AutoGPT leverages GPT-4 with autonomous task decomposition and internet access. It uses vector databases like Pinecone for memory and integrates with 10+ tools including web scraping and file operations. This architecture enables self-prompting and recursive goal refinement.
Configure your agent by setting up API keys for OpenAI and Pinecone in the .env file. Define AI_NAME, AI_ROLE, and up to 5 goals using the `ai_settings.yaml` file. Use the `--continuous` flag for autonomous loops with configurable iteration limits from 1 to 100.
Implement short-term memory via token limits (default 4000 tokens) and long-term memory using Pinecone with 1536-dimensional embeddings. Configure memory_backend in .env as 'pinecone' and set memory_index for persistent context across 50+ sessions.
Extend AutoGPT with custom plugins using the plugin framework supporting 20+ built-in commands like browse_website and write_file. Add new tools by creating Python modules in the `plugins` directory. Use `ALLOWLISTED_COMMANDS` and `DENYLISTED_COMMANDS` for access control.
Set `EXECUTE_LOCAL_COMMANDS` to `False` for safety and enable `FAST_TOKEN_LIMIT` for speed. Configure `RESTRICT_TO_WORKSPACE` to limit file access. Use `TEMPERATURE` (0.0 to 2.0) and `TOP_P` (0.0 to 1.0) to balance creativity and determinism in outputs.
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Expect $0.02 to $0.10 per task cycle with GPT-4, depending on token usage. Use GPT-3.5-turbo for cheaper runs at $0.002 per cycle.
Set a maximum iteration limit (e.g., 50) via `--max-iterations` and define clear, measurable goals. Use `CONTINUOUS_MODE=false` for manual approval between steps.
Yes, configure `OPENAI_API_BASE` to your local endpoint and set `SMART_LLM_MODEL` and `FAST_LLM_MODEL` to models like Llama 2 or Mistral, though performance may vary.