Skip to content

Claudeway

Verifiable multi-agent consensus for Claude. The coordination layer every agent framework punted on.

Every agent framework can make one Claude answer. Most can run several in parallel. None of them make agents genuinely agree — with the disagreement surfaced and the result cryptographically signed. That's Claudeway.

Buzz gave agents a room. Goose gave one agent tools. LangGraph makes you wire coordination by hand. Claudeway is how agents reach agreement.

The 30-second pitch

from claudeway import AgentConfig, Swarm, SwarmConfig, Task

swarm = Swarm(SwarmConfig(
    name="ArchReview",
    agents=[
        AgentConfig("StrongConsistency", "Distributed Systems Engineer", "..."),
        AgentConfig("Operations", "SRE / Platform Lead", "..."),
        AgentConfig("Pragmatist", "Staff Engineer", "..."),
    ],
), api_key=...)

result = await swarm.process(Task(
    id="q1",
    description="Active-active Postgres, FoundationDB, or eventual consistency for payments?",
    input_data={},
))

print(result.result["final_answer"])
print(f"agreement: {result.result['agreement']:.0%}  disagreed: {result.result['disagreed']}")

Three specialists. One signed answer. Disagreement surfaced, not averaged away.

Why this exists

The agent-framework field is crowded but lopsided:

Tool What it solves The gap
LangGraph Production orchestration, manual graphs Coordination is DIY wiring
CrewAI DX, fast prototyping Coordination is shallow; +48% token cost
Goose (Block) Single-agent harness + MCP No multi-agent at all
Buzz (Block) The room agents talk in Explicitly punted on coordination
Anthropic SDK Base Claude calls No coordination layer
MCP marketplaces Tool distribution No agreement primitive

The whitespace, plain: everyone built where agents act or where agents talk. Nobody built how agents agree. Claudeway fills exactly that.

What makes it defensible

Consensus results aren't text — they're signed, tamper-evident attestations anyone can verify:

from claudeway import ConsensusReceipt, Ed25519Backend

receipt = ConsensusReceipt.from_result(result, swarm_name="ArchReview", task_id="q1")
Ed25519Backend().sign_receipt(receipt, private_key)

# Anyone, anywhere, can verify this later:
Ed25519Backend().verify_receipt(receipt)  # -> True
# ...and tampering with the answer invalidates the signature.
  • Ed25519 by default — no new native deps, works everywhere.
  • Swappable signature backend — a post-quantum (ML-DSA) backend drops in later without touching consensus code.
  • Three transports — the same signed receipt renders as plain JSON, a W3C Verifiable Credential, or a Nostr NIP-78 event that drops into a Buzz room.

This is the layer that makes consensus worth acquiring rather than reimplementing in a weekend. See Transports for the tradeoffs and API Reference for the surface area.

Install

pip install claudeway              # the SDK (4 deps, lean)
pip install claudeway[mcp]         # + expose consensus as an MCP server
pip install claudeway[nostr]       # + sign Nostr events for Buzz interop
pip install claudeway[langgraph]   # + drop consensus into a LangGraph StateGraph

Use it three ways

1. As a Python SDK

from claudeway import Swarm, WeightedVote, Debate

# Cheap default — one round, picks highest-confidence answer
swarm = Swarm(config, consensus=WeightedVote())

# For hard questions — agents see peers' answers and revise once
swarm = Swarm(config, consensus=Debate())

2. As an MCP server

claudeway-mcp            # stdio — for Claude Code, Cursor
claudeway-mcp --http     # HTTP/SSE — for remote agents

Any MCP-capable agent (Claude Code, Goose, Buzz rooms) gains two tools: reach_consensus and verify_consensus. One tool call gets a signed agreement — no framework to learn, no graphs to wire.

3. As a coordinator (hierarchical decomposition)

from claudeway import Coordinator, CoordinatorConfig

coord = Coordinator(CoordinatorConfig())
coord.add_sub_agent("Researcher", ...)
coord.add_sub_agent("Analyst", ...)
result = await coord.coordinate(task)

Next

  • Transports — JSON vs W3C VC vs Nostr NIP-78, and when to pick each.
  • Adapters — Buzz and LangGraph integration paths.
  • Benchmarks — Claudeway vs single Claude vs CrewAI on a hard question.
  • API Reference — auto-generated from the source.
  • Changelog