Python · TypeScript SDK|Works with LangChain · CrewAI · AutoGen

Your agents need
an operating system.

Most observability stops at the LLM call. AgentOS is the rest of the stack — isolated runtime, queryable traces, inline security at the syscall layer, and evals on every deploy. One event stream. One SQL query away.

agentos · traces · liveLIVE
19:47:12.418invokeagent_research_v3 → tool.search ✓
19:47:12.512spantool.search.exec 94ms ✓
19:47:12.604invokeagent_research_v3 → llm.complete
19:47:13.241evalgroundedness 0.91 ✓
19:47:13.302invokeagent_writer_v1 → llm.complete
19:47:14.018spansec.scan(prompt) ok ✓
19:47:14.122evalpii_leak 0/0 ✓
19:47:14.241invokeagent_writer_v1 → tool.publish
19:47:14.302spantool.publish.exec 220ms ✓
19:47:14.418committrace_5811 sealed · $0.0042 ✓
19:47:15.102invokeagent_support_v2 → tool.fetch
19:47:15.318evalpolicy.refund pass ✓
19:47:15.412spanmemory.store ok
19:47:15.901committrace_5812 sealed · $0.0031 ✓
19:47:12.418invokeagent_research_v3 → tool.search ✓
19:47:12.512spantool.search.exec 94ms ✓
19:47:12.604invokeagent_research_v3 → llm.complete
19:47:13.241evalgroundedness 0.91 ✓
19:47:13.302invokeagent_writer_v1 → llm.complete
19:47:14.018spansec.scan(prompt) ok ✓
19:47:14.122evalpii_leak 0/0 ✓
19:47:14.241invokeagent_writer_v1 → tool.publish
19:47:14.302spantool.publish.exec 220ms ✓
19:47:14.418committrace_5811 sealed · $0.0042 ✓
19:47:15.102invokeagent_support_v2 → tool.fetch
19:47:15.318evalpolicy.refund pass ✓
19:47:15.412spanmemory.store ok
19:47:15.901committrace_5812 sealed · $0.0031 ✓
§ 02THE GAP

PROBLEM.

Six failure modes every team running agents in production has felt. AgentOS is built to make each one impossible.

01INVISIBLE FAILURES

Step 3 broke. You'll find out tomorrow.

A 4-agent pipeline fails at step 3. You need the exact handoff that broke — not a 24-hour grep across four services.

63%undetected for 24+ hrs
02RUNAWAY COSTS

$4,000 bill. No idea which agent.

One agent quietly consumes 10× the tokens of every other combined. The bill shows the total — never the cause.

#1reason AI projects get cancelled
03DELEGATION LOOPS

A → B → C → A. $50 burned.

Agents delegate in cycles. The loop runs silently, burning tokens with no output until somebody notices the spend.

$50+burned per minute by one cycle
04SILENT REGRESSIONS

Eval scores dropped. Users told you first.

Someone updates a system prompt. Eval scores drop 23% overnight. Users complain — and you hear about it from support.

3.2 daysto detect a prompt regression
05SHIPPING BLIND

Felt better. Performed worse.

You spent two hours iterating. The new prompt felt sharper. You shipped to 100%. Three days later, retention is down — and you can't tell if it was your change, the model update, or last week's deploy. No A/B, no holdout, no answer.

78%ship without statistical validation
06SECURITY GAPS

PII leaked. Found out days later.

Agents touch customer data, API keys, secrets. A single prompt injection or PII leak means regulatory fines and lost trust.

7%of global revenue — EU AI Act, 2026
§ 03HOW IT WORKS

ONE IMPORT.

One import. The entire platform — stdlib, isolated runtime, full observability, and security.

01

Integrate in 5 minutes

One import drops in the full platform — stdlib primitives, isolated process execution, span capture, and security scanning. No new infrastructure. Works with your current stack.

02

Get every layer

Typed memory and tool primitives. Bounded process isolation. Full span trees with cost per call. Eval scores against your goldens. PII and prompt-injection scanning — all wired together.

03

Deploy production-ready

Set SLOs on eval scores and latency. Runtime policy enforces tool allowlists. Cryptographic trace seals for audit. Replay any failure exactly. Ship knowing the whole stack is solid.

agent.py 
from agentos import trace, memory, tools # One import. Your existing code, unchanged.@trace(project="support-agent", env="production")async def run_agent(query: str) -> str:    ctx = await memory.recall(query)  # stdlib primitive    response = await llm.complete(system_prompt, query, ctx)    return response # AgentOS wires the full platform on every run:# ✓ Stdlib — memory, tools, retries as typed primitives# ✓ Isolated process — bounded memory, mediated syscalls# ✓ Full span tree — every tool call, every LLM hop, cost# ✓ Security — PII scan, prompt injection, policy enforcement# ✓ Eval score vs. golden dataset — graded on every deploy
§ 04THE PLATFORM

FOUR PILLARS.

L01 · AUTHORING

AgentBuild

A standard library for autonomous software. Memory, tools, retries, and eval harnesses ship as primitives — not as code your team rewrites every quarter.

AgentBuild
DESCRIBE YOUR AGENT
BUILD →
STDLIB
Typed primitives for memory, tools, retries, planners.
TEMPLATES
Forkable starting points: research, support, ops.
DEPLOY.SPEC
Declarative manifest — one file from local to prod.
EVAL HARNESS
Goldens, replays, regression gates built in.
L02 · EXECUTION

AgentRun

A kernel for agents. Real process isolation, mediated syscalls, bounded memory, networked state. The runtime that makes "agent" mean something concrete.

PROCESS MONITORsyscalls: 1,847
0x1f42research_v3ISOLATED
244 MB · mediated
0x2a91writer_v1ISOLATED
118 MB · mediated
0x3c07support_v2ISOLATED
67 MB · mediated
KERNEL ACTIVE · 3 POLICIES ENFORCED
PROCESSES
Each agent run is a first-class isolated process.
SYSCALLS
Tool calls go through a mediated, audited interface.
MEMORY
Bounded, persistent, addressable per agent.
NETWORKING
Outbound is policy-controlled, not free-for-all.
L03 · OBSERVABILITY

AgentHog

Datadog for autonomous systems. Every span captured, every prompt versioned, every run replayable. Evals are first-class — not an afterthought.

TRACE · 58111,241ms · $0.0000
agent.invoke1,241ms
llm.complete943ms
tool.search394ms
tool.publish222ms
sec.scan124ms
✓ groundedness 0.91✓ pii_leak 0/0
TRACING
Span-level capture across tools, prompts, models.
REPLAY
Re-run any historical trace bit-for-bit.
EVALS
Goldens, drift, groundedness — graded on every commit.
MONITORS
SLOs and alerts that understand non-determinism.
L04 · SECURITY

AgentSec

Adversarial-grade security for agent stacks. Continuous red-teaming, runtime policy, and the only PII / prompt-injection scanner that runs inside the syscall layer.

SECURITY SCAN · trace_5811
scanning...
TRACE SEALED · SOC2-ALIGNED
RED TEAM
Always-on adversarial probes. Reports against goldens.
DEFENSE
Inline scans for prompt-injection, PII, credential leaks.
POLICY
RBAC and tool-allowlists at the syscall layer.
AUDIT
Cryptographic trace seals. SOC2-aligned.
§ 05ONE EVENT STREAM

Four pillars.
One event stream.

Every build artifact, runtime event, trace, eval score, and security event lands in one PostgreSQL table — with the same identity columns. Cross-cutting questions become one SQL query.

QUERIES OTHER STACKS CAN'T ANSWER
›Cost per failed eval, by agent
›Traces that triggered a security alert
›Latency p95 after the last deploy
›Eval drift since prompt v3.2 shipped
Build
AUTHORING
build.deploy
Run
EXECUTION
run.syscall
Hog
OBSERVABILITY
hog.eval
Sec
SECURITY
sec.scan
events
PostgreSQL · partitioned · one identity model
SELECT * FROM events WHERE trace_id = '5811'
§ 06VS THE FIELD

Tracing tools end where AgentOS begins.

Langfuse, LangSmith, Helicone, Arize — strong observability surfaces, but the data lives in their silo. AgentOS gives you the runtime, the stdlib, security at the syscall layer, and one event stream you can query with SQL.

CAPABILITY
AgentOS
OURS
Langfuse
LangSmith
Helicone
Arize
LLM call tracing
✓
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Evals · scores · drift
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Prompt versioning
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Process isolation for agent runs
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Inline security at the syscall layer
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Stdlib for agent authors
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One event stream in your PostgreSQL
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Cross-cutting SQL across the stack
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✓ covered◐ partial— not offeredhighlighted rows = where AgentOS uniquely operates