Colab demo

Enables creation of specialized AI agents with distinct roles (e.g., programmer, reviewer, tester) that communicate through a message-passing architecture to collaboratively solve tasks. Agents maintain role-specific system prompts and can chain reasoning across multiple turns, with built-in support for agent-to-agent communication patterns including hierarchical delegation and peer collaboration. The framework handles agent lifecycle management, message routing, and conversation state across distributed agent instances.

Generates code through a specialized programmer agent that receives iterative feedback from reviewer and tester agents, implementing a continuous improvement loop. The system uses role-specific prompts to guide code quality assessment, test case generation, and bug detection. Agents exchange code artifacts through structured message formats and can request revisions with specific improvement directives, creating a collaborative development workflow that mirrors human code review processes.

Provides a message-passing infrastructure where agents send structured messages containing task descriptions, code artifacts, feedback, and execution results to each other. Messages are routed based on agent roles and task dependencies, with support for broadcast (one-to-many) and directed (one-to-one) communication patterns. The protocol preserves message history and enables agents to reference prior messages, creating a persistent conversation context that agents can query and reason about.

Abstracts LLM interactions behind a unified interface that supports multiple providers (OpenAI, Anthropic, local models) and allows agents to use different models simultaneously. The abstraction handles API key management, request formatting, response parsing, and error handling across providers with different API signatures. Agents can be configured to use specific models (e.g., GPT-4 for complex reasoning, GPT-3.5 for simple tasks), enabling cost and performance optimization.

Automatically breaks down complex tasks into subtasks and assigns them to agents based on role compatibility and capability matching. The decomposition uses the LLM to analyze task requirements and generate a task tree with dependencies, then routes subtasks to appropriate agents (e.g., database schema design to a database specialist agent). The system tracks task completion status and handles task dependencies, ensuring subtasks are executed in the correct order.

Maintains conversation history and context across multiple agent interactions, allowing agents to reference prior messages, decisions, and artifacts. The system stores conversation state (messages, agent states, task progress) and provides query interfaces for agents to retrieve relevant context. Context is automatically passed to new agents joining a conversation, ensuring continuity and reducing redundant information exchange.

Enables humans to intervene in agent workflows by reviewing agent decisions, providing feedback, and manually overriding agent actions. The system pauses agent execution at configurable checkpoints (e.g., before code deployment, after major decisions) and presents human-readable summaries of agent reasoning and proposed actions. Humans can approve, reject, or modify agent outputs before the workflow continues.

Tracks and logs agent performance metrics including token usage, execution time, error rates, and task completion status. The system generates detailed logs of agent actions, decisions, and reasoning steps, enabling post-execution analysis and debugging. Metrics are aggregated across agents and tasks, providing visibility into workflow efficiency and bottlenecks.