A neuroscience-inspired memory engine for AI agents featuring private vaults, smart inbox review, and natural-language forgetting. Portable, federated, and zero-dependency.
Project description
Synapse AI Memory
A local-first memory engine for AI agents. Store facts, context, and conversation history in one place, then retrieve them as structured context with policy + privacy controls.
Current release: 0.13.0
Why this release
Recent work focuses on onboarding + runtime + integrations + policy receipts. The repo now provides a cleaner zero-prompt setup path, explicit runtime mode controls, and practical auditability for policy enforcement decisions.
Install
pip install synapse-ai-memory
Optional one-step installer still available:
curl -fsSL https://synapse.ai/install.sh | bash
Quick start (recommended)
# interactive quickstart (flow does onboarding + integration probes)
synapse onboard --flow quickstart
# CI/non-interactive default setup
synapse onboard --flow quickstart --non-interactive --json
# advanced flow with explicit defaults
synapse onboard --flow advanced \
--policy-template private \
--default-scope private \
--default-sensitive on \
--enable-service \
--service-schedule daily
Python API is straightforward:
from synapse import Synapse
s = Synapse()
s.remember("I prefer dark mode")
print(s.recall("what theme?"))
Core flow model
- Vault-aware memory: isolate by
user_idwhen enabled. - Review-first path for sensitive/uncertain captures via the Inbox.
- Natural language forgetting for targeted cleanup.
- Scope + sensitive controls with conservative defaults.
- Portable formats (
.brain, checkpoints, imports/exports). - Federation primitives (
synapse federate,sync,push,pull) available when configured. - Zero-LLM indexing for core recall/ingest.
Runtime and service management
synapse up --port <port>starts the appliance daemon (MCP stdio server + HTTP JSON-RPC fallback)synapse downstops it.synapse statusshows running status and basic store stats.synapse service install|uninstall|statusmanages autostart (launchd/systemd).
Runtime defaults are local-only; integrations may add network calls only when used.
Integrations (client + contract metadata)
synapse install and synapse integrations are now split by use-case:
# quick probe + integration ops
synapse integrations list --db ~/.synapse
synapse integrations list --json
# install / verify / repair / open flows for detected clients
synapse integrations install claude
synapse integrations test cursor
synapse integrations repair windsurf
synapse integrations open continue
synapse integrations list --json includes connector contract metadata (type, tier, commands, capabilities, doctor_checks, example_prompt) for supported built-ins.
synapse install still handles broader install targets (for example telegram, ollama, nanoclaw) as a direct installer path.
Policy receipts (Phase-1)
Audit trail for permit decisions is now available from policy hooks.
synapse permit receipts --last 5
synapse permit receipts --last 5 --json
Notes:
- Receipts are written to
<db>/receipts/permit_receipts.jsonl. - Schema is stable (
synapse.permit.receipt.v1) for JSON output. - This is a Phase-1 implementation: it records enforcement decisions and reasons, but log volume and retention policies are intentionally minimal.
MCP server
The project exposes MCP tools via:
synapse serve(default appliance mode)synapse serve --http --port 8765(HTTP JSON-RPC)synapse-mcpwrapper (managed launcher at~/.synapse/bin/synapse-mcp)
Use:
synapse serve # local stdio-compatible mode
synapse serve --http --port 8765
For client integrations, see synapse install <client> and the onboarding docs.
Key CLI commands
# onboarding
synapse onboard --flow quickstart
synapse onboard --flow advanced --non-interactive
# integrations & connectors
synapse integrations list --json
synapse integrations install claude
synapse integrations test claude
synapse integrations repair claude
# policy and runtime
synapse permit receipts --last 10 --json
synapse up --port 8765
synapse down
synapse status
synapse service install
# runtime memory operations (examples)
synapse ingest "User prefers concise responses"
synapse clip "Meeting notes: ..."
synapse watch --clipboard
synapse inbox list
synapse nlforget "forget my old job" --dry-run
synapse pack --topic project-x --range 30d
Data model highlights (for API users)
- Contextual recall (
Synapse.recall) supports multiple retrieval paths and query options. - Structured triples + graph indexing are available for explainability and consistency checks.
- Contradiction detection and belief lineage expose confidence/conflicts for recall.
- Scope policy can enforce conservative output behavior at runtime.
Python examples
from synapse import Synapse
s = Synapse("~/.synapse/synapse_store")
# memory with scope
s.remember("Team deadline is Friday", scope="shared", shared_with=["team:ops"])
# compile context for downstream LLM call
pack = s.compile_context("Prepare a concise status update", budget=1800, policy="balanced")
system_prompt = pack.to_system_prompt()
# maintenance + safety
report = s.sleep(verbose=True)
print(report.to_digest())
Compatibility and links
- Docs: this README and
README_MCP.md - Examples:
examples/,docs/,integrations/ - Tests:
tests/anddocs/benchmarks/ - Installable on: macOS / Linux (and compatible Python 3.10+)
Security and licensing
See SECURITY.md. Synapse remains MIT-licensed under LICENSE.
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