惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

Hugging Face - Blog
Hugging Face - Blog
Google DeepMind News
Google DeepMind News
云风的 BLOG
云风的 BLOG
WordPress大学
WordPress大学
Vercel News
Vercel News
Apple Machine Learning Research
Apple Machine Learning Research
T
Tailwind CSS Blog
I
InfoQ
小众软件
小众软件
Recent Announcements
Recent Announcements
博客园 - 【当耐特】
The GitHub Blog
The GitHub Blog
大猫的无限游戏
大猫的无限游戏
美团技术团队
T
The Blog of Author Tim Ferriss
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
酷 壳 – CoolShell
酷 壳 – CoolShell
MongoDB | Blog
MongoDB | Blog
V
V2EX
J
Java Code Geeks
有赞技术团队
有赞技术团队
博客园 - 聂微东
B
Blog RSS Feed
博客园 - 司徒正美

Hacker News - Newest: "AI"

AI can't read an investor deck AI as an attorney? Student uses ChatGPT, Gemini to sue UW over alleged racial discrimination Hacking MCP Servers in AI Systems – The Rug Pull: Tool Changes After Approval GitHub - MeepCastana/KubeezCut: Free Web based video editor Can AI judge journalism? A Thiel-backed startup says yes, even if it risks chilling whistleblowers Coming soon: 10 Things That Matter in AI Right Now DARPA built an AI to fact-check enemy weapons claims What explains heterogeneity in AI adoption? When AI Meets Muscle: Context-Aware Electrical Stimulation Promises a New Way to Guide Human Movements - Department of Computer Science AI Changed How We Build. It Did Not Change What Matters. Linux rules on using AI-generated code - Copilot is OK, but humans must take 'full responsibility for the… Meta spins up AI version of Mark Zuckerberg to engage with employees Code Mode: Let Your AI Write Programs, Not Just Call Tools | TanStack Blog GitHub - Delavalom/graft: Go framework for building AI agents. Type-safe tools, multi-provider (OpenAI, Anthropic, Gemini, Bedrock), zero vendor SDKs. India's TCS tops estimates, says new AI models did not dent services demand Gen Z's fading AI hype Strong feeling: we are in a folded AI reality GitHub - machinarii/total-recall-catalog: A reference catalog of latest knowledge retrieval, memory & RAG systems GitHub - mensfeld/code-on-incus: Give each AI agent its own isolated machine with root, Docker, and systemd. Active defense detects and stops threats automatically.. Quantization, LoRA, and the 8% Problem: Benchmarking Local LLMs for Production AI Iran war: We spoke to the man making Lego-style AI videos that experts say are powerful propaganda Powell, Bessent discussed Anthropic's Mythos AI cyber threat with major U.S. banks GitHub - immartian/bellamem: Persistent belief-graph memory for AI agents. Retrieves decisive context by importance — not recency, not RAG, not /compact. recursive-mode: The Repo-Native Operating System for AI Engineering After the attack on Sam Altman's home, will AI CEO's go on the offensive? The biggest advance in AI since the LLM Opus 4.6 vs GPT 5.4 One Prompt Unity World Generation Test “AI polls” are fake polls Client Challenge Can AI be a 'child of God'? Inside Anthropic's meeting with Christian leaders
GitHub - ajensenwaud/recursant: Recursant, Inc.
hestefisk · 2026-05-07 · via Hacker News - Newest: "AI"

An open-source agentic mesh — governance, security, compliance, and observability for AI agents communicating across the enterprise.

License: MIT


What is Recursant?

Recursant is an enterprise-grade agentic mesh platform — Istio for AI agents. It provides governance, security, compliance, and observability for AI agent deployments where agents talk to each other (A2A protocol) and to tools (MCP).

The architecture has two planes:

  • Control plane (registry/) — Flask + React web app backed by PostgreSQL, Redis, and Kafka. The single source of truth for agent metadata, policies, certificates, and audit history. Includes a full web UI for governance workflows.
  • Data plane (mesh/) — A Python sidecar process injected next to every agent pod, mediating all inter-agent traffic over mTLS via the A2A protocol.

Recursant is a runtime governance layer for any AI agent — LangChain, LangGraph, CrewAI, ServiceNow (in-flight), custom HTTP — not a framework for writing agents. Bring your own agent code; Recursant gives it identity, policy enforcement, observability, and compliance guarantees.

Why use it?

If you're running AI agents at any meaningful scale, you eventually hit the same questions:

  • Who's allowed to call whom? Hub-and-spoke topologies, sovereignty zones, data classification.
  • What's leaking? PII redaction, prompt-injection guardrails, chain-of-thought audit.
  • How did this decision get made? Hash-chained audit trail per request, end-to-end traces.
  • Is the agent safe to deploy? Automated security scans + LLM-as-judge eval suites + human approval gates.
  • What is it costing me? Per-agent token consumption, model breakdown, budget thresholds.
  • Did the guardrail catch the attack? Real-time effectiveness matrix, adversarial test runs, false-positive tracking.

Recursant answers all of these without requiring you to rewrite your agents.

How it works

┌────────────────────────────────────────────────────────────────────┐
│                      Control Plane (Registry)                       │
│  ┌──────────┐ ┌──────────┐ ┌──────────────┐ ┌─────────────────┐   │
│  │ Agent    │ │ Policy   │ │ Cert         │ │ Observability   │   │
│  │ Registry │ │ Engine   │ │ Authority    │ │ (Kafka + UI)    │   │
│  └──────────┘ └──────────┘ └──────────────┘ └─────────────────┘   │
└──────────────────────────────────────────────────┬─────────────────┘
                                  │ policy + identity (gRPC / REST)
        ┌─────────────────────────┴─────────────────────────┐
        ▼                                                   ▼
┌──────────────────┐                                ┌──────────────────┐
│  Pod: Agent A    │                                │  Pod: Agent B    │
│  ┌────┐ ┌─────┐  │     mTLS, JSON-RPC 2.0         │  ┌────┐ ┌─────┐  │
│  │App │◀┤Side-│◀─┼────────── A2A protocol ────────┼─▶│App │◀┤Side-│  │
│  │    │ │car  │  │                                │  │    │ │car  │  │
│  └────┘ └─────┘  │                                │  └────┘ └─────┘  │
└──────────────────┘                                └──────────────────┘

Every agent pod gets a sidecar injected by a Kubernetes mutating admission webhook. The sidecar runs an interceptor pipeline — authentication, authorisation, compliance, PII redaction, guardrails, audit, rate limiting, resilience — for every inbound and outbound message. Agent code is unchanged.

For the full architecture, see ARCHITECTURE.md. For the catalog of capabilities, see FEATURES.md.


Quick start

Recursant runs in Kubernetes (Kind for local dev, any cluster in production). The fastest path to a working demo:

# 1. Configure secrets (at minimum: an LLM API key)
cp .env.sample .env
$EDITOR .env       # set OPENROUTER_API_KEY (or ANTHROPIC_/OPENAI_/GOOGLE_API_KEY)

# 2. One command to bring everything up — Kind cluster, build all images,
#    deploy via Helm, smoke test
./scripts/install.sh

# 3. Open the registry UI (login: admin / value of ADMIN_PASSWORD)
open http://localhost:8030

Full install + deployment guide: INSTALL.md.


Try the mortgage demo

Recursant ships with a complete mortgage origination demo — a Customer Agent hub coordinating Auth, KYC (n8n workflow), Credit, Core Banking, and Compliance (CrewAI) spokes, with hub-and-spoke NetworkPolicy enforcement and a full audit trail.

# Mortgage demo UI
open http://localhost:8031

# End-to-end test — walks the full mortgage application journey
python3 demo/mortgage/scripts/test_e2e.py

# Continuous traffic generator (great for live demos / recordings)
python3 demo/mortgage/scripts/generate_demo_traffic.py --interval 8

Watch traffic animate live in the Mesh Visualiser and Observability tabs of the registry UI.


Repository layout

recursant/
├── registry/              # Control plane — Flask API + React frontend
│   ├── app/
│   │   ├── api/           # REST blueprints (agents, mesh, guardrails, ...)
│   │   ├── consumers/     # Kafka consumer services (pg-writer, ws-broadcaster, ...)
│   │   ├── models/        # SQLAlchemy models
│   │   ├── services/      # Business logic
│   │   ├── llm/           # LLM provider abstraction
│   │   └── schemas/       # Marshmallow schemas
│   ├── frontend/          # React + Vite + Tailwind UI
│   ├── test_agent/        # LangGraph test agent for evaluation
│   ├── scripts/           # Seed scripts (admin, security tests, eval suites)
│   └── migrations/        # Alembic migrations
│
├── mesh/                  # Data plane — Python sidecar
│   ├── runtime/sidecar/   # Sidecar with interceptor pipeline
│   ├── runtime/gateway/   # External A2A ingress gateway
│   └── examples/          # Agent A & B example agents
│
├── demo/mortgage/         # Mortgage origination demo
│   ├── agents/            # Customer, KYC/Credit, Core Banking, Compliance
│   ├── frontend/          # Demo React frontend
│   ├── mcp_servers/       # MCP tool implementations
│   └── stubs/             # Mock banking APIs
│
├── k8s/                   # Kubernetes deployment
│   ├── charts/recursant/  # Helm chart
│   ├── webhook/           # Sidecar injection webhook
│   └── scripts/           # Cluster lifecycle scripts
│
├── sdk/                   # Python SDK + CLI for agent developers
├── scripts/               # Top-level install / quickstart / teardown scripts
├── ARCHITECTURE.md        # System architecture deep-dive
├── FEATURES.md            # Capability catalog with code references
├── INSTALL.md             # Detailed installation guide
├── CONTRIBUTING.md        # How to contribute
└── AUTHOR.md              # About the author

Documentation

Document Purpose
README.md This file
ARCHITECTURE.md Two-plane architecture, sidecar internals, observability pipeline
FEATURES.md Feature-by-feature catalog with code references
INSTALL.md Full install guide (prerequisites, env, alternatives)
CONTRIBUTING.md How to file issues and submit PRs
AUTHOR.md About the author
sdk/README.md Python SDK and CLI for agent developers

Useful commands

make k8s-all              # Full bring-up: cluster + build + deploy + smoke test
make k8s-test             # Run all integration tests
make k8s-status           # Pod and service status
make k8s-logs             # Tail registry + webhook logs
make k8s-port-forward     # Forward registry, frontend, mortgage demo to localhost
make k8s-down             # Tear down the Kind cluster

Run make help for the full list (Docker Compose, multi-cluster, individual test suites, etc.).


Tech stack

  • Backend: Python 3.11+, Flask, SQLAlchemy, Alembic, Marshmallow
  • Frontend: React 18, Vite, Tailwind CSS, D3 (visualizations)
  • Storage: PostgreSQL (with pgvector), Redis, Weaviate
  • Streaming: Apache Kafka (KRaft mode, no ZooKeeper)
  • Mesh: A2A protocol (a2a-sdk 1.0+) over mTLS, JSON-RPC 2.0
  • Observability: OpenTelemetry, Socket.IO (live updates), Prometheus-compatible metrics
  • Deploy: Helm 3, Kubernetes 1.27+, Calico CNI (NetworkPolicy enforcement)

License

MIT — use, fork, and adapt freely. Attribution appreciated.

Status

Recursant is an active project. The architecture is stable; APIs may evolve before a 1.0 release. Issues and PRs welcome — see CONTRIBUTING.md.