Agent Invents Nonexistent Library Function — AttributeError at Runtime
Symptom
AttributeError: module 'pandas' has no attribute 'read_excel_cached'AttributeError: 'OpenAI' object has no attribute 'complete'(correct method ischat.completions.create)- Code passes a code review but fails immediately at runtime
- Agent confidently uses
.upsert_many()which doesn’t exist in the ORM - Agent mixes function signatures from two different library versions
Root Cause
LLM training data includes library docs, Stack Overflow, and GitHub code from multiple versions and forks. The model synthesizes plausible-sounding function names that don’t actually exist, or uses outdated APIs from an older version. This is especially common for: infrequently used modules, recently released libraries, and functions that sound like they should exist but don’t.
Fix
Option 1: Verify function existence before using it
import inspect
def verify_api_exists(obj, method_name: str, check_callable: bool = True) -> bool:
"""
Check that a method/attribute exists on an object before calling it.
Use this when an agent generates code using library APIs.
"""
if not hasattr(obj, method_name):
available = [m for m in dir(obj) if not m.startswith("_")]
# Find closest match
import difflib
closest = difflib.get_close_matches(method_name, available, n=3, cutoff=0.6)
raise AttributeError(
f"'{type(obj).__name__}' has no attribute '{method_name}'.\n"
f"Did you mean: {closest}\n"
f"Available methods: {available[:20]}"
)
attr = getattr(obj, method_name)
if check_callable and not callable(attr):
raise TypeError(f"'{method_name}' exists but is not callable — it's a {type(attr).__name__}")
return True
# Usage before executing agent-generated code:
import pandas as pd
verify_api_exists(pd, "read_excel") # OK
verify_api_exists(pd, "read_excel_cached") # Raises with suggestions
Option 2: Use introspection to get real API surface
import importlib
import inspect
def get_real_api_surface(module_name: str, class_name: str = None) -> dict:
"""
Get the actual public API of a module/class.
Feed this into the agent prompt so it uses real function names.
"""
try:
module = importlib.import_module(module_name)
except ImportError as e:
return {"error": f"Module not installed: {e}"}
target = module
if class_name:
target = getattr(module, class_name, None)
if target is None:
return {"error": f"No class '{class_name}' in {module_name}"}
methods = {}
for name in dir(target):
if name.startswith("_"):
continue
attr = getattr(target, name)
if callable(attr):
try:
sig = str(inspect.signature(attr))
doc = (inspect.getdoc(attr) or "")[:100]
methods[name] = {"signature": sig, "doc": doc}
except (ValueError, TypeError):
methods[name] = {"signature": "(...)", "doc": ""}
return {
"module": module_name,
"class": class_name,
"version": getattr(module, "__version__", "unknown"),
"methods": methods
}
# Include in agent system prompt:
api = get_real_api_surface("anthropic", "Anthropic")
# → {methods: {complete: ..., messages: ...}} — agent sees actual methods
Option 3: Sandbox test before returning generated code
import subprocess
import tempfile
import textwrap
def test_generated_code(code: str, timeout: int = 10) -> dict:
"""
Run generated code in a subprocess to catch AttributeError before delivery.
Returns {"ok": True} or {"ok": False, "error": "..."}
"""
# Wrap in try/except to capture runtime errors
test_code = textwrap.dedent(f"""
import sys
try:
{textwrap.indent(code, ' ')}
print("OK")
except AttributeError as e:
print(f"ATTRIBUTE_ERROR: ", file=sys.stderr)
sys.exit(1)
except ImportError as e:
print(f"IMPORT_ERROR: ", file=sys.stderr)
sys.exit(1)
except Exception as e:
print(f"RUNTIME_ERROR: : ", file=sys.stderr)
sys.exit(1)
""")
with tempfile.NamedTemporaryFile(suffix=".py", mode="w", delete=False) as f:
f.write(test_code)
tmp_path = f.name
result = subprocess.run(
["python", tmp_path],
capture_output=True,
text=True,
timeout=timeout
)
if result.returncode != 0:
return {"ok": False, "error": result.stderr.strip()}
return {"ok": True}
# After agent generates code:
check = test_generated_code("import pandas as pd\ndf = pd.read_excel_cached('test.xlsx')")
if not check["ok"]:
print(f"Agent hallucinated API: {check['error']}")
# Re-prompt agent with error
Option 4: System prompt with version-pinned API references
def build_library_context(libraries: list[tuple[str, str]]) -> str:
"""
Build a system prompt section with the exact installed versions.
Prevents agent from using API from wrong version.
"""
lines = ["Exact library versions installed in this environment:"]
for pkg, version in libraries:
lines.append(f" - {pkg}=={version}")
lines.append("")
lines.append("Rules:")
lines.append("- ONLY use functions that exist in these exact versions")
lines.append("- Do NOT use functions from older or newer versions")
lines.append("- If unsure whether a function exists, say so rather than guessing")
lines.append("- Prefer official documentation examples over memory")
return "\n".join(lines)
import pkg_resources
def get_installed_versions(packages: list[str]) -> list[tuple[str, str]]:
result = []
for pkg in packages:
try:
version = pkg_resources.get_distribution(pkg).version
result.append((pkg, version))
except pkg_resources.DistributionNotFound:
result.append((pkg, "NOT INSTALLED"))
return result
versions = get_installed_versions(["pandas", "anthropic", "httpx", "sqlalchemy"])
context = build_library_context(versions)
# → "pandas==2.2.1\nanthropic==0.40.0\n..."
Option 5: Lint agent output with AST before execution
import ast
import importlib
class HallucinatedAPIDetector(ast.NodeVisitor):
"""
Walk the AST of agent-generated code and check that called
attributes actually exist on the module being used.
"""
def __init__(self):
self.issues = []
self._imports = {} # alias -> module_name
def visit_Import(self, node):
for alias in node.names:
name = alias.asname or alias.name
self._imports[name] = alias.name
self.generic_visit(node)
def visit_ImportFrom(self, node):
for alias in node.names:
name = alias.asname or alias.name
self._imports[name] = f"{node.module}.{alias.name}"
self.generic_visit(node)
def visit_Attribute(self, node):
if isinstance(node.value, ast.Name) and node.value.id in self._imports:
module_name = self._imports[node.value.id]
try:
mod = importlib.import_module(module_name)
if not hasattr(mod, node.attr):
self.issues.append(
f"Line {node.lineno}: '{module_name}' has no attribute '{node.attr}'"
)
except ImportError:
pass # Can't check uninstalled modules
self.generic_visit(node)
def detect_hallucinated_apis(code: str) -> list[str]:
try:
tree = ast.parse(code)
detector = HallucinatedAPIDetector()
detector.visit(tree)
return detector.issues
except SyntaxError as e:
return [f"SyntaxError: {e}"]
# Example:
issues = detect_hallucinated_apis("""
import pandas as pd
df = pd.read_excel_cached('file.xlsx')
df2 = pd.DataFrame.from_records_lazy(data)
""")
# → ["Line 2: 'pandas' has no attribute 'read_excel_cached'",
# "Line 3: 'pandas' has no attribute 'from_records_lazy'"]
Option 6: Correct common hallucinations with a known-issues map
# Maintain a map of commonly hallucinated functions and their real equivalents
HALLUCINATED_TO_REAL = {
# Anthropic SDK
("anthropic", "complete"): "client.messages.create(model=..., messages=[...])",
("anthropic", "chat"): "client.messages.create(model=..., messages=[...])",
# Pandas
("pandas", "read_excel_cached"): "pd.read_excel()",
("pandas", "DataFrame.upsert"): "Use pd.concat() then drop_duplicates()",
("pandas", "merge_many"): "Use functools.reduce with pd.merge()",
# SQLAlchemy
("sqlalchemy", "upsert_many"): "session.bulk_insert_mappings() or PostgreSQL ON CONFLICT",
("sqlalchemy", "Session.upsert"): "Use merge() for upsert semantics",
# Httpx
("httpx", "get_json"): "client.get(url).json()",
("httpx", "Client.fetch"): "client.get() or client.request()",
}
def check_hallucination(module: str, attr: str) -> str | None:
"""Return correction hint if this is a known hallucination"""
key = (module.split(".")[0], attr)
return HALLUCINATED_TO_REAL.get(key)
# When agent uses a known hallucinated API:
hint = check_hallucination("anthropic", "complete")
if hint:
print(f"Hallucinated API detected. Real equivalent: {hint}")
Common Hallucinated APIs by Library
| Library | Hallucinated | Real |
|---|---|---|
anthropic |
.complete() |
.messages.create() |
pandas |
pd.read_excel_cached() |
pd.read_excel() |
openai |
.ChatCompletion.create() (v0) |
.chat.completions.create() (v1) |
sqlalchemy |
session.upsert() |
session.merge() |
httpx |
.get_json() |
.get().json() |
boto3 |
s3.upload() |
s3.upload_file() |
Expected Token Savings
Debugging hallucinated API + fix cycle: ~8,000 tokens API verification before execution catches it instantly: ~200 tokens
Environment
- Any agent that generates code using external libraries
- Source: direct experience; API hallucination is most common for SDK clients and data libraries
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