+ P Act for LangChain agents
Capture every LangChain run as it actually executes — via LangChain's own callback system, not a network proxy — with the full chain → tool → retriever call tree reconstructed from real run IDs.
Prefactor acts on your LangChain agents at runtime — block, throttle, sandbox, or escalate a tool call or data access before it executes, stop an agent instantly with the kill switch, and delete or redact tagged PII the moment it's encountered.
How to add act to LangChain
Same middleware, no extra package
The prefactor-langchain middleware you already installed for observability is what enforcement runs through — nothing separate to add.
Define a runtime policy
Write a policy — block, throttle, sandbox, or escalate — scoped to an action type, a data tag, or a risk level.
Evaluated before the action runs
Every intercepted LangChain call is evaluated against your policy before it's allowed to proceed — not flagged after the fact.
# pip install prefactor-langchain
import os
from prefactor_langchain import PrefactorMiddleware
middleware = PrefactorMiddleware.from_config(
api_url="https://app.prefactorai.com",
api_token=os.environ["PREFACTOR_API_TOKEN"], # never hardcode tokens
agent_id="my-agent",
agent_name="My Agent",
)How the LangChain integration actually works
- Attaches via the prefactor-langchain middleware, which registers as a LangChain BaseCallbackHandler — no gateway in your request path, no change to how your chains run.
- LangChain fires every callback with a run_id and parent_run_id; Prefactor uses that parent/child relationship to reconstruct the full call tree — a chain's sub-chains, tool calls, and retriever lookups nested under the run that triggered them, not a flat list of events.
- Capture and sampling are controlled by PREFACTOR_CAPTURE_INPUTS / _OUTPUTS / PREFACTOR_SAMPLE_RATE.
- LangChain runs synchronous callback handlers inline and swallows any exception a handler raises unless that handler sets raise_error — the middleware sets it, so a policy violation raised from on_tool_start genuinely blocks the tool call before it executes, not just logs it afterward.
What Prefactor captures for LangChain agents
Runtime policy enforcement
Every tool call, API request, or data access is evaluated against your rules at the point the agent tries to act — block, throttle, sandbox, or escalate, in sub-millisecond time.
Kill switch
Stop a single run, an agent, or every agent a team owns — immediately, no code deploy, triggered natively from Prefactor or programmatically from a custom span.
Human-in-the-loop approval
A flagged action pauses execution and routes to the right approver with full context — what the agent was trying to do, why, and its recent history — through Slack, Teams, or email.
PII deletion
Find every field carrying a tag and remove it in one action, wherever it appears — plus real-time redact, block, or escalate the moment PII is first encountered.
Trace anything — not just LLM calls
The SDK captures LLM, tool, and agent spans automatically. With withSpan you wrap any operation in your own span type — an API call, a database query, a quality check, a business action — each with its own payload and schema. It all flows into the same Observe, Evaluate, and cost views.
import { withSpan } from "@prefactor/core";
// Wrap ANY operation in a span you define — an API call, a quality
// check, a business action — with its own spanType, inputs and schema
await withSpan(
{
name: "research competitor",
spanType: "research_competitor",
inputs: { competitor },
},
async () => {
const results = await search(competitor); // your tool / API calls
return summarize(results); // captured as one span
},
);Nest spans to capture business-level actions, and start with permissive schemas you tighten over time. Instrumentation strategy →
An example run, span by span
Illustrative — a single LangChain run as nested spans.
Illustrative example.
Manual logging vs DIY vs Prefactor
| Capability | Manual | DIY OpenTelemetry | Prefactor |
|---|---|---|---|
| LLM / tool / agent spans | Hand-rolled | ✓ build it | ✓ via the SDK |
| Token usage captured per call | — | Build it | ✓ |
| Configurable capture & sampling | — | Partial | ✓ |
| Hosted Admin UI (agents, instances) | — | — | ✓ |
| Risk profiles & audit trail | — | — | ✓ |
LangChain act — FAQ
How do runtime policies work for LangChain agents?+
A policy engine sits at the point a LangChain agent calls a tool, queries an API, or touches data — the same interception point Prefactor uses across every framework. Every attempted action is evaluated against the agent's identity, permissions, and current context before it's allowed to proceed.
Does enforcement add latency to LangChain runs?+
No meaningfully — policy evaluation runs in sub-millisecond time, so enforcement doesn't add noticeable latency to the agent's response.
Can I kill a single LangChain run without stopping the whole agent?+
Yes — the kill switch scopes to a single run, a single agent, or every agent a team owns. Trigger it from the Prefactor dashboard directly, or programmatically via a custom span the moment your own code detects a problem.
How does human-in-the-loop approval work for LangChain?+
When a policy flags a LangChain agent's action for escalation, execution pauses and an approval request goes out with full context. The approver's decision is logged and either lets the action proceed or cancels it.
How is PII actually deleted from LangChain agent data?+
Detection and tagging happen continuously; deletion is one action against the tag — find every field carrying it for a given person or record, and remove it, wherever it appears across your LangChain runs.
How do I add Prefactor to a LangChain app?+
Install prefactor-langchain, create the middleware with PrefactorMiddleware.from_config(...) using your API URL, token, and agent, and add it to your LangChain agent — it registers as a standard LangChain callback handler.
What does Prefactor capture from LangChain?+
Prefactor hooks LangChain's own callback events — on_chain_start/end, on_tool_start/end, on_retriever_start/end, on_llm_start/on_chat_model_start/on_llm_end, and agent actions — and uses each event's run_id and parent_run_id to reconstruct the full call tree as structured, timestamped spans, so every LangChain run is captured as trace data you can reconstruct, search and export end to end.
Does Prefactor add latency or change how LangChain runs?+
No. Observability capture is designed to stay off your agent's critical path, so it doesn't alter your LangChain logic or your users' responses. The only part that acts inline is the optional runtime guardrails you enable per agent — by design, so a high-risk or low-confidence action can be held for human approval before it executes.
Can I evaluate agents built with LangChain and catch regressions?+
Yes. Once runs are captured, eval suites score quality and groundedness on real traffic, drift detection flags behaviour changes after deployment, and versioned eval history catches regressions before they ship — the observe → evaluate → improve loop applied to your LangChain agents.
Keep going
Act for other frameworks
CrewAI →LangGraph →OpenAI Agents SDK →Claude Agent SDK →Microsoft AutoGen →LlamaIndex →See it on your LangChain agents
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