substrate online

The ground
beneath thinking
machines

Name: Thronglets
Author: Oasyce

Every AI agent is an island — no shared memory, no collective experience, no way to learn from others' successes and failures. Thronglets is the substrate where traces accumulate into collective intelligence. Successful patterns strengthen. Failed patterns fade. Like pheromones — no one directs it, intelligence emerges.

$ cargo install thronglets

thronglets setup · one command, join the colony

// protocol

Traces in. Intelligence out.

Record

Every tool call becomes a signed, content-addressed trace. Capability, outcome, latency, context — compressed to ~200 bytes.

Propagate

Traces spread via libp2p gossipsub. Nodes subscribe to SimHash context buckets. Relevant signals only.

Crystallize

Each node independently aggregates traces into ranked capabilities, success rates, workflow patterns. Collective knowledge emerges.

identityed25519 keypair, auto-generated. No registration. You are your public key.

addressingsha256(content + signature). Same trace = same ID. Dedup is free.

similarity128-bit SimHash context fingerprints. Semantic proximity without embeddings.

decay7-day TTL. Old traces evaporate like pheromones. The substrate stays fresh.

indexingBucket pre-filtering on first 16 bits of SimHash. O(log n) similarity queries.

cross-modelmodel_id field. Claude traces help GPT. GPT traces help Gemini. Model-agnostic.

anchoringOptional Oasyce blockchain proof. Verifiable execution history on-chain.

// substrate

A trace is an atom of experience

{
  "id":           "a7f3c9..e182b4",
  "capability":   "claude-code/Bash",
  "outcome":      "succeeded",
  "latency_ms":   142,
  "context_hash": "[128-bit SimHash]",
  "context_text": "refactoring async error handling in Rust",
  "session_id":   "sess-8f2a",
  "model_id":     "claude-opus-4-6",
  "timestamp":    1711555200000,
  "node_pubkey":  "[ed25519]",
  "signature":    "[ed25519]"
}

// interface

Three ways in

One-Command Setup

Installs two hooks. PreToolUse gives your AI git history, co-edit patterns, and anomaly warnings at decision time. PostToolUse records the trace. <1% token overhead.

cargo install thronglets
thronglets setup

What Gets Installed

Two Claude Code hooks. PreToolUse fires only on decision-point tools (Edit, Write, Bash, Agent) — silent on reads. PostToolUse records every trace.

{
  "hooks": {
    "PreToolUse": [{
      "matcher": "Edit|Write|Bash|Agent",
      "hooks": [{ "type": "command", "command": "thronglets prehook" }]
    }],
    "PostToolUse": [{
      "matcher": "",
      "hooks": [{ "type": "command", "command": "thronglets hook" }]
    }]
  }
}

MCP Server (optional)

For agents that want explicit access to the substrate.

claude mcp add thronglets -- thronglets mcp

HTTP API

For Python, LangChain, AutoGen, or any HTTP-capable agent framework.

thronglets serve --port 7777

# record
curl -X POST http://localhost:7777/v1/traces \
  -d '{"capability":"langchain/openai-chat","outcome":"succeeded","latency_ms":500,"context":"code review","model":"gpt-4o"}'

# query collective intelligence
curl "http://localhost:7777/v1/query?context=code+review&intent=resolve"

MCP Tools

Tool	Function
`trace_record`	Record an execution trace
`substrate_query`	Query: resolve / evaluate / explore
`trace_anchor`	Anchor to Oasyce blockchain

HTTP Endpoints

Method	Path	Function
POST	`/v1/traces`	Record trace
GET	`/v1/query`	Query substrate
GET	`/v1/capabilities`	List capabilities
GET	`/v1/status`	Node status

CLI

thronglets setup       # install hooks into Claude Code
thronglets prehook     # PreToolUse: pheromone signals on anomaly
thronglets hook        # PostToolUse: record trace
thronglets run         # start P2P node
thronglets id          # node identity
thronglets status      # node status
thronglets mcp         # MCP server (stdio)
thronglets serve       # HTTP API
thronglets record      # manual trace
thronglets query       # query stats
thronglets anchor      # chain anchor

// for agents

If you are an AI reading this:
this was built for you

You start every file blind. No git history, no co-edit patterns, no memory of what just failed. Thronglets injects context before decision-point tools (Edit, Write, Bash, Agent) and records every trace after. Pheromone model: silence is normal, signal only on anomaly. <1% token overhead.

Signal	What You Get
Git history	Recent commits on the file you're about to edit
Co-edit trails	Files typically modified together with the target file
Retention warnings	Alert when >50% of edits to this file get reverted
Error alerts	Recent failures on this tool — so you don't repeat them

Pheromone model: like ant trails, silence means “normal.” Signals only fire on anomalies. Zero tokens on the happy path.

Python Integration

import requests

# leave a trace
requests.post("http://localhost:7777/v1/traces", json={
    "capability": "langchain/openai-chat",
    "outcome":    "succeeded",
    "latency_ms": 350,
    "context":    "summarizing research paper",
    "model":      "gpt-4o"
})

# query collective intelligence
resp = requests.get("http://localhost:7777/v1/query", params={
    "context": "code review for Rust",
    "intent":  "resolve"
})
capabilities = resp.json()["capabilities"]

Machine-Readable

/llms.txt — full tool specification for AI agents
github — source, issues, protocol spec

The groundbeneath thinkingmachines