrade()`` which correctly passes
|
||||
``connect_args={"check_same_thread": False}`` for SQLite.
|
||||
|
||||
Bug: https://git.cleverthis.com/cleveragents/cleveragents-core/issues/10487
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import shutil
|
||||
import sqlite3
|
||||
import tempfile
|
||||
import threading
|
||||
from pathlib import Path
|
||||
from unittest.mock import patch
|
||||
|
||||
from behave import given, then, when
|
||||
from behave.runner import Context
|
||||
from sqlalchemy import create_engine as real_create_engine
|
||||
|
||||
from cleveragents.infrastructure.database.migration_runner import MigrationRunner
|
||||
|
||||
|
||||
@given("a MigrationRunner configured with a temporary SQLite database")
|
||||
def step_migration_runner_with_temp_db(context: Context) -> None:
|
||||
"""Create a MigrationRunner pointing at a temporary SQLite database file."""
|
||||
context.tdd_threading_tmpdir = tempfile.mkdtemp(
|
||||
prefix="tdd_migration_runner_threading_10487_"
|
||||
)
|
||||
db_path = Path(context.tdd_threading_tmpdir) / "test.db"
|
||||
context.tdd_threading_db_url = f"sqlite:///{db_path}"
|
||||
context.tdd_threading_runner = MigrationRunner(context.tdd_threading_db_url)
|
||||
context.tdd_threading_db_path = str(db_path)
|
||||
|
||||
def _cleanup() -> None:
|
||||
shutil.rmtree(context.tdd_threading_tmpdir, ignore_errors=True)
|
||||
|
||||
context.add_cleanup(_cleanup)
|
||||
|
||||
|
||||
@given("the database has been initialized via init_or_upgrade")
|
||||
def step_database_initialized(context: Context) -> None:
|
||||
"""Initialize the database schema using init_or_upgrade."""
|
||||
context.tdd_threading_runner.init_or_upgrade(require_confirmation=False)
|
||||
|
||||
|
||||
@when("I call get_current_revision from a background thread")
|
||||
def step_call_get_current_revision_from_thread(context: Context) -> None:
|
||||
"""Call get_current_revision() from a background thread and capture any errors.
|
||||
|
||||
To reproduce the SQLite threading bug, we create a raw sqlite3 connection
|
||||
in the main thread (without ``check_same_thread=False``) and wrap it in
|
||||
a SQLAlchemy engine. We then monkey-patch ``create_engine`` so that
|
||||
``get_current_revision()`` reuses this main-thread engine when called
|
||||
from the background thread. When the background thread tries to use
|
||||
the main-thread connection, SQLite raises ``ProgrammingError``.
|
||||
|
||||
This reproduces the bug: ``get_current_revision()`` calls
|
||||
``create_engine(self.database_url)`` without
|
||||
``connect_args={"check_same_thread": False}``. With a shared
|
||||
main-thread connection, SQLite rejects the cross-thread usage.
|
||||
"""
|
||||
errors: list[Exception] = []
|
||||
|
||||
# Create a raw sqlite3 connection in the main thread WITHOUT
|
||||
# check_same_thread=False. This connection will be used by the
|
||||
# background thread, triggering the SQLite threading error.
|
||||
raw_main_thread_conn = sqlite3.connect(context.tdd_threading_db_path)
|
||||
|
||||
# Create a SQLAlchemy engine that wraps the raw connection.
|
||||
# We use a creator function that always returns the same raw connection.
|
||||
main_thread_engine = real_create_engine(
|
||||
"sqlite://",
|
||||
creator=lambda: raw_main_thread_conn,
|
||||
)
|
||||
|
||||
def call_from_thread() -> None:
|
||||
try:
|
||||
# Patch create_engine so get_current_revision() uses the
|
||||
# main-thread engine (simulating a shared/cached engine).
|
||||
with patch(
|
||||
"cleveragents.infrastructure.database.migration_runner.create_engine",
|
||||
return_value=main_thread_engine,
|
||||
):
|
||||
context.tdd_threading_runner.get_current_revision()
|
||||
except Exception as exc:
|
||||
errors.append(exc)
|
||||
|
||||
thread = threading.Thread(target=call_from_thread)
|
||||
thread.start()
|
||||
thread.join()
|
||||
|
||||
raw_main_thread_conn.close()
|
||||
main_thread_engine.dispose()
|
||||
context.tdd_threading_errors = errors
|
||||
|
||||
|
||||
@then(
|
||||
"get_current_revision should not raise any exception from the background thread"
|
||||
)
|
||||
def step_no_exception_from_thread(context: Context) -> None:
|
||||
"""Assert that get_current_revision() raised no exception from the thread.
|
||||
|
||||
This assertion FAILS on the unfixed code because SQLite raises:
|
||||
ProgrammingError: SQLite objects created in a thread can only be
|
||||
used in that same thread.
|
||||
|
||||
The root cause is that ``get_current_revision()`` calls
|
||||
``create_engine(self.database_url)`` without
|
||||
``connect_args={"check_same_thread": False}``. When the connection
|
||||
pool returns a connection created in the main thread to a background
|
||||
thread, SQLite rejects the cross-thread usage.
|
||||
|
||||
Once the fix is applied (adding ``connect_args={"check_same_thread": False}``
|
||||
to the ``create_engine`` call in ``get_current_revision()``), this test
|
||||
will pass and the ``@tdd_expected_fail`` tag should be removed.
|
||||
"""
|
||||
errors: list[Exception] = context.tdd_threading_errors
|
||||
assert not errors, (
|
||||
f"get_current_revision() must not raise from a background thread; "
|
||||
f"got: {errors!r}"
|
||||
)
|
||||
@@ -0,0 +1,25 @@
|
||||
@tdd_issue @tdd_issue_10487
|
||||
Feature: TDD Issue #10487 — MigrationRunner.get_current_revision() SQLite threading error
|
||||
As a developer
|
||||
I want to verify that MigrationRunner.get_current_revision() works correctly
|
||||
when called from a background thread
|
||||
So that the SQLite threading bug is captured and will be caught by a regression test
|
||||
|
||||
``MigrationRunner.get_current_revision()`` creates a new SQLAlchemy engine
|
||||
with ``create_engine(self.database_url)`` but does not pass
|
||||
``connect_args={"check_same_thread": False}`` for SQLite databases.
|
||||
When called from a thread other than the one that created the engine,
|
||||
SQLite raises ``ProgrammingError: SQLite objects created in a thread can
|
||||
only be used in that same thread``.
|
||||
|
||||
Compare with ``init_or_upgrade()`` which correctly passes
|
||||
``connect_args={"check_same_thread": False}`` for SQLite.
|
||||
|
||||
Bug: https://git.cleverthis.com/cleveragents/cleveragents-core/issues/10487
|
||||
|
||||
@tdd_issue @tdd_issue_10487 @tdd_expected_fail
|
||||
Scenario: get_current_revision() called from background thread raises no error
|
||||
Given a MigrationRunner configured with a temporary SQLite database
|
||||
And the database has been initialized via init_or_upgrade
|
||||
When I call get_current_revision from a background thread
|
||||
Then get_current_revision should not raise any exception from the background thread
|
||||
@@ -11,8 +11,6 @@ site_dir: build/site
|
||||
nav:
|
||||
- Specification: specification.md
|
||||
- Architecture: architecture.md
|
||||
- Guides:
|
||||
- Getting Started: guides/getting-started.md
|
||||
- API Reference:
|
||||
- Overview: api/index.md
|
||||
- Core Utilities: api/core.md
|
||||
|
||||
Reference in New Issue
Block a user