GitHub hosts HunyuanVideo, Tencent's open-source framework for large-scale video generation models, enabling AI-driven video creation.
Tracely
$49Starting price
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About Tracely
Tracely provides trace-native CI/CD for AI agents by converting production failures into regression tests. It ingests agent traces via OTLP, enabling real-time grading of agent behavior using LLM-as-judge evaluators. The platform clusters failures into actionable issues, allowing teams to identify patterns and root causes efficiently. Failing traces can be frozen into hermetic test cases, complete with bundled inputs and contracts, which are then replayed in CI gates to prevent regressions before deployment. This approach eliminates the need for manual test authoring, as the system instruments any agent stack automatically. Replays are deterministic and incur zero model spend, ensuring cost-effective and reliable validation. A centralized dashboard visualizes traces, evaluator scores, and failure trends, providing teams with clear insights into agent performance. Additionally, an MCP server allows coding agents to directly inspect failures, streamlining debugging and resolution workflows.
Key features
- OTLP-based trace ingestion with durable storage
- LLM-as-judge evaluators for hallucination, grounding, and correctness
- Automated failure clustering into named issues with suggested fixes
- One-click freezing of failing traces into hermetic test cases
- Deterministic CI gate replay with zero model spend
- MCP server for agent-assisted debugging
- Trend analysis with failure rates and cross-metric correlations
- Judge calibration against human review for evaluator accuracy
Use cases
- Converting production AI agent failures into regression tests for CI
- Detecting and blocking regressions in AI agent behavior before deployment
- Debugging agent conversations via replayable traces in a dashboard
Pros
- Automated conversion of production traces into regression tests
- Real-time LLM-based evaluation with per-span or conversation-level grading
- Hermetic replay of failing cases without API calls or model costs
- CI gate integration that blocks merges on regression detection
- Supports any agent stack via OTLP instrumentation
Cons
- Requires OTLP-compatible tracing setup
- No free tier or open-source option mentioned
- Limited to agent-based workflows
- Pricing page details not provided
Frequently asked questions about Tracely
What does Tracely do?
Tracely provides trace-native CI/CD for AI agents by converting production failures into regression tests. It ingests agent traces via OTLP, grades them in real time using LLM-as-judge evaluators, and clusters failures into actionable issues.
Who is Tracely suited for?
Tracely is designed for teams developing AI agents or applications that rely on LLM-based workflows, particularly those needing automated regression testing and observability in CI/CD pipelines.
How does Tracely integrate with existing agent stacks?
Tracely instruments any agent stack without requiring manual test authoring, supporting frameworks like OpenAI, Anthropic, LangChain, LangGraph, LiteLLM, CrewAI, and Mistral via OTLP.
What is the pricing model for Tracely?
Pricing details are not specified in the provided content, but the tool offers a free tier and paid plans based on usage, with features like hermetic replay and CI gate blocking included.
Can Tracely replay production traces deterministically?
Yes, Tracely replays failing traces in CI gates deterministically with zero model spend, using bundled inputs and contracts to ensure consistent results.
How do I get started with Tracely?
To get started, install the Tracely SDK, initialize it in your agent code, and configure the CI workflow to stream traces and run the gate on pull requests.
