Pensyve
Memory serverOfficial Major 7 Apps LLC project
Created by Major 7 Apps LLC
Published in major7apps/pensyve. Publisher page
100 GitHub stars
What it is
Universal memory runtime for AI agents
AutoGineer overview, from the brain’s own files
Pensyve is an open-source memory runtime designed to provide AI agents with persistent, cross-session recall. It functions as a memory layer that stores facts, conversations, observations, and action outcomes outside of the language model itself. The system is built in Rust and utilizes SQLite for local storage, with optional PostgreSQL support. It supports semantic search via locally run embedding models and provides multiple interfaces, including a Python SDK, an MCP server for integration with tools like Claude Code and Cursor, and an HTTP gateway for REST-based access. Pensyve is designed to be self-hosted, requiring no external accounts or API keys for local operation.
How it works
Memory model: Hybrid
AutoGineer overview, from the brain’s own files
Pensyve operates as a memory server that stores data in a local SQLite database or a PostgreSQL instance. It organizes information into four categories: facts (semantic memory), conversations (episodic memory), action outcomes (procedural memory), and observations. The system uses embedding models to enable semantic search across these records. Agents interact with Pensyve through various SDKs or the MCP protocol to save and retrieve information, which is then injected into the agent's prompt to provide context across different sessions and processes.
| Folder | Role | Files |
|---|---|---|
| .github | agent-config | 10 |
| docs | docs | 15 |
| scripts | scripts | 3 |
| tests | tests | 11 |
| .agents | folder | 1 |
| .claude-plugin | folder | 1 |
| benchmarks | folder | 8 |
| integrations | folder | 324 |
| loadtest | folder | 3 |
| pensyve-benchmarks | folder | 17 |
| pensyve-cli | folder | 3 |
| pensyve-core | folder | 90 |
| pensyve-go | folder | 10 |
| pensyve-mcp | folder | 6 |
| pensyve-mcp-gateway | folder | 36 |
| pensyve-mcp-tools | folder | 5 |
| pensyve-python | folder | 19 |
| pensyve-ts | folder | 11 |
| pensyve-wasm | folder | 4 |
| pensyve_server | folder | 3 |
| results | folder | 6 |
Requirements
Runtimes: Python >=3.10, Rust
Databases: PostgreSQL, SQLite
Works with: Claude Code, OpenAI Codex, GitHub Copilot, Cursor, Kiro, OpenCode, Amp, Cline, JetBrains Junie, Devin (ex-Windsurf)
Set it up
This brain runs code automatically: 8 hooks (claude-code, codex). Review integrations/claude-code/hooks/hooks.json, integrations/codex-plugin/hooks/hooks.json before opening the folder in Claude Code. No step below turns a hook on for you.
integrations/claude-code/hooks/hooks.json:cat "${CLAUDE_PLUGIN_ROOT}/hooks/session-start.md"integrations/claude-code/hooks/hooks.json:cat "${CLAUDE_PLUGIN_ROOT}/hooks/post-tool-write-edit.md"integrations/claude-code/hooks/hooks.json:cat "${CLAUDE_PLUGIN_ROOT}/hooks/post-tool-bash.md"integrations/claude-code/hooks/hooks.json:cat "${CLAUDE_PLUGIN_ROOT}/hooks/pre-compact.md"integrations/claude-code/hooks/hooks.json:cat "${CLAUDE_PLUGIN_ROOT}/hooks/stop.md"integrations/claude-code/hooks/hooks.json:cat "${CLAUDE_PLUGIN_ROOT}/hooks/user-prompt-submit.md"integrations/codex-plugin/hooks/hooks.json:cat "${PLUGIN_ROOT}/hooks/session-start.md"integrations/codex-plugin/hooks/hooks.json:cat "${PLUGIN_ROOT}/hooks/user-prompt-submit.md"
- Not scanned by AutoGineerThe author’s MCP configuration (README.md) also starts it in a way AutoGineer does not offer. Read that file: AutoGineer did not scan that path; what it runs is listed under “This brain runs”. https://github.com/major7apps/pensyve/blob/d2c56fa44d1ee7b2d504f0008c6b9c860bc7d25f/README.md
- Not scanned by AutoGineerThe author’s MCP configuration (smithery.yaml) also starts it in a way AutoGineer does not offer. Read that file: AutoGineer did not scan that path; what it runs is listed under “This brain runs”. https://github.com/major7apps/pensyve/blob/d2c56fa44d1ee7b2d504f0008c6b9c860bc7d25f/smithery.yaml
- Not scanned by AutoGineerThe author’s README also describes installing it as the PyPI package pensyve. Read its install section: AutoGineer did not scan that path. https://github.com/major7apps/pensyve/blob/d2c56fa44d1ee7b2d504f0008c6b9c860bc7d25f/README.md#python-quick-start
- Not scanned by AutoGineerThe author also offers a container image (ghcr.io/major7apps/pensyve-mcp:5.0.0). https://github.com/major7apps/pensyve/tree/d2c56fa44d1ee7b2d504f0008c6b9c860bc7d25f
Created by Major 7 Apps LLC. Published in major7apps/pensyve. Licence: Apache License 2.0.
Usage
AutoGineer overview, from the brain’s own files
- Add persistent memory to Python-based AI agents using the Python SDK.
- Connect AI coding assistants like Claude Code, Cursor, or Codex to a shared memory store via the MCP server.
- Share a centralized memory store across multiple services using the HTTP gateway.
- Integrate with LangChain or LangGraph frameworks for enhanced agent memory capabilities.
- Perform semantic search and retrieval of past conversations or facts via the command-line interface.
- “Save a fact about the user's preference for dark mode.”
- The agent stores the fact in the local SQLite database, allowing it to recall this preference in future sessions.
- “Recall previous editor settings for the current project.”
- The agent retrieves relevant facts or conversation history from the memory store and uses them to inform its current task.
Documentation by Major 7 Apps LLC
Pensyve
Image: CI Image: License: Apache 2.0 Image: Python 3.10+ Image: Rust 1.94+
Pensyve is an open-source runtime for persistent AI agent memory. It stores facts, conversations, observations, and action outcomes so an agent can retrieve them in later sessions. You can use it through Python, a Model Context Protocol (MCP) server, a command-line tool, or a REST API.
The Rust engine uses SQLite for local storage and runs embedding models locally to search by meaning. TypeScript and Go clients connect to a gateway you run yourself. Local use does not require a Pensyve account or API key. Your application or client integration calls Pensyve to save and retrieve memories.
Choose a setup
| Use case | Start here |
|---|---|
| Add persistent memory to a Python agent | Python quick start |
| Give Claude Code, Cursor, or Codex access to memory tools | MCP server and client setup guides |
| Share a memory store through TypeScript, Go, or REST | HTTP gateway |
| Use a LangChain or LangGraph adapter | Python integration or TypeScript integration |
Project status
Pensyve Cloud closed on October 1, 2026. The open-source project continues in this repository under the Apache 2.0 license, with the engine, SDKs, integrations, and documentation available here.
Pensyve is in maintenance mode. Releases cover security fixes and dependency updates, with no new features planned. See the maintenance policy for contribution and support expectations. If you have a saved Cloud export, the self-hosting guide explains how to use it with your own gateway.
What you can do
Store facts about users, projects, or other named entities, and record conversations as episodes.
Search memories using text matching, embeddings, and relationships between entities.
Record observations and action outcomes, and use consolidation to promote repeated facts and update memory retention.
Keep data in local SQLite storage, or run the HTTP gateway with SQLite or PostgreSQL.
Embedding models may download when first loaded unless they are already cached. Running without network access requires preparing the model files first. See the model setup and deployment guide.
How agent memory works
Pensyve stores memory outside the language model. Your application saves facts or conversations, searches for relevant records, and adds the results to a later prompt. Reusing the same storage path and namespace lets the agent retrieve information across processes and sessions. Saving a memory does not train the language model or change its weights.
The store holds four kinds of records: facts (semantic memory), conversations (episodic memory), action outcomes (procedural memory), and observations. Your integration decides what to record and when to retrieve it. Recording a conversation does not automatically turn it into a successful procedure. See how storage and retrieval work and the usage recipes for the APIs and their limits.
Python quick start
Python 3.10 or newer is required.
pip install pensyveCreate a local store, save a fact, and retrieve it:
import pensyve
p = pensyve.Pensyve(path="./memories", namespace="my-agent")
user = p.entity("user", kind="user")
p.remember(
entity=user,
fact="Prefers dark mode and vim keybindings",
confidence=0.95,
)
for memory in p.recall("editor preferences", entity=user):
print(memory.content)Reuse the same path and namespace in a later process to retrieve saved memories. The Python SDK runs the engine in your process and does not need the HTTP gateway.
You can also record a conversation with the same p and user:
with p.episode(user) as episode:
episode.message("user", "Use dark mode in my editor")
episode.message("agent", "I updated the editor settings")
episode.outcome("success")
groups = p.recall_grouped("editor settings", limit=10)
for group in groups:
for memory in group.memories:
print(memory.content)recall_grouped() groups results by source session for use in an agent's
prompt. See the Python SDK guide and
recipes for more examples.
MCP server
Pensyve's local MCP memory server lets clients such as Claude Code, Codex, and Cursor store and retrieve memories across sessions. From a checkout of this repository, install the server with Rust 1.94 or newer:
git clone https://github.com/major7apps/pensyve.git
cd pensyve
cargo install --path pensyve-mcp --lockedMake sure Cargo's binary directory is on your client's PATH, then add a
stdio server entry to its MCP configuration:
{
"mcpServers": {
"pensyve": {
"command": "pensyve-mcp",
"args": ["--stdio"],
"env": {
"PENSYVE_NAMESPACE": "my-project"
}
}
}
}The server provides tools such as pensyve_remember, pensyve_recall,
pensyve_observe, and pensyve_inspect. It stores data locally and needs no
API key. The configuration file location depends on your client. See the
MCP setup guide and
integration guides.
HTTP gateway
Run the gateway to access Pensyve over REST or MCP HTTP, including from the TypeScript and Go SDKs. From the repository root, start a local instance:
HOST=127.0.0.1 PENSYVE_API_KEYS=psy_local_example \
cargo run --release -p pensyve-mcp-gatewayIn another terminal, save and recall a fact:
curl http://localhost:3000/v1/remember \
-H "Authorization: Bearer psy_local_example" \
-H "Content-Type: application/json" \
-d '{"entity":"user","fact":"Prefers Python","confidence":0.95}'
curl http://localhost:3000/v1/recall \
-H "Authorization: Bearer psy_local_example" \
-H "Content-Type: application/json" \
-d '{"query":"programming language","entity":"user"}'The MCP HTTP endpoint is http://localhost:3000/mcp. Use the same API key in
your client's Authorization: Bearer header.
The example key is for local testing. For a deployment, choose your own key
with the required psy_ prefix and configure HTTPS and allowed hosts. See
the self-hosting guide,
gateway configuration, and
security documentation.
SDKs and integrations
The TypeScript and Go SDKs require a running gateway. Configure its URL and, when authentication is enabled, an API key from that gateway.
| Interface | Installation or guide |
|---|---|
| Python | pip install pensyve, SDK guide |
| TypeScript | npm install @pensyve/sdk, SDK guide |
| Go | go get github.com/major7apps/pensyve/pensyve-go/v5@latest, SDK guide |
| Claude Code | Plugin setup |
| Cursor | MCP setup and rules |
| Codex | Plugin setup |
| LangChain and LangGraph | Python adapter, TypeScript adapter |
| Other clients and frameworks | Integration index |
Command-line tool
From the repository root, install the CLI and inspect its commands:
cargo install --path pensyve-cli --locked
pensyve --help
pensyve status
pensyve recall "editor preferences" --entity userThe binary is named pensyve. Its default output is JSON, and --format text
selects text output. The CLI uses its own default local storage location;
it does not automatically open the ./memories directory from the Python
example. See the CLI source and command definitions
for storage and namespace options.
Documentation
| Guide | Contents |
|---|---|
| Documentation index | Choose a setup, find API guides, and identify historical plans |
| Getting started | Setup by client or SDK |
| Self-hosting | Gateway deployment, model files, backups, and saved Cloud exports |
| Recipes | Recall, facts, episodes, observations, and other API examples |
| Architecture | Storage, retrieval, and component boundaries |
| Security | Authentication, namespace isolation, and execution limits |
| Reliability | Tests and runtime guarantees |
| Changelog | Release history and breaking changes |
Development and contributions
Start with CONTRIBUTING.md for prerequisites and setup. From the repository root:
uv sync --extra dev
make build
make checkmake build compiles Rust and builds the Python extension. make check
runs the Rust and Python lint and test commands. TypeScript and Go have
separate checks documented in the contribution guide.
Issues and pull requests follow the maintenance policy. Report security issues through private vulnerability reporting.
License
Pensyve is licensed under Apache 2.0.
integrations/hermes/LICENSE
Shown as plain text.
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- pensyve-core/tests/test_consolidation_supersession_guard.rs (not served here)
- pensyve-core/tests/test_diversity.rs (not served here)
- pensyve-core/tests/test_embedding_migration.rs (not served here)
- pensyve-core/tests/test_embedding_space.rs (not served here)
- pensyve-core/tests/test_extractor_cancellation.rs (not served here)
- pensyve-core/tests/test_gate_wiring.rs (not served here)
- pensyve-core/tests/test_intent_router.rs (not served here)
- pensyve-core/tests/test_migration_idempotency.rs (not served here)
- pensyve-core/tests/test_ms_card_v2.rs (not served here)
- pensyve-core/tests/test_multi_session_card.rs (not served here)
- pensyve-core/tests/test_namespace_scoping.rs (not served here)
- pensyve-core/tests/test_no_network_invariants.rs (not served here)
- pensyve-core/tests/test_pensyve_handle_policy_propagation.rs (not served here)
- pensyve-core/tests/test_retrieval_card_trait.rs (not served here)
- pensyve-core/tests/test_schema_v2_migration.rs (not served here)
- pensyve-core/tests/test_schema_v4_migration.rs (not served here)
- pensyve-core/tests/test_schema_v5_migration.rs (not served here)
- pensyve-core/tests/test_schema_v6_migration.rs (not served here)
- pensyve-core/tests/test_single_session_user_card.rs (not served here)
- pensyve-core/tests/test_supersession_card.rs (not served here)
- pensyve-core/tests/test_typed_slots.rs (not served here)
- pensyve-go/LICENSE
- pensyve-go/README.md (not served here)
- pensyve-go/client.go (not served here)
- pensyve-go/client_test.go (not served here)
- pensyve-go/episode.go (not served here)
- pensyve-go/errors.go (not served here)
- pensyve-go/go.mod (not served here)
- pensyve-go/retry.go (not served here)
- pensyve-go/retry_test.go (not served here)
- pensyve-go/types.go (not served here)
- pensyve-mcp-gateway/Cargo.toml (not served here)
- pensyve-mcp-gateway/Dockerfile (not served here)
- pensyve-mcp-gateway/README.md (not served here)
- pensyve-mcp-gateway/entrypoint.sh (not served here)
- pensyve-mcp-gateway/models/manifest.sha256 (not served here)
- pensyve-mcp-gateway/models/revisions.env (not served here)
- pensyve-mcp-gateway/scripts/fetch-model-bundle.sh (not served here)
- pensyve-mcp-gateway/scripts/test-model-bundle.sh (not served here)
- server.json
- smithery.yaml
Advantages
- Provides persistent, cross-session memory that functions independently of the language model's training. evidence: README.md
- Supports multiple integration methods including Python, MCP, and REST API for flexibility across different agent architectures. evidence: README.md
Limitations
- Embedding models may require initial download, and running without network access requires pre-caching model files. evidence: README.md
- The project is in maintenance mode, meaning no new features are planned, with updates limited to security and dependency maintenance. evidence: README.md
Security
Risk level: No risk found
This brain runs
Agent instructions that run or fetch something (48)
AGENTS.md:**`README.md`** — overview, install, quick-start examples.AGENTS.md:**`docs/GETTING_STARTED.md`** — first-run walkthrough for new contributors.AGENTS.md:make build # Full build (Rust + PyO3 into .venv)AGENTS.md:make test # Rust + Python testsAGENTS.md:make lint # clippy --workspace + ruff check + pyrightAGENTS.md:make format # cargo fmt + ruff formatAGENTS.md:make check # Rust + Python lint and tests; run SDK checks separatelyAGENTS.md:.venv/bin/pytest tests/python/ -v # Python tests (requires `make build` first)AGENTS.md:cargo run -p pensyve-mcp-gateway # HTTP gateway (port 3000)AGENTS.md:cargo run -p pensyve-cli -- recall "q" # CLIAGENTS.md:cargo run -p pensyve-mcp # MCP stdio serverAGENTS.md:Python env setup: `uv sync --extra dev && uv run maturin develop --manifest-path pensyve-python/Cargo.toml`AGENTS.md:Use `git add` with specific filenames.AGENTS.md:Run `make check` before pushing.integrations/antigravity-plugin/skills/context-loader/SKILL.md:If the user explicitly opts into cross-entity discovery, run targeted recall queries with `types: ["episodic", "procedural", "semantic"]`, `limit: 5`, and label the results as top matches ranked by relevance/RRF score.integrations/antigravity-plugin/skills/memory-informed-refactor/SKILL.md:Run multiple `pensyve_recall` queries to gather comprehensive context about the target within the shared recall budget:integrations/antigravity-plugin/skills/memory-review/SKILL.md:If no entity is specified, run a broad `pensyve_recall` query with `"*"` (limit: 50) to discover entities, then inspect the top entities found.integrations/antigravity-plugin/skills/session-memory/SKILL.md:Then run `pensyve_recall` with a query matching the sanitized candidate fact.integrations/claude-code/skills/context-loader/SKILL.md:Run the following `pensyve_recall` queries to gather session context:integrations/claude-code/skills/memory-informed-design/SKILL.md:If the design process revealed a reusable way to evaluate similar decisions (e.g., "run spike vs.integrations/claude-code/skills/memory-informed-longitudinal-work/SKILL.md:| What you learned | Type | Example | |---|---|---| | Per-run outcome (what this run showed) | episodic | "Run N+1: V7r accuracy improved +3.5% with new threshold" | | Stable truth about the system | semantic | "Haiku classifier plateaus above temp 0.3" | | Reusable experiment procedure …[+83 chars]integrations/claude-code/skills/memory-informed-longitudinal-work/SKILL.md:When a run ends with an unresolved question ("X improved but we don't know why"), capture it as an episodic observation with open-question provenance:integrations/claude-code/skills/memory-informed-longitudinal-work/SKILL.md:Before Stop fires, briefly summarize (inline, 3-5 lines): what this run taught us vs.integrations/claude-code/skills/memory-informed-refactor/SKILL.md:Run multiple `pensyve_recall` queries to gather comprehensive context about the target:integrations/claude-code/skills/memory-review/SKILL.md:If no entity is specified, run a broad `pensyve_recall` query with `"*"` (limit: 50) to discover entities, then inspect the top entities found.integrations/claude-code/skills/memory-review/SKILL.md:**Consolidation**: Suggest running `/consolidate` (this skill does not run consolidation directly).integrations/claude-code/skills/session-memory/SKILL.md:Before storing, run `pensyve_recall` with a query matching the candidate fact to check for duplicates.integrations/codex-plugin/AGENTS.md:| What you learned | Type | Call | |---|---|---| | Per-run outcome | episodic | `pensyve_observe(..., content: "[proactive/in-flight/tier-1] Run N+1: ...", source_entity: "codex", about_entity: <entity>, content_type: "text")` | | Stable truth | semantic | `pensyve_remember(entity, fac …[+280 chars]integrations/codex-plugin/AGENTS.md:Before wrapping: summarize what this run taught vs.integrations/codex-plugin/skills/context-loader/SKILL.md:Run the following `pensyve_recall` queries to gather session context:integrations/codex-plugin/skills/memory-informed-refactor/SKILL.md:Run multiple `pensyve_recall` queries to gather comprehensive context about the target:integrations/codex-plugin/skills/memory-review/SKILL.md:If no entity is specified, run a broad `pensyve_recall` query with `"*"` (limit: 50) to discover entities, then inspect the top entities found.integrations/codex-plugin/skills/session-memory/SKILL.md:Before storing, run `pensyve_recall` with a query matching the candidate fact to check for duplicates.integrations/hermes/AGENTS.md:| What you learned | Type | Call | |---|---|---| | Per-run outcome | episodic | `pensyve_observe(..., content: "[proactive/in-flight/tier-1] Run N+1: ...", source_entity: "hermes", about_entity: <entity>, content_type: "text")` | | Stable truth | semantic | `pensyve_remember(entity, fa …[+282 chars]integrations/hermes/AGENTS.md:Before wrapping: summarize what this run taught vs.integrations/openclaw-plugin/AGENTS.md:| What you learned | Type | Call | |---|---|---| | Per-run outcome | episodic | `pensyve_observe(..., content: "[proactive/in-flight/tier-1] Run N+1: ...", source_entity: "openclaw", about_entity: <entity>, content_type: "text")` | | Stable truth | semantic | `pensyve_remember(entity, …[+286 chars]integrations/openclaw-plugin/AGENTS.md:Before wrapping: summarize what this run taught vs.integrations/opencode-plugin/AGENTS.md:| What you learned | Type | Call | |---|---|---| | Per-run outcome | episodic | `pensyve_observe(..., content: "[proactive/in-flight/tier-1] Run N+1: ...", source_entity: "opencode", about_entity: <entity>, content_type: "text")` | | Stable truth | semantic | `pensyve_remember(entity, …[+286 chars]integrations/opencode-plugin/AGENTS.md:Before wrapping: summarize what this run taught vs.pensyve-ts/CLAUDE.md:Use `bun <file>` instead of `node <file>` or `ts-node <file>`pensyve-ts/CLAUDE.md:Use `bun test` instead of `jest` or `vitest`pensyve-ts/CLAUDE.md:Use `bun build <file.html|file.ts|file.css>` instead of `webpack` or `esbuild`pensyve-ts/CLAUDE.md:Use `bun install` instead of `npm install` or `yarn install` or `pnpm install`pensyve-ts/CLAUDE.md:Use `bun run <script>` instead of `npm run <script>` or `yarn run <script>` or `pnpm run <script>`pensyve-ts/CLAUDE.md:Use `bunx <package> <command>` instead of `npx <package> <command>`pensyve-ts/CLAUDE.md:Use `bun test` to run tests.pensyve-ts/CLAUDE.md:Then, run index.tspensyve-ts/CLAUDE.md:bun --hot ./index.ts
MCP servers (2)
README.md(pensyve):pensyve-mcp --stdiointegrations/codex-plugin/.mcp.json(pensyve):pensyve-mcp --stdio
Hooks (8)
integrations/claude-code/hooks/hooks.json(SessionStart):cat "${CLAUDE_PLUGIN_ROOT}/hooks/session-start.md"integrations/claude-code/hooks/hooks.json(PostToolUse):cat "${CLAUDE_PLUGIN_ROOT}/hooks/post-tool-write-edit.md"integrations/claude-code/hooks/hooks.json(PostToolUse):cat "${CLAUDE_PLUGIN_ROOT}/hooks/post-tool-bash.md"integrations/claude-code/hooks/hooks.json(PreCompact):cat "${CLAUDE_PLUGIN_ROOT}/hooks/pre-compact.md"integrations/claude-code/hooks/hooks.json(Stop):cat "${CLAUDE_PLUGIN_ROOT}/hooks/stop.md"integrations/claude-code/hooks/hooks.json(UserPromptSubmit):cat "${CLAUDE_PLUGIN_ROOT}/hooks/user-prompt-submit.md"integrations/codex-plugin/hooks/hooks.json(SessionStart):cat "${PLUGIN_ROOT}/hooks/session-start.md"integrations/codex-plugin/hooks/hooks.json(UserPromptSubmit):cat "${PLUGIN_ROOT}/hooks/user-prompt-submit.md"
Attribution and licence
- Created by
- Major 7 Apps LLC
- Declared authors
- Major7 Apps (declared in pyproject.toml)
- Publisher
- Major 7 Apps LLC (Official Major 7 Apps LLC project)
- Source
- major7apps/pensyve · commit d2c56fa
- Licence
- Apache License 2.0 (files served here)
- ATTRIBUTION.md (written by AutoGineer)
- Synced
AutoGineer is not affiliated with or endorsed by Major 7 Apps LLC.
Install from source
482 of its files are not served here, so there is no ZIP. major7apps/pensyve at d2c56fa