Instrukt

Provides a native terminal UI for real-time interaction with AI agents, enabling developers to send prompts, view agent reasoning chains, and monitor execution state without leaving the command line. Uses a TUI framework (likely Textual or similar) to render multi-pane layouts with agent output, logs, and input buffers, supporting keyboard navigation and context persistence across sessions.

Manages the lifecycle of AI agent execution including initialization, step-by-step execution control, state snapshots, and rollback capabilities. Implements an execution engine that tracks agent memory, tool invocations, and decision points, allowing developers to pause, inspect, and resume agent runs with full context preservation across terminal sessions.

Provides a unified interface for agents to invoke external tools, APIs, and functions with automatic schema validation and error handling. Supports registration of custom tools with type hints, manages tool discovery and routing, and handles asynchronous execution of tool calls with timeout and retry logic built into the orchestration layer.

Enables multiple AI agents to communicate within a shared conversation context, with automatic message routing, context aggregation, and conversation history management. Implements a message bus pattern where agents can send and receive messages, with the framework handling context window management and conversation state across multiple agent instances.

Manages agent memory across sessions using a pluggable storage backend, supporting both short-term (conversation) and long-term (episodic) memory. Implements memory retrieval and summarization to keep context within LLM token limits, with support for semantic search over historical interactions and automatic memory pruning based on relevance or age.

Collects and visualizes real-time metrics about agent execution including token usage, latency, tool call success rates, and decision quality. Implements a metrics pipeline that aggregates data from each step of agent execution and renders dashboards in the terminal UI, with support for exporting metrics for external analysis.

Provides a configuration system for defining agent behavior, tools, memory backends, and execution parameters using declarative YAML or JSON files. Supports agent templates that can be instantiated with different parameters, enabling rapid prototyping and standardization of agent configurations across teams.

Allows developers to extend Instrukt with custom tools, memory backends, and UI components through a plugin architecture. Implements a discovery and loading mechanism for plugins, with standardized interfaces for each extension point, enabling the ecosystem to grow without modifying core code.

Records all agent interactions, decisions, and outputs to enable deterministic replay of sessions for debugging and analysis. Implements a recording format that captures the full execution trace including LLM inputs/outputs, tool calls, and state changes, allowing developers to step through recorded sessions and inspect behavior at any point.

Abstracts away LLM provider differences through a unified interface, supporting multiple providers (OpenAI, Anthropic, local models, etc.) with automatic fallback and provider-specific optimization. Handles provider-specific features like function calling schemas, token counting, and rate limiting transparently, allowing agents to switch providers without code changes.