greydgl--pentestgpt
Gelei Deng
e238d701f2
* feat: 🎸 version 1.0 agentic workflow
Major rewrite of PentestGPT to use an agentic pipeline architecture:
Core Changes: - New event-driven architecture with EventBus for
TUI-agent decoupling - Implemented AgentController with 5-state
lifecycle (IDLE->RUNNING->PAUSED->COMPLETED->ERROR) - Added AgentBackend
interface with ClaudeCodeBackend implementation - Session management
with file-based persistence for resumable pentests - Langfuse
integration for observability and tracing Interface: - New Textual-based
TUI with real-time activity feed - Keyboard shortcuts: F1 help, Ctrl+P
pause, Ctrl+Q quit - Enhanced CLI with --target, --instruction,
--non-interactive, --debug flags Project Structure: - Moved legacy
multi-LLM version (v0.15) to legacy/ directory - New pentestgpt/core/
for agent, controller, events, session modules - New
pentestgpt/interface/ for TUI and CLI components - New
pentestgpt/benchmark/ for xbow benchmark integration - Comprehensive
test suite in tests/ with unit and integration tests DevOps: - Docker
support with Ubuntu 24.04 container - GitHub Actions CI/CD pipeline -
Makefile with dev commands (test, lint, format, typecheck) - Added
xbow-validation-benchmarks as submodule
* style: format code with Black
This commit fixes the style issues introduced in abe3be0 according to the output
from Black.
Details: https://github.com/GreyDGL/PentestGPT/pull/325
* fix: 🐛 fix test pipeline
* feat: 🎸 update format
* feat: 🎸 update
---------
Co-authored-by: deepsource-autofix[bot] <62050782+deepsource-autofix[bot]@users.noreply.github.com>
42 行
1.4 KiB
Python
42 行
1.4 KiB
Python
import os
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from datetime import datetime
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# get keys for your project
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os.environ[
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"LANGFUSE_PUBLIC_KEY"
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] = "pk-lf-5655b061-3724-43ee-87bb-28fab0b5f676" # do not modify
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os.environ[
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"LANGFUSE_SECRET_KEY"
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] = "sk-lf-c24b40ef-8157-44af-a840-6bae2c9358b0" # do not modify
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from langfuse import Langfuse
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langfuse = Langfuse()
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from langfuse.model import CreateTrace, CreateSpan, CreateGeneration, CreateEvent
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trace = langfuse.trace(CreateTrace(name="llm-feature"))
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retrieval = trace.span(CreateSpan(name="retrieval"))
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retrieval.generation(CreateGeneration(name="query-creation"))
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retrieval.span(CreateSpan(name="vector-db-search"))
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retrieval.event(CreateEvent(name="db-summary"))
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trace.generation(CreateGeneration(name="user-output"))
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generationStartTime = datetime.now()
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generation = trace.generation(
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CreateGeneration(
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name="summary-generation",
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startTime=generationStartTime,
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endTime=datetime.now(),
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model="gpt-3.5-turbo",
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modelParameters={"maxTokens": "1000", "temperature": "0.9"},
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prompt=[
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{"role": "system", "content": "You are a helpful assistant."},
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{
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"role": "user",
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"content": "Please generate a summary of the following documents \nThe engineering department defined the following OKR goals...\nThe marketing department defined the following OKR goals...",
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},
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],
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metadata={"interface": "whatsapp"},
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)
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)
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