agent7 — AI Agent Governance Platform
The problem
You deployed agents. agent-mesh governs them. mem7 stores their decisions. Now you need answers :
- Which agents are running ? What are they doing ? You have JSONL traces and terminal logs across machines. No single view.
- Who approved what, when, and why ? Decisions are in mem7, but querying them requires knowing the key format and tag conventions.
- Is this new agent safe to promote to production ? There's no scoring, no lifecycle, no diff showing what changed since the last review.
- Three agents are waiting for approval at 2am. Nobody is watching the terminal. The requests time out.
These are management plane problems. agent-mesh is the data plane (runtime enforcement), mem7 is the memory substrate. agent7 is the visibility and control layer that makes them manageable at scale.
What agent7 is
A web-based governance platform for AI agents. Dashboard, approval UI, audit trail, governance engine.
agent7 (management plane)
├── Agent Catalog — registry, metadata, lifecycle, scoring
├── Trace Viewer — ingests agent-mesh JSONL, aggregates stats
├── Memory Viewer — reads mem7 via SDK, displays decisions + facts
├── Approval UI — shows pending approvals, human clicks approve/reject
├── Governance Engine — rules, 3-axis scoring, severity, validation verdicts
├── Dependency Graph — declared (YAML) + inferred (traces), impact analysis
└── Diff Engine — breaking change detection on agent config changes
Stack : Next.js 16 + FastAPI + PostgreSQL. Auth-agnostic (Supabase SaaS or API key on-prem).
How it fits
agent7 (visibility + control)
┌────────────────────────────┐
│ dashboard, approval UI, │
│ governance, audit trail │
└──────┬──────────┬──────────┘
│ │
reads via │ │ reads via
HTTP API │ │ Python SDK
│ │
▼ ▼
┌─────────────────┐ ┌─────────────────┐
│ agent-mesh │───►│ mem7 │
│ (runtime) │ │ (memory) │
│ │ │ │
│ • policy │ │ • facts │
│ • approvals │ │ • decisions │
│ • traces │ │ • observations │
└─────────────────┘ └─────────────────┘
agent7 is a thin client of agent-mesh and mem7. If agent7 goes down, everything keeps working — agents are still governed, decisions are still stored. agent7 adds visibility, not runtime dependency.
Agent-agnostic. agent7 doesn't know or care which SDK produced the tool call. Claude Code, Managed Agents, LangChain, cron scripts — if it goes through agent-mesh, agent7 sees it.
What it enables
For the solo dev : trace viewer shows what your agents did today. Memory viewer shows what decisions were made. You don't need agent7 on day 1 — agent-mesh + mem7 are enough. Add agent7 when you want a dashboard instead of curl.
For the team : approval UI lets any team member resolve pending approvals from a browser. Governance scoring flags risky agents before they hit production. Audit trail answers "who approved that email send at 3am."
For compliance : every decision is a fact in mem7. Every tool call is a trace. agent7 joins them : "this agent called this tool, it was auto-approved because of these 3 past decisions, here's the full chain." Query, don't grep.
What makes it different
| Anthropic Console | LangSmith / LangFuse | agent7 | |
|---|---|---|---|
| Scope | Anthropic agents only | LangChain ecosystem | Any agent through agent-mesh |
| Governance | Permission policies (allow/ask) | None | Rules, scoring, lifecycle, diffs |
| Approvals | Inline in SDK | None | Web UI + API, team-accessible |
| Memory | None | Trace replay | mem7 integration (decisions as facts) |
| Policy enforcement | Basic | None | Full (agent-mesh data plane) |
Anthropic Console is great for Managed Agents visibility. agent7 complements it with governance and cross-agent visibility for heterogeneous deployments.
Current state (May 2026)
- Dashboard — agent-mesh trace viewer, session browser, memory debug view
- Supervisor — Python service with rule engine + Ollama LLM evaluation
- Working — trace ingestion, session aggregation, pending approval polling
- Next — governance engine (scoring, lifecycle), approval UI, mem7 SDK integration, dependency graph
Progressive adoption
Day 1: agent-mesh only — policies + tracing (CLI, zero UI)
Day 30: + mem7 — persistent memory, decision history, auto-approve
Day 60: + agent7 — dashboard, team approval UI, governance scoring
Each step is independently valuable. agent7 is the last layer, not the first.