paperclipai

Orchestrates teams of AI agents through a command-line interface, enabling agents to be spawned, coordinated, and managed as autonomous workers. Uses a task-queue-based architecture where agents receive work items, execute them independently, and report results back to a central coordinator. Agents can communicate with each other through a message-passing system to handle interdependent tasks.

Allows developers to define specialized agent roles with specific system prompts, capabilities, and behavioral constraints. Each agent role is configured with a unique persona, set of allowed tools, and decision-making parameters. The system enforces role boundaries so agents stay within their domain of responsibility and cannot exceed their defined capabilities.

Provides built-in error handling mechanisms including automatic retries with exponential backoff, circuit breakers for failing services, and fallback agents when primary agents fail. Implements timeout handling to prevent agents from hanging indefinitely. Supports custom error handlers that can implement domain-specific recovery logic. Errors are logged with full context for debugging.

Enables version control of agent configurations, allowing teams to track changes, rollback to previous versions, and compare configurations across versions. Configurations include agent prompts, tool bindings, role definitions, and execution parameters. Supports configuration templates for creating similar agents with minimal duplication. Enables environment-specific configurations (dev, staging, production).

Provides a registry system for binding external tools and APIs to agents, enabling them to take actions beyond text generation. Tools are registered with schemas defining inputs, outputs, and execution logic. Agents can discover available tools, invoke them with appropriate parameters, and handle results. Supports both synchronous and asynchronous tool execution with error handling and retry logic.

Enables agents to send and receive messages from other agents in the team, facilitating coordination on complex tasks. Messages are routed through a central message broker that handles delivery, ordering, and acknowledgment. Agents can subscribe to message types and react to incoming messages, enabling event-driven workflows where one agent's output triggers another agent's action.

Manages a task queue where work items are submitted and distributed to available agents for execution. Tasks are enqueued with metadata (priority, deadline, dependencies) and assigned to agents based on availability and capability matching. The queue system tracks task status, handles retries for failed tasks, and provides visibility into queue depth and agent utilization.

Provides real-time monitoring of agent execution with detailed logging of actions, decisions, and outcomes. Each agent execution generates logs capturing the agent's reasoning process, tool calls, and results. Logs are structured and queryable, enabling debugging and auditing of agent behavior. Includes metrics collection for performance analysis and error tracking.

Enables agents to save and restore their state across executions, allowing long-running workflows to survive restarts and failures. Agent state includes memory of past interactions, learned preferences, and execution context. State can be persisted to local storage or external backends. Recovery mechanisms restore agent state on restart, allowing workflows to resume from the last checkpoint.

Analyzes agent execution performance to identify bottlenecks and optimization opportunities. Collects metrics on execution time, token usage, API call latency, and success rates. Provides recommendations for improving agent efficiency, such as caching results, reducing context size, or parallelizing independent tasks. Enables A/B testing of different agent configurations.

Enables definition of complex multi-step workflows where agents execute tasks in sequence, parallel, or conditional branches. Workflows are defined declaratively using a configuration format (YAML or JSON) that specifies task dependencies, branching logic, and error handling. The execution engine interprets workflow definitions and orchestrates agent execution according to the specified flow, handling retries and fallbacks.

Manages scaling of agent teams based on workload, automatically spawning new agent instances when queue depth exceeds thresholds and terminating idle agents to conserve resources. Implements resource limits per agent (memory, execution time) and enforces them to prevent runaway processes. Provides visibility into resource utilization across the team and recommendations for capacity planning.