AI Agent Monitoring Dashboard in 2026: 7 Open-Source Tools Compared

You cannot improve an agent you cannot see. These 7 dashboards show token spend, traces, and eval scores.

## AI Agent Monitoring Dashboard in 2026 An AI agent dashboard answers three questions: what did the agent do, what did it cost, and is it getting better. Here are 7 open-source tools that answer all three, ranked by GitHub stars (data pulled from the ainomam star tracker on 2026-07-31). ### The 7 tools 1. **Langfuse** (28k+ stars) - the most complete LLM observability suite: traces, evals, prompts, and cost tracking in one dashboard 2. **Arize Phoenix** (14k+ stars) - open-source AI observability with built-in eval runs and dataset versioning 3. **Helicone** (8k+ stars) - proxy-based logging, works with any OpenAI-compatible API, one-line setup 4. **MLflow** (27k+ stars) - the classic ML lifecycle tool now tracks LLM traces and evals too 5. **LangSmith** (proprietary, free tier) - tightest LangChain integration, strongest trace viewer 6. **WandB Weave** (free tier) - Weights and Biases LLM tracing, great for research teams 7. **AgentOps** - purpose-built for agent loops, shows step-by-step agent reasoning ### What a good dashboard must show - **Per-step trace**: every LLM call, tool call, and retrieval in one view - **Cost breakdown**: tokens by model, by session, by user - **Eval score timeline**: did the agent answer quality improve after your last prompt change? - **Failure rate**: tool errors and retries, colored red when they spike ### Recommendation Start with Langfuse for production apps and MLflow if you already run MLflow for model training. Both are self-hostable on one small server. ### FAQ **Can I self-host these?** Langfuse, Phoenix, and Helicone all support Docker self-hosting. **Do they work with Claude or local models?** Yes, any OpenAI-compatible API works through the proxy-based tools.