zetryn-trading 1.1.0

AI Agent Trading from Zetryn AI. Graph-based agents for Solana memecoin trading: scanner + sniper, with structured LLM reasoning.

Zetryn AI Agent Trading Framework

AI Agent Trading Framework from Zetryn AI.

A Python framework that turns raw Solana memecoin data into structured, auditable trading decisions. You bring the bot, the wallet, and the RPC; Zetryn provides the decision engine — eight reference agents built from a graph of LLM analysts and hard-rule guardrails, plus a declarative graph-spec layer if you'd rather configure than code.

BOT (yours)                   ZETRYN AI AGENT TRADING (this framework)
─────────────                 ─────────────────────────────────────────
gather token data  ──push──>  safety_gate → intel_gate → market_gate
                                                  │
                                         ┌────────┘
                                         ▼
                              ai analyst (LLM): safety / market /
                                         wallets / social verdict
                                                  │
                                         guardrail (sanity check)
                                                  │
                                                  ▼
                                       Decision { action, confidence,
                                                  analysis, reasons }
                                                  │
execute (or not) <───────────────────────────────┘

Zetryn decides; the bot executes. The framework never holds your private key and never touches the chain.

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[!NOTE] v1.0.0 — API stable. Public APIs are frozen across minor releases; behaviour changes only at major versions. The Zetryn platform (subscription auth, hosted Hardes / Medifus / Easfus models) is not yet live — today the framework runs on public LLM providers (Groq / Gemini / OpenRouter / Cerebras / Mistral / SambaNova / NVIDIA NIM) with your own keys.

Paper-test first. No warranty, no fiduciary — this is a decision engine, not investment advice. See LICENSE.

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Quickstart — no API key needed

git clone https://github.com/zetryn/zetryn-trading
cd zetryn-trading
pip install -e ".[dev]"
cd examples
python walkthrough.py

Runs the scanner on 16 dummy memecoin scenarios — gem, rug, bundle attack, smart-money entry, pumpfun near-graduation, sell pressure — and prints, for each:

1. INPUT — the signals the bot pushed in
2. PROCESSING — which gate stopped it, or the full analyst pipeline
3. ANALYSIS — per-aspect AI verdict with signals + reasoning
4. OUTPUT — the Decision returned to the bot

Uses a heuristic stub LLM so you can see the shape of the output without spending a token. With a real provider key, the reasoning is dramatically richer.

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Install for real use

pip install zetryn-trading
cp .env.example .env
# fill GROQ_API_KEY_1=... (free at https://console.groq.com)

A single free Groq key is enough to drive every agent end-to-end. For production, configure 2+ providers via LLMRouter — see docs/GUIDE.md §2.

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Why Zetryn?

Three properties this framework refuses to compromise on:

• Boundary clean. Framework decides, bot executes. Zetryn never fetches data, holds keys, signs transactions, or tracks positions across calls — ever. Your bot owns I/O; the framework owns the decision graph. That separation is enforced in code, documented in tests, and is the reason the same agent runs unchanged in unit test, backtest, and live trading.

• Graceful by design. LLM failure → conservative fallback verdict, never a crash. Tool error → empty result + flag, never a crash. Rate-limited provider → LLMRouter fails over to the next entry transparently. The graph keeps running so the bot keeps making decisions; the loud failure modes that bite live trading bots are designed out.

• Audit-first. Every node execution produces a StepTrace — scratch before, duration, error if any, what came next. Every LLM call is structured (Pydantic-validated) and stored. Every Decision is loggable to DecisionLog and replayable through Backtester. The postmortem question "why did the agent buy that one?" always has a concrete answer.

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What's in the box

Layer	What it does	Learn more
Core engine	State, Node, Edge, Graph, Command, trace, compile-time validator	zetryn/core/ · CLAUDE.md
LLM router	7 providers (Groq, Gemini, OpenRouter, Cerebras, Mistral, SambaNova, NVIDIA NIM), key rotation on 429, per-model RPM/RPD/TPM/TPD throttle, three tier presets	docs/GUIDE.md §1–2
Knowledge	Markdown rules + JSON data files loaded once into the system prompt — facts the LLM reads	docs/GUIDE.md §3
Skills	Tool + ToolRegistry + LLM-driven tool-use loop — functions the LLM invokes mid-decision	docs/GUIDE.md §4
Memory	Blacklist, DecisionLog, ReflectiveNode (loss-pattern extractor), JSON or in-memory stores	docs/GUIDE.md §6
8 reference agents	Scanner · Sniper · KOL Copy-Trade · Graduation · Lifecycle · Confluence · Dip-Buy · Organic Growth	docs/CAPABILITIES.md · docs/STRATEGIES.md
YAML loader	Declarative graph specs — no Python wiring required for simple strategies	See section below
Backtest	Backtester.run(items) over a list of (id, context) pairs, same compiled graph as live	docs/GUIDE.md §8
Observability	Structured per-node JSON logging, Hooks protocol, trace_to_dicts serializer	zetryn/observability/

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YAML loader — declarative graphs (v0.17.0)

For strategies that don't need bespoke Python wiring, build the graph from a YAML file. The same Graph object falls out — runnable, traceable, testable, identical to a hand-built one.

# scanner.yaml
name: yaml_scanner
entry: safety_gate

nodes:
  - {name: safety_gate, type: rule, fn: strategies.nodes.filters:fast_safety}
  - {name: analyst,     type: llm,
     client: ${llm.tier_speed},
     schema: trading.schemas:ScannerVerdict,
     prompt_fn: strategies.nodes.analyst:scanner_prompt}
  - {name: decide,      type: rule, fn: strategies.nodes.decide:aggregate}

edges:
  - {from: safety_gate, to: analyst, when: "scratch.safety_ok"}
  - {from: safety_gate, to: END,     when: "not scratch.safety_ok"}
  - {from: analyst,     to: decide}
  - {from: decide,      to: END}

from zetryn.config import load_graph

graph = load_graph("scanner.yaml", registry={"llm.tier_speed": my_router})
state = await graph.run(State(context=ctx))

Validation is eager (module:attr references must import; ${name} placeholders must be in the registry; every edge target must exist; the when: DSL is AST-whitelisted — no eval() at runtime). Failures raise ConfigError pointing at the offending line.

CLI: python -m zetryn.config <file.yaml> — exit 0 if valid, 1 with detailed message if not.

Full design rationale + DSL grammar + supported node types → docs/plans/2026-06-27-yaml-loader-m13.md.

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Eight reference agents

All eight share the same contract: the bot fills an input context, the framework returns a Decision. All accept decision_log + reflect_* parameters to enable the reflective learning loop (LLM sees recent losses before deciding).

#	Agent	Strategy	Modes	Builder
A	Scanner	AI-first token discovery	analyst	build_scanner
B	Sniper	Sub-ms launch entry	rule / llm / hybrid / hybrid_audit	build_sniper
C	KOL Copy-Trade	Copy pre-vetted KOL wallets	rule / confirmed / audit	build_kol_copytrade
D	Graduation Snipe	Pump.fun → Raydium migration entry	rule / llm / hybrid / hybrid_audit	build_graduation
E	Position Lifecycle	Hold / TP / scale-out / emergency exit	rule / llm / hybrid / hybrid_audit	build_lifecycle
F	Smart Money Confluence	≥N pre-vetted wallets accumulate same token	rule / llm / hybrid / hybrid_audit	build_confluence
G	Early-Stage Dip Buy	Post-dump recovery entry	rule / llm / hybrid / hybrid_audit	build_dip_buy
H	Organic Growth Detector	Post-launch chart-pattern triage filter	rule / llm / hybrid / hybrid_audit	build_organic_detector

Per-agent specs, signatures, decision shapes, and config knobs → docs/GUIDE.md §7. Strategy catalog (shipped / considered / rejected, with tier rationale) → docs/STRATEGIES.md.

Decision modes (shared shape across most agents)

Mode	Latency	LLM in hot path?	When to use
rule (default)	< 1 ms	No	Production, latency-critical paths
llm	200–500 ms	Yes (decides)	Richer reasoning, older tokens
hybrid	200–500 ms	Yes + rule guardrail	LLM decides, rules veto rug / cap size
hybrid_audit	< 1 ms + async AI verify	Async (non-blocking)	Best of both worlds

hybrid_audit is the speed–intelligence bridge: the rule path returns a Decision instantly, then a background coroutine runs the LLM to second-opinion it. Result lands in state.scratch["audit_task"] for the bot to await and log. Full pattern walkthrough → docs/GUIDE.md §5.

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Boundary — what Zetryn owns vs what the bot owns

Zetryn (framework)	Bot (caller)
Graph orchestration	RPC, wallet, signing
LLM calls (advisor / analyst / decider)	Hot loop, mempool watching
Scoring, decision aggregation	Trade execution, slippage, MEV
Memory (blacklist, decision log)	Position tracking, PnL
Observability (trace, hooks)	Pre-filter, fetch budgeting
Backtest harness	Live market data feeds

The line is non-negotiable: Zetryn decides, bot executes. Never the reverse. New schema field? Probably yes. New async fetcher inside zetryn/? No — it belongs in your bot.

Integration patterns (push vs pull, pre-filter funnel, when to bring your own schemas) → docs/GUIDE.md §9.

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Architecture

zetryn-trading/
├── zetryn/              ← the library (only this ships in the wheel)
│   ├── core/            ← graph engine
│   ├── llm/             ← clients, router, key pool, structured output, tool-use
│   ├── knowledge/       ← KnowledgePack
│   ├── tools/           ← Tool + ToolRegistry
│   ├── memory/          ← Blacklist, DecisionLog, ReflectiveNode
│   ├── observability/   ← logging, hooks, trace serialization
│   ├── auth/            ← SubscriptionAuth seam (platform-pending)
│   ├── backtest/        ← Backtester
│   └── config/          ← YAML graph loader
├── trading/             ← shared contract (TokenInput, Decision, ...)
└── strategies/          ← reference agents (move to your bot repo for production)

Dependency rule (strict, enforced): zetryn/ imports nothing from trading/ or strategies/; trading/ imports nothing; strategies/ imports both. You can import zetryn in your own bot and bring your own trading-shaped schemas if your domain isn't Solana memecoins.

Deeper architecture (per-module conventions, design ADRs, runtime flow) → CLAUDE.md + docs/plans/.

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Status

v1.0.0 — public API stable. Breaking changes will bump to v2.0.0.

Roadmap, per-feature status, and milestone history live in docs/CAPABILITIES.md. What's shipped vs what's planned is updated there on every release; the README does not duplicate that table.

Known limits

• The Zetryn platform (hosted Hardes / Medifus / Easfus models, subscription auth) is not yet live. The seam is stubbed in zetryn/auth/; production today uses public providers with your own keys.
• Single-provider free tier can spike p95 latency past targets. Recommended production pattern: LLMRouter with ≥ 2 providers (e.g. Groq + Gemini Flash). Working example: examples/run_with_router.py.

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Tests

pytest                            # all
pytest tests/test_scanner.py      # one suite
pytest tests/test_sniper.py -v    # one suite, verbose

No API key required. Tests use offline stubs + MockDataProvider.

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Documentation

Doc	Purpose
GUIDE.md	Developer guide — providers, key rotation, knowledge, skills, decision modes, memory & reflective loop, per-agent reference, backtesting, bot integration
CAPABILITIES.md	Source of truth — capability matrix, roadmap, milestone status
STRATEGIES.md	Strategy catalog — shipped, considered, rejected (tier rationale)
CHANGELOG.md	Version-by-version notes
CLAUDE.md	Architecture conventions for contributors
plans/	Design docs (one per roadmap feature)

If anything in plans/ disagrees with CAPABILITIES.md, CAPABILITIES.md wins — the plans are frozen snapshots of decisions, not live tracking.

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License

MIT. Use it, fork it, build something better with it.

Zetryn's own models (Hardes / Medifus / Easfus) and hosted serving will live behind a subscription at zetryn.com when the platform goes live. Public providers (Groq / Gemini / OpenRouter / Cerebras / Mistral / SambaNova / NVIDIA NIM) work today, free of charge to Zetryn, with your own keys.