agent-in-the-loop 0.2.3

Client library for Agent In The Loop confidence evaluation API

agent-in-the-loop

A lightweight Python client for the Agent In The Loop (AITL) confidence evaluation API. Attach a callback to your LangChain / LangGraph run, then get a structured confidence score for the whole agent run — no manual context or trace-ID wiring required.

---

Installation

pip install "agent-in-the-loop[langchain]"

The langchain extra is required because agent runs are captured via a LangChain callback handler (get_agent_guard). Requires Python 3.9+.

---

Quick Start

from agent_in_the_loop import get_agent_guard, evaluate_confidence

# agent_name must be a stable, unique name for this agent graph — the
# backend uses it to track the graph's network profile across runs.
guard = get_agent_guard("research-agent")

# Attach the guard to your LangGraph / LangChain invocation
graph.invoke(inputs, config={"callbacks": [guard]})

# context, trace_id, and agent_name are all picked up automatically from
# the guard above — nothing else to pass in.
result = evaluate_confidence(api_key="your-api-key")

print(result.score)        # int, 1-10
print(result.explanation)  # str, human-readable reasoning

---

Environment Variables

The SDK always talks to the managed AITL backend at https://api.trellar.io — this is fixed and cannot be overridden via an environment variable or function argument.

Instead of passing api_key on every call, set it as an environment variable:

Variable	Description	Default
AGENT_IN_THE_LOOP_API_KEY	Bearer token for authentication	(required)

export AGENT_IN_THE_LOOP_API_KEY=your-api-key

from agent_in_the_loop import get_agent_guard, evaluate_confidence

guard = get_agent_guard("research-agent")
graph.invoke(inputs, config={"callbacks": [guard]})

result = evaluate_confidence()

---

How It Works

get_agent_guard(agent_name) returns a LangChain callback handler that:

• Records every LLM, tool, and chain/graph lifecycle event fired during the run (on_llm_start, on_tool_end, on_chain_start, etc.), in order.
• Captures the root run's run_id as the trace_id for the whole graph.
• Registers itself in a ContextVar as the "active" guard for the current thread/async task, so evaluate_confidence() can find it automatically — no need to pass it around.

Once the graph run finishes, calling evaluate_confidence() sends the accumulated events, trace ID, and agent name to the AITL backend and returns a confidence score.

Different graphs in the same codebase must use different agent_name values.

---

API Reference

get_agent_guard

get_agent_guard(agent_name: str) -> BaseCallbackHandler

Parameter	Type	Description
agent_name	str	Stable, unique name identifying this agent graph (e.g. "research-agent")

Returns a LangChain callback handler bound to agent_name. Pass it to graph.invoke(..., config={"callbacks": [guard]}).

Raises:

• ValueError — if agent_name is empty or blank

evaluate_confidence

evaluate_confidence(
    *,
    api_key: str | None = None,
    timeout: float = 30.0,
) -> AgentLoopResult

Parameter	Type	Description
api_key	str | None	Bearer token. Falls back to AGENT_IN_THE_LOOP_API_KEY
timeout	float	HTTP request timeout in seconds (default 30.0)

context, trace_id, and agent_name are all resolved automatically from the active guard created by get_agent_guard — there is no way to pass them manually. Requests are always sent to the fixed backend domain (https://api.trellar.io); there is no way for callers to redirect them elsewhere.

Raises:

• ValueError — if no active guard is found, its trace_id cannot be resolved, or api_key is missing
• requests.HTTPError — on non-2xx HTTP responses

AgentLoopResult

A frozen dataclass with two fields:

Field	Type	Description
score	int	Confidence score from 1 (low) to 10 (high)
explanation	str	Human-readable explanation of the score

---

Running Tests

pip install -e ".[test]"
pytest

---

Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/my-feature)
3. Commit your changes (git commit -m "Add my feature")
4. Push to the branch (git push origin feature/my-feature)
5. Open a Pull Request

---

License

MIT — see LICENSE for details.