AI Skill Hub 推荐使用:Traccia AI工作流平台 是一款优质的Agent工作流。AI 综合评分 7.8 分,在同类工具中表现稳健。如果你正在寻找可靠的Agent工作流解决方案,这是一个值得深入了解的选择。
Traccia AI工作流平台 是一套完整的 AI Agent 自动化工作流方案。通过可视化的节点编排,将复杂的多步骤任务拆解为清晰的自动化流程,实现全程无人值守的智能处理。支持与数百种外部服务和 API 无缝集成,适合构建数据处理管线、业务自动化和 AI 辅助决策系统。
Traccia AI工作流平台 是一套完整的 AI Agent 自动化工作流方案。通过可视化的节点编排,将复杂的多步骤任务拆解为清晰的自动化流程,实现全程无人值守的智能处理。支持与数百种外部服务和 API 无缝集成,适合构建数据处理管线、业务自动化和 AI 辅助决策系统。
# 方式一:pip 安装(推荐)
pip install traccia-py
# 方式二:虚拟环境安装(推荐生产环境)
python -m venv .venv
source .venv/bin/activate # Windows: .venv\Scripts\activate
pip install traccia-py
# 方式三:从源码安装(获取最新功能)
git clone https://github.com/traccia-ai/traccia-py
cd traccia-py
pip install -e .
# 验证安装
python -c "import traccia_py; print('安装成功')"
# 命令行使用
traccia-py --help
# 基本用法
traccia-py input_file -o output_file
# Python 代码中调用
import traccia_py
# 示例
result = traccia_py.process("input")
print(result)
# traccia-py 配置文件示例(config.yml) app: name: "traccia-py" debug: false log_level: "INFO" # 运行时指定配置文件 traccia-py --config config.yml # 或通过环境变量配置 export TRACCIA_PY_API_KEY="your-key" export TRACCIA_PY_OUTPUT_DIR="./output"
OpenTelemetry-based observability, distributed tracing, governance, and compliance for AI agents and LLM applications
Traccia is a production-ready Python SDK for observability, distributed tracing, governance, and compliance across AI agents, LLM applications, agentic workflows, and multi-agent systems.
Built on OpenTelemetry standards, Traccia provides automatic instrumentation, token and cost tracking, guardrail detection, AI governance evidence, and OTLP-compatible exports for modern AI applications.
Traccia is available on PyPI.
init() call with automatic configuration discovery@observe decorator---
api_key = ""
pip install -e ".[dev]"
pip install traccia
```bash
```python from traccia import init, observe
@observe() def process_data(data): return transform(data)
init(sample_rate=0.1)
Traccia merges configuration from multiple sources with the following priority (highest to lowest):
init(endpoint="...", agent_id="...") or start_tracing(...)TRACCIA_ENDPOINT, TRACCIA_AGENT_ID, etc.traccia.toml (current directory) or ~/.traccia/config.tomlExample: If you set TRACCIA_ENDPOINT in your environment and pass endpoint=... to init(), the explicit parameter wins.
---
Create a traccia.toml file in your project root:
traccia config init
This creates a template config file:
```toml [tracing]
[exporters]
[logging] debug = false # Enable debug logging enable_span_logging = false # Enable span-level logging
[advanced]
All config parameters can be set via environment variables with the TRACCIA_ prefix:
Tracing: TRACCIA_API_KEY, TRACCIA_ENDPOINT, TRACCIA_SAMPLE_RATE, TRACCIA_AUTO_START_TRACE, TRACCIA_AUTO_TRACE_NAME, TRACCIA_USE_OTLP, TRACCIA_SERVICE_NAME
Exporters: TRACCIA_ENABLE_CONSOLE, TRACCIA_ENABLE_FILE, TRACCIA_FILE_PATH, TRACCIA_RESET_TRACE_FILE
Instrumentation: TRACCIA_ENABLE_PATCHING, TRACCIA_ENABLE_TOKEN_COUNTING, TRACCIA_ENABLE_COSTS, TRACCIA_AUTO_INSTRUMENT_TOOLS, TRACCIA_MAX_TOOL_SPANS, TRACCIA_MAX_SPAN_DEPTH, TRACCIA_OPENAI_AGENTS, TRACCIA_CREWAI, TRACCIA_GUARDRAIL_HEURISTICS
Rate Limiting: TRACCIA_MAX_SPANS_PER_SECOND, TRACCIA_MAX_QUEUE_SIZE, TRACCIA_MAX_BLOCK_MS, TRACCIA_MAX_EXPORT_BATCH_SIZE, TRACCIA_SCHEDULE_DELAY_MILLIS
Runtime: TRACCIA_SESSION_ID, TRACCIA_USER_ID, TRACCIA_TENANT_ID, TRACCIA_PROJECT_ID, TRACCIA_AGENT_ID, TRACCIA_AGENT_NAME, TRACCIA_ENV
Legacy alias: TRACCIA_PROJECT (maps to project_id)
Logging: TRACCIA_DEBUG, TRACCIA_ENABLE_SPAN_LOGGING
Advanced: TRACCIA_ATTR_TRUNCATION_LIMIT
```python from traccia import init
init( endpoint="http://tempo:4318/v1/traces", sample_rate=0.5, enable_costs=True, max_spans_per_second=100.0, agent_id="my-agent", agent_name="My Agent", env="production", ) ```
Create a new traccia.toml configuration file:
traccia config init
traccia config init --force # Overwrite existing
#
#
export TRACCIA_PRICING_OVERRIDE_JSON='{"gpt-4o": {"prompt": 0.005, "completion": 0.015}}' ```
Deprecation notice:AGENT_DASHBOARD_PRICING_JSONis accepted as a back-compat alias forTRACCIA_PRICING_OVERRIDE_JSONbut will be removed in a future minor version. Rename the variable in your environment.
Platform overrides (org-level): Org admins can set pricing overrides in Settings → Pricing on the Traccia platform. These apply to the platform-recomputed cost (platform_cost_usd) for all agents in the org. They do not change llm.cost.usd on existing spans retroactively unless you explicitly enable the "Also recompute past traces" option in the save dialog.
---
init(debug=True)
load_config(config_file=None, overrides=None) -> TracciaConfigLoad and validate configuration.
Parameters: - config_file (str, optional): Path to config file - overrides (dict, optional): Override values
Returns: Validated TracciaConfig instance
Raises: ConfigError if invalid
validate_config(config_file=None, overrides=None) -> tuple[bool, str, TracciaConfig | None]Validate configuration without loading.
Returns: Tuple of (is_valid, message, config_or_none)
---
python -m venv venv source venv/bin/activate # On Windows: venv\Scripts\activate
Traccia automatically detects and instruments the OpenAI Agents SDK when installed. No extra code needed:
from traccia import init
from agents import Agent, Runner
init() # Automatically enables Agents SDK tracing
agent = Agent(
name="Assistant",
instructions="You are a helpful assistant"
)
result = Runner.run_sync(agent, "Write a haiku about recursion")
Configuration: Auto-enabled by default when openai-agents is installed. To disable:
```python init(openai_agents=False) # Explicit parameter
endpoint = "https://api.traccia.ai/v2/traces"
sample_rate = 1.0 # 0.0 to 1.0 auto_start_trace = true # Auto-start root trace on init auto_trace_name = "root" # Name for auto-started trace use_otlp = true # Use OTLP exporter
metrics_sample_rate = 1.0 # Metrics sampling rate (1.0 = 100%)
[runtime]
If you do not set endpoint (in config, environment, or when calling init() / start_tracing()), the SDK uses the Traccia platform by default (https://api.traccia.ai/v2/traces). You can override it to send traces to your own OTLP-compatible backend.
The default is defined in traccia.config: DEFAULT_OTLP_TRACE_ENDPOINT. The alias DEFAULT_ENDPOINT is kept for backward compatibility (same value).
Traccia includes a powerful CLI for configuration and diagnostics:
traccia pricing clear ```
---
git clone https://github.com/traccia-ai/traccia-py.git cd traccia-py
traccia.instrumentation.*: Infrastructure and vendor instrumentation.requests).traccia.integrations.*: AI/agent framework integrations.---
Traccia填补AI工作流可观测性空白,OpenTelemetry原生设计体现专业度。治理合规功能前瞻,但社区规模待扩大,适合有合规需求的企业探索。
AI Skill Hub 为第三方内容聚合平台,本页面信息基于公开数据整理,不对工具功能和质量作任何法律背书。
建议在沙箱或测试环境中充分验证后,再部署至生产环境,并做好必要的安全评估。
✅ Apache 2.0 — 宽松开源协议,可商用,需保留版权声明和 NOTICE 文件,含专利授权条款。
总体来看,Traccia AI工作流平台 是一款质量良好的Agent工作流,在同类工具中具备一定竞争力。AI Skill Hub 将持续追踪其更新动态,建议收藏备用,结合自身场景选择合适时机引入使用。
| 原始名称 | traccia-py |
| 原始描述 | 开源AI工作流:OpenTelemetry-native observability, governance, and compliance for AI agents and。⭐63 · Python |
| Topics | AI工作流可观测性智能体治理OpenTelemetry合规监控 |
| GitHub | https://github.com/traccia-ai/traccia-py |
| License | Apache-2.0 |
| 语言 | Python |
收录时间:2026-06-06 · 更新时间:2026-06-11 · License:Apache-2.0 · AI Skill Hub 不对第三方内容的准确性作法律背书。
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