Top 7 AI Agent Observability Tools

AI agents are notoriously hard to debug. Unlike traditional software, an agent's behavior depends on LLM outputs, tool calls, and external data -- all of which are non-deterministic. Without proper observability, a failing agent is a black box. You know the output is wrong, but you cannot see why.

Observability tools for AI agents track every decision, tool call, token usage, and latency across your agent pipeline. This guide covers the top 7 tools that actually help you debug, monitor, and optimize AI agents in production.

Key Takeaways

• Agent observability is different from LLM observability -- you need to trace the full agent loop, not just individual LLM calls
• LangSmith and Weave are the two most mature options for agent tracing, but newer tools like Braintrust offer better evaluation workflows
• Free tiers exist across all tools -- start with one before committing to a paid plan
• SearchHive's agent tools (SwiftSearch, ScrapeForge, DeepDive) integrate with any observability stack through standard callbacks

1. LangSmith (by LangChain)

LangSmith is the most widely adopted agent observability platform, built by the LangChain team. It traces the full execution of LangChain and LangGraph agents, showing every LLM call, tool invocation, and intermediate state.

Key features:

• Full trace visualization of agent execution chains
• Prompt versioning and A/B testing
• Dataset management for evaluation
• Collaboration features for teams

Pricing: Free for personal use (limited traces), Team from $39/user/month, Enterprise custom pricing.

Best for: Teams already using LangChain/LangGraph who need deep integration with their existing agent framework.

Limitation: Tight coupling to LangChain ecosystem. Works best with LangChain agents -- tracing custom agents requires manual instrumentation.

2. Weave (by Weights & Biases)

Weave extends W&B's experiment tracking into the LLM/agent space. It traces agent runs, logs prompts and completions, and provides evaluation tooling.

Key features:

• Native integration with W&B experiment tracking
• Automatic tracing for popular frameworks
• Evaluation suites with custom metrics
• Dashboard for comparing agent runs

Pricing: Free for individuals, Team from $50/user/month.

Best for: ML teams already using W&B for model training who want a unified experiment + agent observability pipeline.

Limitation: The UI can feel overwhelming if you are not already a W&B user. Evaluation setup requires more configuration than LangSmith.

3. Braintrust

Braintrust focuses on AI evaluation and observability with a developer-friendly approach. It traces agent calls, evaluates outputs against test cases, and surfaces regressions automatically.

Key features:

• Prompt engineering playground
• Automated regression detection
• Evaluation datasets with scoring functions
• Fast, lightweight SDK with minimal overhead

Pricing: Free tier available, Pro plans start at $49/month.

Best for: Teams that prioritize evaluation over pure tracing. Braintrust makes it easy to define what "good" output looks like and detect when agents drift.

4. Arize Phoenix

Arize Phoenix is an open-source observability platform for LLM applications. You can self-host it, which makes it attractive for teams with data privacy requirements.

Key features:

• Self-hosted option (Docker deployment)
• Trace visualization with span details
• Embedding analysis and drift detection
• Integrates with LangChain, LlamaIndex, and OpenAI

Pricing: Open-source (free to self-host), cloud version available with paid tiers.

Best for: Teams that need on-premise deployment, compliance requirements, or want full control over their observability data.

5. Helicone

Helicone is a lightweight proxy-based observability tool. It sits between your application and the LLM API, logging every request and response without any code changes.

Key features:

• Zero-code setup (proxy-based)
• Supports OpenAI, Anthropic, Azure, and more
• Request caching to reduce costs
• Basic analytics dashboard

Pricing: Free tier with 100K requests/month, Pro from $29/month.

Best for: Small teams that want observability with minimal engineering effort. The proxy approach means no SDK integration -- just point your API endpoint at Helicone.

Limitation: Agent-level tracing is limited since it operates at the HTTP level. You see individual API calls but not the full agent decision chain.

6. Langfuse

Langfuse is an open-source LLM observability platform with strong tracing, prompt management, and evaluation capabilities. It supports multiple frameworks out of the box.

Key features:

• Open-source with cloud and self-hosted options
• Multi-framework support (LangChain, LlamaIndex, OpenAI, Anthropic)
• Prompt management with versioning
• Score-based evaluation system
• Cost tracking per trace

Pricing: Open-source (free), cloud from $0.0047/trace.

Best for: Cost-conscious teams that want open-source flexibility with the option to self-host. Langfuse has one of the most active open-source communities in this space.

7. Phoenix by Arize

Similar to Arize Phoenix but worth noting separately: Phoenix provides notebook-native debugging. You can start tracing inside a Jupyter notebook and visualize agent traces inline.

Key features:

• Notebook integration (Jupyter, Colab)
• Real-time trace streaming
• Span-level latency breakdown
• LLM-as-a-judge evaluation helpers

Best for: Data scientists and researchers debugging agents in notebooks before deploying to production.

Comparison Table

Integrating Web Search Tools with Observability

Most agent observability tools trace LLM calls but ignore the web search and scraping calls that feed data into the agent. This creates blind spots -- you can see the LLM's response but not the search results that shaped it.

SearchHive's API is designed for observability:

• Every API call returns a request_id that you can log alongside your traces
• Structured free JSON formatter responses are easy to serialize into any observability format
• Credit tracking in every response lets you monitor costs per agent run

import httpx
import json

# Wrap SearchHive calls with trace logging
def search_with_trace(query: str, trace_id: str):
    response = httpx.post(
        "https://api.searchhive.dev/v1/search/web",
        json={"q": query, "limit": 5},
        headers={"Authorization": f"Bearer {API_KEY}"},
    )
    data = response.json()

    # Log to your observability tool
    log_to_observability({
        "trace_id": trace_id,
        "tool": "swift_search",
        "input": query,
        "output_count": len(data.get("results", [])),
        "credits_used": data.get("credits_used", 0),
        "latency_ms": response.elapsed.total_seconds() * 1000,
    })
    return data

Recommendation

Choose based on your stack and priorities:

• Already using LangChain? Go with LangSmith -- the integration is seamless
• Already using W&B? Weave adds agent observability to your existing experiment tracking
• Need self-hosted? Langfuse or Arize Phoenix -- both are fully open-source
• Want minimal setup? Helicone's proxy approach requires zero code changes
• Focused on evaluation? Braintrust has the best evaluation workflows

Whatever observability tool you choose, make sure your web search and scraping tools provide structured, traceable responses. SearchHive returns request IDs, credit usage, and latency data in every response -- making it easy to see exactly what web data shaped your agent's output.

Start with SearchHive's free tier to add web search capabilities to your agents. 500 credits per month, no credit card required.