yicoclaw

Coordinates multiple AI agents with distinct roles and responsibilities, routing tasks to specialized agents based on capability matching and context. Implements a supervisor pattern where a coordinator agent analyzes incoming requests, decomposes them into subtasks, and delegates to worker agents with appropriate system prompts and tool access, aggregating results into coherent outputs.

Provides a declarative function registry system where tools are defined as JSON schemas with execution bindings, enabling agents to discover and invoke external functions with type safety. Supports native integrations with OpenAI and Anthropic function-calling APIs, automatically marshaling arguments and handling response serialization across different LLM provider formats.

Abstracts away provider-specific API differences through a unified interface, allowing agents to switch between LLM providers (OpenAI, Anthropic, Ollama, etc.) without code changes. Handles provider-specific features (function calling formats, streaming, token counting) transparently, with automatic fallback to alternative providers on failure.

Manages agent conversation history and working memory using a sliding window approach that preserves recent interactions while summarizing older context to stay within token limits. Implements automatic summarization of conversation segments when memory exceeds thresholds, maintaining semantic continuity while reducing token overhead for long-running agent sessions.

Provides mechanisms to serialize agent execution state (memory, tool results, decision history) to persistent storage and recover from checkpoints, enabling agents to resume work after interruptions or failures. Supports pluggable storage backends (file system, database) and automatic checkpoint creation at configurable intervals or after significant state changes.

Allows developers to define agent personalities, constraints, and behavioral guidelines through structured system prompt templates and role definitions. Supports prompt composition where base system prompts are combined with role-specific instructions, tool descriptions, and output format requirements, enabling consistent behavior across agent instances while allowing fine-grained customization.

Captures detailed execution traces of agent operations including LLM calls, tool invocations, decision points, and state transitions, with structured logging that enables debugging and performance analysis. Provides hooks for custom logging handlers and integrates with observability platforms, recording latency, token usage, and error context at each step.

Enables multiple agents to execute concurrently while respecting task dependencies and data flow constraints. Implements a DAG-based execution model where tasks are defined with explicit dependencies, allowing the framework to parallelize independent tasks while serializing dependent ones, with automatic result aggregation and error propagation.

Automatically matches agent capabilities to available tools based on semantic similarity and task requirements, enabling agents to discover and use tools without explicit configuration. Uses embeddings or semantic matching to find relevant tools for a given task, with fallback mechanisms when exact matches aren't available, reducing the need for manual tool registration per agent.

Provides configurable error handling and retry logic for agent operations, tool calls, and LLM API requests, with support for exponential backoff, circuit breakers, and custom recovery strategies. Distinguishes between transient errors (retryable) and permanent errors (fail-fast), with hooks for custom error handlers and recovery logic.

Collects and aggregates performance metrics for agent operations including execution time, token usage, API costs, success rates, and tool utilization. Provides real-time dashboards and historical analysis capabilities, with support for custom metrics and integration with monitoring platforms for alerting and trend analysis.