AI Framework Comparison

How Directive compares to popular AI agent frameworks.

Directive's AI adapter doesn't replace your LLM framework – it wraps it with constraint-driven orchestration. This means you keep your existing agent code and gain guardrails, reactive state, time-travel debugging, and declarative patterns on top.

At a Glance

LangChain / LangGraph

LangChain provides a comprehensive toolkit for building LLM applications with chains, agents, and tools. LangGraph adds graph-based orchestration with nodes and edges.

When LangChain is Better

• You need the broadest ecosystem of integrations (100+ LLM providers, vector stores, tools)
• Your team is Python-first
• You want LangSmith's hosted tracing and evaluation platform

When Directive Adds Value

• You want framework-agnostic orchestration that wraps any LLM SDK
• You need declarative constraints that automatically trigger agent runs
• You want reactive state (derivations, scratchpad) that drives UI updates
• You need visual debugging (Timeline, Cost, State) without a hosted service
• You want self-healing with automatic agent rerouting

Using Together

Directive can wrap a LangChain runner. Use LangChain for your LLM calls and tool integrations, Directive for orchestration, guardrails, and state management.

CrewAI

CrewAI provides role-based agent teams with tasks, tools, and process flows. Agents have roles, goals, and backstories.

When CrewAI is Better

• You want the simplest mental model for multi-agent systems
• Role-based metaphors (researcher, writer, reviewer) fit your use case
• You're building in Python

When Directive Adds Value

• You need TypeScript-native orchestration
• You want per-agent and orchestrator-level guardrails (input, output, tool-call)
• You need 8 execution patterns beyond sequential and parallel (including goal-directed resolution)
• You want reactive cross-agent derivations and shared scratchpad
• You need breakpoints, checkpoints, and time-travel debugging

Directive's goal pattern goes beyond CrewAI's role-based goals. CrewAI goals are natural-language descriptions that guide agent behavior (goal="Identify trending topics"). Directive goals are machine-checkable conditions with dependency resolution, quantitative satisfaction tracking, and progressive relaxation – the runtime knows exactly how close you are to done and can self-correct when progress stalls.

AutoGen

Microsoft's AutoGen enables multi-agent conversations where agents chat with each other to solve problems.

When AutoGen is Better

• Conversational multi-agent patterns (round-robin, group chat) are your primary use case
• You want AutoGen Studio's visual builder
• Your team uses Python

When Directive Adds Value

• You need structured execution patterns (DAG, race, reflect, debate) beyond conversation
• You want constraint-driven orchestration with declarative rules
• You need token budgets, circuit breakers, and self-healing
• You want a reactive state backbone that drives UI updates
• You need evals with 10 built-in criteria and LLM-as-judge scoring

Vercel AI SDK

Vercel AI SDK provides streaming-first UI primitives for React, with excellent DX for chatbots and generative UI.

When Vercel AI SDK is Better

• You're building a chat UI and want the fastest path to streaming responses
• You want React Server Components integration
• Your use case is primarily single-agent chat

When Directive Adds Value

• You need multi-agent orchestration with patterns, constraints, and guardrails
• You want framework-agnostic state (works with React, Vue, Svelte, Solid, Lit)
• You need time-travel debugging and visual DevTools (Timeline, Cost, State)
• You want declarative agent routing based on runtime state
• You need production features: evals, OTEL, self-healing, goal pattern

Using Together

Use Vercel AI SDK for the streaming UI layer and Directive for backend orchestration, guardrails, and state management.

Directive's Unique Differentiators

Features no other framework provides:

1. Constraint-driven orchestration – Declare when/require rules; the runtime resolves them automatically
2. Goal pattern – Declare desired end-state with produces/requires declarations; runtime resolves through dependency-ordered agent runs with satisfaction scoring and progressive relaxation
3. Reactive Directive System backbone – Every agent is a namespaced module with reactive facts, derivations, and effects
4. Cross-agent derivations – Compute values across all agent states reactively
5. Visual DevTools – Timeline, Cost, State (plus 5 more views planned: Flamechart, DAG, Health, Breakpoints, Compare)
6. Self-healing – Circuit breakers with automatic agent rerouting and health scoring
7. Pattern checkpoints – Save/resume mid-execution for all 8 pattern types with progress tracking, forking, and diffing
8. Framework-agnostic – Wraps any AgentRunner function, no LLM SDK lock-in

Next Steps

• Overview – Full feature map and reading paths
• Running Agents – Get started with Directive AI
• Execution Patterns – See all 8 patterns in action