Add two lines of code. Agent Doctor auto-captures every step in your pipeline: LLM calls, vector DB lookups, tool executions, API calls, all of it. Override any prompt or model from the dashboard and re-run on your infrastructure. Your data never leaves your servers.
An engineer can wire up the pipeline, but they can't judge a sound medical diagnosis, a well-written paragraph, or a legally airtight clause. Agent Doctor hands those controls to the experts who can. Pair a builder with a domain expert and agent quality multiplies.
Initialize the SDK, register a webhook. Agent Doctor auto-instruments every LLM call, vector DB lookup, tool execution, and API request. No decorators, no wrappers, no code changes.
Install the SDK and call init(). Every LLM call, vector DB query, tool execution, and API request is auto-captured. No decorators. No wrappers.
Domain experts open the UI, pick any node, swap the prompt or model, and hit run. The experiment fires on your servers via webhook.
Traces stream back via OpenTelemetry. Compare runs side-by-side. Deploy the winning prompt. No code deploy needed.
The biggest bottleneck in AI agents isn't the code. It's that the people who understand the domain can't touch the prompts. Agent Doctor gives domain experts a dashboard to iterate on prompts, models, and parameters without ever opening the codebase.
Writes the agent code, connects to databases, sets up tools, deploys to production.
Drops in the SDK. Done. Back to building features.
When the domain expert finds a winning prompt, the dev reviews and deploys, or it ships automatically via the SDK.
Opens the dashboard, sees the full pipeline trace. Every prompt, every output, every decision the agent made.
Spots a bad prompt, rewrites it in plain English. Changes the model or temperature. Hits "Run". No code, no deploys.
Compares runs side-by-side, uses Dr. Pinpoint to debug, runs flakiness tests. All from the dashboard.
Agent Doctor hooks into LLM SDKs, vector databases, and tool calls at the library level. Your pipeline code stays exactly as-is. Every step is auto-captured.
# Add these two lines, that's it import agent_doctor agent_doctor.init(api_key="sk-proj-...") # Your existing agent code, zero changes needed # LLM calls, vector DB queries and tool calls are auto-captured def handle_ticket(query, customer_id): # LLM call → auto-captured intent = openai.chat.completions.create( model="gpt-4o", messages=[{"role": "user", "content": query}] ) # Vector DB lookup → auto-captured docs = pinecone_index.query(vector=embed(query), top_k=5) # Tool call → auto-captured policy = check_refund_policy(customer_id) # LLM call → auto-captured response = openai.chat.completions.create( model="gpt-4o", messages=[{"role": "system", "content": f"Docs: {docs} Policy: {policy}"}, {"role": "user", "content": query}] ) return response # Webhook handler. Overrides auto-injected by the SDK with agent_doctor.run_context(run_id=payload.run_id, overrides=payload.overrides): result = handle_ticket(payload.query, payload.customer_id) # Every step auto-captured. Overrides auto-injected. Done.
Four tools to take your agent from broken to production-ready.
Auto-capture every step: LLM calls, vector DB queries, tool executions. Override any prompt, model, or temperature from the UI.
Chat with an AI debugger: "my agent should say X but it says Y." It asks 3 to 4 questions and pinpoints the exact broken node.
Run your pipeline 10× and see which node is non-deterministic, via a stability heatmap and AI root cause report.
Automatically generate, test, and rank improved prompt variants for any node. Strategies include GEPA (genetic evolution), grid search, and more. Optimize for accuracy, consistency, cost, or latency.
Watch Dr. Patel, a cardiologist with zero coding skills, diagnose a broken AI agent, override the faulty prompt, and get better results. All from the dashboard.
Describe the expected behavior and the actual result. Dr. Pinpoint uses binary search to ask 3 to 4 plain-English questions, then pinpoints the exact node that caused the downstream failure. No logs, no code, no debugging skills needed.
Run your pipeline 10× with identical input. Per-node consistency scores reveal exactly which step is non-deterministic, and an AI report tells you why.
Pick a node, define what "good" looks like, and let the optimizer find the best prompt. Multiple strategies available, starting with GEPA (Genetic Evolutionary Prompt Algorithm), with more coming soon.
Most LLM tracing platforms run experiments on their own cloud, so your agent can't reach your private databases, RAG, or internal tools during a test. They also keep prompt iteration locked behind code, leaving your domain experts out.
init(). It auto-instruments your LLM calls, vector DB lookups, tool executions, and API requests at the library level, with no decorators, wrappers, or refactors required.Two lines of code. Zero changes to your agent. Every step captured automatically.