DeFiPy 2.1.0a2

Python SDK for Agentic DeFi

DeFiPy: Python SDK for Agentic DeFi

DeFiPy is the Python SDK for agentic DeFi — a substrate of composable, typed primitives built on hand-derived AMM math across four families (Uniswap V2, Uniswap V3, Balancer, and Curve-style Stableswap). Most DeFi tools wrap APIs; DeFiPy ships the math. Whether you're building dashboards, simulations, notebook research, or agent-based systems, the primitives compose the same way: stateless construction, exact computation at .apply(), structured dataclass results.

Underneath, DeFiPy is modular by protocol:

• UniswapPy
• BalancerPy
• StableSwapPy

For onchain event access and scripting, pair it with Web3Scout — a companion tool for decoding pool events and interfacing with Solidity contracts.

🔗 SPDX-Anchor: anchorregistry.ai/AR-2026-YdPXB5g

🆕 What's new in v2.1

v2.1 makes the State Twin real. LiveProvider ships for Uniswap V2 and V3 — chain reads compose with every primitive in the library, the same way MockProvider recipes do. The "what would happen if?" loop is now local: pull state once, simulate forever, decide before executing.

• LiveProvider — provider.snapshot("uniswap_v2:0xADDR") and provider.snapshot("uniswap_v3:0xADDR") build V2PoolSnapshot and V3PoolSnapshot from real on-chain state. Block pinning is automatic — "latest" resolves once at the top of .snapshot() and every read inside that snapshot pins to the same block. Pass block_number=N for historical reads.
• Multicall3 batching for V3 — V3 snapshots batch slot0, liquidity, fee, tickSpacing, token0, token1, and block timestamp into one Multicall3 aggregate3 round trip. Hardcoded canonical Multicall3 address; works on every major EVM chain.
• PoolSnapshot enrichment — every snapshot now carries block_number, timestamp, chain_id as optional fields. LiveProvider populates them from chain reads; MockProvider leaves them None to honestly signal "synthetic, not chain state."
• [chain] install extra — pip install defipy[chain] adds web3scout and web3.py for users who want LiveProvider. Core install (no extras) remains free of any chain or LLM dependencies.

V2.1 is a strict superset of v2.0 — every v2.0 primitive, MockProvider recipe, and MCP server pattern works identically. What changes is that the same primitives now run against live chain state without changing call shape.

What's deferred to v2.2: Balancer and Stableswap LiveProvider implementations. V3 tick bitmap walking (active-liquidity-only is the v2.1 stance). Calls for those raise NotImplementedError pointing at the planned version.

v2.0 foundations

The State Twin abstraction, agentic primitives, and MCP server pattern shipped in v2.0. v2.1 builds on that surface without changing it:

• defipy.tools — self-describing schemas for a curated set of 10 leaf primitives, in Model Context Protocol (MCP) format. Any MCP-compatible client can discover and invoke DeFiPy primitives as tools.
• defipy.twin — the State Twin abstraction. MockProvider ships four canonical synthetic pools (V2, V3, Balancer, Stableswap) for notebooks and tests; LiveProvider ships chain reads for V2 and V3 in v2.1.
• MCP server demo at python/mcp/ — a stdio-transport server exposing DeFiPy's tools to Claude Desktop, Claude Code, or any MCP client. Install with pip install defipy[mcp] and see the MCP server README for setup.

What is MCP?

The Model Context Protocol is an open standard for giving LLMs access to tools and data. With DeFiPy's MCP server running, Claude can answer natural-language LP questions backed by exact math:

"Is this V2 pool healthy? Any rug signals?"

Claude reads the tool descriptions, picks CheckPoolHealth, calls it against a twin (synthetic via MockProvider, or live via LiveProvider), receives the typed dataclass result, and synthesizes a response — one that correctly interprets TVL, LP concentration, and activity signals, because the primitives encode the domain, not the LLM.

Substrate, not agent. DeFiPy itself has zero LLM dependencies and zero network calls at core. The library is a substrate that agent runtimes (including forthcoming DeFiMind and any third-party project) build on top of.

🧩 What DeFiPy offers

22 primitives across 7 categories. Each answers a specific LP question with exact math and returns a typed dataclass result:

• Position analysis — "Why is my position losing money? What if price moves X%?" PnL decomposition (IL, fees, net result) and price-move scenarios across Uniswap V2/V3, Balancer, and Stableswap. Includes break-even pricing and time-to-breakeven analysis.
• Pool health — "Is this pool healthy? Any rug signals?" TVL, LP concentration, activity, threshold-based rug detection, fee-anomaly checks (V2/V3).
• Risk — "How exposed am I to a stablecoin depeg? Is my V3 range safe?" Stableswap IL at multiple depeg levels with V2 comparison baseline; V3 tick-range status.
• Execution — "What's my actual slippage? Maximum trade size before it exceeds X%? Did a swap get MEV'd?" (V2/V3).
• Optimization — Zap-in optimal swap fractions, V3 tick range evaluation, rebalance cost analysis.
• Comparison — Side-by-side same-capital analysis across protocols or V3 fee tiers.
• Portfolio — Multi-position aggregation with cross-protocol dispatch.

Full primitive catalog with LP-question mappings lives in the v2 docs.

Legacy event-driven agents (python/prod/agents/) are preserved for chapter 9 of Hands-On AMMs with Python but are not the go-forward architecture — new agentic behavior composes from primitives running against State Twin twins.

📝 Docs

Visit DeFiPy docs for full documentation. The LiveProvider page covers the v2.1 chain-reading surface in detail.

🔍 Install

DeFiPy requires Python 3.10 or later. Install via pip:

> pip install defipy

The core install is the pure analytics engine — AMM math, primitives, State Twin, and all 22 typed analytics functions. It has zero web3 dependencies and zero LLM dependencies. No chain reads, no RPC calls, no MCP. Chain reads come from Web3Scout (via the [chain] or [book] extras); MCP tool serving comes from the [mcp] extra. All optional.

Chain install (LiveProvider — v2.1+)

To use LiveProvider for on-chain pool snapshots, install the [chain] extra:

> pip install defipy[chain]

This pulls in web3scout (which LiveProvider uses internally for ABI loading, contract reads, and token-fetching) plus web3.py. With [chain] installed, you can construct twins from real chain state:

from defipy.twin import LiveProvider, StateTwinBuilder

provider = LiveProvider("https://eth-mainnet.g.alchemy.com/v2/<key>")
snapshot = provider.snapshot("uniswap_v2:0xB4e16d0168e52d35CaCD2c6185b44281Ec28C9Dc")
lp = StateTwinBuilder().build(snapshot)

Note: the [chain] extra pins web3 < 7.0 because web3scout 0.2.0 depends on eth_utils.abi.get_abi_input_types, which was removed in web3 7. If you have web3 7.x installed for other reasons, pip install defipy[chain] will downgrade it. Tracking upstream as v2.2 work.

MCP install (Claude Desktop / Claude Code demo)

To run the MCP server that exposes DeFiPy's primitives as tools to Claude Desktop, Claude Code, or any MCP-compatible client, install the [mcp] extra:

> pip install defipy[mcp]

This adds the mcp Python SDK on top of the core install. The MCP server itself lives at python/mcp/defipy_mcp_server.py; see python/mcp/README.md for Claude Desktop and Claude Code configuration snippets.

Book install (chapter 9 agents)

Chapter 9 of Hands-On AMMs with Python — Building Autonomous DeFi Agents — uses live chain integration via web3scout. To run those examples, install the [book] extra:

> pip install defipy[book]

The [book] extra carries the same package set as [chain] (web3scout + web3). The split is intent-based — [chain] signals production live-state reads via LiveProvider; [book] signals textbook chapter 9 use. Either works for either purpose.

Anvil install (local Foundry workflows)

If you're using ExecuteScript or UniswapScriptHelper against a local Anvil node and don't need the full web3scout event-monitoring stack, the lighter [anvil] extra just adds web3.py:

> pip install defipy[anvil]

[book] and [chain] already include everything in [anvil], so users on either of those don't need it separately.

Source install

To install from source:

> git clone https://github.com/defipy-devs/defipy
> cd defipy
> pip install .

System libraries for gmpy2

DeFiPy depends on gmpy2 for high-precision arithmetic in StableSwap math. On most platforms, pip will install gmpy2 from a prebuilt wheel and no further setup is needed. If the install fails, you may need the GMP, MPFR, and MPC system libraries installed before pip install:

macOS (Homebrew):

> brew install gmp mpfr libmpc

Linux (Debian / Ubuntu):

> sudo apt install libgmp-dev libmpfr-dev libmpc-dev

See the gmpy2 installation docs for other platforms.

🔍 Learning Resources

DeFiPy is accompanied by educational resources for developers and researchers interested in on-chain analytics and DeFi modeling.

📘 Textbook

DeFiPy: Python SDK for On-Chain Analytics

A comprehensive guide to DeFi analytics, AMM modeling, and simulation.

🔗 Buy on Amazon: https://www.amazon.com/dp/B0G3RV5QRB

🎓 Course

On-Chain Analytics Foundations

A practical course on transforming raw blockchain data into structured analytics pipelines using Python.

Topics include:

• retrieving blockchain data via Ethereum RPC
• decoding event logs
• analyzing AMM swap events
• building DeFi analytics pipelines

🔗 Course Page: https://defipy.thinkific.com/products/courses/foundations

🚀 Quick Example (LiveProvider: real chain state + primitives)

---

The fastest way to see DeFiPy at work — pull a real Uniswap V2 pool's state from mainnet and run a primitive against it. Requires the [chain] install extra.

from defipy import AnalyzePosition
from defipy.twin import LiveProvider, StateTwinBuilder

# Pull live state from a real Uniswap V2 pool — WETH/USDC mainnet
provider = LiveProvider("https://eth-mainnet.g.alchemy.com/v2/<key>")
snapshot = provider.snapshot(
    "uniswap_v2:0xB4e16d0168e52d35CaCD2c6185b44281Ec28C9Dc"
)
lp = StateTwinBuilder().build(snapshot)

# Snapshot carries chain context — block_number, timestamp, chain_id
print(f"Block:   {snapshot.block_number}")
print(f"Reserves: {snapshot.token0_name}={snapshot.reserve0:.2f}, "
      f"{snapshot.token1_name}={snapshot.reserve1:.2f}")

# Run any primitive against the live twin — same call shape as MockProvider
result = AnalyzePosition().apply(
    lp,
    lp_init_amt=1.0,
    entry_x_amt=1000,
    entry_y_amt=3_000_000,
)

print(f"Diagnosis:   {result.diagnosis}")
print(f"Net PnL:     {result.net_pnl:.4f}")
print(f"IL %:        {result.il_percentage:.4f}")
print(f"Current val: {result.current_value:.4f}")

The same shape works for V3 — swap uniswap_v2: for uniswap_v3: and the appropriate pool address (e.g. 0x88e6A0c2dDD26FEEb64F039a2c41296FcB3f5640 for USDC/WETH 3000bps). V3 snapshots default to full-range ticks; pass lwr_tick=N, upr_tick=N to override. See the LiveProvider docs for block pinning, the V3 tick-range surface, and the active-liquidity-only caveat.

No chain access? Substitute MockProvider for LiveProvider and pass a recipe name ("eth_dai_v2", "eth_dai_v3", "eth_dai_balancer_50_50", "usdc_dai_stableswap_A10"). Same primitive call, same result shape, no network needed:

from defipy.twin import MockProvider, StateTwinBuilder

provider = MockProvider()
lp = StateTwinBuilder().build(provider.snapshot("eth_dai_v2"))
# ... AnalyzePosition().apply(lp, ...) works identically

The State Twin abstraction is what makes this work: providers know about sources, primitives know about math, the twin is the canonical handoff between them. Same lp shape from a synthetic recipe, a live chain read, or a custom CSV-backed provider — every primitive consumes them identically.

For LLM-driven interaction with these primitives, see the MCP server README.

🧱 Quick Example (low-level: Uniswap V3 pool construction)

---

To construct a Uniswap V3 pool directly (outside MockProvider's canonical recipes and outside LiveProvider's chain reads), you must first create the tokens in the pair using the ERC20 object. Next, create a liquidity pool (LP) factory using IFactory object. Once this is setup, an unlimited amount of LPs can be created; the procedures for such are as follows:

from defipy import *

# Step 1: Define tokens and parameters
eth = ERC20("ETH", "0x93")
tkn = ERC20("TKN", "0x111")
tick_spacing = 60
fee = 3000  # 0.3% fee tier

# Step 2: Set up exchange data for V3
exch_data = UniswapExchangeData(tkn0=eth, tkn1=tkn, symbol="LP", address="0x811", version='V3', tick_spacing=tick_spacing, fee=fee)

# Step 3: Initialize factory
factory = UniswapFactory("ETH pool factory", "0x2")

# Step 4: Deploy pool
lp = factory.deploy(exch_data)

# Step 5: Add initial liquidity within tick range
lwr_tick = UniV3Utils.getMinTick(tick_spacing)
upr_tick = UniV3Utils.getMaxTick(tick_spacing)
join = Join()
join.apply(lp, "user", 1000, 10000, lwr_tick, upr_tick)

# Step 6: Perform swap
swap = Swap()
out = swap.apply(lp, tkn, "user", 10)

# Check reserves and liquidity
lp.summary()

# OUTPUT:
Exchange ETH-TKN (LP)
Real Reserves:   ETH = 999.0039930189599, TKN = 10010.0
Gross Liquidity: 3162.277660168379  

🧪 Tests

DeFiPy ships ~677 tests across primitives, tools, twin, packaging, and the MCP server dispatch layer. Run the full suite:

pytest python/test/ -v

Run just the primitive suite (504 tests, no MCP or twin dependencies):

pytest python/test/primitives/ -v

The twin suite includes opt-in live-RPC tests gated by the DEFIPY_LIVE_RPC environment variable — set it to a mainnet RPC URL to verify LiveProvider against real chain state:

DEFIPY_LIVE_RPC=https://eth-mainnet.g.alchemy.com/v2/<key> pytest -m live_rpc -v

License

Licensed under the Apache License, Version 2.0.
See LICENSE and NOTICE for details.
Portions of this project may include code from third-party projects under compatible open-source licenses.