1. Why Agent Debugging Is a Whole New Sport
Modern agents do more than chat. They chain tools, hit vector stores, fire webhooks, and sometimes decide that deleting your production database is a good idea. When things explode you get
- stack traces for code you never wrote
- prompt histories longer than a novel
- users who watched the bot burn their cloud credits
Classic APM cannot show prompts, tokens, or retrieval context, so every incident becomes archaeology. You need debugging platforms designed for LLM agents.
Further reading
https://www.getmaxim.ai/blog/agent-tracing-for-debugging-multi-agent-ai-systems
Maxim docs
https://www.getmaxim.ai/docs/debugger/overview
2. What Great Agent Debugging Looks Like
| Requirement | Why It Matters | Concrete Feature |
|---|---|---|
| Prompt Timeline | See full conversation | Scrollable tree with token counts |
| Tool Call Capture | Agents fail on external calls | Log input, output, retries |
| Vector Context | RAG bugs hide here | Doc IDs, similarity scores |
| Deterministic Replay | Repeat the bug | One click rerun with fixed seed |
| Parameter Tweaks | Test fixes fast | Change temp or model and diff output |
| Cross-Request Search | Spot trends | Filter by error, latency |
| Safety Signals | Avoid brand damage | PII leak flags, jailbreak alerts |
Checklist guide
https://www.getmaxim.ai/blog/tool-chaos-no-more
Debugging API reference
https://www.getmaxim.ai/docs/debugger/replay
3. 2025 Shortlist
| Platform | Sweet Spot | Signature Power | Deploy | Pricing |
|---|---|---|---|---|
| Maxim AI Debugger | Production agents with tools | Trace capture and deterministic replay | SaaS or self-host | Free 50 k traces then usage |
| Arize Phoenix | Retrieval heavy apps | Embedding drift graphs | OSS or SaaS | OSS free then custom |
| LangSmith | LangChain projects | Dataset replay and prompt diff | SaaS | Free dev then 39 USD/seat |
| LangFuse | Air-gapped stacks | OTel export and JSON traces | OSS or SaaS | OSS free then 99 USD |
| TruLens | Research and CI | Feedback functions and red team tests | OSS or SaaS | OSS free then tiers |
| W&B LLMOps | Multi-modal orgs | Unified run comparison | SaaS | Team and Enterprise |
| OpenAI Inspector | Small GPT bots | Inline prompt viewer | SaaS | Usage only |
Shortlist comparison post
https://www.getmaxim.ai/blog/paperbench-can-ai-agents-actually-replicate-ai-research
4. Deep Dive by Problem
4.1 Maxim AI Debugger
Every request through Bifrost becomes a nested trace:
- Root span: user ID, cost, total latency
- Children: retrieval, reranker, tool calls, LLM completion
- Tokens and dollars per span
Key tricks
- Deterministic replay: lock seed and rerun any span
- Inline guardrail flags: see where jailbreaks were blocked
- Branch and compare: duplicate trace, swap model, diff outputs
from maxim_bifrost import BifrostChatModel
llm = BifrostChatModel(
api_key="BIFROST_KEY",
base_url="https://api.bifrost.getmaxim.ai/v1",
model_name="gpt-4o"
)
reply = llm.chat("Generate a migration plan for our database")
Trace opens in the Maxim dashboard. Press "Replay", adjust temperature, rerun.
Hands-on tutorial
https://www.getmaxim.ai/blog/observing-tool-calls-and-json-mode-responses-from-fireworks-ai
Debugger docs
https://www.getmaxim.ai/docs/debugger/replay
4.2 Arize Phoenix
Best when retrieval quality rules the KPI. Overlay each query with embeddings, drift alerts, and nearest neighbor lists. CLI replay is powerful but less friendly.
4.3 LangSmith
Pure LangChain pipelines plug in instantly. Traces, dataset runs, AI judge scores. No on-prem option.
4.4 LangFuse
Self-host in Kubernetes. Stores traces in Postgres, exports to Prometheus, downloads JSON. Guardrails still early.
4.5 TruLens
Great for research. Write feedback rules like “must cite a source,” attach to CI, get pass–fail. Replay is local.
4.6 W&B LLMOps
If vision and tabular are already in W&B, add LLM runs and sweeps. Debug UI is broad but not tool-aware.
4.7 OpenAI Inspector
Single GPT endpoint only. Shows prompts and completions, no tools or retrieval context. Works for hackathons.
Blog walkthrough
https://www.getmaxim.ai/blog/when-your-ai-cant-tell-the-difference-between-fine-and-frustration
5. Why Maxim AI Often Wins
- Trace plus guardrails in the same pane
- Model agnostic routing via Bifrost
- Zero gateway markup so debugging is free of surcharge
- Hot-swap experiments on a cloned trace
- Export to JSON, Parquet, or SIEM for audit
OS-HARM benchmark overview
https://www.getmaxim.ai/blog/os-harm-the-ai-safety-benchmark-that-puts-llm-agents-through-hell
Debugger architecture docs
https://www.getmaxim.ai/docs/bifrost/architecture
6. Debug Playbook Using Maxim AI
- Capture incident trace link from user or alert
- Open trace tree and locate failing span
- Inspect last tool output or retrieval context
- Toggle verbose logs and replay
- Lower temperature or adjust system prompt, rerun
- Try alternate model and compare token use
- Save new prompt version and add to regression set
- Deploy fix, monitor nightly eval
Step-by-step guide
https://www.getmaxim.ai/blog/building-and-evaluating-a-reddit-insights-agent-with-gumloop-and-maxim-ai
Docs for regression sets
https://www.getmaxim.ai/docs/evaluation/workflows
7. Cost Math
Traffic: 5 M tokens per month
Vendor price: 0.01 USD per 1 k tokens → 50 USD
| Platform | Gateway Markup | Debug Fee | Total |
|---|---|---|---|
| Maxim AI self host | 0 | 0 for first 50 k traces | 50 USD |
| LangSmith | none | 4 seats × 39 | 206 USD |
| Phoenix OSS | 0 | infra only | ~100 USD |
| W&B | none | team plan | ~400 USD |
| OpenRouter + Smith | 5 % | 206 USD + markup | 258 USD |
Cost comparison article
https://www.getmaxim.ai/blog/when-ai-transcription-turns-tasty-burger-into-nasty-murder
Pricing page
https://www.getmaxim.ai/pricing
8. Future of LLM Debugging
- Live code map that shows real-time tool graph
- Automated fault isolation that highlights failing node
- Prompt line blame with Git style diff
- Secure encrypted context replay
Roadmap hint
https://www.getmaxim.ai/blog/building-high-quality-document-processing-agents-for-insurance-industry
Roadmap docs
https://www.getmaxim.ai/docs/roadmap
9. Decision Matrix
| Primary Pain | Best Fit |
|---|---|
| Multi tool agent plus compliance | Maxim AI |
| Retrieval drift questions | Arize Phoenix |
| LangChain only prototype | LangSmith |
| On-prem no SaaS | LangFuse |
| Research red teaming | TruLens |
| Unified vision and text sweeps | W&B LLMOps |
| One GPT bot | OpenAI Inspector |
10. Final Takeaway
LLM agent debugging mixes detective work, statistics, and guardrail audits. You need traces, replay, safety insights, and cost data all in one place. Maxim AI delivers that through its Bifrost powered debugger with no token markup. Sign up, point your SDK at the Bifrost URL, reproduce your next bug in minutes, and ship with confidence.
Happy fixing.
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