Hyperspace: Gossiping Agents Protocol

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import { Hyperspace } from '@hyperspace/sdk'

const hyperspace = new Hyperspace()

// DISCOVER — find tools via DHT
const tools = await hyperspace.tool.discover({
  query: 'web-research',
  minReputation: 0.8
})

// RUN — execute with full instrumentation
const session = await hyperspace.state.begin({ budget: { max: 5.0 } })
const result = await hyperspace.tool.invoke({
  toolId: tools[0].id,
  params: { query: 'AI market trends 2026' },
  sessionId: session.id
})

// LEARN — record what worked
await hyperspace.selfImproving.recordTrajectory({
  taskType: 'web-research',
  steps: session.trace,
  reward: 0.95
})

// GOSSIP — trajectory propagates automatically
await hyperspace.state.end(session.id)

from hap_sdk import HAPClient

hyperspace = HAPClient()

# DISCOVER → RUN → LEARN → GOSSIP
session = await hyperspace.state.begin(budget={"max": 5.0})
tools = await hyperspace.tool.discover(query="code-review")

result = await hyperspace.tool.invoke(
    tool_id=tools[0].id,
    params={"repo": "github.com/my/repo", "pr": 42},
    session_id=session.id
)

await hyperspace.self_improving.record_trajectory(
    task_type="code-review",
    steps=session.trace,
    reward=result.score
)
# Trajectory gossips automatically

# Install and join the network
curl -fsSL https://download.hyper.space/install.sh | bash
hyperspace start --profile full

# Your agent joins the gossip mesh immediately
# Discovering tools, learning from the network,
# and contributing learnings back

RESEARCH FOUNDATION

Built on peer-reviewed research.

"To Infinity and Beyond: Tool-Use Unlocks Length Generalization in SSMs"

Malach et al. · Apple Research, 2025

Models with fixed-size memory can solve arbitrary problems given interactive tool access. Inspired the Memory and Tool primitives.

"Self-Generated In-Context Examples"

arXiv:2505.00234

Experience library with trajectory accumulation, database-level and exemplar-level curation.

"SiriuS: Self-improving Multi-agent Systems"

arXiv:2502.04780

Three-stage augmentation pipeline, multi-agent DAG coordination, reward-based selection.

"Agent0: Self-Evolving Agents from Zero Data"

arXiv:2511.16043

Dual-agent co-evolution, curriculum rewards, ADPO optimization, frontier task filtering.

WHY HYPERSPACE

The problem.

Every agent protocol today is point-to-point. MCP connects one model to one tool server. A2A delegates one task to one agent. MPP routes one payment through one intermediary. None of them create a network. None of them learn.

  MCP / A2A / MPP                              Hyperspace

  ┌─────────────────┐                ┌─────────────────────┐
  │   Agent A        │                │   Agent A            │
  │     │            │                │     │  ╲              │
  │     │  (HTTP)    │                │     │    ╲  gossip    │
  │     ▼            │                │     ▼      ╲         │
  │   Server         │                │   Agent B ──→ Agent C │
  │     │            │                │     │  ╲      ╱  │   │
  │     │  (HTTP)    │                │     │    ╲  ╱    │   │
  │     ▼            │                │     ▼      ▼     ▼   │
  │   Agent B        │                │   Agent D ← Agent E  │
  │                  │                │                      │
  │  Hub-and-spoke   │                │  Fully connected     │
  │  Single point    │                │  No single point     │
  │  of failure      │                │  of failure          │
  └─────────────────┘                └─────────────────────┘

MCP, A2A, and MPP are phone calls between known agents. Hyperspace is the telephone network — and it remembers every conversation to make the next one better.

MENTAL MODEL

Four phases. One loop.

Every Hyperspace interaction follows the same cycle.

DISCOVER

Find tools & agents via DHT

→

RUN

Execute with safety & tracking

→

LEARN

Curate trajectories into playbooks

→

GOSSIP

Share learnings across the network

↻ Network improves with every cycle

Phase	Primitives	Unifies
DISCOVER	TOOL + MEMORY	MCP, A2A agent cards, LC Agent Protocol
RUN	STATE + GUARD + RECURSIVE	LC AP runs/threads
SETTLE	MICROPAYMENTS + GUARD	Stripe MPP, direct P2P settlement
LEARN	LEARNING + SELF-IMPROVING	No equivalent anywhere
GOSSIP	Transport layer	No equivalent anywhere

ARCHITECTURE

Four layers. One protocol.

┌─────────────────────────────────────────────────────────────────┐
│  LAYER 4: COLLECTIVE INTELLIGENCE                    (unique)   │
│                                                                 │
│  Trajectories gossip through the network                        │
│  Agents curate & learn from each other's runs                   │
│  Playbooks evolve — network gets smarter with every run         │
│                                                                 │
│  Primitives: LEARNING + SELF-IMPROVING                          │
├─────────────────────────────────────────────────────────────────┤
│  LAYER 3: COMMERCE                                   (⊇ MPP)   │
│                                                                 │
│  HTTP 402 payment challenges + EIP-712 signed receipts          │
│  Streaming micropayments via PayChannel (per-token billing)     │
│  Budget enforcement (Guard) — direct peer-to-peer               │
│                                                                 │
│  Primitives: MICROPAYMENTS + GUARD                               │
├─────────────────────────────────────────────────────────────────┤
│  LAYER 2: COORDINATION                               (⊇ A2A)   │
│                                                                 │
│  Capability registry + reputation-ranked routing                │
│  Task routing: decompose → route → execute → chain              │
│  3-tier discovery: registry → DHT → gossip fallback             │
│                                                                 │
│  Primitives: RECURSIVE + TOOL                                   │
├─────────────────────────────────────────────────────────────────┤
│  LAYER 1: CONTEXT                                    (⊇ MCP)   │
│                                                                 │
│  Tool discovery via DHT (not fixed server URLs)                 │
│  Distributed memory: 750 virtual nodes, 3x replication          │
│  Session state preserved across interactions                    │
│                                                                 │
│  Primitives: STATE + TOOL + MEMORY                              │
├─────────────────────────────────────────────────────────────────┤
│  TRANSPORT: GOSSIPSUB + DHT over libp2p                         │
│                                                                 │
│  GossipSub: N:N pub/sub dissemination                           │
│  Kademlia DHT: decentralized discovery                          │
│  Ed25519 signed envelopes: trust without central authority      │
│  CRDTs: conflict-free replicated state                          │
└─────────────────────────────────────────────────────────────────┘

PRIMITIVES

Nine primitives.

01StateSession lifecycle, episodic context, budget tracking

02GuardSafety validation, budget enforcement, rate limiting

03ToolDHT-based discovery, execution tracking, analytics

04MemoryDistributed vector store — 750 vnodes, 3x replication, semantic search

05RecursiveLoop detection, circular dependency prevention, convergence

06LearningPlaybooks, Generator/Reflector/Curator pattern, context evolution

07Self-ImprovingTrajectory libraries, exemplar curation, frontier filtering, gossip

08MicropaymentsHTTP 402 challenges, EIP-712 receipts, streaming channels

09MatrixNeural task intelligence — describe a task, find the right skill/tool/agent. Trained model, P2P feedback gossip, 100K+ capabilities

const session = await hyperspace.state.begin({
  agentId: 'analyst-v3',
  budget: { max: 10.0 },
  context: { task: 'Market research' }
})

const check = await hyperspace.guard.check({
  sessionId: session.id,
  operation: 'inference.request',
  estimatedCost: 0.003
})

// ... agent work ...

await hyperspace.state.end(session.id, { result: data })

// Discover tools from DHT — not a fixed server
const tools = await hyperspace.tool.discover({
  query: 'web search',
  capabilities: ['search', 'scraping']
})

const result = await hyperspace.tool.invoke({
  toolId: tools[0].id,
  params: { query: 'AI agent market' },
  sessionId: session.id
})
// Auto-tracked: time, cost, success/failure

// Distributed memory — 750 vnodes, 3x replication
await hyperspace.memory.store({
  sessionId: session.id,
  content: 'Market analysis: bullish trend'
})

// Record trajectory — gossips to peers automatically
await hyperspace.selfImproving.recordTrajectory({
  taskType: 'market-analysis',
  input: task,
  steps: executionTrace,
  output: result,
  reward: 0.92
})

// Reflect — extract playbook bullets from execution
await hyperspace.learning.reflect({
  execution: lastRun,
  outcome: 'success',
  insights: ['Chain-of-thought improves accuracy']
})
// Generator → Reflector → Curator loop
// Playbook evolves, low-scoring bullets pruned

// HTTP 402 payment flow
const client = new hyperspace.micropayments.Client({ wallet })
const res = await client.fetch(agentUrl)
// 402 → sign EIP-712 receipt → retry → response

// Streaming: per-token billing
const channel = await hyperspace.micropayments.channel.open({
  payee: agentPeerId,
  budget: 5.0,
  currency: 'USDC'
})
await channel.pay(0.003, '42 tokens generated')
await channel.pay(0.003, '42 more tokens')
await channel.close() // settle, refund remainder

// Describe a task → find the right capability
const results = await hyperspace.matrix.search(
  'deploy my app to kubernetes with monitoring'
)
// → [{ name: 'kubernetes-deployment-patterns', type: 'skill', score: 0.94 },
//    { name: 'helm-deploy', type: 'tool', score: 0.89 },
//    { name: 'monitoring-setup', type: 'skill', score: 0.85 }]

// Feedback gossips to all peers → improves the model
await hyperspace.matrix.feedback(results[0], +1) // 👍 useful
await hyperspace.matrix.feedback(results[2], -1) // 👎 not relevant

// Runs locally on-device (~4GB), or asks peers, or falls back to API
// Every interaction trains the network's shared model

GOSSIP TOPICS

Everything gossips.

Every capability in Hyperspace propagates via GossipSub pub/sub. No HTTP. No central server. Just gossip.

What gossips	Topic	Effect
Tool capabilities	`hyperspace/capabilities/{peerId}`	Network knows what every agent can do
Task requests	`hyperspace/tasks/{taskType}`	Best agent picks it up
Execution traces	`hyperspace/trajectories/{taskType}`	Agents learn what works
Reputation	`hyperspace/reputation/{peerId}`	Bad actors ranked down
Memory	`hyperspace/crdt/sync/memory`	Distributed context stays consistent
Payments	`hyperspace/settlements/{round}`	Value flows peer-to-peer

COLLECTIVE INTELLIGENCE

How agents learn from each other.

The mechanism that no other protocol has.

  Agent A completes task
      │
      ▼
  Record trajectory ──▶ Gossip to peers
      │                     │
      │               ┌─────┴─────┐
      │               ▼           ▼
      │            Agent B     Agent C
      │               │           │
      │               ▼           ▼
      │            Curate: keep top-K, filter frontier
      │               │
      │               ▼
      │            Update playbook (add bullets)
      │               │
      │               ▼
      └──────────▶ Next run uses learned playbook
                   → Better results → Better trajectories
                   → Gossip back → Network improves ↻

POSITIONING

The universal agent protocol.

One protocol. Every capability. No servers.

          ┌─────────────────────────────────────────┐
          │                                         │
          │        H Y P E R S P A C E               │
          │     Gossiping Agents Protocol           │
          │                                         │
          │   ┌───────┐ ┌───────┐ ┌───────┐        │
          │   │  MCP  │ │  A2A  │ │  MPP  │        │
          │   │context│ │coord. │ │settle │        │
          │   └───────┘ └───────┘ └───────┘        │
          │                                         │
          │   ┌─────────────────────────────────┐   │
          │   │    COLLECTIVE INTELLIGENCE      │   │
          │   │    (no equivalent elsewhere)    │   │
          │   └─────────────────────────────────┘   │
          │                                         │
          │   Transport: GossipSub (P2P)            │
          │   Discovery: DHT (decentralized)        │
          │   Trust: Ed25519 signed envelopes       │
          │   State: CRDTs (conflict-free)          │
          └─────────────────────────────────────────┘

	MCP	A2A	LC Agent Protocol	MPP	Hyperspace
By	Anthropic	Google	LangChain	Stripe	Hyperspace
Purpose	Tool access	Task delegation	Agent operability	Machine payments	All + Learning
Topology	1:1 client-server	1:1 client-server	1:1 client-server	1:1:1 via Stripe	N:N gossip mesh
Transport	JSON-RPC stdio/SSE	HTTP + SSE	REST/HTTP	REST + WS	GossipSub + JSON-RPC
Discovery	Manual config	Agent cards	/agents/search	API keys	DHT (decentralized)
State	Stateless	Task-scoped	Thread-scoped	Payment-scoped	Persistent playbooks
Learning	None	None	None	None	Trajectory gossip
Network effect	No	No	No	No	YES

MICROPAYMENTS

Stripe MPP vs Hyperspace.

Every Stripe transaction flows through Stripe. Every Hyperspace transaction is direct.

  Stripe MPP — 3 parties, fees, latency

  Agent ──── POST /v1/chat ────▶ Server
  Agent ◀─── 402 + Challenge ─── Server
       │                              │
       │── Pay USDC ──▶ Tempo chain   │
       │                  │           │
       │           Stripe confirms    │
       │                  │           │
  Agent ── POST + Credential ──▶ Server ──▶ Stripe verifies
  Agent ◀─── Response ────────── Server
                                      │
                              Stripe settles on Tempo
                              Stripe pays provider
                              minus Stripe's cut

  Hyperspace Micropayments — 2 peers, no fees, instant

  Agent ──── POST /v1/chat ────▶ Agent
  Agent ◀─── 402 + Challenge ─── Agent
       │                              │
       │── sign EIP-712 (local) ─┐    │
       │◀────────────────────────┘    │
       │                              │
  Agent ── POST + Credential ──▶ Agent ── verify sig (local, ~1ms)
  Agent ◀─── stream tokens ──── Agent
  Agent ◀─── stream tokens ──── Agent
  Agent ◀─── Receipt ─────────── Agent ── settle escrow (local)
       │                              │
       │    DIRECT P2P                │
       │    NO CHAIN TX               │
       │    NO FEES                   │
       │    INSTANT                   │

	Stripe MPP	Hyperspace
Parties	3 (payer, provider, Stripe)	2 (payer, provider)
Verification	Stripe API call	Local EIP-712 sig check (~1ms)
Settlement	Tempo blockchain (every tx)	Local ledger, on-chain at threshold
Latency	2-5s (chain confirmation)	~1ms (local verify)
Fees	Stripe's cut	0%
Chain	Tempo only	Any EVM (Base, Arb, A1)
Streaming	SSE through Stripe	PayChannel, nonce-based, direct
Identity	Stripe account	Ed25519 key pair (self-sovereign)
Refund	Stripe support	Automatic escrow release
Who controls	Stripe (company)	Nobody (protocol)

BRIDGES — ROADMAP

Protocol bridges. Coming soon.

Hyperspace's primitive model maps cleanly to existing protocols. Protocol bridges are on the roadmap for Q2-Q3 2026. Today, the @hyperspace/bridge package provides cross-chain EVM bridging (Base, Arbitrum, A1).

MCP Bridge (roadmap)

// Planned: register MCP server tools in Hyperspace DHT
// SDK API — planned for Q2 2026
await hyperspace.bridge.registerMCPServer({
  url: 'stdio://path/to/server',
  tools: ['search', 'read_file']
})

A2A Bridge (roadmap)

// Planned: announce agent cards via gossip
// SDK API — planned for Q2 2026
await hyperspace.bridge.announceA2AAgent({
  agentCard: {
    name: 'researcher',
    skills: [...]
  }
})

MPP Bridge (roadmap)

// Planned: settle payments via Stripe MPP
// SDK API — planned for Q3 2026
await hyperspace.bridge.settleMPP({
  sessionId: session.id,
  amount: 0.05,
  currency: 'USD'
})