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"

# 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)]

pip install pensyve          # Python (PyPI)
npm install @pensyve/sdk     # TypeScript (npm)
go get github.com/major7apps/pensyve/pensyve-go/v3@latest  # Go

pip install pensyve

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}")

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}")

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

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

# Pensyve tracks action→outcome reliability with Bayesian updates.
# Next time a similar issue comes up, recall surfaces the approach that worked.

p.consolidate()
# Promotes repeated episodic facts to semantic knowledge
# Decays memories you never access via FSRS forgetting curve

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 (flat list)
results = p.recall("programming language", entity=entity)

# Recall memories clustered by source session — the canonical entry point
# for "memory as input to an LLM reader" workflows.
groups = p.recall_grouped("programming language", limit=50)

# 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()

cargo build --release --bin pensyve-mcp

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

/plugin marketplace add major7apps/pensyve
/plugin install pensyve@major7apps-pensyve
/reload-plugins

export PENSYVE_API_KEY="psy_your_key_here"

{
  "mcpServers": {
    "pensyve": {
      "type": "http",
      "url": "https://mcp.pensyve.com/mcp",
      "headers": {
        "Authorization": "Bearer ${PENSYVE_API_KEY}"
      }
    }
  }
}

{
  "mcpServers": {
    "pensyve": {
      "type": "http",
      "url": "https://mcp.pensyve.com/mcp"
    }
  }
}

{
  "mcpServers": {
    "pensyve": {
      "command": "pensyve-mcp",
      "args": ["--stdio"]
    }
  }
}

Plugin contents:
├── 7 slash commands   /remember, /recall, /forget, /inspect, /consolidate, /memory-status, /using-pensyve
├── 4 skills           session-memory, memory-informed-refactor, context-loader, memory-review
├── 2 agents           memory-curator (background), context-researcher (on-demand)
└── 6 hooks            SessionStart, Stop, PreCompact, UserPromptSubmit, PostToolUse (Write/Edit, Bash)

codex plugin marketplace add major7apps/pensyve
codex plugin add pensyve@pensyve-codex

export PENSYVE_API_KEY="psy_your_key_here"