Agent Observability MCP server

Agent Observability

<!-- mcp-name: io.github.RudrenduPaul/agent-observability --> <!-- Ownership-proof string for registry.modelcontextprotocol.io publishing. Do not remove. -->

Record your agent's LLM calls once, replay them offline in under 1 ms, zero API calls, zero cost.

Your LangGraph agent fails after step 8. LangSmith shows you what broke. To reproduce it: 8 more LLM calls. 30 more seconds. $0.15 more in API cost. If the failure was caused by a transient model output, you can't reproduce it at all.

Agent Observability fixes this. Record once. Replay offline in 0.93 ms. Zero API calls. Zero cost.

Recording overhead:   0.011%   (0.090 ms added per LLM call)
Replay latency:       0.93 ms  mean (vs ~8,500 ms live on GPT-4o × 10 steps)
Replay fidelity:      100%     (response bytes byte-for-byte identical)
CI cost per replay:   $0

Install

pip install agent-observability-trace-cli
# or
uv add agent-observability-trace-cli

LangGraph support:

pip install agent-observability-trace-cli[langgraph]

OpenAI Agents SDK support:

pip install agent-observability-trace-cli[openai-agents]

30-second CLI quickstart

# Record a live run (your script just needs `import agent_trace` somewhere)
agent-trace run --name my_agent -- python my_agent.py

# List recorded runs
agent-trace list

# Replay offline: zero network, zero cost
agent-trace replay run_<id>

# Show the trace for a run
agent-trace show run_<id>

list, inspect, diff, replay, and run all support --json for machine-parseable output, so an orchestrating agent or CI job can call any of them the same way a person would and parse the result. (run --json prints its own status to stderr and the child process's output to stdout, ending with one final JSON summary line, since the child's own output can't be made structured.) show has no --json mode of its own. It accepts --errors-only to filter its output to failed spans instead. See the full CLI reference below for every subcommand's flags.

Shortened. The full README is on GitHub.