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petfishFramework — a general AI Agent framework

Project description

petfishFramework

A lightweight Python framework for reliable, auditable, budget-aware, and permission-aware AI agents.

Python 3.10+ License: MIT Tests: 187

Status: Alpha — API may change. Core runtime works; see Roadmap.

Quick Start (Zero Cost — No API Key)

pip install petfishframework
from petfishframework import Agent, ReAct
from petfishframework.tools.calculator import Calculator
from petfishframework.models.fake import FakeModel

# FakeModel — runs without any API key, perfect for testing
model = FakeModel.script_tool_then_answer(
    tool_name="calculator",
    tool_args={"expression": "17 * 23"},
    final_answer="391",
)

agent = Agent(
    model=model,
    reasoning=ReAct(),
    tools=(Calculator(),),
)

result = agent.run("What is 17 * 23?")
print(result.answer)  # "391"
print(result.usage.total_tokens)
print(len(result.trajectory.steps), "steps")

Quick Start (Real LLM)

pip install "petfishframework[openai]"
from petfishframework import Agent, ReAct
from petfishframework.tools.calculator import Calculator
from petfishframework.models.openai import OpenAIModel

agent = Agent(
    model=OpenAIModel(model="gpt-4o-mini"),  # or model="openai:gpt-4o-mini"
    reasoning=ReAct(),
    tools=(Calculator(),),
)

result = agent.run("What is 17 * 23?")
print(result.answer)  # "391"

Set OPENAI_API_KEY in .env or environment. Works with OpenAI-compatible APIs (SiliconFlow, etc.) via OPENAI_BASE_URL.

Budget Control (Runtime Hard Limits)

from petfishframework import Budget

# Budget is execution-scoped — pass to run() or session(), not Agent()
result = agent.run(
    "Complex calculation task",
    budget=Budget(max_tokens=1000, max_tool_calls=5, max_steps=10),
)
# Exceeding any limit raises BudgetExceeded

Permission Gate (Runtime Access Control)

from petfishframework.permissions.model import (
    Decision, DecisionEffect, PermissionPolicy,
)

class DenyExpensiveTools:
    """Custom policy: deny tools tagged 'expensive'."""
    def evaluate(self, subject, action, resource, context):
        if "expensive" in resource.tags:
            return Decision(effect=DecisionEffect.DENY, reason="too expensive")
        return Decision(effect=DecisionEffect.ALLOW)

agent = Agent(
    model=model,
    reasoning=ReAct(),
    tools=(Calculator(),),
    permission_policy=DenyExpensiveTools(),
)
# Tool calls pass through the Environment chokepoint — denied calls never execute

Replay & Audit (Event-Sourced Sessions)

session = agent.session("What is 17 * 23?")
result = session.run()

# Every step is recorded — model calls, tool calls, permission decisions
for event in session.replay():
    print(f"{event.type}: {event.data}")

# Events come from session.replay(), not from Result
# Result has: answer, usage, trajectory
# Session has: events, replay(), checkpoint()

MCP Client (External Tool Servers)

pip install "petfishframework[mcp]"
from petfishframework.mcp import connect_stdio

# Connect to a real MCP server
client = connect_stdio("npx", ["-y", "@modelcontextprotocol/server-filesystem", "/tmp"])
tools = client.discover_tools()  # 14 tools: read_file, write_file, list_directory...

agent = Agent(model=model, reasoning=ReAct(), tools=tuple(tools))
result = agent.run("List all files in /tmp")

Reliability Evaluation (Pass^k)

from petfishframework.reliability import pass_at_k_with_perturbations, exact_match
from petfishframework.core.types import Task

# Run same task 8 times — measure consistency
result = pass_at_k_with_perturbations(
    session_factory=lambda task: agent.session(task),
    task=Task(prompt="What is 17 * 23?"),
    k=8,
)
print(result.summary())
# Pass@8 — PASS (100%)
#   canonical:        8/8
#   order_shuffled:   8/8
#   paraphrase:       8/8

Core Concepts

Concept Role
Agent Immutable recipe (model + reasoning + tools)
Session Event-sourced execution (auditable, replayable)
Environment Single chokepoint (all calls audited, budget-metered, permission-gated)
Budget Hard execution limits (tokens, cost, steps, tool calls)
Permission SARC access control with 6 DecisionEffects
Replay AUDIT (event log), RESUME (checkpoint), RERUN (fresh)
Pass^k Reliability metric (k repetitions + perturbation suite)

Features

  • 3 reasoning strategies: ReAct, LATS (MCTS search), LLM+P (symbolic planning)
  • 3 model adapters: OpenAI, Anthropic, FakeModel (deterministic testing)
  • 3 routing axes: ToolRegistry (auto tool selection), Adaptive-RAG (retrieval), ReasoningStrategy
  • MCP client: real stdio transport, tool discovery from external MCP servers
  • Multi-agent: AgentAsTool (supervisor delegates to specialist agents)
  • Structured output: JSON → dataclass (zero regex scoring)
  • Conversation memory: cross-session recall via ConversationStore
  • Async + streaming: dual sync/async interface

Documentation

Roadmap

  • v0.1.x (current): Core runtime, permission semantics, quickstart verified ✅
  • v0.2.x: Enterprise agent examples, structured audit reports
  • v0.3.x: Policy engine (YAML), credential broker
  • v0.4.x: Production hardening, deployment guides

Current Limitations

petfishFramework is Alpha. API may change before v1.0.

Capability Status
Zero-cost quickstart ✅ Available
ReAct / Budget / Pass^k ✅ Available
DENY permission gate ✅ Enforced (pre-execution block)
REQUIRE_APPROVAL ✅ Enforced (pre-execution block)
PARTIAL_ALLOW ✅ Enforced (pre-execution arg filtering)
MASK ✅ Enforced (post-execution result masking)
DEGRADE ⚠️ Modeled (tool switching not yet implemented)
Session replay ✅ Audit replay available
Deterministic rerun / resume 📋 Planned
MCP client stdio ✅ Available
MCP server mode 📋 Planned
Structured output / conversation memory ✅ Available
LATS / LLM+P ⚠️ Lightweight implementations
CRAG / Adaptive-RAG ⚠️ Lightweight reference implementations

Development

git clone https://github.com/kylecui/petfishFramework.git
cd petfishFramework
uv sync --all-extras
uv run pytest              # 187 tests
uv run ruff check src/ tests/

See CONTRIBUTING.md for details.

License

MIT — © 2026 Kyle Cui

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