f107ff8cf0
- **FIX ALL LINT/TYPE ERRORS IN AUTOGPT, FORGE, AND BENCHMARK** ### Linting - Clean up linter configs for `autogpt`, `forge`, and `benchmark` - Add type checking with Pyright - Create unified pre-commit config - Create unified linting and type checking CI workflow ### Testing - Synchronize CI test setups for `autogpt`, `forge`, and `benchmark` - Add missing pytest-cov to benchmark dependencies - Mark GCS tests as slow to speed up pre-commit test runs - Repair `forge` test suite - Add `AgentDB.close()` method for test DB teardown in db_test.py - Use actual temporary dir instead of forge/test_workspace/ - Move left-behind dependencies for moved `forge`-code to from autogpt to forge ### Notable type changes - Replace uses of `ChatModelProvider` by `MultiProvider` - Removed unnecessary exports from various __init__.py - Simplify `FileStorage.open_file` signature by removing `IOBase` from return type union - Implement `S3BinaryIOWrapper(BinaryIO)` type interposer for `S3FileStorage` - Expand overloads of `GCSFileStorage.open_file` for improved typing of read and write modes Had to silence type checking for the extra overloads, because (I think) Pyright is reporting a false-positive: https://github.com/microsoft/pyright/issues/8007 - Change `count_tokens`, `get_tokenizer`, `count_message_tokens` methods on `ModelProvider`s from class methods to instance methods - Move `CompletionModelFunction.schema` method -> helper function `format_function_def_for_openai` in `forge.llm.providers.openai` - Rename `ModelProvider` -> `BaseModelProvider` - Rename `ChatModelProvider` -> `BaseChatModelProvider` - Add type `ChatModelProvider` which is a union of all subclasses of `BaseChatModelProvider` ### Removed rather than fixed - Remove deprecated and broken autogpt/agbenchmark_config/benchmarks.py - Various base classes and properties on base classes in `forge.llm.providers.schema` and `forge.models.providers` ### Fixes for other issues that came to light - Clean up `forge.agent_protocol.api_router`, `forge.agent_protocol.database`, and `forge.agent.agent` - Add fallback behavior to `ImageGeneratorComponent` - Remove test for deprecated failure behavior - Fix `agbenchmark.challenges.builtin` challenge exclusion mechanism on Windows - Fix `_tool_calls_compat_extract_calls` in `forge.llm.providers.openai` - Add support for `any` (= no type specified) in `JSONSchema.typescript_type`
AutoGPT: build & use AI agents
AutoGPT is the vision of the power of AI accessible to everyone, to use and to build on. Our mission is to provide the tools, so that you can focus on what matters:
- 🏗️ Building - Lay the foundation for something amazing.
- 🧪 Testing - Fine-tune your agent to perfection.
- 🤝 Delegating - Let AI work for you, and have your ideas come to life.
Be part of the revolution! AutoGPT is here to stay, at the forefront of AI innovation.
📖 Documentation | 🚀 Contributing | 🛠️ Build your own Agent - Quickstart
🧱 Building blocks
🏗️ Forge
Forge your own agent! – Forge is a ready-to-go template for your agent application. All the boilerplate code is already handled, letting you channel all your creativity into the things that set your agent apart. All tutorials are located here. Components from the forge.sdk can also be used individually to speed up development and reduce boilerplate in your agent project.
🚀 Getting Started with Forge – This guide will walk you through the process of creating your own agent and using the benchmark and user interface.
📘 Learn More about Forge
🎯 Benchmark
Measure your agent's performance! The agbenchmark can be used with any agent that supports the agent protocol, and the integration with the project's CLI makes it even easier to use with AutoGPT and forge-based agents. The benchmark offers a stringent testing environment. Our framework allows for autonomous, objective performance evaluations, ensuring your agents are primed for real-world action.
📦 agbenchmark on Pypi
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📘 Learn More about the Benchmark
💻 UI
Makes agents easy to use! The frontend gives you a user-friendly interface to control and monitor your agents. It connects to agents through the agent protocol, ensuring compatibility with many agents from both inside and outside of our ecosystem.
The frontend works out-of-the-box with all agents in the repo. Just use the CLI to run your agent of choice!
📘 Learn More about the Frontend
⌨️ CLI
To make it as easy as possible to use all of the tools offered by the repository, a CLI is included at the root of the repo:
$ ./run
Usage: cli.py [OPTIONS] COMMAND [ARGS]...
Options:
--help Show this message and exit.
Commands:
agent Commands to create, start and stop agents
benchmark Commands to start the benchmark and list tests and categories
setup Installs dependencies needed for your system.
Just clone the repo, install dependencies with ./run setup, and you should be good to go!
🤔 Questions? Problems? Suggestions?
Get help - Discord 💬
To report a bug or request a feature, create a GitHub Issue. Please ensure someone else hasn’t created an issue for the same topic.
🤝 Sister projects
🔄 Agent Protocol
To maintain a uniform standard and ensure seamless compatibility with many current and future applications, AutoGPT employs the agent protocol standard by the AI Engineer Foundation. This standardizes the communication pathways from your agent to the frontend and benchmark.