Langtrace
OpenTelemetry-based observability for tracing, evaluating, and measuring LLM applications end to end.
- Source repo
- Scale3-Labs/langtrace
- Stars
- ★ 1.2k
- Last updated
- 11mo ago
- License
- AGPL-3.0
- Primary language
- TypeScript
- FA score
- 42/100 · Major gaps
At a glance
- How it runs
- Works with
- Universal · cross-platformOpenAI API · Claude API
- Cost
- Free tier plus a paid hosted plan
- Setup effort
- Medium · a few setup steps
- You'll need
- Typical use
- An application team using OpenAI, Anthropic, or Azure OpenAI that needs to inspect traces, latency, cost, and usage for model calls.
- Not a fit if
- Teams that need fully offline operation with no data leaving their machines
- TypeScript-only projects relying on LangChain, LangGraph, LiteLLM, or CrewAI
- Teams unwilling to operate Postgres and ClickHouse database infrastructure
- Source review
- 42/100 · Major gaps 2 safety controls not found
What does this agent do, and when should you use it?
Langtrace is open-source observability software for capturing, debugging, and analyzing traces and metrics from LLM applications. Its TypeScript and Python SDKs automatically capture calls made through supported LLM providers, application frameworks, and vector databases. It offers real-time monitoring plus analysis of latency, cost, and usage patterns, with tracing-oriented debugging and visualizations. The managed service is connected through projects and API keys, while self-hosting runs a Next.js application with Postgres and ClickHouse. The self-hosted OSS client states that it collects no telemetry and that data does not leave the operator's servers.
For JavaScript/TypeScript applications, install @langtrase/typescript-sdk and call Langtrace.init({ api_key: '<your_api_key>' }); for Python, install langtrace-python-sdk and call langtrace.init(api_key='<your_api_key>'). In TypeScript, the SDK import must precede LLM module imports. The SDK automatically captures calls to supported LLM APIs, frameworks, and vector databases, producing traces that adhere to OpenTelemetry standards. Langtrace uses the resulting traces and metrics for real-time monitoring, latency, cost, and usage analysis, along with workflow debugging and visualizations. For self-hosting, docker compose up starts the Next.js app, Postgres, and ClickHouse, with the application available at http://localhost:3000.
- An application team using OpenAI, Anthropic, or Azure OpenAI that needs to inspect traces, latency, cost, and usage for model calls.
- A Python developer running LangChain, LangGraph, LlamaIndex, LiteLLM, or CrewAI who needs to debug call chains inside a framework workflow.
- A retrieval-augmented application using Pinecone, ChromaDB, Qdrant, Weaviate, or PGVector that needs to analyze vector operations alongside LLM calls.
- A team that must keep observability data on its own infrastructure and can operate the documented Next.js, Postgres, and ClickHouse deployment.
- A developer maintaining a TypeScript Vercel AI or LlamaIndex application, or a Python Mistral integration, who needs SDK-supported automatic tracing.
How do you install or deploy this agent?
For the managed service, create a project in Langtrace and generate an API key. TypeScript/JavaScript: npm i @langtrase/typescript-sdk. Python: pip install langtrace-python-sdk. Self-hosting requires Docker and Docker Compose; adjust .env as needed, run docker compose up, then open http://localhost:3000.
How do you use this agent?
For TypeScript/JavaScript, place import * as Langtrace from '@langtrase/typescript-sdk' before any LLM module imports, then call Langtrace.init({ api_key: '<your_api_key>' }). Alternatively, set LANGTRACE_API_KEY and call LangTrace.init(). For Python, use from langtrace_python_sdk import langtrace followed by langtrace.init(api_key='<your_api_key>'), or set LANGTRACE_API_KEY and call langtrace.init(). Then run application calls through supported providers, frameworks, or vector databases to collect traces and metrics.
What are this agent's strengths and limitations?
- It generates traces based on OpenTelemetry standards and publishes its work on semantic conventions.
- Both TypeScript and Python SDKs support multiple LLM providers, including OpenAI, Anthropic, Azure OpenAI, Cohere, Gemini, and AWS Bedrock.
- Coverage extends beyond model calls to supported frameworks and vector databases, allowing different operations in an application workflow to be observed together.
- It provides a documented self-hosted stack using Next.js, Postgres, and ClickHouse; the self-hosted OSS client states that no telemetry is collected or sent outside the server.
- Integration coverage is asymmetric between SDKs: LangChain, LangGraph, LiteLLM, and CrewAI are listed only for Python, while Vercel AI is listed only for TypeScript.
- Self-hosting requires operating three services and depends on Docker and Docker Compose, adding database and infrastructure overhead.
- Managed usage requires a project and API key, and the TypeScript SDK must initialize before any LLM module imports.
- The repository describes its tracing semantic conventions as ongoing work, so attribute definitions may continue to evolve.
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 |
|---|---|---|---|---|---|---|
| Langtrace This agent | 42 · Major gaps | Library / SDKFreemium | ★ 1.2k | 11mo ago | TypeScript | OpenAI API · Claude API |
| traceAI | 69 · Some gaps | Library / SDKFree | ★ 266 | 4d ago | Python | — |
| TMA1 | 51 · Major gaps | Self-hosted serviceFree + model costs | ★ 124 | 7d ago | Go | Codex · Claude Code |
| Laminar | 41 · Major gaps | Self-hosted serviceFreemium | ★ 3.5k | today | TypeScript | OpenAI API · Claude API |
How does FollowAgents rate this agent?
Why each dimension lost points
Evidence shows: API endpoints have authentication checks (session or API key), but no explicit least privilege implementation such as role-based access control. User confirmation mechanisms are absent; no workflows require user confirmation. Data flow transparency is limited; README mentions no telemetry collection when self-hosting, but does not detail data flows. Sensitive data handling: API keys are processed, but no explicit encryption or redaction policies. Dependency security: many dependencies in package.json, but no evidence of dependency audit or vulnerability scanning. External effects: Docker deployment and external service integrations exist, but no explicit restrictions. Rollback mechanisms are not mentioned. Source attribution: contributor list exists, but per-file authorship is not clear.
Evidence shows: Code structure is consistent, using Next.js and Prisma, but no explicit error handling strategy. Dependency availability: many dependencies, but no lock file or integrity checks. Failure messages: API returns generic error messages, but no detailed error codes or logs.
Evidence shows: README identifies target users (LLM application developers) and use cases (observability), but does not detail capability boundaries. Trigger precision: SDK initialization requires API key, but trigger conditions are not explicit. Environment fit: supports self-hosting and cloud, but no detailed configuration options.
Evidence shows: README has clear structure with table of contents and detailed instructions. Install notes provide npm and pip commands. Naming stability: SDK names and API endpoints are consistent, but no version compatibility notes. Examples and FAQ provide basic examples and common questions. Known limitations are not listed. License is AGPL-3.0, but SDKs are Apache 2.0. Versioning changelog is not provided. Maintenance responsibility: contributor list and community links exist.
Evidence shows: Output usability: API and UI are provided, but no detailed output format documentation. Marginal value: observability features are provided, but no comparison with other tools. Cost-benefit: self-hosting is free, but no performance benchmarks.
Evidence shows: README claims are partially supported by links, but no detailed verification methods. Cross-source corroboration: multiple SDK repositories exist, but no cross-verification. Fact-inference separation: feature claims in README do not clearly distinguish facts from inferences.
- 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.
- No user confirmation mechanisms found; operations may lack explicit consent.
- Many dependencies without lock files, increasing supply chain risk.
- Sensitive data handling policies are unclear; API key handling requires caution.
- Rollback mechanisms are absent, limiting failure recovery.
- Versioning changelog is missing, making upgrade risks unclear.
Recent releases
Latest entries from the project's GitHub Releases. Notes are quoted from upstream as written, not rewritten or tested by FollowAgents.
-
Release notes excerpt
What's Changed * Added support for displaying [Cleanlab](https://cleanlab.ai/) trustworthiness score on the traces tab * Cleanlab span shows additional metadata such as the explanation logs on the span attributesView on GitHub ↗ -
Release notes excerpt
What's Changed * Fixed the setup instructions for the typescript sdk by @kanlanc * fix selfhosting missing team issue + add proper timeout to clickhouse by @alizenhom New Contributors * @kanlanc made their first contributionView on GitHub ↗
FAQ
Is Langtrace Cloud free?
Does self-hosting send telemetry outside my infrastructure?
How does a TypeScript application begin collecting traces?
@langtrase/typescript-sdk, import it before any LLM module, and initialize it with an API key or the LANGTRACE_API_KEY environment variable.