hermes-next 0.7.1

Next-generation memory provider for Hermes Agent — fusing OpenViking vector storage with MemOS cognitive engine

Hermes Next

A self-evolving memory system for AI agents. Python-native, zero-bridge, brain-like cognition.

Hermes Next fuses OpenViking vector storage with a Python-native MemOS cognitive engine, giving Hermes Agent agents persistent, self-evolving memory — no JSON-RPC bridge, no TypeScript dependency, no process overhead.

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Why Hermes Next?

Most agent memory systems today are TypeScript plugins bolted onto Python agents via JSON-RPC bridges — every memory operation crosses a process boundary, serializes and deserializes, and introduces a failure point.

Hermes Next is Python-native from day one. It runs in-process, shares memory with the agent, and eliminates the bridge entirely.

Feature	Traditional (memos-local)	Hermes Next
Language	TypeScript + JSON-RPC bridge	Python native
Search	Brute-force cosine	6-step fusion (semantic + FTS5 + timeline + RRF + MMR + recency)
Lifecycle	None (data grows forever)	Auto-archive (90d) + confidence decay + pruning
Native memory sync	❌	✅ MEMORY.md promotion + session_search fallback
Cross-agent sharing	JSON-RPC	✅ OpenViking namespaces
Cognitive pipeline	L1/L2/L3/Skill	L1→Reward→L2→L3→Skill fully wired
Setup	npm install + bridge config	pip install

Bottom line: memos-local was built for the TypeScript era. Hermes Next is built for the Python-native era.

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Quick Start

pip install hermes-next

Requires Python 3.10+ and a running OpenViking server (v0.3.22+).

Basic Usage

from hermes_next import HermesNextProvider

# Initialize
provider = HermesNextProvider()
provider.initialize(session_id="my-session")

# Every turn — automatic capture + retrieval
context = provider.prefetch("What did we discuss about RAG?")
provider.sync_turn(
    user_content="Tell me about RAG optimization",
    assistant_content="Key techniques: chunk size tuning, embedding selection...",
    session_id="my-session",
    tags=["rag", "optimization"],
)

# At session end — cognitive pipeline runs automatically
provider.on_session_end(messages=[])

# Check pipeline health
status = provider.handle_tool_call("memos_status", {})
print(status)

CLI

# Start the built-in viewer
hermes-next-viewer --port 8080

# Migrate from legacy memos-local-plugin database
hermes-next-migrate --old-db ~/.hermes/memos-plugin/data/memos.db

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Architecture

                         Hermes Agent (Python)
                              │
                    HermesNextProvider
                     ┌────────┴────────┐
                     │                 │
              RetrievalPipeline   CognitivePipeline
              ┌─────┼───┬───┐    ┌───┼───┬───┬───┐
              │     │   │   │    │   │   │   │   │
           Semantic FTS5 Timel. MMR  L1  L2  L3  Skill
           (OV)   (SQLite)    (Rerank)  (Policy)(WM)(Cryst.)
                     │                 │
                  ┌──┴──┐          ┌───┴───┐
             OpenViking  SQLite  MEMORY.md  state.db
             (Vector)   (Cache)  (Native)  (Fallback)

4-Layer Retrieval Chain

1. Hot Memory — MEMORY.md / USER.md (always in system prompt)
2. Semantic Search — OpenViking vector store (primary, 95% of queries)
3. FTS5 Full-text — Local SQLite cache (keyword fallback)
4. session_search — Hermes Agent native state.db (last resort)

Cognitive Pipeline

Every session triggers an automatic induction chain:

L1 Traces → Reward Backprop → L2 Policy Induction → L3 World Model → Skill Crystallization
   ↓              ↓                  ↓                   ↓                 ↓
 Raw turns    Score each       Extract reusable       Build domain      Package as
              interaction     behavioral patterns     knowledge         invocable skills

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Configuration

# ~/.hermes-next.yaml
openviking:
  base_url: "http://localhost:1933"

cognitive:
  enable_l2_induction: true
  enable_l3_world_model: false   # opt-in, GPT-intensive
  enable_skill_crystallization: false

lifecycle:
  trace_retention_days: 90
  policy_decay_rate: 0.03

integration:
  sync_memory_md: false           # promote to Hermes Agent MEMORY.md
  session_search_fallback: true    # fallback to native state.db FTS5

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Tools Exposed to the Agent

Tool	Description
memos_search(query, k)	Semantic search across all memories
memos_get(trace_id)	Read a specific trace
memos_timeline(limit)	Recent memory activity
memos_status	Pipeline health + promotion stats

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Project Structure

hermes-next/
├── hermes_next/
│   ├── ov/            # OpenViking REST client
│   ├── memos/         # MemOS cognitive engine
│   ├── cache/         # SQLite local cache (FTS5 + vector)
│   ├── retrieval/     # 6-step fusion pipeline
│   ├── integration/   # Hermes Agent native memory bridge
│   └── viewer/        # Built-in SPA dashboard
├── tests/             # 141 tests, 0 flaky
└── docs/              # Architecture + upgrade guides

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Comparison with memos-local-plugin

Aspect	memos-local-plugin	Hermes Next
Runtime	TypeScript, separate process	Python, in-process
Communication	JSON-RPC over stdio	Direct function calls
Vector search	Brute-force cosine (SQLite)	OpenViking (real vector DB)
Full-text search	❌	✅ FTS5
Memory lifecycle	❌	✅ Auto archive + decay
Native Hermes sync	❌	✅ MEMORY.md + session_search
Cross-agent sharing	Per-process	✅ OpenViking namespaces
Viewer	HTTP + SSE	HTTP SPA
Pipeline	3-tier retrieval	6-step fusion + cognitive

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Roadmap

• v0.4.0 — Feedback loop (user 👍/👎 → policy update → better retrieval)
• v0.5.0 — Decision Repair (failure patterns → prevention)
• v0.6.0 — Hub cross-agent search (native, no OV dependency)

See docs/strategic-positioning.md for the full strategy.

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License

AGPL-3.0 — This project is a derivative of OpenViking (AGPL-3.0).

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