stability-analysis-agent 1.2.1

Stability Analysis Agent: AI-assisted crash log analysis toolchain (CLI, daemon, analyzers, agent)

Stability Analysis Agent

An AI Agent for App Stability — from crash log to root cause in one step
_{Crash · ANR · OOM · Freeze analysis | addr2line / atos symbolizer | LangGraph AI Agent | RAG knowledge base}

English | 简体中文

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Stability Analysis Agent is an open-source AI Agent purpose-built for app stability analysis — covering crashes, ANR (Application Not Responding), OOM (Out of Memory), freezes / watchdog kills, and more. Feed it a stability log, and it will parse, symbolize, extract code, reason about the root cause, and generate fix suggestions — automatically. Supports iOS, Android, macOS, Linux, and Windows with built-in addr2line / atos integration, LangGraph multi-turn reasoning, and a RAG knowledge base (ChromaDB).

Why not just paste the log into an AI coding tool?

General-purpose AI coding tools (Cursor, Copilot, Claude Code, etc.) can read a crash log, but they hit hard limits on stability analysis:

• Raw addresses are meaningless — AI tools cannot run addr2line / atos; they see 0x1a2b3c instead of MyClass::process() at main.cpp:42.
• Stability logs are noisy — hundreds of system frames drown the real signal; without structured parsing, the LLM wastes tokens on irrelevant context.
• No domain memory — every conversation starts from scratch; learned patterns (crash signatures, ANR deadlock traces, OOM heuristics) are lost.

This Agent solves all three:

	AI Coding Tool	Stability Analysis Agent
Address symbolization	Cannot run native tools	Built-in addr2line / atos integration
Log parsing	Sees raw text, high noise	Structured parser extracts signal, threads, key frames; classifies crash / ANR / OOM / freeze
Knowledge accumulation	Stateless, starts from zero	RAG: rule table + vector DB, patterns improve over time
Workflow	Single-prompt, one-shot	Multi-step Agent with conditional multi-turn reasoning
Extensibility	Prompt-only	Tool + Workflow plugin system, config-driven

Agent Engine

Three execution modes to fit different needs:

Mode	Engine	Best for
Direct	One-shot prompt assembly	Fast, simple, no framework dependency
LangChain	LangChain Agent	Flexible tool calling with chain-of-thought
LangGraph	LangGraph state machine	Multi-turn reasoning, the Agent can request more context and re-invoke tools

Select via --engine direct|langchain|langgraph. All modes share the same tool chain and RAG knowledge base.

No LLM API key required to run the core toolchain (parsing + symbolization + code extraction). Plug in any OpenAI-compatible model (GPT, DeepSeek, ERNIE, GLM, etc.) when you're ready for AI analysis.

Key Features

Feature	Description
Multi-Step AI Agent	LangGraph / LangChain / Direct — multi-turn reasoning with conditional branching
Address Symbolization	Resolves raw addresses to function names & line numbers via addr2line / atos
Structured Log Parsing	Auto-detects iOS / Android / macOS / Linux / Windows; classifies crash, ANR, OOM, freeze; extracts signal, threads, key frames
Source Code Context	Extracts code snippets around crash points
RAG Knowledge Base	Rule table (fast path) + vector retrieval (ChromaDB) with feedback loop
Tool + Workflow System	Pluggable architecture — register custom tools and workflows via config or decorators
Multiple Interfaces	CLI, HTTP Daemon (streaming / SSE), Python API

Architecture

                  ┌──────────┐   ┌──────────┐   ┌──────────┐
                  │   CLI    │   │  Daemon  │   │  Python  │
                  │          │   │  (HTTP)  │   │   API    │
                  └────┬─────┘   └────┬─────┘   └────┬─────┘
                       │              │              │
                       └──────────────┼──────────────┘
                                      │
                            ┌─────────▼─────────┐
                            │   Tool + Workflow │
                            │   (tool_system)   │
                            └─────────┬─────────┘
                                      │
          ┌───────────────────────────┼───────────────────────────┐
          │                           │                           │
          ▼                           ▼                           ▼
   ┌────────────┐            ┌────────────┐            ┌────────────┐
   │  Crash Log │            │  Address   │            │    Code    │
   │   Parser   │            │ Symbolizer │            │  Provider  │
   └────────────┘            └────────────┘            └────────────┘
                                      │
                            ┌─────────▼─────────┐
                            │    AI Agent       │
                            │  ┌─────────────┐  │
                            │  │  LangGraph  │  │
                            │  │  State      │  │
                            │  │  Machine    │  │
                            │  └──────┬──────┘  │
                            │         │         │
                            │    ┌────▼────┐    │
                            │    │   RAG   │    │
                            │    │ Rules + │    │
                            │    │ Vectors │    │
                            │    └────┬────┘    │
                            │         │         │
                            │    ┌────▼────┐    │
                            │    │   LLM   │    │
                            │    └─────────┘    │
                            └───────────────────┘

Agent Pipeline:

Crash Log → Parse → Symbolize → Extract Code
                                      ↓
                              RAG (rules + vectors)
                                      ↓
                                LLM Reasoning ←──→ Request More Context (multi-turn)
                                      ↓
                                 Fix Report

For detailed architecture diagrams, see docs/architecture.

Quick Start

Prerequisites

• Binary usage: no Python runtime required
• Source usage: Python 3.9+
• (Optional) atos (macOS, built-in) or addr2line (Linux, via binutils) for symbolization

1. Install via PyPI (Recommended)

# Install (for Mainland China, add -i https://pypi.tuna.tsinghua.edu.cn/simple)
pip install stability-analysis-agent

# Verify installation
sa-agent --help

# One command onboarding (auto-check + guided setup + analysis)
sa-agent

When a previous analysis record exists, the interactive menu shows 5) Analyze recent log again for one-click rerun. The onboarding interaction is inspired by Claude-style CLI UX: arrow-key menus, grouped "More options", clear back actions, and post-action confirmation panels.

Config files are saved in ~/.config/stability-analysis-agent/:

• agent_config.local.json — LLM provider / API key / model
• add2line_resolver_config.local.json — addr2line / atos tool paths

Provider template is available at tools/configs/agent_config.local.example.json.
Besides setting API keys/authorization, you must set llm_config.active_provider to the provider key you want to use (for example openai, deepseek, or zhipu_bigmodel). AI requests currently use OpenAI Chat Completions compatible format by default; for non-compatible providers, an adapter is required (config-only changes may not be sufficient).

Even without config initialization, you can run the full non-AI toolchain with --skip-ai.

The PyPI package includes full runtime dependencies (vector DB, tree-sitter, and LangGraph chain).

Upgrade with: pip install -U stability-analysis-agent

2. Use Prebuilt CLI Binary (No Python Required)

Download the latest binary from GitHub Releases, then run:

# Example for v1.2.1 macOS arm64 package
unzip StabilityAnalyzer-v1.2.1-mac-arm64.zip
cd output/cli_release/stability_analyzer_cli/v1.2.1-mac-arm64

chmod +x StabilityAnalyzer

# If macOS Gatekeeper blocks launch (unsigned binary)
xattr -d com.apple.quarantine StabilityAnalyzer

./StabilityAnalyzer --help

# Optional: install a stable command name into ~/.local/bin (also ships in release zips)
chmod +x install.sh
./install.sh
# then: sa-agent --help

3. Developer Setup (from Source)

git clone https://github.com/baidu-maps/stability-analysis-agent.git
cd stability-analysis-agent
pip install -e .

pip install -e . is intended for development workflows. It also exposes the sa-agent command locally.

4. Run the Built-in Demo (No API Key Needed)

After installing via PyPI (pip install stability-analysis-agent) or from source (pip install -e .), clone the repo to get the bundled demo cases, then run:

sa-agent \
  --crash-log examples/crash_cases/demo_basic/logs/mac/NullPtr_SIGSEGV_2026-04-08_10-43-08.crash \
  --library-dir examples/crash_cases/demo_basic/lib/mac \
  --code-root examples/crash_cases/demo_basic/code_dir \
  --skip-ai

Output is saved to ./cli_reports/<timestamp>/ (under your current working directory) with structured JSON reports.

5. Analyze Your Own Crash Log

sa-agent \
  --crash-log <your-crash-log> \
  --library-dir <path-to-libs-and-symbols> \
  --code-root <path-to-source-code>

Add --skip-ai to skip AI analysis, or --parse-only to only parse + symbolize.

CLI Options

Flag	Required	Description
--crash-log	Yes	Path to the crash log file
--library-dir	Yes*	Directory with libraries (.dylib/.so) and debug symbols (.dSYM)
--code-root	No	Source code root for extracting code context
--skip-ai	No	Skip AI — run toolchain only (parser + resolver + code provider)
--parse-only	No	Parse + symbolize only (no --code-root needed)
--parse-log-only	No	Parse crash log only (no --library-dir needed)
--daemon <url>	No	Delegate to a running daemon instance

* Not required when using --parse-log-only.

Daemon Mode

The daemon provides streaming output (SSE), process reuse (no cold start), and task cancellation — ideal for IDE integration and high-frequency analysis:

# Start the daemon
sa-agent --daemon-server --host 127.0.0.1 --port 8765

# Analyze via daemon
sa-agent --daemon http://127.0.0.1:8765 \
  --crash-log <crash-log> --library-dir <lib-dir> --code-root <code-root>

See Daemon Server Guide for the full HTTP API reference.

Python API

from tool_system import (
    ToolAndWorkflowRegistry, SystemConfig, WorkflowConfig,
    ConfigDrivenExecutor, register_all_tools_and_workflows
)

registry = ToolAndWorkflowRegistry()
register_all_tools_and_workflows(registry)

config = SystemConfig(
    workflows=[WorkflowConfig(name="crash_analysis", enabled=True)]
)
executor = ConfigDrivenExecutor(registry, config, llm_adapter=None)

result = executor.execute_workflow("crash_analysis", {
    "crash_log": open("crash.crash").read(),
    "library_dir": "./lib",
    "code_root": "./code"
})
print(result)

LLM and Tool Configuration

AI analysis is optional. You can still run full non-AI toolchain with --skip-ai without any initialization.

For AI analysis and add2line customization after PyPI install, run:

sa-agent

Default local config directory:

~/.config/stability-analysis-agent/

• agent_config.local.json for LLM provider/key/model
• add2line_resolver_config.local.json for addr2line/atos tool paths

If you prefer manual editing, edit these files directly in that directory.

Advanced: add2line config override

You can override add2line config file location via environment variable:

export STABILITY_AGENT_ADD2LINE_CONFIG_FILE="/abs/path/add2line_resolver_config.local.json"

Project Structure

stability-analysis-agent/
├── agent/              # AI Agent engine (LangGraph state machine)
├── cli/                # CLI entry point
├── daemon/             # HTTP daemon (streaming, SSE)
├── tools/              # Tool implementations (parser, resolver, code provider)
│   └── configs/        # Configuration templates
├── tool_system/        # Tool + Workflow registration & dispatch framework
├── workflows/          # Workflow definitions (crash analysis)
├── rag/                # RAG: rule store + vector index (ChromaDB) + metadata
├── prompts/            # Prompt templates for LLM analysis
├── protocol/           # Unified request/response protocol
├── examples/           # Bundled crash cases
│   └── crash_cases/
│       ├── demo_basic/         # NullPtr, DivZero, Abort, DoubleFree, etc.
│       └── demo_multithread/   # Race condition, deadlock, atomic failure, etc.
├── test/               # Test suite
└── docs/               # Documentation

Documentation

Topic	Link
CLI Guide	docs/cli/CLI_GUIDE.md
CLI Commands Reference	docs/cli/CLI_COMMANDS_REFERENCE.md
Daemon Server Guide	docs/cli/DAEMON_SERVER_GUIDE.md
PyPI Release Scripts	docs/scripts/PYPI_RELEASE_SCRIPTS.md
System Architecture	docs/architecture/README.md
Architecture Diagram	docs/architecture/ARCHITECTURE_DIAGRAM.md
Tool System Overview	docs/tools/tool_system/TOOL_SYSTEM_OVERVIEW.md
Tool Extension Guide	docs/tools/tool_system/TOOL_SYSTEM_EXTENSION.md
Workflow System	docs/workflows/WORKFLOWS.md
RAG Vector Database	docs/rag/README.md
Crash Demos	docs/crash_demos/README.md

Testing

# Regression tests
python3 test/tool_system/test_regression.py

# LLM connection test
python3 test/llm/test_llm_connection.py --provider openai

# Code content provider test
python3 test/agent_py_tool/test_code_content_provider.py

# Vector database test
python3 test/agent_py_tool/test_vector_db.py

FAQ

Q: Symbolization failed? Ensure --library-dir contains the binary files (.dylib / .so) along with their debug symbols (.dSYM directories or DWARF info).

Q: LLM call failed? Verify your API key is set correctly. Quick check: python3 test/llm/test_llm_connection.py --provider openai

Q: Code context extraction returns empty? Ensure --code-root points to the source directory that contains the files listed in the symbolized stack trace.

Q: Can I use this without an LLM key? Yes. Use --skip-ai to run the full toolchain (parse + symbolize + extract code). The structured JSON output is useful on its own for triage and debugging.

Contributing

Contributions are welcome! Please read CONTRIBUTING.md before submitting a PR.

# All commits require DCO sign-off
git commit -s -m "feat: describe your change"

License

Apache License 2.0

Contact

Channel	Link
GitHub Issues	Report a bug or request a feature
Email	[e-mail]

Maintainer:

Name	GitHub	Email
liuhong	@liuhong996	[e-mail]

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