agent-flow

AgentFlow implements a native Model Context Protocol orchestration layer that binds AI agent reasoning directly to MCP server capabilities without intermediate abstraction layers. The framework maps LLM tool calls to MCP resource handlers and server functions, enabling agents to invoke remote tools with full context preservation across the protocol boundary. This eliminates the impedance mismatch between LLM function-calling schemas and MCP's resource/tool model by implementing bidirectional schema translation and response marshaling.

AgentFlow provides a declarative workflow engine that models agent execution as a state machine where transitions are triggered by LLM reasoning outputs and tool execution results. Each state represents an agent task or decision point, and transitions encode conditional logic based on tool outcomes, allowing complex multi-step workflows to be defined as configuration rather than imperative code. The engine maintains execution context across state transitions and provides rollback/retry semantics for failed branches.

AgentFlow enables workflows to adapt their behavior based on runtime context such as available resources, user preferences, or prior execution results. Workflows can define conditional branches that evaluate context at runtime and select different execution paths, and can dynamically adjust parameters (model selection, tool choices, retry policies) based on current conditions. This enables workflows to optimize for different scenarios (cost vs. quality, speed vs. accuracy) without requiring separate workflow definitions.

AgentFlow implements a memory system that automatically indexes MCP resources and tool outputs into a searchable context store, allowing agents to retrieve relevant prior results and resource metadata during reasoning. The system uses semantic similarity and metadata filtering to surface relevant context without explicit retrieval calls, reducing token usage and improving reasoning coherence. Memory is scoped to workflow execution with optional persistence across sessions via configurable backends.

AgentFlow abstracts away provider-specific LLM APIs (OpenAI, Anthropic, local models) behind a unified interface, allowing workflows to specify model requirements (reasoning capability, cost, latency) and automatically route requests to the best available provider. The system implements intelligent fallback logic that retries failed requests on alternative providers and can switch models mid-workflow based on task complexity or cost constraints. Provider configuration is declarative and supports dynamic provider selection based on workflow state.

AgentFlow enforces structured output formats by providing agents with JSON schemas that define expected response structures, then validates and parses LLM outputs against these schemas before passing results to downstream workflow steps. The system implements automatic schema inference from tool definitions and workflow state requirements, reducing the need for manual schema specification. Failed validations trigger automatic retry logic with schema refinement prompts to guide the LLM toward valid outputs.

AgentFlow provides comprehensive execution tracing that captures every agent decision, tool invocation, and state transition with full context and timing information. The system logs LLM reasoning outputs, tool inputs/outputs, and decision rationales in a structured format that enables post-execution analysis and debugging. Traces can be exported in multiple formats (JSON, OpenTelemetry) and integrated with external observability platforms for real-time monitoring of agent behavior.

AgentFlow enables developers to define reusable workflow templates and agent patterns that can be composed into larger workflows, reducing duplication and enabling workflow libraries. Templates support parameterization for different use cases, and the system provides a registry for discovering and sharing common patterns. Composition is declarative and supports both sequential and conditional nesting of sub-workflows with automatic context propagation.

AgentFlow implements sophisticated error handling that distinguishes between transient failures (network timeouts, rate limits) and permanent failures (invalid tool calls, schema violations), applying appropriate recovery strategies for each type. The system supports configurable retry policies with exponential backoff, circuit breakers for cascading failures, and fallback actions when retries are exhausted. Error context is preserved and logged for debugging, and workflows can define custom error handlers for domain-specific recovery logic.

AgentFlow provides built-in profiling and cost analysis that tracks token usage, API call counts, execution latency, and estimated costs across agent workflows. The system breaks down costs by provider, model, and workflow step, enabling identification of expensive operations and optimization opportunities. Profiling data is collected automatically during execution and can be aggregated across multiple runs for trend analysis and cost forecasting.

AgentFlow provides a testing framework that enables developers to simulate agent execution with mocked tool responses, allowing workflows to be tested without external dependencies. The system supports scenario-based testing where different tool response sequences can be replayed to verify agent behavior under various conditions. Test results include execution traces and decision logs that enable detailed assertion on agent reasoning and behavior.