power-loop 0.10.0

Embeddable agent execution kernel — LLM loop, hooks, events, tools, dynamic sub-agents.

power-loop

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Embeddable, stateful agent execution for Python.

power-loop gives application code one small interface, StatefulAgentLoop, and handles the repetitive agent runtime work around it: multi-turn LLM loops, tool calls, hooks, events, context compaction, sub-agents, retry/cancel, structured output, memory, and SQLite-backed session persistence.

It is a library, not a service or a full application framework. You keep ownership of product logic, HTTP APIs, auth, queues, RAG, UI, and deployment.

Scope: orchestration, not isolation

power-loop orchestrates the agent loop; it does not sandbox tool execution. The built-in bash / file tools run in-process (a subprocess shell inheriting the host environment) — convenient for trusted, local use, but not a security boundary. If your agent runs model-authored or otherwise untrusted commands, run them in your own sandbox (container / gVisor / microVM) and inject it via the ShellBackend seam (runtime.exec_backend); power-loop launches the persistent shell through your backend. Keep secrets in your orchestrator — the loop does not scrub the tool environment for you.

One store file = one process. Per-session serialization is an in-process asyncio.Lock; the SQLite store itself happily opens from multiple processes, but two processes calling send() on the same session bypass all ordering guarantees and can interleave histories. Run one process per store file (scale by sharding sessions across processes/files), or put your own distributed lock in front.

Likewise there is no built-in scheduler/timer: a session only runs while a send() / resume() call is in flight. "Wake this agent again in 10 minutes" is orchestrator state — keep it in your own durable store and call send() when it fires (that survives restarts; an in-process timer would not).

Install

pip install power-loop

For local development:

git clone https://github.com/PL-play/power-loop.git
cd power-loop
pip install -e ".[dev]"

Python 3.10+ is required.

Quick Example

import asyncio

from power_loop import AgentLoopConfig, StatefulAgentLoop, create_llm_service_from_env


async def main() -> None:
    llm = create_llm_service_from_env()
    loop = StatefulAgentLoop(
        llm=llm,
        db_path="./power_loop_sessions.db",
        config=AgentLoopConfig(
            system_prompt="You are a concise assistant.",
            max_rounds=4,
        ),
    )

    sid = loop.new_session(metadata={"user_id": "demo"})
    first = await loop.send("My favorite color is teal.", session_id=sid)
    second = await loop.send("What is my favorite color?", session_id=sid)

    print(second.final_text)


asyncio.run(main())

Configure any OpenAI-compatible endpoint with environment variables:

POWER_LOOP_BASE_URL=https://api.openai.com/v1
POWER_LOOP_API_KEY=sk-...
POWER_LOOP_MODEL=gpt-4o-mini

See Getting Started for the complete first run.

What It Provides

Capability	Where to read more
Stateful sessions and cross-process resume	Sessions
Tool calling with JSON Schema validation	Tools
Lifecycle hooks for control flow	Hooks
Typed events for streaming, audit, and metrics	Events
Context compaction	Compaction
Sub-agents with AgentSpec	Sub-agents
Retry, timeout, and cancellation	Retry & Cancel
Structured JSON output	Structured Output
Pluggable cross-session memory	Memory
Provider configuration	Providers

Per-call overrides

Build one loop and reuse it across callers; restrict tools or swap the system prompt per send without rebuilding (the model only sees the allowed tools). Ideal for multi-tenant hosts.

# loop registered with all tools; this run exposes only "get_weather"
await loop.send("…", session_id=sid, tools=["get_weather"])

# per-run system prompt override (precedence: per-call > session > config)
await loop.send("…", session_id=sid, system_prompt="You are a terse bot.")

The same overrides are available on send_sync(). When follow_up() is idle and falls back to a new send, it accepts them too. A follow-up queued into an already running call keeps that call's active tool and prompt policy.

For a multi-tenant host that reuses one registry across workspaces, build an unbound registry and supply the workspace at invocation time:

from power_loop import RuntimeEnv, create_default_tool_registry, runtime_env_context

registry = create_default_tool_registry(preset="core", bind=False)
with runtime_env_context(RuntimeEnv(workspace_dir=tenant_workspace)):
    result = await registry.invoke_async("read_file", {"path": "README.md"})

See examples/23_per_send_overrides.py.

Token usage accounting

Every send() returns the run's cumulative token usage — summed over all LLM calls of that run (tool loops make several) — so cost accounting needs no event plumbing:

res = await loop.send("…", session_id=sid)
res.usage
# {"prompt_tokens": 1234, "completion_tokens": 56, "cache_read_tokens": 0,
#  "reasoning_tokens": 0, "total_tokens": 1290, "calls": 2}

For per-call, real-time metering subscribe to the usage_updated event (one per LLM call, tagged with session_id). See examples/25_token_usage.py.

Budget guardrail — cap real provider tokens per run (rounds are cheap, tokens are money):

config = AgentLoopConfig(max_rounds=24, max_tokens_per_run=50_000)
res = await loop.send("…", session_id=sid)
res.status  # "budget_exceeded" when the cap is hit

Checked at round boundaries: the round that crosses the budget finishes cleanly (no dangling tool_calls), then the loop stops without paying for the next LLM call. A status_changed event with kind="budget_exceeded" fires.

Session statistics — cumulative accounting persisted in the store, bumped once per finished send:

stats = loop.get_session_stats(sid)
# SessionStatsRow(sends=12, rounds=29, llm_calls=31, tool_calls=18,
#                 prompt_tokens=…, completion_tokens=…, total_tokens=…,
#                 first_send_at=…, last_send_at=…)
loop.list_session_stats()  # every session, most recently active first

Structured event logging — one JSON line per event, stdlib-only:

from power_loop.contrib.logging_sink import attach_logging_sink

bus = AgentEventBus(suppress_subscriber_errors=True)
attach_logging_sink(bus)   # or events={AgentEventType.USAGE_UPDATED}
loop = StatefulAgentLoop(llm=llm, event_bus=bus, ...)

Crash recovery: heal_pending

A run killed mid tool-call leaves the session with unresolved tool_calls; the next send() raises SessionPendingError (the message protocol forbids continuing). Orchestrators whose runs can legitimately die (human interrupts, process restarts) can opt into self-healing:

res = await loop.send("…", session_id=sid, heal_pending=True)
# stale tool_calls are aborted with synthetic <aborted> results, then the
# send proceeds. Default remains raise — healing discards in-flight work.

Or recover explicitly with resume(sid) / abort_pending(sid).

Public API

Stable imports are re-exported from power_loop:

from power_loop import (
    AgentLoopConfig,
    StatefulAgentLoop,
    StatefulResult,
    ToolDefinition,
    ToolRegistry,
)

The stability tiers are:

Tier	Meaning
Stable	Backward compatible across minor releases. Listed in power_loop.STABLE_API.
Provisional	Available from the top-level package during 0.x, but may change.
Internal	Submodule imports such as power_loop.core.*; no compatibility promise.

See the API reference for the current surface.

Examples

The examples/ directory is ordered from minimal usage to full chatbot composition:

python examples/00_hello_world.py
python examples/02_tool_calling.py
python examples/19_full_chatbot.py

The full list is in examples/README.md.

Development

pip install -e ".[dev]"
ruff check .
pytest -q --no-real

Real LLM examples/tests use POWER_LOOP_* or the legacy OPENAI_COMPAT_* variables.

Project Links

• Documentation index
• Architecture
• Roadmap
• Changelog
• Contributing
• License