network-ai

Provides a unified TypeScript interface that abstracts over 27+ distinct AI agent frameworks (LangChain, AutoGen, CrewAI, OpenAI Assistants, LlamaIndex, Semantic Kernel, Haystack, DSPy, Agno, MCP, LangGraph, Anthropic Compute, etc.) through a common adapter pattern. Each framework gets a dedicated adapter that translates between the framework's native agent lifecycle (initialization, execution, tool binding, response handling) and Network-AI's standardized agent contract, enabling single-codebase orchestration across heterogeneous agent systems without rewriting business logic.

Implements native Model Context Protocol (MCP) server integration allowing agents to discover, invoke, and compose tools exposed via MCP servers without manual schema translation. The framework handles MCP server lifecycle management (connection pooling, reconnection logic, capability discovery), marshals tool calls from agents into MCP-compliant requests, and unmarshals responses back into agent-consumable formats. Supports both stdio and SSE transport modes for MCP server communication.

Provides testing utilities for agent behavior including mock LLM providers for deterministic testing, tool call simulation, and execution trace comparison. Implements property-based testing for agents (testing invariants across multiple executions) and scenario-based testing (testing agent behavior in specific situations). Supports snapshot testing of agent outputs and execution traces for regression detection.

Provides configuration management for agents including environment-specific configurations (dev, staging, production), secrets management (API keys, credentials), and deployment orchestration. Supports configuration validation against schemas, hot-reloading of agent configurations without restart, and configuration versioning with rollback capabilities. Integrates with infrastructure-as-code tools and CI/CD pipelines for automated agent deployment.

Provides profiling tools to identify performance bottlenecks in agent execution including LLM call latency, tool invocation overhead, and decision-making latency. Implements automatic performance recommendations (e.g., 'caching tool results would save 500ms per execution') and supports performance regression detection. Tracks performance metrics over time and correlates performance changes with code/configuration changes.

Implements input validation and sanitization for agent prompts, tool parameters, and outputs to prevent prompt injection, tool misuse, and data exfiltration. Supports configurable validation rules (regex patterns, schema validation, semantic validation) and automatic detection of suspicious patterns (e.g., attempts to override system prompts). Integrates with security scanning tools and provides audit logs for security events.

Translates tool/function definitions between incompatible schema formats used by different frameworks (OpenAI function calling format, Anthropic tool_use format, LangChain StructuredTool, CrewAI Tool, etc.) into a canonical internal representation and back. Handles parameter validation, type coercion, and error mapping so a single tool definition can be used across frameworks without duplication. Supports JSON Schema, TypeScript interfaces, and Zod schema inputs for tool definition.

Orchestrates agent execution across multiple LLM providers (OpenAI, Anthropic, Ollama, local models, etc.) with dynamic routing based on cost, latency, or capability requirements. Handles agent lifecycle management (initialization, step execution, tool invocation, termination), maintains execution context across provider boundaries, and implements fallback logic if a provider fails. Supports both synchronous and asynchronous execution modes with configurable timeout and retry policies.

Provides serialization and deserialization of agent execution state (current step, tool call history, context window, intermediate results) to enable agent resumption after interruption or failure. Supports multiple persistence backends (in-memory, file system, Redis, database) through a pluggable storage interface. Handles state versioning and migration to support agent code updates without losing execution history. Captures full execution traces for debugging and audit purposes.

Enables composition of multiple agents into hierarchical structures where parent agents can spawn child agents, delegate tasks, and aggregate results. Implements task decomposition patterns (breaking complex goals into subtasks) with automatic dependency resolution and parallel execution where possible. Handles inter-agent communication through a message queue abstraction and manages resource allocation (token budgets, concurrent execution limits) across the agent hierarchy.

Captures detailed execution metrics, logs, and traces for all agent operations (LLM calls, tool invocations, decision points, errors) with structured logging and optional integration with observability platforms (OpenTelemetry, Datadog, New Relic, etc.). Provides real-time dashboards for agent health, token usage, latency, and error rates. Implements distributed tracing across multi-agent systems to track request flows through hierarchical agent structures.

Provides a centralized system for managing, versioning, and deploying agent prompt templates across frameworks. Supports template inheritance, variable substitution, and conditional prompt sections based on agent context or capabilities. Implements A/B testing infrastructure for comparing prompt variants and their impact on agent behavior. Tracks prompt versions with git-like history and enables rollback to previous versions.

Implements configurable error handling and recovery strategies for agent failures including automatic retries with exponential backoff, circuit breaker patterns for cascading failures, graceful degradation when tools fail, and fallback agent invocation. Distinguishes between transient errors (network timeouts, rate limits) and permanent errors (invalid tool calls, authentication failures) with different recovery strategies for each. Provides hooks for custom error handlers and recovery logic.

Automatically discovers agent capabilities (supported tools, LLM models, execution modes) at runtime and dynamically binds tools based on agent requirements and availability. Implements capability negotiation between agents and tool providers, with fallback to alternative tools if preferred tools are unavailable. Supports capability constraints (e.g., 'agent requires tools with <100ms latency') and automatic tool selection based on constraints.