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/fetchai/agentverse-skills/agentverse-memorynpx skills add fetchai/agentverse-skills --skill agentverse-memorygit clone --depth 1 https://github.com/fetchai/agentverse-skillsWrote 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/fetchai/agentverse-skills/agentverse-memory)<a href="https://agentmods.dev/skills/fetchai/agentverse-skills/agentverse-memory"><img src="https://agentmods.dev/badge/skills/fetchai/agentverse-skills/agentverse-memory.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 | $0.00127 | $0.10247 |
| Opus 5 | $0.00063 | $0.05123 |
| Sonnet 5 | $0.00025 | $0.02049 |
| Haiku 4.5 | $0.00013 | $0.01025 |
Grade A, and why
agentverse-memory scanned grade A with 1 finding 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 4d ago.
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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
allowed-tools: Read Bash(python3 *) Bash(curl *) Bash(mem *) Bash(pip install requests) How it starts
The opening of the file, as written. The whole thing — 684 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agentverse Memory
Overview
Give any AI agent persistent, graph-native memory. Agentverse Memory is a managed MCP service that exposes 35 JSON-RPC 2.0 tools for:
| Memory Type | What it stores | Key tools |
|---|---|---|
| Episodic | Time-stamped events, observations, conversations | memory_store_episode, memory_search_episodes |
| Entity | Named entities with typed properties | memory_store_entity, memory_get_entity |
| Graph | Knowledge graph triples, traversal, pathfinding | memory_traverse_graph, memory_find_path |
| Procedural | Goal-directed skill sequences with outcome tracking | memory_store_procedure, memory_match_procedure |
| Working | Ephemeral key-value scratchpad (TTL-aware) | memory_set_working, memory_get_working |
| Shared | Multi-agent shared knowledge spaces | memory_create_shared_space, memory_shared_query |
| Pheromone | Stigmergic trails on memory paths | memory_deposit_pheromone, memory_get_pheromone |
Key differentiators:
- 🚀 <5ms writes, $0 ingest — zero LLM inference at write time. Embeddings are computed lazily on the read path and cached per agent, so ingestion never pays an LLM/embedding bill. This is the core cost moat.
- 🌐 Graph memory on every tier — including free. Knowledge triples, BFS graph traversal, and all 4 memory types are available on the free Explorer tier (most vector-only free tiers don't include graph at all).
- 🔀 Hybrid retrieval by default — lexical (TF-IDF) and dense (
text-embedding-3-small) candidate sets fused via Reciprocal Rank Fusion (RRF, k=60). Live default-on in production. - 🐜 Pheromone-guided retrieval (opt-in) — stigmergic trails that boost frequently-recalled memories. Best for warm-cache / repeated-access and multi-agent shared workloads; off by default for single-pass queries.
- 🔗 MCP-native — 35 tools over JSON-RPC; works with Claude, Cursor, Codex, Copilot, Gemini CLI.
Positioning (honest): Agentverse Memory competes on total cost of ownership ($0 write-time inference), graph at every tier, native MCP, and multi-agent pheromone transfer — not on a claim of higher raw retrieval accuracy than other systems. Keep the headline on cost and capabilities.
What ships with it
1 file 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.
- 4d ago First seen · 684 lines · 127 tokens per session scan A c6d3279e92fc
agentverse-memory is a skill published in the GitHub repository fetchai/agentverse-skills (2 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 127 tokens to every session and 10,247 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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