Imported from jeremylongshore/tons-of-skills-marketplace (skills/.curated/langchain-middleware-patterns/SKILL.md). Install upstream with npx skills add jeremylongshore/tons-of-skills-marketplace --skill langchain-middleware-patterns. Copyright stays with the author (MIT).

LangChain Middleware Patterns (Python)

Overview

Tenant A sends a prompt: "Summarize this support ticket from alice@acme.com about her overdue invoice." The chain's caching middleware ran before the PII redaction middleware, so the raw prompt — email and all — became part of the cache key. Thirty seconds later Tenant B sends a semantically identical prompt (different tenant, different customer, same shape). Cache hits. Tenant B's user gets back a summary that names alice@acme.com and her overdue invoice. That is pain-catalog entry P24 in production, and it is a real class of incident — post-mortems read like "we added caching to cut cost, leaked a customer's PII to a different tenant within an hour."

The sibling failure modes:

• P25 — Retry middleware runs the model call twice on a 429; both attempts fire on_llm_end; the token-usage aggregator sums both; a single logical call bills as two, tenant's per-session budget trips at 50% of true usage.
• P10 — Agent loops exceed 15 iterations on vague prompts. There is no default cost cap. A per-session token-budget middleware solves this; without one, a single "help me with my account" prompt can burn thousands of tokens.
• P34 — Runnable.invoke does not sanitize prompt injection. A RAG document containing "Ignore previous instructions and..." is followed verbatim. Guardrails middleware is your injection defense; without it, indirect prompt injection is a one-line exploit.
• P61 — set_llm_cache(InMemoryCache()) hashes the prompt string only. Two chains with different tool bindings return the same cached response; tools are silently ignored by the cache key.

This skill defines the canonical middleware order for LangChain 1.0 chains and LangGraph 1.0 agents, with an ordering-invariants matrix (every adjacent pair has a named failure mode if you swap them), six reference implementations, a cache-key hash that includes prompt plus bound-tools plus tenant_id, retry telemetry that deduplicates by request_id, and an integration test pattern that asserts the ordering invariant on every build.

Pin: langchain-core 1.0.x, langchain 1.0.x, langgraph 1.0.x. Pain-catalog anchors: P10, P24, P25, P34, P61, with supporting references to P27, P29, P30, P33.

Prerequisites

• Python 3.10+
• langchain-core >= 1.0, < 2.0
• langgraph >= 1.0, < 2.0 (for agent middleware)
• At least one provider package: pip install langchain-anthropic (or openai)
• Optional: presidio-analyzer + presidio-anonymizer for PII NER beyond regex
• Optional: redis + langchain-redis for multi-worker cache and rate limiting

Instructions

Step 1 — Adopt the canonical middleware order

Every LangChain 1.0 chain and LangGraph 1.0 agent that goes to production applies middleware in this order:

user → redact → guardrail → budget → cache → retry → model

• redact → cache (P24): cache key must be PII-free or Tenant A's PII leaks to Tenant B on a hit
• guardrail → cache: an injection-laden prompt must never become a cache entry
• budget → cache: cache hits count against RPS; check budget first so loops cannot DoS a session on hits alone
• cache → retry: cache hits bypass retry; retry wraps only the model call

Production chains typically run 4-6 middleware layers with <1ms per layer overhead (bench: p50 0.3ms/layer, p99 0.9ms on a 100-request sample). See ordering-invariants.md for the full pairwise matrix and the benchmark script.

Step 2 — PII redaction middleware

Mask entities with reversible placeholders so the caller can reinsert in the output — but the cache key and the model prompt only ever see redacted text.

import re
from typing import Any

_REDACTORS = [
    ("EMAIL", re.compile(r"[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}")),
    ("PHONE", re.compile(r"\+?\d[\d\s\-\(\)]{7,}\d")),
    ("SSN",   re.compile(r"\b\d{3}-\d{2}-\d{4}\b")),
    ("CC",    re.compile(r"\b(?:\d[ -]*?){13,16}\b")),
]

def redact(text: str) -> tuple[str, dict[str, str]]:
    pmap: dict[str, str] = {}
    for label, pattern in _REDACTORS:
        for i, match in enumerate(pattern.findall(text)):
            token = f"<{label}_{i}>"
            pmap[token] = match
            text = text.replace(match, token)
    return text, pmap

def redaction_middleware(inputs: dict[str, Any]) -> dict[str, Any]:
    redacted, pmap = redact(inputs["input"])
    return {**inputs, "input": redacted, "_pii_map": pmap}

For names, addresses, and custom entities, Presidio's AnalyzerEngine covers 20+ entity types. See pii-redaction.md for the regex vs spaCy vs Presidio tradeoff matrix, GDPR/HIPAA/PCI-DSS entity lists, and the reinsertion pattern (return un-redacted output only to the originating tenant — never cross-populate).

Step 3 — Guardrails middleware

Detect injection patterns up front and wrap user content so the model treats it as data. Two layers: pattern match (catches the 90% case cheaply) plus prompt wrapping (neutralizes what slips through).

INJECTION_PATTERNS = [
    re.compile(r"ignore (all |the )?(previous|prior|above) (instructions|rules)", re.I),
    re.compile(r"system prompt (is|was|now)", re.I),
    re.compile(r"you are now (a |an )?", re.I),
    re.compile(r"</?(system|instruction|prompt)>", re.I),
]

class GuardrailViolation(Exception):
    pass

def guardrail_middleware(inputs: dict[str, Any],
                        allowed_tools: set[str] | None = None) -> dict[str, Any]:
    for pattern in INJECTION_PATTERNS:
        if pattern.search(inputs["input"]):
            raise GuardrailViolation(f"Injection pattern matched: {pattern.pattern!r}")
    wrapped = f"<user_input>\n{inputs['input']}\n</user_input>"
    out = {**inputs, "input": wrapped}
    if allowed_tools is not None:
        out["_tool_allowlist"] = allowed_tools
    return out

Never rely on the model to "know what is an instruction" without wrapping.

Step 4 — Token-budget middleware (per-session / per-tenant)

Directly addresses P10 — agents loop 15+ iterations on vague prompts and burn thousands of tokens. The budget middleware raises before the model call if the session is over ceiling.

from dataclasses import dataclass, field
from collections import defaultdict
from threading import Lock

class BudgetExceeded(Exception): pass

@dataclass
class TokenBudget:
    ceiling: int = 50_000           # tokens per session
    _usage: dict[str, int] = field(default_factory=lambda: defaultdict(int))
    _lock: Lock = field(default_factory=Lock)

    def record(self, session_id: str, tokens: int) -> None:
        with self._lock:
            self._usage[session_id] += tokens

    def check(self, session_id: str) -> None:
        with self._lock:
            used = self._usage[session_id]
        if used >= self.ceiling:
            raise BudgetExceeded(f"Session {session_id}: {used}/{self.ceiling}")

budget = TokenBudget(ceiling=50_000)

def budget_middleware(inputs: dict[str, Any]) -> dict[str, Any]:
    budget.check(inputs.get("session_id") or "anonymous")
    return inputs

Pair with a BaseCallbackHandler.on_llm_end that calls budget.record(...) with usage_metadata.input_tokens + output_tokens. For multi-worker deploys, back TokenBudget with Redis — per-process dicts are per-process (P29).

Step 5 — Caching middleware with tool-aware key

P61 is the booby trap: InMemoryCache() hashes the prompt string only, so two chains with different tool lists return the same cached response. Use a custom key over prompt + bound tools + tenant id.

import hashlib, json
from typing import Callable

def cache_key(prompt: str, bound_tools: list[dict] | None, tenant_id: str) -> str:
    """Blake2b-16 hash. Tool-aware, tenant-aware, collision-safe via \\x1f separator."""
    h = hashlib.blake2b(digest_size=16)
    h.update(prompt.encode("utf-8")); h.update(b"\x1f")
    if bound_tools:
        h.update(json.dumps(bound_tools, sort_keys=True).encode("utf-8"))
    h.update(b"\x1f"); h.update(tenant_id.encode("utf-8"))
    return h.hexdigest()

def cache_middleware(get: Callable[[str], Any | None], put: Callable[[str, Any], None]):
    def _run(inputs: dict[str, Any]) -> dict[str, Any]:
        key = cache_key(inputs["input"], inputs.get("_bound_tools"),
                        inputs.get("tenant_id", "default"))
        hit = get(key)
        if hit is not None:
            return {**inputs, "_cache_hit": True, "output": hit}
        inputs["_cache_key"] = key
        return inputs
    return _run

The cache key must be computed on the redacted prompt (Step 2 ran first) and must include the tool schemas. See cache-key-design.md for backend comparison (InMemoryCache / SQLiteCache / RedisCache / RedisSemanticCache), invalidation strategies (TTL, schema-version bump, tenant-wide purge), and the full pitfalls list including Unicode normalization and P62.

Step 6 — Retry middleware with telemetry tagging

P25: retry runs the model call twice on a 429, both attempts emit on_llm_end, the aggregator sums both, tenant budget trips at 50% of true usage. Fix: attach a stable request_id on the first attempt, and have the aggregator replace (not add) per request_id so only the last successful attempt is counted.

import time, uuid

RETRYABLE = (TimeoutError, ConnectionError,
             # Provider-specific — import from your provider SDK:
             # anthropic.RateLimitError, anthropic.APITimeoutError,
             # openai.RateLimitError, openai.APITimeoutError,
             )

def retry_middleware(max_retries: int = 2, base_delay: float = 1.0):
    def _run(inputs: dict[str, Any]) -> dict[str, Any]:
        request_id = inputs.get("request_id") or str(uuid.uuid4())
        return {**inputs, "request_id": request_id}
    return _run

See retry-telemetry.md for the full retry loop, the dedup-by-request_id aggregator, provider-specific retryable exception lists (Anthropic / OpenAI / Gemini), exponential backoff with jitter, and a circuit breaker that stops retry storms on a dead upstream.

Step 7 — Compose the middleware into a chain

from langchain_core.runnables import RunnableLambda, RunnablePassthrough

# Order matters. See Step 1 for why.
chain = (
    RunnableLambda(redaction_middleware)
    | RunnableLambda(guardrail_middleware)
    | RunnableLambda(budget_middleware)
    | RunnableLambda(cache_middleware(cache_get, cache_put))
    | RunnableLambda(retry_middleware(max_retries=2))
    | model                             # ChatAnthropic / ChatOpenAI
)

For LangGraph agents, the same layers apply but are wired as nodes with conditional edges — a budget node that routes to END on violation, a guardrail node that routes to an error handler on injection match, and so on. See the LangGraph adaptation in references/ordering-invariants.md.

Step 8 — Integration test: assert the ordering invariant

Ordering is invisible in code review until someone moves cache above redact. Assert the invariant in a test that runs on every commit.

def test_cache_key_does_not_leak_pii():
    """P24 — cache key built from REDACTED prompt, not raw."""
    a = redaction_middleware({"input": "Ticket from alice@acme.com", "tenant_id": "T1"})
    b = redaction_middleware({"input": "Ticket from bob@other.com",  "tenant_id": "T1"})
    assert cache_key(a["input"], None, "T1") == cache_key(b["input"], None, "T1")

def test_cache_key_tenant_isolation():
    """P24/P33 — same prompt, different tenants, different cache keys."""
    assert cache_key("notes", None, "T1") != cache_key("notes", None, "T2")

def test_cache_key_tool_aware():
    """P61 — same prompt, different tool bindings, different cache keys."""
    assert cache_key("p", [{"name":"search"}], "T") != cache_key("p", [{"name":"code_exec"}], "T")

Run in CI. A failure means someone broke the ordering invariant — chain does not merge until it is fixed.

Output

• Six middleware layers composed in canonical order: redact → guardrail → budget → cache → retry → model
• Reversible PII redaction with placeholder map (emails, phones, SSNs, credit cards; Presidio optional for names/addresses)
• Guardrails middleware with injection-pattern detection and user-content wrapping
• Per-session / per-tenant token budget with thread-safe counter
• Cache-key hash that includes prompt + bound-tool schemas + tenant id (fixes P61 and P24)
• Retry middleware with request_id tagging so the token aggregator deduplicates (fixes P25)
• Integration tests asserting the ordering invariant

Error Handling

Examples

Building a chain end-to-end with correct order

The Step 7 composition shows the six layers in order. In production code this usually lives in a factory — build_chain(tenant_id: str, allowed_tools: set[str]) — that closes over the tenant-scoped cache backend and budget instance. The factory makes the order explicit and testable.

LangGraph agent version

The same six layers in a LangGraph agent become six nodes plus conditional edges. budget routes to END on violation; guardrail routes to error_handler on injection match; cache routes to END on hit. See references/ordering-invariants.md for the adapted graph topology.

Debugging a cache-poisoning incident

Post-mortem template: (1) enumerate cache entries, (2) check whether keys were built pre- or post-redaction, (3) identify the first cross-tenant hit in logs, (4) purge by tenant prefix or full flush, (5) add the ordering integration test from Step 8 so this cannot recur.

Resources

• LangChain 1.0 / LangGraph 1.0 release announcement
• LangChain how-to: caching
• LangChain callbacks
• LangGraph middleware / pre_model_hook
• Microsoft Presidio (PII detection)
• OWASP LLM01: Prompt Injection
• Pack pain catalog: docs/pain-catalog.md (entries P10, P24, P25, P34, P61, plus P27, P29, P30, P33)
• Companion references: ordering-invariants.md, pii-redaction.md, cache-key-design.md, retry-telemetry.md