Neo
Back to use cases

Workflow

Build Agents

Multi-agent swarms, autonomous research agents, tool-use orchestration, self-healing pipelines, and memory management.

The problem

Agent systems need tools, memory, orchestration, and observability; glue code piles up fast and every change risks silent failures in production.

  • A swarm works in the demo, then falls apart the first time two agents disagree.
  • Nobody can tell you which agent made a bad call without reading raw logs.
  • Memory either resets every session or grows until it leaks between users.

How NEO helps

NEO composes agents with clear tool boundaries, shared memory patterns, and retries, so swarms, research flows, and self-healing jobs stay maintainable as requirements change.

  • Coordinates specialized agents over a pub/sub bus with pre- and post-action risk checks
  • Adds tiered memory with automatic PII redaction so context doesn't leak across sessions
  • Instruments every agent step with OpenTelemetry tracing and full session replay
  • Wires human-approval gates between pipeline stages for high-risk actions

Scenarios (5)

Trading Agent Swarm
+4.62% returns, 10 agents

Build Agents

Trading Agent Swarm

10 specialized agents coordinating over async message bus: +4.62% returns across 250 days of S&P 500 data.

Read case study
Multi-Agent Memory Management
35-45% token reduction

Build Agents

Multi-Agent Memory Management

Three-tier memory with hot context, cold storage, and auto-summarization. 35-45% token reduction at 87% semantic recall.

Read case study
Agent Observability Platform
Self-hosted, full session replay

Build Agents

Agent Observability Platform

OpenTelemetry-compatible tracing, real-time cost tracking, and LLM-powered anomaly detection for multi-agent AI systems.

Read case study
Autonomous Research Agent
3 literature sources, ranked hypotheses

Build Agents

Autonomous Research Agent

Generates hypotheses, searches arxiv/PubMed/Semantic Scholar, synthesizes findings into structured reports with chain-of-thought reasoning.

Read case study
Spec-to-Ship Multi-Agent Pipeline
5-agent spec-to-code pipeline

Build Agents

Spec-to-Ship Multi-Agent Pipeline

Five-agent engineering pipeline: plain-language spec in, typed TypeScript code + test suites + security audit out.

Read case study

Run this workflow in your workspace

Install NEO in VS Code or Cursor and ship training, evals, and deployment from chat.

Get started
CursorCursor
Claude CodeClaude Code
CodexCodex
WindsurfWindsurf
ZedZed
AntigravityAntigravity
ContinueContinue
CursorCursor
Claude CodeClaude Code
CodexCodex
WindsurfWindsurf
ZedZed
AntigravityAntigravity
ContinueContinue
eval_pipeline.py
  def run_eval(model, tasks):-     results = model.predict(tasks)+     results = batch_eval(model, tasks)+     log_regression(results)      return score(results)