OpenAGI

Implements the ReAct (Reasoning + Acting) pattern through ReactAgent class that extends BaseAgent, enabling agents to interleave chain-of-thought reasoning with tool invocation. The framework manages the reasoning loop by accepting LLM outputs, parsing tool calls, executing tools, and feeding results back into the reasoning chain. This architecture decouples reasoning logic from tool execution, allowing agents to reason about which tools to use before invoking them.

Provides a factory pattern implementation (AgentFactory class) that handles agent creation, configuration loading, activation, and lifecycle coordination. The factory abstracts agent instantiation by loading configuration from JSON files, resolving dependencies, and managing agent state across creation and execution phases. This enables standardized agent deployment and reduces boilerplate for agent setup.

Implements standardized agent packaging through directory structure (pyopenagi/agents/{author}/{agent_name}/), configuration files (config.json), and dependency specifications (meta_requirements.txt). This enables consistent agent distribution, dependency resolution, and metadata tracking. Agents can be packaged with all dependencies and shared through the Agent Hub.

Integrates with AIOS (AI Operating System) kernel as the primary agent creation system, with an explicit migration path to Cerebrum SDK for future versions. The integration enables agents to run within the AIOS environment, accessing kernel services and resources. The architecture supports both current AIOS integration and future Cerebrum SDK compatibility.

Implements a pluggable tool system through BaseTool abstract class with concrete implementations for RapidAPI, Huggingface, and custom tools. Each tool type has its own adapter that handles API authentication, request formatting, response parsing, and error handling. Tools are registered with agents and invoked through a standardized interface, allowing agents to seamlessly call external APIs without knowing implementation details.

Implements the Interactor system that manages downloading and uploading of agent implementations to/from a centralized Agent Hub. The interactor handles agent packaging, versioning, and repository management, enabling community-driven agent sharing. Agents can be published to the hub with metadata and dependencies, then discovered and downloaded by other users for local execution.

Implements a Queues system that manages requests to language model backends, handling the flow of prompts and responses between agents and LLM services. The queue system abstracts LLM provider details, allowing agents to submit prompts without knowing which backend processes them. This enables load balancing, request batching, and provider switching without agent code changes.

Enables agents to be customized through JSON configuration files (config.json) that specify agent parameters, tool selections, and execution settings. The BaseAgent class loads and validates configurations, allowing non-developers to customize agent behavior without modifying code. Configuration includes tool selections, model parameters, and agent-specific settings that control runtime behavior.

Implements AgentProcess class that manages agent execution flow, breaking down complex tasks into steps and executing them sequentially. The process system tracks execution state, handles step transitions, and manages context between steps. This enables agents to handle multi-step tasks where each step depends on previous results, with visibility into execution progress.

Provides a compressor utility (pyopenagi/utils/compressor.py) that optimizes prompts and context for LLM consumption, reducing token usage while preserving semantic meaning. The compressor applies techniques like summarization, deduplication, and selective context inclusion to minimize prompt size. This reduces LLM API costs and latency while maintaining agent reasoning quality.

Implements a logging system (pyopenagi/utils/logger.py) that provides structured logging for agent execution, tool calls, and LLM interactions. The logger captures execution traces with timestamps, step details, and error information, enabling debugging and monitoring of agent behavior. Logs can be exported in multiple formats for analysis and auditing.

Provides BaseAgent abstract class that serves as the foundation for all agent implementations, defining core interfaces for configuration loading, tool management, and task execution. Agents extend BaseAgent and implement specific reasoning patterns (e.g., ReactAgent implements ReAct). This inheritance-based architecture enables code reuse and standardized agent development patterns.