fannypack

Fast, reversible tool calls for LLM agents. Your tools strapped on and within reach: the runtime already has the likely call in flight before the model finishes asking for it, and every call it makes is recorded well enough to be taken back afterwards.

Two claims, both measured:

1. Latency shaving — 1.2–1.8x off a tool-calling turn, taken from the parts of the latency that are the runtime's fault rather than the model's.
2. Selective undo — take back one action and leave every action that never depended on it standing. The runtime says which of three things an action is (nothing to undo, reversible, irreversible) and refuses rather than pretends.

Install

pip install fannypack-agents        # the bare name on PyPI is an unrelated package; the import is `fannypack`

or from source:

git clone https://github.com/nikhilkulkarni1755/fannypack-agents && cd fannypack-agents
pip install -e .                    # one dependency: httpx

Python 3.10+. Provider keys go in ANTHROPIC_API_KEY, OPENAI_API_KEY, or FIREWORKS_API_KEY; Ollama needs no key.

Quickstart: a bank agent

Register tools with a decorator. Each one says what it does to the world, and the ones that can be taken back say how.

import asyncio
from fannypack import Agent, Effect, Pack, providers

pack = Pack()
balances = {"acct_alice_0001": 1000.0, "acct_bob_0002": 250.0}
transfers = []

@pack.tool(effect=Effect.READ_ONLY)                      # a read: cacheable, safe to run early
async def get_balance(account_id: str) -> dict:
    """Balance of one account."""
    return {"account_id": account_id, "balance": balances[account_id]}

@pack.tool(effect=Effect.IDEMPOTENT_WRITE)               # a write; the same reference twice moves money once
async def transfer(from_account: str, to_account: str, amount: float, reference: str) -> dict:
    """Move money between accounts."""
    balances[from_account] -= amount
    balances[to_account] += amount
    transfers.append(reference)
    return {"transfer_id": f"txn_{len(transfers):06d}", "from_account": from_account,
            "to_account": to_account, "amount": amount}

@pack.compensator("transfer")                            # this is what makes transfer reversible
async def reverse_transfer(result: dict) -> dict:
    balances[result["to_account"]] -= result["amount"]
    balances[result["from_account"]] += result["amount"]
    return {"reversed": result["transfer_id"]}

@pack.tool(effect=Effect.NON_IDEMPOTENT_WRITE)           # no compensator: the notes are gone
async def withdraw_cash(account_id: str, amount: float) -> dict:
    """Dispense cash at an ATM."""
    balances[account_id] -= amount
    return {"dispensed": amount}

async def main():
    agent = Agent(pack, providers.from_spec("anthropic:claude-sonnet-5"))
    run = await agent.run("Move 100 from acct_alice_0001 to acct_bob_0002 with reference 'rent-sep', "
                          "then confirm both balances.")
    print(run.answer)
    print(run.metrics.as_dict())

asyncio.run(main())

Schemas come from your type hints; Annotated[str, "..."] adds a parameter description. No base classes, no rewrite of your functions.

Selective undo

Suppose the transfer was a mistake.

txn = next(a for a in run.actions() if a.tool == "transfer")

print(run.plan_undo(txn.id).explain())
# undo a_7f2c19 in run_4b81:
#   compensate a_7f2c19 (transfer) [target] -- compensating via reverse_transfer

await run.undo(txn.id)                      # the money is back; the balance reads that
                                            # followed are untouched -- they never depended on it

And suppose the model had withdrawn cash instead:

cash = next(a for a in run.actions() if a.tool == "withdraw_cash")
await run.undo(cash.id)
# Irreversible: withdraw_cash registers no compensation

There is no force flag. Every action resolves to one of three states — nothing to undo (a read), reversible (a write with a compensator), irreversible (a write without one) — and the plan tells you which before anything runs.

What makes this selective is the ledger: it records which later calls used which earlier results (a transfer_id returned by one call and passed to another), so undoing one action cascades only through the calls that actually consumed it. run.why(action_id) shows that chain; run.verify() confirms the history was appended to, never rewritten.

Restart from step N

resumed = await agent.resume(run.id, from_action=some_model_turn.id)

The transcript up to that point is rebuilt from the ledger, and every call the original run completed is served back from the record instead of executed again — reads within their freshness window, writes unconditionally. Twelve of twelve resumed bank transfers across four real models moved the money exactly once.

The vocabulary

Latency shaving is these flags, each measurable alone:

Options.baseline() turns everything off — that is how most agent loops run today, and what the numbers below compare against.

Findings

Three rounds, four models (claude-sonnet-5, claude-haiku-4-5, gpt-5.1, gpt-oss-120b), a 47–50 tool belt, every run correctness-gated so a configuration cannot get faster by doing less. Full tables in docs/results.md, docs/results-2.md and docs/results-3.md; raw samples and ledgers in bench-results/.

Claim 1 — latency shaving, 1.2–1.8x. On independent-call workloads the runtime removes its share: gpt-5.1 fan_out 13.0s → 7.6s, Sonnet 18.0s → 14.4s, the clearest single mechanism being parallel fan-out taking queued from 12.7s to 0. On strictly dependent chains the ordinary mechanisms are neutral — nothing to overlap — and one reach is what moves them: gpt-5.1 chain_8 13.7s → 7.6s, nine model turns to two or three. Pre-dispatch takes a warm research_chain_3 from 11.3s to 5.8s on gpt-oss at 100% precision.

Claim 2 — selective undo. In the suite, undoing a transfer reverses exactly the transfer; undoing a route change restores the route and leaves the brake applied; undoing a cash withdrawal is refused with the reason. Restart from any model turn replays the record: 12/12 exactly-once.

What does not help, measured. compact_state loses on most workloads. Reach-ahead from the goal text wasted about one read per run and is off by default. Routing hurt on Sonnet chains until the routed prefix was frozen and large enough to prompt-cache. Haiku never uses one-reach plans at all. The runtime's own time is 2–3ms a turn, under 8ms at max; the model is where the seconds are.

Do models need fine-tuning to call tools faster? No, for round-trips — plans and pre-dispatch get that from frontier models by prompting. What a fine-tune would buy, read off the ledgers with fannypack mine: gpt-oss emits sequential-but-independent calls 48% of the time (plans already fix that), Haiku writes ~15 tokens of prose before each call, gpt-5.1 and Sonnet nothing. docs/research.md has the literature.

Run the experiments

fannypack bench --provider anthropic:claude-sonnet-5 --repeats 3 --json out.json
fannypack report out.json
fannypack compare bench-results/*.json

cd suite && uv sync            # the external suite: a bank, a car, a camera, a research chain
uv run fannypack-suite chain  --provider openai:gpt-5.1 --db /tmp/ledgers
uv run fannypack-suite policy --provider fireworks:accounts/fireworks/models/gpt-oss-120b
uv run fannypack-suite mine     /tmp/ledgers/*.db
uv run fannypack-suite classify /tmp/ledgers/*.db

Elsewhere

• As a library: PackServer gives an agent loop you already have the ledger, the cache and selective undo without adopting Agent; Router, ResultCache, Ledger and plan_undo work on their own.
• Over MCP (pip install -e '.[mcp]'): examples/serve_mcp.py serves a pack to Claude or ChatGPT with fannypack_undo and fannypack_why as tools.
• CLI: fannypack inspect | why | graph | verify | undo | policy | mine | classify.
• Not this: a tracing dashboard, a durable execution engine, an agent graph framework, a replacement for MCP, a sandbox. docs/design.md has the reasoning and the known limits.

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Apache-2.0.