Trusted by engineering teams at
Cornell University
Boston University
TikTok
The observation and security layer for production AI agents. Full trace visibility and real-time threat detection — from one integration.
Cornell University
Boston University
TikTok
Every LLM call, tool use, cost, and anomaly — captured, analyzed, and turned into concrete decisions. Not just logs. Answers.
Your agent processes untrusted data at every call. Jasmind scans every prompt and completion in real time, scores each trace, and surfaces threats before they become incidents.
read_file → send_email — early warning of jailbreak or compromise.No rewrites. Observation and security both activate from the same integration.
pip install jasmind
docker-compose up -d and go to localhost:3000.from jasmind import Jasmind, wrap_openai from openai import OpenAI jm = Jasmind(endpoint="http://localhost:8080") # captures every call for observation + security client = wrap_openai(OpenAI(), tracer=jm) @jm.trace(name="research_task") def run_agent(query: str) -> str: # your original agent code — unchanged for step in range(10): response = client.chat.completions.create( model="gpt-4o", messages=[{"role": "user", "content": query}], ) # ↑ logged: tokens, cost, latency (Observation) # ↑ scanned: injection, PII, secrets (Security) ... return result
Every plan includes both Observation and Security. Your data never leaves your infrastructure.
Add Jasmind in 5 minutes. Full observation and security from the very first agent run.