SuperAPI AI Directory
Partner with us中文AI newsFavorites
All toolsAI agentsTraccia

Traccia

Traccia is an AI agent control plane for production-grade AI systems, providing observability, evaluation, governance, and runtime policy enforcement. It connects to frameworks such as LangChain, CrewAI, OpenAI Agents SDK, AutoGen, and LlamaIndex through an OpenTelemetry-native SDK, and uses a unified dashboard to centrally manage all agents: view end-to-end traces, cost and token attribution for 2,500+ models, PII and sensitive data detection, prompt registry and experiment evaluation, and layered policy guardrail monitoring with hard blocking. Its distinctive features include the ability to enforce interception mid-execution of an agent (such as overspending, infinite loops, or PII leakage), cost accounting that is unaffected by sampling rates, and compliance evidence export mapped to the EU AI Act. It is suitable for AI teams that need unified observability, governance, and compliance auditing across frameworks.

Open website

About this tool

Traccia is an AI agent control plane, used for observability, evaluation, governance, and runtime policy enforcement for AI systems in production. Its core proposition is: let agents do more without losing control.

I. Purpose and Positioning Traccia is aimed at teams running AI agents in production, solving the problem of fragmented tools. According to the official description, teams no longer need to switch back and forth between LangSmith, self-built Grafana dashboards, and spreadsheets; Traccia provides a single view for all AI agents and is not limited to a single framework. It focuses on four actions: Observe, Evaluate, Govern, and Audit.

II. Main Capabilities

  1. Unified agent registry: a single source of truth across LangChain, CrewAI, and OpenAI Agents SDK, tracking versions, health status, environments, and ownership, with zero code changes required.
  2. End-to-end tracing: OpenTelemetry-native, covering every LLM call, tool use, and agent decision; supports LangChain, CrewAI, OpenAI Agents SDK, AutoGen, and LlamaIndex.
  3. Policy monitoring and enforcement: three-layer guardrail detection, covering explicit annotations, provider-native signals (OpenAI, Anthropic, Google), and heuristics. Hard blocking can stop an agent mid-execution rather than handling it after the fact. When native OpenAI, Anthropic, or Gemini clients point to the Gateway, Spend Cap and Model Boundary also take effect.
  4. Cost and token attribution: accurate costs for 2,500+ models; calculated locally at span end, independent of sampling. Even if the trace sampling rate is only 10%, token and cost totals remain 100% accurate.
  5. PII and sensitive data detection: flag PII exposure (such as patient names, credentials, or user data) before agent traces enter logs; annotate violations, enforce hard blocking, and automatically maintain compliance.
  6. Prompt registry and Playground: named prompts with immutable versions and protected production labels;compare wording and models side by side, view latency, tokens, and cost, then load the live version from the SDK so every trace can be traced back to what was actually shipped.
  7. Datasets, graders, and experiments: organize test cases, score with built-in checks, LLM-as-judge, or custom graders, and save immutable experiment reports; compare baseline vs candidate, with evidence-based promotion.
  8. Built-in compliance evidence: out-of-the-box, clause-mapped EU AI Act evidence—Art. 12 GovernanceEvent spans, Art. 14 human review, Art. 50 disclosure() in the SDK. Optionally enable HIPAA Controls for PHI-capable agent inventories, draft safeguards, and labeled exports. One-click export of evidence packages.

III. How to Use Traccia provides an SDK covering the various frameworks mentioned above. According to the official example, its initialization method is traccia.init(), a single initialization call is all it takes to trace, calculate costs, and govern every agent. For LLMs, OpenAI and Anthropic include automatic token and cost tracking; any HTTP-based provider is traced via OpenTelemetry, with deeper coverage continuously being added. The installation command is pip install traccia.

The official documentation provides two starting paths:

  • Local use of the SDK: export traces to your own observability stack, compatible with Jaeger, Grafana Tempo, Zipkin, SignNoz, and any OpenTelemetry-compatible backend. No account required, standard OTLP export, full SDK control.
  • Use the Traccia platform: access the full Traccia dashboard, including agent-first governance, cost attribution, policy guardrails, and team collaboration. Platform-side capabilities include policy guardrails, cost attribution, prompt registry, and evaluation.

IV. Applicable scenarios and limitations Applicable scenarios: teams that need unified observability and governance of AI agents across multiple frameworks; organizations that need to control LLM costs, prevent agents from running away or entering infinite loops, and prevent PII leakage; enterprises that need to generate EU AI Act / HIPAA compliance evidence and conduct audit trails; teams that need prompt version management and A/B experiment evaluation. Limitations and notes: The official documentation currently explicitly states that SOC 2 Type II certification is in progress; OIDC/SAML SSO is on the enterprise roadmap; ML PII detection is marked as planned (the SDK currently provides optional pattern-based redaction, masking before export via init(redact_pii=True)).

More details about pricing, specific supported platforms, or languages are not currently provided in the available material.

Related tools

Browse category