Fences

Runtime governance for AI agents. Budget limits, loop protection, token governance, and a decision audit trail — in three lines of code.

import agentfences
from agentfences import governed, checkpoint, log_decision

agentfences.init(local_only=True)

@governed(budget_usd=0.50, max_iterations=20, max_tokens=50_000)
async def run_agent(query: str, messages: list):
    while True:
        log_decision(reasoning="calling LLM", action="llm_call")
        response = call_llm(messages)

        result = await checkpoint(
            cost_delta_usd=compute_cost(response.usage),
            tokens_used=response.usage.total_tokens,
        )

        if result.breached:
            # Agent handles the limit gracefully — no exception, no crash
            messages.append({"role": "system", "content": result.system_prompt})
            return call_llm(messages)  # LLM summarises what it found

        if response.is_done():
            return response

Install

pip install agentfences

How it works

checkpoint() returns a CheckpointResult after every call — not an exception. The agent reads it and decides what to do: stop, summarise, ask for more budget. No try/catch in your orchestration layer.

result = await checkpoint(cost_delta_usd=0.02, tokens_used=450)

result.ok           # True if within all limits
result.breached     # True if a limit was crossed
result.breach_type  # "budget_exceeded" | "iteration_limit" | "time_limit" | "token_limit"
result.message      # "I've reached my budget limit... I'll summarise what I found."
result.system_prompt  # ready to inject into LLM conversation context

Quickstart — no backend needed

import agentfences
from agentfences import governed, checkpoint, log_decision

agentfences.init(local_only=True)  # no account, no API key

@governed(budget_usd=0.10, max_iterations=5, max_tokens=2000)
async def my_agent(query: str):
    for step in range(100):
        log_decision(reasoning=f"step {step}: searching", action="search")

        result = await checkpoint(cost_delta_usd=0.02, tokens_used=200)
        if result.breached:
            return result.message  # or inject result.system_prompt into your LLM

    return "done"

Limits

Legacy exception mode

If you prefer exceptions over result objects:

@governed(budget_usd=0.50, raise_on_breach=True)
async def my_agent():
    ...
    await checkpoint(cost_delta_usd=0.02)  # raises BudgetExceeded, IterationLimitReached, etc.

Modes

Local (free) — governance runs entirely in-process. No account, no backend, no API key.

agentfences.init(local_only=True)

Cloud (coming soon) — persistent audit trails, live dashboard, server-authoritative enforcement. Same code, one line changes.

agentfences.init(api_key="fc_...", endpoint="https://your-fences-instance.com")

License

MIT