LangChain, LangGraph, CrewAI, Microsoft AutoGen, OpenAI Agents SDK, Anthropic Agent SDK. Different shapes — multi-step graphs, role-based crews, multi-agent conversations, single-vendor harnesses. Plus how wmcp.sh sits underneath any of them as the MCP tool layer.
Not a leaderboard. Different problems pick different frameworks. Honest tradeoffs below.
All descriptions are based on each project's public documentation and source repos as of 2026-05-28. We are not affiliated with the vendors listed. Features and licensing change — always check the canonical project repo before depending on it.
The broadest open-source agent framework: chains, retrievers, prompts, dozens of LLM provider integrations, tools, agents. Often the substrate other frameworks build on or interoperate with.
A library inside the LangChain ecosystem for building stateful, graph-shaped agent workflows. Strong fit when you need explicit control flow — cycles, branches, human-in-the-loop, checkpointed state.
Role-based multi-agent orchestration: define agents with roles + goals + tools, compose them into a crew with a process (sequential, hierarchical). Purpose-built for "team of specialized agents" patterns.
Multi-agent conversation framework from Microsoft Research. Agents talk to each other (and to humans) to solve tasks. Often the canonical pick for research-style multi-agent setups and code-execution loops.
OpenAI's official agent SDK, tightly integrated with the Responses API, tool calling, and hosted runs. Lowest friction if your agent runs entirely on OpenAI models. Handoffs + guardrails + tracing built in.
Anthropic's first-party Agent SDKs (Python + TypeScript) for building Claude-driven agents. MCP-native — Claude harnesses speak MCP directly, so adding MCP tools is a one-liner.
| Capability | LangChain | LangGraph | CrewAI | AutoGen | OpenAI Agents SDK | Anthropic Agent SDK |
|---|---|---|---|---|---|---|
| Multi-agent handoff | Yes (agent + tools) | Yes (graphs) | Yes (crew) | Yes (conversations) | Yes (handoffs) | Yes (subagent pattern) |
| Tool calling | Yes (@tool) | Yes | Yes | Yes | Yes (typed tools) | Yes (MCP-native) |
| MCP integration | First-party adapter | Via langchain adapter | Community adapters | Community adapters | Community adapters | Native |
| Built-in observability | LangSmith (commercial) | LangSmith | Native traces | Native traces | Tracing built in | Via harness |
| Cross-vendor models | ✅ Many providers | ✅ Many | ✅ Many | ✅ Many | ⚠️ OpenAI-centric | ⚠️ Anthropic-centric |
| License | MIT | MIT | MIT | See repo | Open SDK + hosted | Open SDK + hosted |
Cell values reflect typical patterns from each project's public docs. "First-party adapter" / "community adapters" notes the canonical MCP integration story but does not preclude other patterns.
wmcp.sh isn't a framework — it's an MCP endpoint. Any of the six frameworks above can consume wmcp.sh tools via MCP. The integration is shallow: point the framework's MCP client at wmcp.sh, get tools.
# LangGraph + wmcp.sh:
from langchain_mcp_adapters.client import MultiServerMCPClient
client = MultiServerMCPClient({
"wmcp": { "url": "https://wmcp.sh/mcp/openapi?url=https://...", "transport": "streamable_http" }
})
# Anthropic Agent SDK + wmcp.sh:
# Add https://wmcp.sh/mcp/... as an MCP server in the harness config — done.
# OpenAI Agents SDK + wmcp.sh:
# Use a community MCP adapter to ingest tools from wmcp.sh as Agent tools.You want the broadest integration ecosystem, cross-vendor model support, and you're OK building your own control flow on top.
Your agent needs explicit graphs — cycles, branches, retries, checkpointed state, human-in-the-loop. The right pick for production-grade multi-step.
Your problem fits "team of specialized agents with roles + goals". CrewAI's process abstraction is the cleanest for that shape.
Research-style multi-agent conversations, code-execution loops, or you want the canonical Microsoft Research multi-agent pattern.
You're all-in on OpenAI models + Responses API and want the lowest-friction first-party tool calling, handoffs, and tracing.
You're all-in on Claude and want first-party MCP + Skills + memory. The tightest Claude integration is the official SDK.
Custom MCP adapter + hosted endpoint + framework integration tested end-to-end. From $499 one-time setup; Managed Retainer $999/mo; Enterprise $4,999+/mo.