pensyve

Pensyve

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

Without memory

User: "I prefer dark mode and use vim keybindings"
Agent: "Got it!"

[next session]

User: "Update my editor settings"
Agent: "What settings would you like to change?"
User: "I ALREADY TOLD YOU"

With Pensyve

# Session 1 — agent stores the preference
p.remember(entity=user, fact="Prefers dark mode and vim keybindings", confidence=0.95)

# Session 2 — agent recalls it automatically
memories = p.recall("editor settings", entity=user)
# → [Memory: "Prefers dark mode and vim keybindings" (score: 0.94)]

Your agent stops being amnesiac. Decisions, patterns, and outcomes persist across sessions — and the right context surfaces when it's needed.

Why Pensyve

Install

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

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

Quick Start

pip install pensyve

Episode: your agent remembers a conversation

import pensyve

p = pensyve.Pensyve()
user = p.entity("user", kind="user")

# Record a conversation — Pensyve captures it as episodic memory
with p.episode(user) as ep:
    ep.message("user", "I prefer dark mode and use vim keybindings")
    ep.message("agent", "Got it — I'll remember your editor preferences")
    ep.outcome("success")

# Later (even in a new session), the agent recalls what happened
results = p.recall("editor preferences", entity=user)
for r in results:
    print(f"[{r.score:.2f}] {r.content}")

Recall grouped: feed an LLM reader without rebuilding session blocks

When the consumer of recalled memories is another LLM (the dominant "memory for an AI agent" pattern), recall_grouped() returns memories already clustered by source session and ordered chronologically — ready to format as session blocks in a reader prompt.

import pensyve

p = pensyve.Pensyve()
groups = p.recall_grouped("How many projects have I led this year?", limit=50)

# Each group is one conversation session — feed it to a reader directly.
for i, g in enumerate(groups, start=1):
    print(f"### Session {i} ({g.session_time}):")
    for m in g.memories:
        print(f"  {m.content}")

No more manual OrderedDict clustering, no more reordering by date string, no more boilerplate every consumer has to reinvent.

Remember: store an explicit fact

p.remember(entity=user, fact="Prefers Python over JavaScript", confidence=0.9)

Procedural: the agent learns what works

# After a debugging session that succeeded:
ep.outcome("success")