TraceRoot
Observability that turns AI-agent production failures into verified fixes and regression evals.
- Source repo
- traceroot-ai/traceroot
- Stars
- ★ 1.3k
- Last updated
- today
- License
- NOASSERTION
- Primary language
- TypeScript
- FA score
- 42/100 · Major gaps
At a glance
- How it runs
- Works with
- Universal · cross-platformCodex · Claude Code · OpenAI API · Claude API
- Cost
- Free tier plus a paid hosted plan
- Setup effort
- Medium · a few setup steps
- You'll need
- Typical use
- An engineering team operating a customer-facing agent can detect production hallucinations or intent drift, then retain confirmed incidents as regression examples.
- Not a fit if
- Teams that must stay fully offline or keep data on-premises
- Teams unwilling to expose production source code and GitHub history to a sandbox
- Teams needing a stable production self-hosted deployment; the K8s/Helm/Terraform path is experimental
- Source review
- 42/100 · Major gaps 2 safety controls not found
What does this agent do, and when should you use it?
TraceRoot is an open-source observability and improvement platform for AI agents, combining tracing, production detection, root-cause analysis, and offline evaluation. Its OpenTelemetry-compatible SDKs capture LLM calls, agent actions, and tool usage, while Detectors screen incoming traces for selected failure types. Confirmed findings can become golden datasets, which teams can use with the TraceRoot CLI or SDK to run offline evals after a change. The Agentic Debugging layer is described as working against a sandbox containing production source code and correlating failures with GitHub commits, pull requests, and issues to identify a failing line and open a fix PR. It is available as TraceRoot Cloud or through local Docker workflows; its AWS Kubernetes, Helm, and Terraform production deployment path is explicitly experimental.
In Python, an application can call traceroot.initialize(integrations=[Integration.OPENAI]) and decorate a function with @observe(name="my_agent", type="agent"); in TypeScript, it can call TraceRoot.initialize({ instrumentModules: { openAI: OpenAI } }) and wrap an async function with observe({ name: "my_agent", type: "agent" }, ...), then call TraceRoot.shutdown(). These SDK paths capture LLM calls, agent actions, and tool usage as traces. Detectors apply an LLM-as-judge evaluator to incoming traces for hallucinations, tool or logic failures, safety violations, and intent drift, with email and Slack alerts and automatic root-cause-analysis triggering described in the project. The debugging layer analyzes a sandbox with production source code, correlates against GitHub commits, PRs, and open issues, identifies an exact failing line, and is described as opening verified fix PRs. Confirmed production findings can be converted into golden datasets, and offline evals can run through the TraceRoot CLI or SDK inside coding agents including Claude Code, Codex, and Cursor.
- An engineering team operating a customer-facing agent can detect production hallucinations or intent drift, then retain confirmed incidents as regression examples.
- A Python or TypeScript service using OpenAI Chat Completions or the Responses API can instrument calls and agent functions through the documented SDK setup.
- A team investigating tool-call instability or logic failures can use the described sandbox-and-GitHub workflow to trace an incident to a source line.
- Developers using LangChain, LangGraph, CrewAI, AutoGen, or the OpenAI Agents SDK can use one of the framework integrations listed by the project.
- A team validating agent fixes during coding work can turn production findings into golden datasets and run offline evals from Codex, Claude Code, or Cursor.
How do you install or deploy this agent?
For Python, run pip install traceroot openai. For TypeScript, run npm install @traceroot-ai/traceroot openai. To work on the repository locally, run git clone https://github.com/traceroot-ai/traceroot.git, cd traceroot, then make dev; to run everything locally in Docker, use make prod. The supplied material does not document the Cloud authentication variables, SDK ingestion endpoint configuration, or exact model-provider credential setup. Because the examples instantiate an OpenAI client, a working authentication configuration for that client is required for actual model calls.
How do you use this agent?
For Python, import traceroot, Integration, observe, and OpenAI; call traceroot.initialize(integrations=[Integration.OPENAI]); then decorate the function that calls client.chat.completions.create(...) with @observe(name="my_agent", type="agent"). For TypeScript, call TraceRoot.initialize({ instrumentModules: { openAI: OpenAI } }), wrap work with observe({ name: "my_agent", type: "agent" }, async (...) => ...), and call await TraceRoot.shutdown() in finally. For a local platform deployment, run make dev or make prod from the cloned repository. The AWS deployment path is under deploy/ and is described as Kubernetes with Helm and Terraform, still experimental.
What are this agent's strengths and limitations?
- It connects LLM-as-judge production detection, root-cause analysis, golden datasets, and offline evals in one stated workflow rather than stopping at trace capture.
- Its debugging design explicitly combines a sandbox containing production source code with GitHub commits, PRs, and issues to identify a failing line and create a fix PR.
- It provides native Python and TypeScript SDKs and documents integrations across agent frameworks and providers including OpenAI, Anthropic, Gemini, Mistral, and OpenRouter.
- Teams can choose TraceRoot Cloud, local Docker workflows, or an AWS Kubernetes/Helm/Terraform route, while the project states BYOK support for model providers.
- The supplied documentation does not specify Cloud, SDK-ingestion, or GitHub token variables, authentication steps, or permission scopes.
- The described debugging workflow requires access to a sandbox with production source code and GitHub history, creating source-access, isolation, and permission-review work.
- The Kubernetes, Helm, and Terraform AWS production-hosting route is explicitly experimental.
- Detectors rely on an LLM-as-judge, but the supplied material does not document judge model selection, thresholds, false-positive handling, retention, or alert configuration.
How does this agent compare with similar options?
Key facts side by side with the most closely related agents.
| Agent | Source review | Form / cost | Stars | Updated | Language | Full support on |
|---|---|---|---|---|---|---|
| TraceRoot This agent | 42 · Major gaps | Hosted serviceFreemium | ★ 1.3k | today | TypeScript | Codex · Claude Code · OpenAI API · Claude API |
| DataBuff | 41 · Major gaps | Self-hosted serviceFree + model costs | ★ 723 | 1mo ago | Java | OpenAI API |
| Laminar | 41 · Major gaps | Self-hosted serviceFreemium | ★ 3.5k | 4d ago | TypeScript | OpenAI API · Claude API |
| HolmesGPT SRE Agent | 57 · Major gaps | CLIFree + model costs | ★ 3.8k | today | Python | OpenAI API |
How does FollowAgents rate this agent?
Why each dimension lost points
Evidence shows: README mentions BYOK and self-hosting, but least privilege not explicit; no user confirmation mechanism; data flow transparency limited, only mentions OpenTelemetry collection; sensitive data handling has SECURITY.md but lacks details; dependencies have version bounds but not fully audited; external effects include sending notifications and creating PRs, but authorization not specified; no rollback mechanism; source attribution has contributor list but unverified. Deductions: lack of implementation details.
Evidence shows: tests cover detector conditions and trace naming, but not entire system; dependencies declared but availability not verified; failure messages partially in tests, but overall insufficient. Deductions: static review cannot verify runtime behavior.
Evidence shows: README describes multiple scenarios and integrations, but capability boundaries unclear; trigger conditions tested but not fully documented; environment fit has Docker and Terraform but experimental. Deductions: lack of detailed configuration.
Evidence shows: information architecture clear, with README, SECURITY, CONTRIBUTING; install notes exist but not detailed; naming stable but version not explicit; examples present but no FAQ; known limitations not explicit; license Apache 2.0 but with enterprise edition; versioning changelog missing; maintenance responsibility has contributors but not explicit. Deductions: missing changelog and detailed limitations.
Evidence shows: output usability has example code; marginal value high, solves real problems; cost-benefit not quantified. Deductions: no cost data.
Evidence shows: README claims partially supported by docs, but not fully traceable; cross-source verification limited; facts and inferences not clearly separated. Deductions: static review cannot verify.
- Not found in source: confirmation before actingTurn on (or add) a confirmation step before it acts, and try it in a sandbox or test environment before real data.
- Not found in source: rollback or recovery pathBack up first, or work on a git branch or snapshot, so its changes can be undone.
- Static review cannot verify runtime behavior; all scores based on source and docs.
- Dependency security not fully audited; recommend checking for vulnerabilities.
- Enterprise features may affect license compliance.
Recent releases
Latest entries from the project's GitHub Releases. Notes are quoted from upstream as written, not rewritten or tested by FollowAgents.
-
v1.3.6 v1.3.6 - Security fixes: model provider keys, better-auth
Release notes excerpt
Security - Reject blank model provider API keys. A bring-your-own-key model provider saved with a whitespace-only key is now rejected when created or updated, and a stored blank key is treated as no key when a provider is resolved. This closes a path by which a workspace admin could make the server use its own environment provider key. Details in the [security advisory](https://github.com/traceroot-ai/traceroot/security/advisories/GHSA-r4ph-92m3-xw89). - Upgrade better-auth to 1.7.15 for security advisories. …View on GitHub ↗ -
v1.3.5 v1.3.5 - Async redact in TypeScript
Release notes excerpt
This release documents async redact in the TypeScript SDK. From @traceroot-ai/traceroot 0.8.0, redact runs at export rather than during the request, may return a promise, and so can call a detection service to remove names and addresses that a pattern match misses. Improvements - Async redact in TypeScript. The Data Capture and Privacy page now covers how TypeScript redact differs from Python: it runs at export, may be async, and each batch gets 5 seconds. A promise that rejects or does not settle in time fails closed the same way a throw does, storing [redaction failed]. …View on GitHub ↗ -
v1.3.4 v1.3.4 - Tool call cost, improved detector judge
Release notes excerpt
This release counts the reported cost of tool and API calls, so a paid search, scrape or data API call reaches the trace total and shows on its span. The detector judge reads a compact text view of the trace instead of raw span JSONL, which removes most of the false positives caused by cut-off input. The rest is follow-through on last release's onboarding: the manual steps now say how the key reaches the app and name TRACEROOT_HOST_URL for self-hosted deployments, the device page is worded for setup as well as login, and the SQL gateway describes a result's columns even when it has no rows. …View on GitHub ↗
FAQ
Is TraceRoot only a tracing product?
Is it tied to one model provider?
What engineering resources does Agentic Debugging need?
Can it be self-hosted?
make dev supports a local development setup and make prod runs everything locally in Docker. An AWS Kubernetes, Helm, and Terraform path is available but marked experimental.