pensyve 1.0.1

Universal memory runtime for AI agents — framework-agnostic, protocol-native, offline-first

Pensyve

Universal memory runtime for AI agents. Framework-agnostic, protocol-native, offline-first.

Agents use Pensyve to remember across sessions, learn from outcomes, and share knowledge — all backed by a Rust core engine with zero cloud dependencies required.

Why Pensyve

Most AI agents lose all context between sessions. Pensyve gives them durable, intelligent memory:

• Three memory types — Episodic (what happened), Semantic (what is known), Procedural (what works)
• Multimodal content — Text, code, images, tool outputs, structured data
• 8-signal fusion retrieval — Vector similarity, BM25 lexical, graph proximity, intent classification, recency, access frequency, confidence, type boost
• Learns from outcomes — Bayesian tracking on action→outcome procedures automatically surfaces what works
• Forgetting curve — FSRS-based memory decay with retrieval-induced reinforcement (memories you use get stronger)
• Consolidation — Background "dreaming" promotes repeated episodic facts to semantic knowledge
• Offline-first — SQLite storage, ONNX embeddings, optional local LLM extraction. No API keys needed.
• Scales to Postgres — Feature-gated Postgres backend with pgvector for multi-node deployments
• Cross-encoder reranking — BGE reranker on top-k results for precision
• Access control — RBAC memory mesh with owner/writer/reader roles and private/shared/public visibility

Install

pip install pensyve          # Python (PyPI)
npm install pensyve          # TypeScript (npm)
go get github.com/major7apps/pensyve/pensyve-go@pensyve-go/v1.0.0  # Go

Or use the MCP server directly with Claude Code, Cursor, or any MCP client — see MCP Setup.

Quick Start

Prerequisites (building from source)

• Rust 1.88+
• Python 3.10+ with uv
• Bun (optional, for TypeScript SDK)
• Go 1.21+ (optional, for Go SDK)

Install

git clone https://github.com/major7apps/pensyve.git && cd pensyve

# Set up Python environment and install deps
uv sync --extra dev

# Build the Python SDK (compiles Rust → native Python module)
uv run maturin develop --release -m pensyve-python/Cargo.toml

# Verify
uv run python -c "import pensyve; print(pensyve.__version__)"

5-Line Demo

import pensyve

p = pensyve.Pensyve()
with p.episode(p.entity("agent", kind="agent"), p.entity("user")) as ep:
    ep.message("user", "I prefer dark mode and use vim keybindings")
print(p.recall("what editor setup does the user prefer?"))

Interfaces

Pensyve exposes its core engine through multiple interfaces — use whichever fits your stack.

Python SDK

Direct in-process access via PyO3. Zero network overhead.

import pensyve

p = pensyve.Pensyve(namespace="my-agent")
entity = p.entity("user", kind="user")

# Remember a fact
p.remember(entity=entity, fact="User prefers Python", confidence=0.95)

# Recall memories
results = p.recall("programming language", entity=entity)

# Record an episode
with p.episode(entity) as ep:
    ep.message("user", "Can you fix the login bug?")
    ep.message("agent", "Fixed — the session token was expiring early")
    ep.outcome("success")

# Consolidate (promote repeated facts, decay unused memories)
p.consolidate()

MCP Server

Works with Claude Code, Cursor, and any MCP-compatible client.

cargo build --release --bin pensyve-mcp

{
  "mcpServers": {
    "pensyve": {
      "command": "./target/release/pensyve-mcp",
      "env": { "PENSYVE_PATH": "~/.pensyve/default" }
    }
  }
}

Tools exposed: recall, remember, episode_start, episode_end, forget, inspect

Claude Code Plugin

Full cognitive memory layer for Claude Code — install from the marketplace or manually.

pensyve-plugin/
├── 6 slash commands   /remember, /recall, /forget, /inspect, /consolidate, /memory-status
├── 4 skills           session-memory, memory-informed-refactor, context-loader, memory-review
├── 2 agents           memory-curator (background), context-researcher (on-demand)
└── 4 hooks            SessionStart, Stop, PreCompact, UserPromptSubmit

See integrations/claude-code/README.md for details.

REST API

Rust/Axum gateway serving REST + MCP with auth, rate limiting, and usage metering.

</code></pre>
<pre lang="bash"><code># Remember
curl -X POST http://localhost:8000/v1/remember \
  -H "Content-Type: application/json" \
  -d '{"entity": "seth", "fact": "Seth prefers Python", "confidence": 0.95}'

# Recall
curl -X POST http://localhost:8000/v1/recall \
  -H "Content-Type: application/json" \
  -d '{"query": "programming language", "entity": "seth"}'

Endpoints: POST /v1/entities, POST /v1/episodes/{start,message,end}, POST /v1/recall, POST /v1/remember, POST /v1/inspect, GET /v1/stats, DELETE /v1/entities/{name}, POST /v1/consolidate, GET /v1/health, GET /metrics

TypeScript SDK

HTTP client with timeout, retry, and structured errors.

import { Pensyve } from "pensyve";

const p = new Pensyve({ baseUrl: "http://localhost:8000", timeoutMs: 10000, retries: 2 });
await p.remember({ entity: "seth", fact: "Likes TypeScript", confidence: 0.9 });
const memories = await p.recall("programming", { entity: "seth" });

Go SDK

Context-aware HTTP client with structured errors.

import pensyve "github.com/major7apps/pensyve-go"

client := pensyve.NewClient(pensyve.Config{BaseURL: "http://localhost:8000"})
ctx := context.Background()
client.Remember(ctx, "seth", "Likes Go", 0.9)
memories, _ := client.Recall(ctx, "programming", nil)

CLI

cargo build --bin pensyve-cli

# Recall memories
./target/debug/pensyve-cli recall "editor preferences" --entity user

# Show stats
./target/debug/pensyve-cli stats

# Inspect an entity
./target/debug/pensyve-cli inspect --entity user

Architecture

Data Model

Namespace (isolation boundary)
  └── Entity (agent | user | team | tool)
        ├── Episodes (bounded interaction sequences)
        │     └── Messages (role + content)
        └── Memories
              ├── Episodic  — what happened (timestamped, multimodal content type)
              ├── Semantic  — what is known (SPO triples with temporal validity)
              └── Procedural — what works (action→outcome with Bayesian reliability)

Retrieval Pipeline

1. Embed query via ONNX (Alibaba-NLP/gte-base-en-v1.5, 768 dims)
2. Classify intent — Question/Action/Recall/General (keyword heuristics)
3. Vector search — cosine similarity against stored embeddings
4. BM25 search — FTS5 lexical matching
5. Graph traversal — petgraph BFS from query entity
6. Fusion scoring — weighted sum of 8 signals (vector, BM25, graph, intent, recency, access, confidence, type boost)
7. Cross-encoder reranking — BGE reranker on top-20 candidates
8. FSRS reinforcement — retrieved memories get stability boost

Project Structure

pensyve/
├── pensyve-core/       Rust engine (rlib) — storage, embedding, retrieval, graph, decay, mesh, observability
├── pensyve-python/     Python SDK via PyO3 (cdylib)
├── pensyve-mcp/        MCP server binary (stdio, rmcp)
├── pensyve-cli/        CLI binary (clap)
├── pensyve-ts/         TypeScript SDK (bun) — timeout, retry, PensyveError
├── pensyve-go/         Go SDK — context-aware HTTP client
├── pensyve-wasm/       WASM build — standalone minimal in-memory Pensyve
├── pensyve_python/       Shared Python utilities — billing, extraction
├── integrations/       All integrations — IDE plugins, framework adapters, code harnesses
│   ├── claude-code/    Claude Code plugin (commands, skills, agents, hooks)
│   ├── vscode/         VS Code sidebar extension
│   ├── openclaw-plugin/ OpenClaw native memory plugin (TypeScript)
│   ├── opencode-plugin/ OpenCode native memory plugin (TypeScript)
│   ├── cursor/         Cursor MCP setup guide
│   ├── cline/          Cline MCP setup guide
│   ├── windsurf/       Windsurf MCP setup guide
│   ├── continue/       Continue MCP setup guide
│   ├── vscode-copilot/ VS Code Copilot Chat MCP setup guide
│   ├── langchain/      LangChain/LangGraph Python (PensyveStore + legacy PensyveMemory)
│   ├── langchain-ts/   LangChain.js/LangGraph.js TypeScript (PensyveStore)
│   ├── crewai/         CrewAI (PensyveStorage + standalone PensyveCrewMemory)
│   └── autogen/        Microsoft AutoGen multi-agent memory
├── tests/python/       Python integration tests
├── benchmarks/         LongMemEval_S evaluation + weight tuning
├── website/            Astro + Tailwind static site for pensyve.com
└── docs/               Architecture, roadmap, design specs, implementation plans

Development

First-Time Setup

# Install dependencies (creates .venv automatically)
uv sync --extra dev

# Build the native Python module (required before running any Python code)
uv run maturin develop --release -m pensyve-python/Cargo.toml

# Verify the module loads
uv run python -c "import pensyve; print(pensyve.__version__)"

Note: The pensyve Python package is a native Rust extension built with PyO3. You must run uv run maturin develop before pytest or any Python import of pensyve, otherwise you will get ModuleNotFoundError: No module named 'pensyve'.

Build & Test

make build      # Compile Rust + build PyO3 module
make test       # Run all tests (Rust + Python)
make lint       # clippy + ruff + pyright
make format     # cargo fmt + ruff format
make check      # lint + test (CI gate)

To run test suites individually:

cargo test --workspace                                       # Rust tests
uv run maturin develop --release -m pensyve-python/Cargo.toml  # Build PyO3 module first
uv run pytest tests/python/ -v                               # Python tests
cd pensyve-ts && bun test                                    # TypeScript tests
cd pensyve-go && go test ./...                               # Go tests

Additional SDKs

cd pensyve-ts && bun test          # TypeScript (38 tests)
cd pensyve-go && go test ./...     # Go (17 tests)
cd pensyve-wasm && cargo check     # WASM (standalone)

Benchmarks

# Run LongMemEval_S evaluation (builtin dataset: 87.5% baseline)
python benchmarks/longmemeval/run.py --verbose

# Run weight optimization
python benchmarks/tuning/optimize.py --maxiter 50

Competitive Landscape

Feature	Pensyve	Mem0	Zep	Honcho
Offline-first (no cloud required)	Yes	No	No	No
Procedural memory (learns from outcomes)	Yes	No	No	No
Multi-signal fusion scoring	8 signals	1	3	1
Retrieval-induced reinforcement (FSRS)	Yes	No	No	No
Intent-aware retrieval	Yes	No	No	No
Multimodal content types	Yes	Text only	Text only	Text only
RBAC memory mesh	Yes	No	No	No
Cross-platform local LLM extraction	Yes	No	Cloud only	Cloud only
MCP server	Yes	No	No	Plugin
Claude Code plugin	Yes	No	No	No
VS Code extension	Yes	No	No	No
Framework integrations	5	3	1	1
Postgres backend	Yes (feature-gated)	Yes	Yes	Yes
Go SDK	Yes	No	No	No
WASM build	Yes	No	No	No
Open source engine	Apache 2.0	Yes	Partial	Yes

License

Apache 2.0