Retrace
Record, replay, and debug AI agent runs to fix production issues faster.
Retrace is the closest thing to a time machine for AI agent debugging—the fork-and-replay mechanic is genuinely novel and saves hours of guesswork. The platform is still maturing, so expect rough edges, but for production agent teams it's worth every penny. Simpler chatbot projects can get by with lighter tools like LangSmith.
Verified 1d ago · liveness 71/100 · cite: rightaichoice.com/tools/retrace
- AI agent developers building production-grade systems
- Engineering teams debugging multi-step agentic workflows
- DevOps/SRE teams monitoring agent performance and cost
- Teams shipping agents that call external APIs or databases
- Casual or no-code AI users
- Teams that only use simple chatbot APIs without multi-step logic
- Projects requiring real-time monitoring of thousands of concurrent agents
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Skip Retrace if you only build simple chatbot APIs without multi-step logic, need real-time monitoring of thousands of concurrent agents on the free tier, or want a general-purpose log aggregator.
Going past 1,000 traces/month on the Free tier adds no automatic upgrade, but you'll hit a hard cap and must pay $29/mo for Starter to get more.
Retrace's pricing fits production agent teams that need serious debugging—Starter at $29/mo is reasonable for a single developer, Pro at $99/mo for small teams, Teams at $399/mo for more concurrency. Compared to LangSmith's free tier and paid plans, Retrace is pricier at mid-tiers but adds replay and prove-the-fix. For hobbyists, the free tier is a good entry; for large enterprises, custom enterprise pricing is available.
In short
Retrace — Record, replay, and debug AI agent runs to fix production issues faster. Best for AI agent developers building production-grade systems, Engineering teams debugging multi-step agentic workflows, DevOps/SRE teams monitoring agent performance and cost. Free to start; paid plans from $29/mo.
What people actually say about Retrace — is it worth it?
We ran a structured research pass across product reviews, community discussions, and post-purchase forum threads to surface the patterns vendors won't publish themselves. Below: the recurring strengths, the hidden costs people mention most, and the cohort that consistently regrets adopting this tool.
47 mentions across 3 sources (Hacker News, GitHub, Lemmy) · researched Jul 3, 2026.
- +Fork and replay agent runs to reproduce and fix failures deterministically.
- +Step-by-step execution timeline with full state inspection at each turn.
- +Side-effect free re-runs: no external API calls during replay forks.
- +Integrates directly with LangChain, CrewAI, AutoGen, and custom SDKs.
- +Breakpoint debugging on specific tool calls or LLM turns.
- −Code changes during replay cause divergence, limiting what-if experimentation.
- −Very limited community presence, making peer support scarce.
- −No Reddit, YouTube, or Product Hunt coverage to evaluate real-world usage.
- −Notion of separate open-source project with same name causes confusion when searching online.
- −No data on performance with large-scale or high-frequency agent traces.
- • Self-hosted likely requires dedicated infrastructure and team effort to maintain
- • Freemium tier may cap number of runs or team members
Viability Score
How well maintained and how widely used is Retrace? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: September 2026
How we score →Key Features
- Record and replay agent runs
- Fork from any step and re-run in sandbox
- Prove-the-fix verdict: improved, regressed, unchanged
- Step-by-step execution timeline with state inspection
- Guardrails and circuit breakers for loops and budgets
- Eval gates for CI/CD regression testing
- Automatic failure classification (MAST)
- Multi-agent session tracing with causal graphs
- Semantic search: describe bugs in plain language
- Cassette VCR replay for deterministic re-runs
- Real-time streaming of agent events
- Diff view comparing two runs
- Export runs to JSON/CSV for external analysis
- Role-based access control (RBAC)
- Self-hosted deployment option for enterprise
About Retrace
Retrace is a debugging and observability platform built specifically for AI agents. It records every step of an agent's execution—LLM calls, tool invocations, internal reasoning, and errors—and turns them into inspectable timelines. You can step through each decision, view intermediate state, and fork a failed run to test a different prompt or config in a sandboxed replay, all without triggering side effects. After the replay, Retrace gives a prove-the-fix verdict—improved, regressed, or unchanged—so you know whether your change actually resolved the issue. Targeted at developers building production agentic workflows, Retrace integrates with a single decorator and works with major LLM providers and frameworks. It goes beyond replay with runtime guardrails like circuit breakers for loops and budgets, eval gates for CI/CD regression testing, and automatic failure classification (MAST). Multi-agent session tracing with causal graphs helps you understand interactions across agents, and semantic search lets you describe bugs in plain language. Retrace's closed-loop workflow—record, fork, fix, prove—effectively turns production failures into permanent regression tests you can re-run on every PR. It's observability with a debugging engine built in, not just a dashboard. Plans start with a free hobby tier and scale to enterprise with self-hosted options. Compared to traditional observability tools that only show raw traces, Retrace structures runs for active debugging. It's a specialized tool for engineers wrestling with the black-box problem in complex agent systems—not a general-purpose log aggregator.
Behind the Verdict
Retrace positions itself as a debugging-first observability tool for AI agents, and that focus shows. The core value is the ability to record a production run, fork it at any step, modify the prompt or configuration, and replay in a sandbox—no side effects. This turns a painful debugging session into a repeatable experiment, and the prove-the-fix verdict removes guesswork about whether your change actually helped. Strengths: The fork-replay workflow is genuinely differentiated. Most observability tools stop at traces and dashboards; Retrace gives you an active debugging loop. The eval gates are a smart addition, letting you turn failures into permanent regression tests that run on every PR. Multi-agent tracing with causal graphs is useful for systems where multiple agents interact. Semantic search is a time-saver when you remember the symptom but not the exact error. The free tier is generous enough for a hobby project. Weaknesses: The platform is clearly young. Expect rough edges in the UI and occasional instability. The free tier's 1,000 traces/month and 7-day retention are tight for moderate production use. Pro plan caps replays at 500,000 events/month and 90-day retention, which may be restrictive for high-volume systems. The replay sandbox can't fully emulate every external service—rate-limited APIs may fail during replay. Self-hosting requires you to manage infrastructure. Where it fits: Teams building production agentic systems that need to debug complex multi-step failures quickly. DevOps/SRE teams monitoring agent performance and cost. Researchers iterating on agent architectures who need to compare runs. Where it doesn't: Simple chatbot API users who don't need multi-step debugging. Teams needing to monitor thousands of concurrent agents in real time—the free and mid tiers have throughput limits. Anyone looking for a general-purpose log aggregator—Retrace is agent-specific. Compared to LangSmith, which is more of a tracing and evaluation platform for LLM apps, Retrace's fork-and-replay is a step further toward active debugging. It's not a replacement for your existing logging or metrics stack; it's a specialized tool for the agent debugging problem.
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Real-world workflow fit
Concrete scenarios for the personas Retrace actually fits — and what changes day-one when you adopt it.
A user reports an agent failed to book a flight. You open the run timeline, see the tool call that returned an error, fork the run at that step, tweak the prompt, replay in sandbox, and get a 'prove-the-fix' verdict of 'improved'.
Outcome: You identify the root cause and validate the fix in minutes, without touching production.
You set guardrails to detect loops and budget overruns. A circuit breaker triggers on a runaway agent, preventing excessive API spend, and you inspect the causal graph to see why.
Outcome: You stop cost leaks early and gain visibility into agent behavior across sessions.
You export a failed run as a Cassette and create an eval gate that replays it on every PR. A new code change causes the run to regress, and the gate fails the build.
Outcome: You catch regressions before deployment, turning production failures into permanent tests.
Use Cases
- Replay a failed agent run to identify the exact tool call that caused an error.
- Fork a successful run to modify the prompt and test a new variant without side effects.
- Compare two runs side-by-side to understand differences in agent behavior.
- Set breakpoints on LLM calls to inspect intermediate reasoning before it selects a tool.
- Monitor cost and latency across all agent runs to optimize model usage.
- Share a run snapshot with a teammate to collaborate on debugging.
Models Under the Hood
as of 2026-09-01
Limitations
- Free tier includes only 1,000 traces/month and 7-day retention—insufficient for moderate production use.
- Pro plan limits replays to 500,000 events/month and 90-day retention, which may be restrictive for high-volume systems.
- Self-hosted enterprise version has custom limits but requires infrastructure management.
- Replay fork sandbox does not support every external service (e.g., rate-limited APIs may fail during replay).
as of 2026-09-01
Verification history
We have re-verified Retrace 8 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-checked, vendor evidence unchanged
- — re-checked, vendor evidence unchanged
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-checked, vendor evidence unchanged
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
Showing the 6 most recent of 8 verification passes.
Free to cite with attribution — this page re-verifies continuously.
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Plans compared
For each published Retrace tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Free
$0/mo
Ideal for
Individual developers exploring agent debugging with under 1,000 traces/month and short retention needs
What this tier adds
Starting free tier: 1,000 traces/month, 7-day retention, basic guardrails, individual use only.
Starter
$29/mo
Ideal for
Solo developers or small teams needing more replay volume and CI/CD regression gates
What this tier adds
Adds increased replay volume, eval gates, and email support over the Free tier.
Pro
$99/mo
Ideal for
Professional teams requiring multi-agent tracing, semantic search, and advanced guardrails
What this tier adds
Adds multi-agent session tracing, semantic search, advanced guardrails, and priority support over Starter.
Teams
$399/mo
Ideal for
Organizations needing role-based access control, higher concurrency, and API access
What this tier adds
Adds RBAC, higher concurrency limits, shared workspaces, and API access over Pro.
Enterprise
Contact us
Ideal for
Large enterprises with strict security and compliance requirements needing self-hosted deployment
What this tier adds
Custom plan with self-hosted deployment, custom SLAs, dedicated support, and advanced security/compliance.
Where the pricing makes sense
The company stage and team size where Retrace's pricing actually pencils out — and where peers do it cheaper.
Retrace's pricing fits production agent teams that need serious debugging—Starter at $29/mo is reasonable for a single developer, Pro at $99/mo for small teams, Teams at $399/mo for more concurrency. Compared to LangSmith's free tier and paid plans, Retrace is pricier at mid-tiers but adds replay and prove-the-fix. For hobbyists, the free tier is a good entry; for large enterprises, custom enterprise pricing is available.
Setup time & first value
How long it actually takes to get something useful out of Retrace — broken out by persona, not the marketing-page minute.
For a single developer, you can install the decorator, record your first run, and replay it within 30 minutes. For a team, setting up shared workspaces and RBAC on Teams adds an hour. Enterprise self-hosting takes a day to provision and configure.
Switching to or from Retrace
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From LangSmith: Export your traces as JSON and import them into Retrace for replay debugging; you'll need to rewrite your instrumentation to use Retrace's decorator.
- →From manual logging: Start by adding Retrace's decorator to your agent entry points and gradually replace your custom logging with Retrace's timelines.
- ↗To LangSmith: Export your runs to JSON/CSV and use LangSmith's import tools if you need a broader LLM evaluation platform.
- ↗To a general observability platform: Export runs to JSON/CSV and ship them to your existing logging system, but you'll lose replay capability.
Integrations
Resources & Guides
Tutorials & Learning
Official links
Featured Head-to-Head Comparisons
Retrace vs Spider Cloud
Choose Retrace if you obsess over agent run quality and need to debug/fork every step. Choose Spider Cloud if your agent's main bottleneck is getting fresh web data cheaply and at scale. For most AI developers, these tools are complementary rather than competitive.
Retrace vs Temporal Ai
If you need a robust orchestration engine to build fault-tolerant AI agents and workflows that survive crashes, Temporal is the obvious choice. If your pain point is debugging existing agent runs—replaying and forking them to find issues—Retrace is purpose-built for that. Many teams may benefit from both: use Temporal for production execution and Retrace for debugging. Choose Temporal for durability at scale; choose Retrace for deep agent debugging.
Retrace vs Voyage Ai
Voyage AI and Retrace serve completely different needs: Voyage AI provides specialized embedding models for high-accuracy retrieval in verticals like finance and law, while Retrace is an observability and debugging platform for AI agent workflows. Choose Voyage if you need better retrieval for enterprise RAG; choose Retrace if you're building complex agents and need to debug them effectively. They are not direct competitors.
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