AgentPilot

Manages creation, configuration, and execution of multiple AI agents within a unified desktop environment. Implements agent state persistence, parameter management, and inter-agent communication patterns through a centralized agent registry that tracks agent instances, their configurations, and execution contexts across sessions.

Implements a unified chat UI that maintains separate conversation histories per agent while allowing seamless switching between agents without losing context. Uses a message buffer architecture that stores conversation turns with metadata (agent ID, timestamp, token count) and retrieves relevant context on agent switch, enabling agents to reference prior exchanges.

Manages agent context windows by maintaining conversation history and implementing strategies for context truncation when conversations exceed token limits. Supports configurable context window sizes per agent and implements sliding window or summarization strategies to preserve relevant context.

Abstracts LLM API calls behind a unified interface supporting OpenAI, Anthropic, and local Ollama models. Routes requests based on agent configuration, handles provider-specific request/response formatting, manages API keys securely in encrypted config storage, and implements fallback logic when a provider is unavailable or rate-limited.

Enables agents to call external tools and functions through a schema-based registry system. Agents define available tools as JSON schemas with input/output specifications, and the system translates LLM function-calling responses into actual Python function invocations with argument validation and error handling.

Provides a templating system for agent prompts that supports variable substitution, conditional logic, and reusable instruction blocks. System instructions are stored per-agent with version history, enabling A/B testing of prompts and rollback to previous versions without code changes.

Serializes agent configurations (model, provider, tools, prompts, parameters) to JSON/YAML files and stores them in a local database. Supports importing configurations from files or templates, enabling agent sharing and version control through standard file formats.

Builds a native desktop application using PyQt5/PyQt6 with a tabbed interface for agent management, chat windows, and configuration editing. Implements responsive UI patterns including async message handling to prevent blocking on LLM calls, and native file dialogs for import/export operations.

Persists conversation messages to a local SQLite database with metadata (agent ID, timestamp, token count, message role). Implements efficient retrieval of conversation history with filtering by agent, date range, or search terms, and supports exporting conversations to JSON or markdown formats.

Allows per-agent configuration of LLM parameters (temperature, max_tokens, top_p, frequency_penalty) and other hyperparameters through a configuration UI. Parameters are stored in agent config and passed to the LLM provider at request time, enabling fine-tuning of agent behavior without code changes.

Executes agent logic by sending prompts to the configured LLM provider and streaming responses back to the UI in real-time. Implements token counting for cost tracking, handles streaming response buffering, and manages request timeouts and error recovery.