agent-harnessed 0.2.1

A runtime environment that coordinates context, memory, prompts, tools, guardrails, output formatting, and a bounded reasoning loop around an LLM.

agent-harness

pip install agent-harnessed

A clean, async-first Python library that provides the runtime environment around an LLM.

Agent = LLM + Context + Memory + Tools + Control Flow + Guardrails + State

The LLM reasons and proposes actions. The harness decides what it sees, validates and executes what it asks for, controls how long the loop runs, formats the result for its delivery channel, and hands back the final answer.

The model client is not part of this library. You inject an async (CompiledPrompt) -> LLMTurnStep callable, so agent-harness depends on pydantic>=2.0 and nothing else — no LLM SDK, no templating engine, no vector store.

Install

The distribution is published as agent-harnessed; the import name is agent_harness:

from agent_harness import LoopController, ToolGateway

New here? GETTING_STARTED.md walks from an empty folder to a real agent calling your own tool, in about ten minutes.

For local development:

pip install -e ".[dev]"

Architecture

Module	Responsibility	Key types
state	Task continuity. Explicit pivot-vs-continuation, never inferred from chat history.	TaskFrame
memory	Typed recall: durable facts, what happened, how to do things. Backends are injected.	MemoryKind, MemoryRecord, MemoryBackend, InMemoryBackend, MemoryStore
context	Decides what the model gets to see this turn, and owns the pivot decision.	ContextEngine, ScopedContext
prompt	Versioned prompt assembly — a compiled artifact, not scattered f-strings.	PromptCompiler, CompiledPrompt
tools	The only path from a proposal to a real side effect: allowlist, authorize, rate-limit, redact.	Tool, ToolGateway, ToolProposal, ToolResult
guardrails	Composable checks with a typed payload per stage.	GuardrailPipeline, Guardrail, GuardrailStage, GuardrailViolation
output	Channel-aware rendering of the final answer. Injected like the model client.	RenderChannel, OutputFormatter, PlainTextFormatter
loop	The bounded reasoning loop. Owns every exit decision and all memory write-back.	LoopController, LoopLimits, LoopResult, LLMTurnStep, TurnStepType

The loop has exactly three exits — and the LLM controls none of them

stopped_reason	final_text	OUTPUT guardrail	Formatter
final_answer	the answer	✅ runs	✅ applied
clarification_needed	the question	❌ skipped	❌ skipped
max_turns_exceeded	None	❌ skipped	❌ skipped
max_tool_calls_exceeded	None	❌ skipped	❌ skipped
max_seconds_exceeded	None	❌ skipped	❌ skipped
repeated_call_detected	None	❌ skipped	❌ skipped
guardrail_violation:{input|context|tool|output}	None	—	❌ skipped

A model can emit final on every turn; the loop still ends only when LoopController says so. The OUTPUT guardrail is skipped for a clarification because a question is not a claim — there is nothing to ground it against.

Guardrail payload contract

Each stage's check() receives exactly one type, so guardrails are written against concrete objects rather than an untyped blob:

Stage	Payload
INPUT	str — the raw incoming user message
CONTEXT	ScopedContext — assembled context, pre-compile
TOOL	ToolProposal — proposed tool + args, pre-execution
OUTPUT	tuple[str, list[ToolResult]] — final text plus every ToolResult from the turn

A violation never reaches your process: LoopController catches GuardrailViolation, records it, and returns a clean LoopResult with final_text=None. Output guardrails must fail safe when evidence is missing, not crash the caller.

A complete agent, wired to a real model

An order-support agent over Claude. Everything below is the whole program.

pip install agent-harnessed anthropic
export ANTHROPIC_API_KEY=sk-ant-...      # setx on Windows

import asyncio

from anthropic import AsyncAnthropic

from agent_harness import (
    ContextEngine, GuardrailPipeline, InMemoryBackend, LLMTurnStep, LoopController,
    MemoryStore, PromptCompiler, Tool, ToolGateway, TurnStepType,
)

ORDERS = {"A-1001": {"status": "shipped", "carrier": "UPS", "eta": "2026-08-03"}}


async def lookup_order(session_id: str, arguments: dict) -> dict:
    return ORDERS[str(arguments["order_id"]).upper()]


lookup = Tool(
    name="lookup_order",
    description="Look up an order's status by its id, e.g. A-1001.",
    parameters=["order_id"],
    execute=lookup_order,
    read_only=True,
)


def make_call_llm(client: AsyncAnthropic, tools: list[Tool]):
    """The only piece this library leaves to you: prompt in, LLMTurnStep out."""
    schemas = [
        {
            "name": tool.name,
            "description": tool.description,
            "input_schema": {
                "type": "object",
                "properties": {p: {"type": "string"} for p in tool.parameters},
                "required": list(tool.parameters),
            },
        }
        for tool in tools
    ]

    async def call_llm(prompt) -> LLMTurnStep:
        response = await client.messages.create(
            model="claude-sonnet-5",
            max_tokens=16000,
            messages=[{"role": "user", "content": prompt.render()}],
            tools=schemas,
        )
        for block in response.content:
            if block.type == "tool_use":
                return LLMTurnStep(
                    step_type=TurnStepType.TOOL_CALL,
                    tool_name=block.name,
                    tool_arguments=dict(block.input),
                )
        text = "".join(b.text for b in response.content if b.type == "text")
        return LLMTurnStep(step_type=TurnStepType.FINAL, text=text.strip())

    return call_llm


async def main() -> None:
    memory = MemoryStore(InMemoryBackend())
    controller = LoopController(
        context_engine=ContextEngine(memory),
        prompt_compiler=PromptCompiler(role="You are a concise order-support agent."),
        tool_gateway=ToolGateway([lookup]),
        guardrails=GuardrailPipeline(),
        memory=memory,
        call_llm=make_call_llm(AsyncAnthropic(), [lookup]),
    )

    result = await controller.handle_turn("session-1", "Where is order A-1001?")

    print(result.stopped_reason)        # final_answer
    print(result.final_text)            # Order A-1001 has shipped with UPS.
    print(result.tool_results[0].data)  # {'status': 'shipped', 'carrier': 'UPS', ...}


asyncio.run(main())

Two round trips to the model, and you wrote neither of them. The first returns a tool_use block, so the gateway runs lookup_order and folds the result into the next prompt as history; the second sees that evidence and answers. handle_turn returns once, when LoopController decides the turn is over.

The adapter is the whole integration

make_call_llm above is the entire model-specific surface. Three mappings do all the work:

The model does this	The adapter returns
emits a tool_use block	TurnStepType.TOOL_CALL
calls the ask_user tool	TurnStepType.CLARIFICATION
replies with text only	TurnStepType.FINAL

Tool schemas are derived from the same list[Tool] handed to the gateway, so what the model is told about and what the gateway will actually run cannot drift apart.

The middle row is worth a note. ask_user is a sentinel: declared to the model as an ordinary tool but never registered with the ToolGateway, so the adapter intercepts the call rather than executing it. That lets the model ask a question through the same native tool-calling channel it uses to act, instead of you parsing its prose to guess whether an answer was really a question.

Nothing here is Claude-specific beyond the SDK call. Any model with native tool calling maps the same three ways; one without it needs the adapter to parse a structured response instead.

The example

python examples/refund_agent.py --session naveed --trace

examples/refund_agent.py is a refund agent over SQLite — the first thing in a support agent that can actually cost money. Three independent mechanisms stand between the model's proposal and a debited account: ownership via Tool.authorize, a $500 ceiling as a TOOL guardrail, and an atomic conditional UPDATE capped by max_calls_per_session. Each stops a different request at a different stage.

--trace prints which stages a turn passed through before it stopped, so the pipeline is something you read rather than infer:

PASS  input guardrail    input-size: passed
  ->  context            intent=refund_order entities={'order_id': 'A-1007'}
  ->  state              fresh frame (pivot -- earlier task dropped)
  ->  memory             recalled 1 semantic, 1 procedural, 0 episodic
  ->  prompt             v1.0.0, 6 sections: task_frame, known_facts, procedures...
  ->  model              turn 1: proposes issue_refund(order_id=A-1007)
STOP  tool guardrail     refund-ceiling: refund of $800.00 exceeds the $500.00 limit
  ->  memory             wrote episodic: guardrail_violation
  ==  stopped            guardrail_violation:tool

The model never writes SQL. It names a tool and supplies a value; every statement is parameterized and lives in your code. And note the second-to-last line — a blocked turn still writes its audit record.

Clarification: a return, not a suspension

When the model needs to ask something, handle_turn returns immediately. It does not suspend, block, or hold an open coroutine. The caller owns the wait — print the question, collect an answer over whatever transport it has (HTTP request, websocket, SMS, tomorrow), then call handle_turn again with that answer and the frame it got back. The controller keeps no state between calls beyond what you pass in.

asked = await controller.handle_turn("session-1", "Can you check on my order?")
# asked.stopped_reason == "clarification_needed"
# asked.final_text     == "Which order number should I look up?"

# ... your transport waits here, for however long it takes ...

resolved = await controller.handle_turn(
    "session-1",
    "It's A-1001 - where is it?",
    current_frame=asked.task_frame,      # hand the same frame back
)
# resolved.stopped_reason == "final_answer"

Because the intent matches and no entity conflicts, that second turn is a continuation: ContextEngine.build extends the frame via merged_with() and is_pivot is False. Ask about a different order and the same intent now carries a conflicting order_id, so the frame is rebuilt via TaskFrame.fresh() with is_pivot=True — dropping the abandoned task's plan and in-flight tool ids.

Running the example with --trace shows which of the two happened on every turn.

What the episodic record looks like

Memory write-back is a correctness requirement, not an optimization. Tools that caused real side effects are recorded the moment they complete, so nothing about a forced stop can erase them.

Exit	Episodic records written
tool call → final	one tool_call per executed tool, then one turn_complete holding the user message and the final answer
clarification	one turn_complete holding the user message and the question — no tool records, since no tool ran
max_*_exceeded / repeated_call_detected	every tool_call that already ran, then one forced_stop naming the specific stopped_reason
guardrail_violation:*	every tool_call that already ran, then one guardrail_violation naming the stage and reason

So a forced stop after two lookups leaves [tool_call, tool_call, forced_stop] — no answer was produced, but the audit trail shows both executions and why the turn was cut short. A resolved turn never loses that granularity either: the consolidated turn_complete record sits alongside the per-tool records, not instead of them.

Out of scope

• The model client. Injected as an async callable. This library ships no model client.
• Pause-in-place clarification. Return-based only, as described above.
• Long-term persistence. Beyond in-process state, that is the caller's job — implement MemoryBackend against your own store.
• Concurrency safety. A single instance is not guaranteed coroutine-safe. If you might call handle_turn concurrently for the same session, serialize it yourself (one asyncio.Lock per session).

Tests

pytest

99 tests covering tool authorization/redaction/rate-limiting, pivot-vs-continuation semantics and the fresh()/merged_with() branch in ContextEngine.build, all three loop exits, every forced stop (including that already-executed ToolResults survive one), guardrail violations converting to clean results at each stage, LLMTurnStep validation rejecting malformed steps, repeated-call detection over non-JSON-native arguments, the per-stage guardrail payload contract, and that the topic-change note reaches the prompt on a real pivot but never on a session's opening message.