# 🤖 GenericGPT Interactive Mode
GenericGPT is a production-hardened autonomous software engineering agent with session persistence, model switching, and multi-provider support.
When you run `autogpt` with **no subcommand or flags**, it launches an interactive AI TUI powered by GenericGPT:
```sh
autogpt
```
<video src="https://github.com/user-attachments/assets/6aae0f5e-1137-4866-bc86-8a081ce067c4"></video>
The interactive shell supports the following commands:
| `<your prompt>` | Send a task to the GenericGPT autonomous agent |
| `/help` | Show available commands |
| `/provider` | Switch AI provider (Gemini, OpenAI, Anthropic, XAI, Cohere, HuggingFace) |
| `/models` | Browse and switch between provider-native models |
| `/sessions` | List and resume previous sessions |
| `/status` | Show current model, provider, and directory |
| `/workspace` | Show the current workspace path |
| `/clear` | Clear the terminal |
| `exit` / `quit` | Save session and quit |
> Press `ESC` at any time to interrupt a running generation.
## 🔀 Mixture of Providers (MoP)
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. **Length calibration** (rewarding detail, penalizing fluff).
1. **Code quality** (bonus for language-tagged Markdown blocks).
1. **Structural richness** (headings, lists, hygiene).
1. **Reasoning depth** (connectivity words and logical flow).
1. **Completeness** (punctuation and closing delimiters).
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.
## The `.autogpt` Directory
GenericGPT maintains all persistent state inside the workspace root (defaults to the **current directory**):
```sh
.autogpt/
├── sessions/ # Markdown conversation snapshots, auto-saved after every response
│ ├── <uuid>.md
│ └── ...
└── skills/ # TOML lesson files, injected into future prompts automatically
├── rust.toml
├── web.toml
└── python.toml
```
Control the workspace root with `AUTOGPT_WORKSPACE`:
```sh
export AUTOGPT_WORKSPACE=/my/project # scope all file ops to a specific directory
autogpt
```
## Model Selection
Models are sourced dynamically from each provider's crate. Override the active model without entering the shell:
```sh
export GEMINI_MODEL=gemini-2.5-pro-preview-05-06
export OPENAI_MODEL=gpt-4o
export MODEL=<any-model-id> # global fallback for any provider
```
## How GenericGPT Works
Each prompt travels through a multi-phase pipeline, with every phase reflected live in the terminal UI:
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.
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.
1. **Task Synthesis**: The agent breaks your goal down into a concrete, numbered list of actionable sub-tasks.
1. **Implementation Plan**: A structured plan is generated and displayed, giving you a clear overview of what will be built before execution begins.
1. **Reasoning**: Before tackling each sub-task, the agent thinks through its approach, anticipated risks, and the best execution strategy.
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.
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.
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.
1. **Metacognition**: The agent tracks outcome patterns across tasks. When it detects repeated failures or inefficiencies, it recalibrates its strategy for the remaining work.
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.
```mermaid
flowchart TD
A([autogpt CLI]) --> B{CLI args?}
B -- none --> C["GenericGPT\nInteractive Shell"]
B -- "-p / --prompt" --> DP["Direct LLM Prompt\n--mixture for fan-out"]
B -- subcommand --> SA
subgraph SA ["Specialized Agent Roster"]
direction LR
ARCH[ArchitectGPT] --- BACK[BackendGPT] --- FRONT[FrontendGPT]
DES[DesignerGPT] --- MGR[ManagerGPT] --- MAIL[MailerGPT]
end
C --> PS["Provider & Model Setup\ngemini · openai · xai · anthropic · cohere · hf"]
PS --> RL["Prompt REPL Loop\nESC → abort_token triggers abort"]
RL --> IC["classify_intent\nINTENT_DETECTION_PROMPT"]
IC -- DirectAnswer --> SR["generate_safe\nstream reply to TUI"]
IC -- ToolCall --> TE["MCP / built-in tool\nMcpCall action"]
IC -- TaskPlan --> MOP
SR --> RL
TE --> RL
subgraph FTP ["Full Task Pipeline · GenericAgent · AgentGPT base class"]
direction TB
MOP{"Mixture of\nProviders?"}
MOP -- Yes --> MF["Fan-out across providers\nmerge best response"]
MOP -- No --> SP["Single provider\ngenerate_tracked with token stats"]
MF & SP --> WS["scan_workspace + walk_glob\nfile-tree snapshot injected as LLM context"]
WS --> SL["SkillStore.load_for_domain\n.autogpt/skills/domain.toml injected"]
SL --> SY["Phase: Synthesizing\nnumbered sub-task list"]
SY --> PG["Phase: Planning\nmarkdown implementation plan"]
PG --> AP{"Phase: AwaitingApproval\n--yolo to skip gate"}
AP -- Abort --> ID([Phase: Idle])
AP -- Approved --> EX
subgraph EL ["Execution Loop: one iteration per sub-task"]
direction TB
EX["Phase: Executing n / N"]
EX --> RS["Reasoning\nReasoningResult: thought · approach · risks"]
RS --> AR["LLM emits ActionRequest JSON array"]
AR --> AD{Action type}
AD -- "CreateFile / WriteFile\nPatchFile / AppendFile" --> FW[Filesystem writes]
AD -- "ReadFile / ListDir\nFindInFile / GlobFiles" --> FR["Filesystem reads\nwalk_glob + pattern_matches"]
AD -- RunCommand --> SH["Shell execution\ncwd + timeout"]
AD -- GitCommit --> GC[git stage and commit]
AD -- WebSearch --> WEB[DuckDuckGo search]
AD -- McpCall --> MCP["MCP server tool\nstdio / SSE transport"]
AD -- MultiPatch --> MPT[Atomic multi-patch]
FW & FR & SH & GC & WEB & MCP & MPT --> RC["ActionResult\nstdout · stderr · success"]
RC --> BV{"Build artifact\ndetected?"}
BV -- "Cargo.toml / package.json / Makefile" --> TB["auto-build\nretry up to 3 · self-fix prompt on failure"]
TB -- pass --> RF
TB -- fail --> RS
BV -- No --> RF
RF["Phase: Reflecting\nReflectionResult: outcome · corrective_actions"]
RF -- Retry --> RS
RF -- "Skip / Success" --> MC
MC{"mta feature\nenabled?"}
MC -- No --> NT
MC -- Yes --> MTE["Phase: MetaCognizing\nrecord_task_outcome → MetacognitionEntry\nshould_adjust_strategy?"]
MTE -- ok --> NT
MTE -- adjust --> MH["METACOGNITION_PROMPT\nstrategy hint injected for next task"]
MH --> NT
end
NT{"More sub-tasks?"}
NT -- Yes --> EX
NT -- No --> FU{"Follow-up tasks\nneeded?"}
FU -- Yes --> SY
FU -- No --> SE
SE["Skill Extraction\nextract_lessons → .autogpt/skills/domain.toml"]
SE --> SS["Session walkthrough\n.autogpt/sessions/"]
end
SS --> RL
```