GitHub Repository

Provides a composable framework for building autonomous agents that can decompose complex tasks into subtasks, manage execution state across multiple steps, and coordinate tool invocations. Uses a graph-based task decomposition pattern where agents define workflows as directed acyclic graphs (DAGs) of operations, with built-in support for parallel execution, error handling, and state persistence across agent boundaries.

Enables agents to invoke external tools and APIs through a schema-based function registry that maps tool definitions to callable functions. Implements a declarative approach where tools are registered with JSON schemas describing inputs/outputs, and the framework handles marshaling arguments, executing the tool, and returning structured results back to the agent for decision-making.

Supports coordination between multiple independent agents working on related tasks, with a message-passing protocol that allows agents to share context, delegate subtasks to specialized agents, and aggregate results. Implements agent-to-agent communication through a standardized interface where agents can discover available peer agents, send requests with context, and receive responses without tight coupling.

Provides mechanisms for agents to maintain persistent memory across task executions, including short-term working memory for current task context and long-term memory for learned patterns and historical interactions. Implements memory storage with retrieval capabilities, allowing agents to query relevant past interactions and use them to inform current decisions without replaying entire conversation histories.

Manages the lifecycle of agent execution from initialization through completion, including task scheduling, progress tracking, and real-time monitoring of agent behavior. Provides observability hooks that emit execution events (task started, tool invoked, decision made, error occurred) allowing external systems to track agent progress, collect metrics, and intervene if needed.

Provides tools and abstractions for defining and refining agent behavior through prompt templates, system instructions, and behavioral parameters. Allows developers to experiment with different prompting strategies, instruction sets, and model parameters without modifying core agent logic, supporting A/B testing of agent behaviors and iterative improvement of agent performance.

Implements automatic error detection and recovery mechanisms that allow agents to handle failures gracefully, including retry logic with exponential backoff, fallback strategies when primary tools fail, and error classification to determine appropriate recovery actions. Agents can learn from errors and adjust their approach on subsequent attempts without manual intervention.

Provides configuration management for agent definitions, allowing agents to be defined declaratively through configuration files (YAML/JSON) and deployed across different environments without code changes. Supports environment-specific overrides, secret management for API keys, and deployment templates that standardize how agents are instantiated and run.