Enterprise Ready : VPC | On-Prem | Air-Gapped

End-to-End Observability for AI Agents and Applications

Understand agentic paths, and ensure your AI behaves as expected—all with zero guesswork.

Try out Tracing
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Trace the Full Lifecycle of Your AI Application

Achieve real-time AI observability by tracing every step from user input to agent response:

  • User prompts, system messages, and tool inputs
  • LLM calls (OpenAI, Anthropic, Cohere, etc.)
  • Tool invocations (LangChain-style or custom functions)
  • Workflow decisions and execution paths
Visualize how your AI systems reason, process, and respond—enabling faster debugging, performance optimization, and better decision-making through complete AI observability.

Trace and Time Every LLM & Tool call in Real Time

Achieve high-fidelity AI observability and actionable metrics:

  • Identify LLM performance bottlenecks and slow responses
  • Track token usage, model selection, and request cost
  • Understand how tools and functions impact workflows
  • Spot repeated patterns, unexpected outputs, and failures

Agent Observability with Auto-Instrumentation

Automatically capture detailed traces from your AI agents:

  • LLM requests and model metadata
  • Tool/function calls and execution durations
  • Workflow logic, branching, and decision flow
  • User/session-level trace context
Agent tracing with span-by-span tracking of how your agents reason, make decisions, and act.

Built on OpenTelemetry for AI Observability

Works across LLM-powered APIs, RAG pipelines, autonomous agents, and complex agent networks.

Take advantage of the open standard for distributed tracing and AI observability:

  • Compatible with any OpenTelemetry SDK
  • Vendor-agnostic, open-source, and scalable
  • Designed for AI observability and agent tracing use cases
  • Includes a modern UI to filter, query, and analyze spans
  • Ships with an OpenTelemetry collector backend for seamless ingestion

Trace Agent-to-Agent Interactions

Support complex use cases with complete AI tracing and observability

  • Trace requests as they move across multiple agents
  • Understand multi-agent orchestration, delegation, and task routing
  • Visualize handoffs, tool invocations, and final decisions in a unified view

Seamless Integrations for AI Observability

Easily connect your stack with prebuilt integrations, designed for modern AI tracing

LangGraph
CrewAI
Agno
OpenAI
FastAPI
OpenTelemetry
PydanticAI
Haystack
Python Code

Enterprise-Ready

Your data and models are securely housed within your cloud / on-prem infrastructure

  • Compliance & Security

    SOC 2, HIPAA, and GDPR standards to ensure robust data protection

  • Governance & Access Control

    SSO + Role-Based Access Control (RBAC) & Audit Logging

  • Enterprise Support & Reliability

    24/7 support with SLA-backed response SLAs
Deploy TrueFoundry in any environment

VPC, on-prem, air-gapped, or across multiple clouds.

No data leaves your domain. Enjoy complete sovereignty, isolation, and enterprise-grade compliance wherever TrueFoundry runs

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Frequently asked questions

What is AI observability and why is it important?

AI observability helps teams monitor, debug, and optimize AI systems by providing visibility into model behavior, workflows, and decisions. It's critical for reliable performance in production environments.

How does this enable agent observability?

Agent observability captures detailed traces of how agents operate—tracking LLM calls, tool usage, and decision logic—to provide a complete view of autonomous workflows.

What is agent tracing, and how does it work?

Agent tracing shows how a request flows through multiple agents, capturing interactions, decisions, and execution steps. This is key for debugging multi-agent systems and ensuring they behave as expected.

Does this support LangChain, LlamaIndex, or CrewAI?

Yes. It’s fully compatible with LangChain, LlamaIndex, CrewAI, and Agno—supporting agent observability across all major frameworks.

Can I use OpenTelemetry for AI observability here?

Absolutely. This system is built on OpenTelemetry and supports any compatible SDK. It
gives you vendor-neutral, scalable observability with rich AI-specific context.

How do I track token usage and model costs?

Every LLM span can include metadata such as model name, token count, temperature, and completion time—enabling cost and performance insights as part of your AI observability stack.

How is this different from traditional APM tools?

Traditional APM tools aren't built for LLMs or agents. This system is designed specifically for AI observability and agent tracing—giving you context-rich insights into model behavior and reasoning paths.

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