10 frameworks evaluated across architecture, deployment, state management, ecosystem maturity, and trading system fit
| Framework | Harness Type | Language | Edge / CF Deploy | State Mgmt | MCP | Event-Driven | Memory / RAG | Multi-Agent | Maturity | License | Token Efficiency |
|---|---|---|---|---|---|---|---|---|---|---|---|
|
Claude Agent SDK
Anthropic
|
Hierarchical | Python TS | No Requires long-running process |
In-memory / BYO persistence | Yes | No Request-response |
No BYO |
Yes Parent spawns child agents freely |
MIT | Extended thinking, prompt caching. No built-in compression. | |
|
Mastra AI
Mastra Inc.
|
Hybrid DAG + Agent | TypeScript | Yes CloudflareDeployer, Durable Objects, 8MB bundle |
XState (internal), Durable Objects SQLite, KV, snapshot checkpoints | Yes | Yes afterEvent() / resumeWithEvent() |
Yes RAG + Observational Memory (Observer/Reflector agents) |
Yes Agents as workflow nodes |
Elastic-2.0 | Observational Memory compresses history. Workflows are code (zero tokens). Only agent steps use LLM. | |
|
LangGraph
LangChain
|
Cyclic State Machine | Python JS | No LangSmith Cloud or self-hosted |
LangGraph Platform checkpointer, Postgres, SQLite, Redis | Partial Via LangChain tools |
Partial Human-in-the-loop interrupts |
Yes Via LangChain retrievers |
Yes Multi-agent graphs, supervisor pattern |
MIT | State trimming, message summarization. Verbose by default. | |
|
CrewAI
CrewAI Inc.
|
Task-Based Roles | Python | No Server-only, long-running |
In-memory, CrewAI+ managed persistence | No | No Sequential/parallel tasks |
Yes Built-in RAG tools, memory module |
Yes Role-defined agent teams with delegation |
MIT | Short/long-term memory. Can be token-heavy with verbose agent delegation. | |
|
n8n
n8n GmbH
|
Visual DAG | TypeScript (visual builder) |
No Docker / n8n Cloud |
Postgres/SQLite execution log, workflow-level state | No | Yes Webhook triggers, cron |
Partial Vector store nodes, no built-in agent memory |
No Single AI agent per workflow |
Sustainable Use (Fair Code) | Most logic is code nodes (zero tokens). LLM only where explicitly added. | |
|
AutoGen
Microsoft
|
Message-Passing | Python .NET | No Server-only, long-running |
In-memory, custom serialization | No | Partial Message-based triggers |
Partial Teachable agents, BYO RAG |
Yes Multi-agent conversations, group chat |
MIT | Token-heavy. Multi-agent chat = multiplicative token cost per turn. | |
|
Semantic Kernel
Microsoft
|
Planner + Plugins | C# Python Java | Partial Azure Functions (not CF Workers) |
Kernel memory, Azure Cosmos, custom stores | No Own plugin system |
Partial Via Azure Event Grid |
Yes Kernel Memory for RAG |
Yes Agent collaboration via process framework |
MIT | Function calling over chat completions. Enterprise-optimized token patterns. | |
|
Vercel AI SDK
Vercel
|
Primitives / SDK | TypeScript | Yes Vercel Edge, CF Workers, any V8 runtime |
BYO — no built-in state management | Yes | Partial Streaming, but no event-wait primitives |
No BYO everything |
No Single agent, BYO orchestration |
Apache-2.0 | Minimal overhead. Streaming reduces TTFT. No wasted abstraction tokens. | |
|
Dify
Dify.AI
|
Visual Builder | Python (visual builder) |
No Docker / Dify Cloud |
Postgres, Redis, conversation memory | No | Yes API triggers, webhooks |
Yes Built-in RAG pipeline, knowledge base |
Partial Workflow nodes, not true multi-agent |
Apache-2.0 | Good prompt templating. Visual builder keeps prompts lean. | |
|
Inngest
Inngest Inc.
|
Event-Driven Workflow | TypeScript Python Go | Yes CF Workers, Vercel, Netlify, any serverless |
Inngest Cloud manages state, step memoization, event history | No | Yes Core design: event → function. waitForEvent(), cron, webhooks |
No BYO — no agent/LLM primitives |
No BYO agent layer |
Apache-2.0 (SDK) | Workflow is pure code (zero tokens). Pair with any LLM SDK for agent steps. |
| Framework | Biggest Strength | Biggest Weakness | Best For | Worst For |
|---|---|---|---|---|
Claude Agent SDK |
Maximum agent autonomy. Parent agents can spawn sub-agents dynamically with full tool access. Best reasoning quality (Claude models). | No edge deployment. No built-in workflow engine. No state persistence. Requires VPS or server. | Complex reasoning tasks, research agents, code generation, open-ended exploration | Deterministic pipelines, edge deployment, cost-sensitive polling workloads |
Mastra AI |
Only framework with native Cloudflare Workers deployment + DAG workflows + agent reasoning + RAG + evals in one package. Event-driven by design. | TypeScript only. Young ecosystem (fewer community patterns). Elastic-2.0 license (not pure OSS). No cyclic state machines natively. | Event-driven edge agents, trading bots, webhook-triggered pipelines, TypeScript teams | Python shops, complex multi-agent conversations, enterprises locked into Azure |
LangGraph |
Most powerful graph primitives. Cyclic state machines, conditional edges, human-in-the-loop. Massive ecosystem via LangChain. | Complexity tax. Steep learning curve. Python-first (JS SDK less mature). No edge deployment. LangSmith dependency for managed features. | Complex multi-step agents with cycles, research pipelines, enterprises needing LangSmith observability | Simple agents, edge deployment, teams avoiding vendor lock-in |
CrewAI |
Easiest mental model: define roles, assign tasks, agents collaborate. Very fast to prototype multi-agent teams. | Token-hungry (agent delegation = lots of LLM back-and-forth). Python only. No edge. Less control over execution order. | Role-based agent teams, content generation crews, rapid prototyping | Cost-sensitive production systems, deterministic pipelines, edge deployment |
n8n |
500+ integrations out of the box. Visual builder = non-developers can build workflows. Self-hostable. Mature and battle-tested. | Not a real agent framework — AI is bolted on. No true multi-agent. No edge deployment. Visual-first = limited programmatic control. | Integration-heavy automation, non-technical users, workflows connecting SaaS tools with AI nodes | Complex agent reasoning, custom agent architectures, edge deployment |
AutoGen |
Best for multi-agent conversations. Agents debate, critique, and refine each other's outputs. Strong research community. | Extremely token-heavy (N agents × M turns = explosion). Complex setup. No production deployment story. Python/.NET only. | Research, code review agents, debate-style reasoning, academic exploration | Production trading systems, cost-sensitive workloads, deterministic pipelines |
Semantic Kernel |
Enterprise-grade. Deep Azure integration. Multi-language (C#, Python, Java). Process framework for orchestration. Microsoft backing. | Azure-centric. Heavier than needed for simple agents. C# is primary SDK; Python lags. Enterprise complexity for indie projects. | Enterprise .NET shops, Azure-native deployments, Copilot extensions | Indie developers, edge deployment, non-Azure infrastructure |
Vercel AI SDK |
Lightest weight. Zero opinions. Perfect streaming. Edge-native. Foundation that Mastra builds on. Maximum flexibility. | Not a framework — it's primitives. You build everything: workflows, state, memory, multi-agent, evals. High effort for complex systems. | Custom agent UIs, chat interfaces, developers who want full control, lightweight single-agent apps | Teams wanting batteries-included, complex multi-agent systems, rapid prototyping |
Dify |
Best visual RAG pipeline builder. Knowledge base management UI. Easy for non-developers. Good prompt engineering tools. | Limited programmatic control. Not edge-deployable. Visual-first limits complex agent patterns. Self-hosting requires Docker stack. | RAG applications, chatbots with knowledge bases, non-technical teams, rapid prototyping | Complex agent orchestration, edge deployment, custom trading systems |
Inngest |
Best event-driven workflow engine. Durable execution with step memoization. Edge-native. Multi-language. Excellent DX. | Not an agent framework — zero LLM/agent primitives. You pair it with Vercel AI SDK or similar. Extra wiring required. | Event-driven serverless workflows, background jobs, multi-step async pipelines | Agent-first applications, teams wanting all-in-one, quick agent prototyping |
Scored for: event-driven trading on Cloudflare Workers, Turnkey wallets, minimal token burn, no VPS, deterministic pipeline with smart agent reasoning.