Nexent is a zero-code platform for creating AI agents from natural-language instructions, combining tools, skills, memory, orchestration, constraints, feedback loops, and control planes. It is intended for people who need to build and deploy agents without writing orchestration code. The catalogue entries are rules and skills for working with Nexent.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/modelengine-group/nexent/external-memory-pluginnpx skills add ModelEngine-Group/nexent --skill external-memory-plugingit clone --depth 1 https://github.com/ModelEngine-Group/nexentWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/modelengine-group/nexent/external-memory-plugin)<a href="https://agentmods.dev/skills/modelengine-group/nexent/external-memory-plugin"><img src="https://agentmods.dev/badge/skills/modelengine-group/nexent/external-memory-plugin.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00085 | $0.00936 |
| Opus 5 | $0.00043 | $0.00468 |
| Sonnet 5 | $0.00017 | $0.00187 |
| Haiku 4.5 | $0.00009 | $0.00094 |
Grade A, and why
external-memory-plugin scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured today.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 65 lines — stays where its author put it; the contents beside it link to each section on GitHub.
External Memory Plugin
Develop external memory adapters against the current Nexent contract and prove them without live-service CI dependencies.
Establish scope
- Read the repository
AGENTS.mdand invoke its SPEC workflow for production behavior changes. - Inspect, rather than assume, these current contracts:
sdk/nexent/memory/providers/base.pysdk/nexent/memory/models.pybackend/services/memory_provider_plugin_loader.pybackend/services/memory_external_provider_service.py
- Read references/plugin-contract.md before creating or reviewing a manifest or provider.
- Read references/mem0-example.md when implementing HTTP transport, error mapping, or tests.
- Preserve tenant, user, Agent, and conversation scope. Never print or persist credentials.
Implement the adapter
- Create
<plugin-dir>/<name>/plugin.yamlandprovider.py. - Declare only implemented capabilities in
implements. - Make protocol methods asynchronous and return the exact Nexent models.
- Honor provider-level
timeout_secondsand result limits. - Map remote failures to
ProviderErrorand the retry exception matching recovery semantics. - Keep OTel attributes low-cardinality and free of queries, memory content, user identifiers, tenant identifiers, and secrets. Rely on the orchestration service's standard instrumentation unless provider-specific spans add actionable detail.
- Treat the deployment search/ingest switches as kill switches and the provider
enabledfield as the instance switch; do not bypass them in normal runtime paths.
Test without external dependencies
- Test manifest discovery and protocol validation with a temporary plugin directory.
- Mock the network at the HTTP client transport boundary; for
httpx, useMockTransport. - Cover successful, empty, partial, malformed, unauthorized, forbidden, rate-limited, timeout, connection, and server-error responses as applicable.
- Assert request scope and authentication shape without using real credentials.
- Assert every ingest unit receives a result and retry classification is correct.
- Run the narrow plugin and loader suites first, then affected service and integration suites.
- Reach at least 90% coverage for each new or modified module.
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- today First seen · 65 lines · 85 tokens per session scan A 73b12333aea4
external-memory-plugin is a skill published in the GitHub repository ModelEngine-Group/nexent (5,849 stars, last pushed today), licensed MIT. It adds 85 tokens to every session and 936 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-05.
Other skills, from other repositories
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agent-v3-memory-specialist
Agent skill for v3-memory-specialist - invoke with $agent-v3-memory-specialist.
agent-memory-coordinator
Agent skill for memory-coordinator - invoke with $agent-memory-coordinator.
agent-swarm-memory-manager
Agent skill for swarm-memory-manager - invoke with $agent-swarm-memory-manager.
agent-collective-intelligence-coordinator
Agent skill for collective-intelligence-coordinator - invoke with $agent-collective-intelligence-coordinator.
memory-management
AgentDB memory system with HNSW vector search. Provides 150x-12,500x faster pattern retrieval, persistent storage, and semantic search capabilities for learning and knowledge management. Use when: need to store successful patterns, searching for similar solutions, semantic lookup of past work, learning from previous…