Retrace

Product Introduction

1. Definition: Retrace is a specialized AI agent observability and reliability platform. It is a developer tool designed for the technical category of AI agent lifecycle management, specifically focusing on execution recording, deterministic replay, and regression testing.
2. Core Value Proposition: Retrace exists to solve the inherent unpredictability and debugging difficulty of AI agents. Its primary value is enabling developers to record, replay, fork, and share AI agent executions to diagnose failures, test fixes, and prevent regressions before they reach production, effectively providing CI/CD for AI agent behavior.

Main Features

1. Record & Trace: A single SDK decorator (@retrace.record()) automatically instruments and captures every LLM call, tool invocation, internal state change, error, cost, and latency within an agent's execution. This creates a complete, immutable execution trace ("tape") that serves as a permanent regression test. It works with any LLM provider (OpenAI, Anthropic, Gemini) and major frameworks (LangChain, CrewAI, LlamaIndex, Vercel AI SDK) through auto-instrumentation.
2. Fork & Cascade Replay: This is the core technical innovation. Users can select any specific step (span) within a recorded trace, modify its input (e.g., a prompt, tool parameter), and "fork" from that point. The platform then cascade-re-executes the agent from the forked step forward, preserving the full context of the original run. This allows developers to test hypothetical fixes directly on the failed execution path without re-running the entire agent from scratch.
3. Prove-the-Fix Verification: After forking and modifying a step, Retrace can automatically re-run the original failed trace with the applied fix and provide a deterministic verdict: "improved," "regressed," or "unchanged." This creates a closed feedback loop where every production failure generates a test case, and fixes are validated against that exact scenario.
4. Runtime Guardrails & Enforcement: Unlike passive observability, Retrace provides active runtime safety. Developers can set guardrails like cost budgets, loop detection limits, context window caps, and latency thresholds. If breached, the agent receives a HALT command, preventing runaway loops or budget blow-outs. A pre-call gateway can also block specific actions for manual approval.
5. Eval Gates & CI/CD Integration: Retrace enables quality gates for AI behavior. Recorded traces can be used as evaluation datasets. The platform's retrace eval gate CLI command or API can score new runs against these baselines and fail a CI/CD build if behavior regresses below a defined threshold (e.g., correctness score < 0.8). This gates on agent-specific metrics like trajectory correctness, tool-call accuracy, and multi-agent hand-off success.
6. Multi-Agent Sessions & MAST Taxonomy: For complex systems, Retrace groups related agent activities into Sessions, displaying a causal graph of agent topology (e.g., planner → researcher → writer). It also employs a MAST (Multi-Agent System Taxonomy) to automatically classify why a run failed (e.g., planning error, tool failure, coordination issue), moving beyond simple error logging to root-cause analysis.

Problems Solved

1. Pain Point: The "black box" nature of AI agents makes debugging nearly impossible. Traditional logging shows an error at step N, but the root cause often occurred at step N-3. Retrace solves this by providing full execution visibility and the ability to rewind and replay from any point.
2. Pain Point: There is no reliable way to test agent behavior changes. A minor prompt edit can silently break complex, multi-step reasoning in production. Retrace solves this by turning every failure into a replayable test case and enabling "prove-the-fix" verification and CI gating.
3. Target Audience: AI Engineers and Developer Teams building production-grade LLM applications with agents (e.g., customer support bots, data analysis pipelines, automated research agents). DevOps and Platform Engineers responsible for the reliability and cost control of AI systems. Product Teams needing to ensure consistent quality and safety of AI-driven features.
4. Use Cases: Post-Incident Debugging: Replay a customer-reported agent failure, fork from the misstep, and identify the exact flawed logic or prompt. Safe Deployment: Run an "eval gate" on every Pull Request that replays critical past failures to ensure a code change doesn't regress agent behavior. Cost Control: Set a $0.10 guardrail on an agent to immediately halt execution if it begins looping and exceeding its budget.

Unique Advantages

1. Differentiation vs. Observability Tools (e.g., LangSmith): While tools like LangSmith excel at tracing and monitoring, Retrace adds an interactive execution layer. The key difference is Fork & Cascade Replay and Prove-the-Fix—capabilities that move from passive observation to active debugging and validation. Retrace also emphasizes runtime enforcement (guardrails) to stop problems before they escalate, not just alert on them afterward.
2. Differentiation vs. Traditional Testing: Unit tests are ill-suited for non-deterministic LLM behavior. Retrace's approach is behavioral regression testing based on real execution traces. It captures and tests the actual input/output flow and reasoning trajectory of an agent, which is a more accurate reflection of real-world performance than mocked LLM calls.
3. Key Innovation: The deterministic replay engine is the core technical innovation. The ability to take a recorded trace, fork its state at an arbitrary step, inject a change, and deterministically re-execute the remainder of the agent's workflow—while maintaining context—is a unique capability that enables fast, iterative debugging of stochastic AI systems.

Frequently Asked Questions (FAQ)

1. How does Retrace's "fork and replay" work technically? Retrace's SDK records a complete serializable state snapshot at each step (span) of an agent's execution. When you fork from a step, the platform loads that state, applies your input modification, and re-executes the agent's code from that point using the same environment and context, generating a new, comparable trace branch.
2. Is my prompt and LLM response data secure with Retrace? Yes. Retrace employs TLS encryption in transit and AES-256-GCM encryption at rest. The platform offers PII auto-redaction on all plans. For enhanced security, you can connect your own model provider API keys (e.g., Google Gemini), so evaluation and replay LLM calls are billed to your account and never transit Retrace's systems.
3. Can Retrace work with my existing LangChain or LlamaIndex application? Absolutely. The Retrace SDK is framework-agnostic. You wrap your main agent function with the @retrace.record() decorator. It auto-instruments popular LLM providers and frameworks, capturing calls without requiring you to rewrite your existing LangChain, LlamaIndex, or CrewAI chains.
4. What is the difference between a "guardrail" and a "circuit breaker" in Retrace? A guardrail is a runtime policy that monitors a specific metric (cost, loop count, latency) and can halt an active agent run when a threshold is crossed. A circuit breaker is a broader system-level enforcement that can block certain actions or patterns from being executed at all, often used as a pre-call check to prevent known bad states.
5. How do "Eval Gates" integrate with my CI/CD pipeline like GitHub Actions? Retrace provides a CLI command retrace eval gate that takes an evaluation ID and a trace ID, runs the evaluation, and returns a pass/fail exit code based on a score threshold. You can run this command directly in a GitHub Actions workflow step; if the agent behavior regresses, the command exits with code 1, failing the build and blocking the merge.