agentlens

Tamper-evident audit logging for Claude agents — Claude Code hooks and Anthropic SDK. Local-first, append-only, OSS.

Why

Anthropic logs API calls for their own safety monitoring — but that log is not yours. When your Claude-powered agent takes an action, you need your own tamper-evident record: for compliance (EU AI Act Art. 12, ISO/IEC 42001 A.6.2.8), incident response, and accountability.

agentlens captures every tool_use / tool_result event into a SHA-256 hash-chained JSONL file on your own machine — via Claude Code hooks (recommended) or as a drop-in Anthropic SDK wrapper. It can also block dangerous tool calls before they execute (deterministic rules, no LLM in the loop).

Quickstart: Claude Code / Claude Agent SDK (v0.6.0+)

pip install agentlens-io
agentlens hook install   # prints the settings.json snippet

.claude/settings.json:

{
  "hooks": {
    "PreToolUse": [
      {"matcher": "*", "hooks": [
        {"type": "command", "command": "agentlens hook pre --log ~/.agentlens/audit.jsonl --block critical"}
      ]}
    ],
    "PostToolUse": [
      {"matcher": "*", "hooks": [
        {"type": "command", "command": "agentlens hook post --log ~/.agentlens/audit.jsonl"}
      ]}
    ]
  }
}

Now every tool call in Claude Code is audit-logged, and rm -rf /-class commands are denied before execution:

agentlens view   ~/.agentlens/audit.jsonl        # colorized event viewer
agentlens summary ~/.agentlens/audit.jsonl       # per-session stats
agentlens verify ~/.agentlens/audit.jsonl        # ✅ hash-chain integrity / ❌ tamper detected
agentlens feedback ~/.agentlens/audit.jsonl --emit-code   # suggest whitelist rules from suppressed violations (v0.8.0+)

feedback reads the accumulated log — including the suppressed_violations that the whitelist keeps instead of deleting — and proposes narrowly-scoped WhitelistRules for rules with a high false-positive rate. It is suggestion-only: it never rewrites your ruleset. A ruleset that auto-tunes from its own logs can be poisoned, so a human stays in the loop. Flags: --min-occurrences N (default 3), --threshold F (default 0.9), --emit-code.

Options: --block critical|high|off (default critical), --whitelist rules.json (false-positive suppression — suppressed violations stay in the log), --standalone (post-hook logs tool_use+result when no pre-hook is registered). Hooks are fail-open: the logger can never break your agent loop.

Design principles

• Read-only interception — requests and responses are never altered
• Append-only writes — log entries cannot be edited after creation
• No AI in the logger — capture logic is deterministic code, not an LLM
• Your data stays local — FileWriter (default) writes to your own machine; no data leaves your environment

Usage: SDK wrapper

from agentlens import AuditedAnthropic

# Drop-in replacement for anthropic.Anthropic()
client = AuditedAnthropic(log_path="./audit.jsonl")

response = client.messages.create(
    model="claude-opus-4-6",
    max_tokens=1024,
    tools=[...],
    messages=[{"role": "user", "content": "..."}],
)
# Every tool_use and tool_result is now in audit.jsonl

Async (v0.7.0+)

AsyncAuditedAnthropic is the drop-in for anthropic.AsyncAnthropic — same audit logging and pre-execution blocking, awaited:

from agentlens import AsyncAuditedAnthropic

client = AsyncAuditedAnthropic(log_path="./audit.jsonl", block_on_critical=True)

response = await client.messages.create(
    model="claude-opus-4-6",
    max_tokens=1024,
    tools=[...],
    messages=[{"role": "user", "content": "..."}],
)
# Raises PreExecutionBlockedError before a critical tool call reaches you.

Streaming (v0.9.0+)

messages.stream() is wrapped too. Text passes through untouched; the audit and the pre-execution gate fire when the message completes — before your code reads the finished tool_use and acts on it. Works on the sync and async clients:

with client.messages.stream(
    model="claude-opus-4-6",
    max_tokens=1024,
    tools=[...],
    messages=[{"role": "user", "content": "..."}],
) as stream:
    for text in stream.text_stream:
        print(text, end="")
    message = stream.get_final_message()  # tool_use audited + gated here
# block_on_critical raises PreExecutionBlockedError before you touch tool_use.

Log format (JSONL)

{"event_type": "tool_use", "tool_use_id": "toolu_01xxx", "tool_name": "bash", "tool_input": {"command": "ls -la"}, "model": "claude-opus-4-6", "timestamp": "2026-04-05T10:00:00+00:00", "session_id": "..."}
{"event_type": "tool_result", "tool_use_id": "toolu_01xxx", "result_content": "file1.txt\nfile2.txt", "is_error": false, "timestamp": "2026-04-05T10:00:01+00:00", "session_id": "..."}

Custom writer

from agentlens.writers import BaseWriter

class MyWriter(BaseWriter):
    def write(self, event) -> None:
        # send to your own DB, S3, SIEM, etc.
        my_db.insert(event.to_json())

client = AuditedAnthropic(writer=MyWriter())

Run tests

pip install -e ".[dev]"
pytest tests/

License

MIT