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|`/models`| Browse and switch between provider-native models |
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|`/sessions`| List and resume previous sessions |
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|`/status`| Show current model, provider, and directory |
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|`/workspace`| Show the current workspace path |
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|`/clear`| Clear the terminal |
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|`exit` / `quit`| Save session and quit |
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> Press `ESC` at any time to interrupt a running generation.
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## 🔀 Mixture of Providers (MoP)
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AutoGPT introduces a high-availability **Mixture of Providers** architecture. When enabled via the `--mixture` or `-m` flag, every prompt is fanned out concurrently to all configured AI providers (Gemini, OpenAI, HuggingFace, etc.). A weighted scoring engine evaluates responses based on:
1.**Reasoning depth** (connectivity words and logical flow).
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1.**Completeness** (punctuation and closing delimiters).
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The highest-scored response is selected as the winner and injected into the agent's context, promoting the best "intelligence" available from your configured keys.
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## The `.autogpt` Directory
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GenericGPT maintains all persistent state inside the workspace root (defaults to the **current directory**):
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```sh
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.autogpt/
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├── sessions/ # Markdown conversation snapshots, auto-saved after every response
Control the workspace root with `AUTOGPT_WORKSPACE`:
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```sh
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export AUTOGPT_WORKSPACE=/my/project # scope all file ops to a specific directory
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autogpt
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```
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## Model Selection
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Models are sourced dynamically from each provider's crate. Override the active model without entering the shell:
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```sh
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export GEMINI_MODEL=gemini-2.5-pro-preview-05-06
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export OPENAI_MODEL=gpt-4o
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export MODEL=<any-model-id># global fallback for any provider
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```
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## How GenericGPT Works
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Each prompt travels through a multi-phase pipeline, with every phase reflected live in the terminal UI:
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1.**Intent Detection**: The agent reads your message and decides whether it can answer directly, call a specific tool, or needs to plan and execute a full multi-step task.
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1.**Multi-Provider Fan-out**: When mixture mode is enabled, the prompt is sent to all configured AI providers simultaneously and the best response is selected.
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1.**Task Synthesis**: The agent breaks your goal down into a concrete, numbered list of actionable sub-tasks.
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1.**Implementation Plan**: A structured plan is generated and displayed, giving you a clear overview of what will be built before execution begins.
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1.**Reasoning**: Before tackling each sub-task, the agent thinks through its approach, anticipated risks, and the best execution strategy.
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1.**Execution**: The agent carries out the sub-task by performing file operations, running shell commands, searching the web, calling external tools, and more, atomically and in order.
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1.**Build & Verify**: If a buildable project is detected, the agent automatically compiles or runs it and attempts to self-correct any failures, retrying up to three times.
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1.**Reflection**: After completing each sub-task, the agent reviews its own output and decides whether to accept it, retry with corrections, or skip and move on.
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1.**Metacognition**: The agent tracks outcome patterns across tasks. When it detects repeated failures or inefficiencies, it recalibrates its strategy for the remaining work.
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1.**Skill Extraction**: At the end of a session, the agent distills domain-specific lessons from what worked and stores them for automatic reuse in future sessions on similar topics.
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```mermaid
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flowchart TD
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A([autogpt CLI]) --> B{CLI args?}
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B -- none --> C["GenericGPT\nInteractive Shell"]
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B -- "-p / --prompt" --> DP["Direct LLM Prompt\n--mixture for fan-out"]
The `mta` (Meta-Thinking Agent) feature adds a self-evaluation phase to `GenericAgent`. After every task reflection, the agent records the outcome and, every 3 tasks or after 2+ consecutive failures, queries the LLM with a structured metacognition prompt to derive strategy adjustments for remaining tasks. The TUI status bar shows **MetaCognizing** during this phase.
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### 🐳 Using Docker
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To install and run the AutoGPT CLI via Docker, use the following command:
Copy file name to clipboardExpand all lines: README.md
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@@ -43,9 +43,7 @@ AutoGPT is a pure rust framework that simplifies AI agent creation and managemen
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## 🧠 Framework Overview
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### ⚙️ Agent Core Architecture
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AutoGPT agents are modular and autonomous, built from composable components:
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AutoGPT agents are modular, autonomous, and designed for flexibility:
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- 🔌 **Tools & Sensors**: Interface with the real world via actions (e.g., file I/O, APIs) and perception (e.g., audio, video, data).
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- 🧠 **Memory & Knowledge**: Combines long-term vector memory with structured knowledge bases for reasoning and recall.
@@ -55,16 +53,10 @@ AutoGPT agents are modular and autonomous, built from composable components:
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- 🧑🤝🧑 **Collaboration**: Agents can delegate, swarm, or work in teams with other agents.
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- 🪞 **Self-Reflection**: Introspection module to debug, adapt, or evolve internal strategies.
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- 🔄 **Context Management**: Manages active memory (context window) for ongoing tasks and conversations.
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- 🔌 **MCP (Model Context Protocol)**: Seamlessly connect to external tool servers (Stdio, SSE, HTTP) to extend agent capabilities with thousands of existing tools.
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- 🔌 **MCP (Model Context Protocol)**: First-class support to seamlessly connect external tool servers (Stdio, SSE, HTTP) to extend capabilities.
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- 📅 **Scheduler**: Time-based or reactive triggers for agent actions.
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### 🚀 Developer Features
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AutoGPT is designed for flexibility, integration, and scalability:
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- 🧪 **Custom Agent Creation**: Build tailored agents for different roles or domains.
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- 📋 **Task Orchestration**: Manage and distribute tasks across agents efficiently.
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- 🔌 **MCP Integration**: First-class support for the Model Context Protocol to unify tool access.
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- 🧱 **Extensibility**: Add new tools, behaviors, or agent types with ease.
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- 💻 **CLI Tools**: Command-line interface for rapid experimentation and control.
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- 🧰 **SDK Support**: Embed AutoGPT into existing projects or systems seamlessly.
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### 0. 🤖 GenericGPT Interactive Mode (Default)
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When you run `autogpt` with **no subcommand or flags**, it launches an interactive AI TUI powered by **GenericGPT**, a production-hardened autonomous software engineering agent with session persistence, model switching, and multi-provider support:
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When you run `autogpt` with **no subcommand or flags**, it launches an interactive AI TUI powered by **GenericGPT**, a production-hardened autonomous software engineering agent. GenericGPT features intent detection, a complete seven-step reasoning and execution pipeline, automatic build-and-verify loops, and metacognition for learning across tasks.
|`/models`| Browse and switch between provider-native models |
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|`/sessions`| List and resume previous sessions |
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|`/status`| Show current model, provider, and directory |
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|`/workspace`| Show the current workspace path |
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|`/clear`| Clear the terminal |
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|`exit` / `quit`| Save session and quit |
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> Press `ESC` at any time to interrupt a running generation.
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### 🔀 Mixture of Providers (MoP)
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AutoGPT introduces a high-availability **Mixture of Providers** architecture. When enabled via the `--mixture` or `-m` flag, every prompt is fanned out concurrently to all configured AI providers (Gemini, OpenAI, HuggingFace, etc.). A weighted scoring engine evaluates responses based on:
1.**Reasoning depth** (connectivity words and logical flow).
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1.**Completeness** (punctuation and closing delimiters).
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The highest-scored response is selected as the winner and injected into the agent's context, promoting the best "intelligence" available from your configured keys.
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### The `.autogpt` Directory
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GenericGPT maintains all persistent state inside the workspace root (defaults to the **current directory**):
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```sh
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.autogpt/
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├── sessions/ # Markdown conversation snapshots, auto-saved after every response
Control the workspace root with `AUTOGPT_WORKSPACE`:
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```sh
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export AUTOGPT_WORKSPACE=/my/project # scope all file ops to a specific directory
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autogpt
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```
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### Model Selection
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Models are sourced dynamically from each provider's crate. Override the active model without entering the shell:
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```sh
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export GEMINI_MODEL=gemini-2.5-pro-preview-05-06
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export OPENAI_MODEL=gpt-4o
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export MODEL=<any-model-id># global fallback for any provider
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```
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### How GenericGPT Works
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Each prompt goes through a seven-step pipeline:
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1.**MoP Fan-out** (optional): Parallel execution across multiple providers.
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1.**Reasoning**: structured internal monologue stored in the session log.
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1.**Task synthesis**: decomposition into typed actions (`CreateFile`, `PatchFile`, `RunCommand`, ...).
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1.**Execution**: file edits via `PatchFile`; shell execution via `RunCommand`.
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1.**Build-and-verify**: auto-detects `Cargo.toml` / `package.json` / `Makefile` and runs the build; retries on failure up to 3 times.
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1.**Reflection**: reviews outcomes and lesson candidates.
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1.**Skill extraction**: lessons written to `.autogpt/skills/<domain>.toml` and injected in future sessions.
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```mermaid
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flowchart TD
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A([User enters prompt]) --> B{Mixture mode?}
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B -- Yes --> C[Run Mixture of Providers]
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B -- No --> D[Standard Provider]
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C & D --> E[Reasoning pre-step]
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E --> F[Task synthesis]
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F --> G{User approves?}
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G -- yolo mode / yes --> H[Execute actions]
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H --> I[Build-and-verify loop]
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I -- pass --> J[Reflection]
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I -- fail, retry ≤3 --> H
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J --> K[Save skills & session]
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K --> L([Ready for next prompt])
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```
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```mermaid
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flowchart TD
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A([User launches autogpt]) --> B{Any args?}
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B -- No --> C[GenericGPT Interactive Shell]
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B -- Yes --> D{Subcommand}
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C --> E[Select Provider & Model]
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E --> F[Enter Prompt Loop]
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F --> G{Mixture enabled?}
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G -- Yes --> H[Mixture of Providers]
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G -- No --> I[Standard Prompt]
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H & I --> J[Agent Generates Response]
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J --> F
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D -- arch --> K[ArchitectGPT]
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D -- back --> L[BackendGPT]
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D -- front --> M[FrontendGPT]
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D -- design --> N[DesignerGPT]
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D -- manage --> O[ManagerGPT]
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D -- -p prompt --> P[Direct LLM Prompt]
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```
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> [!NOTE]
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> For an in-depth breakdown of how GenericGPT works under the hood, including its architecture, interactive shell, Mixture of Providers (MoP), and execution pipeline, see the [GenericGPT Documentation](GenericGPT.md).
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