Synapse Openclaw Plugin
OpenClaw plugin for Synapse research orchestration β SSE notifications, MCP tools, experiment lifecycle, autonomous loop
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Research Orchestration for Human Researchers and AI Agents
Synapse is a research orchestration platform that brings human researchers and AI agents together. It manages the full research lifecycle β from literature review and question formulation through experiment execution and report generation β with built-in agent management, compute orchestration, and real-time observability.
Inspired by the AI-DLC (AI-Driven Development Lifecycle) methodology and built upon Chorus.
Milestone: We reproduced Karpathy's auto-research task and outcome pattern on Synapse. Unlike designing the behavior of a single agent through a
program.md, Synapse treats autonomous experimentation as a native platform capability. Research projects, experiment lifecycles, compute allocation, and context accumulation are all managed through structured MCP tools, creating dynamic contexting. Research memory is persisted in the platform and exposed as tool-based state that agents can call, fetch, and expand on demand according to the current situation, rather than forcing everything to be loaded into an agent's context window up front.
What's New
v0.7.0 β PGlite Local Mode & Server Action Fix (2026-04-20) Β π΄ New
- One-command local install:
npm install -g @synapse-research/synapse && synapseβ zero-dependency PGlite mode, no PostgreSQL or Redis required - Fixed critical bug where research question creation silently failed in standalone mode (server actions replaced with REST API)
- Fixed cache directory permission error when installed globally via
sudo npm install -g - Completion notifications now show a checkmark icon instead of a plain dot
- Health check no longer reports "degraded" when Redis is configured but not yet lazy-loaded
v0.6.1 β Experiment Board UI Polish (2026-04-15)
- Project descriptions on the dashboard now preserve intentional blank lines when expanded, improving readability for structured briefs
- The experiment plan side panel now renders above the detail sheet overlay, uses the shared panel background, and allows normal text selection
- The autonomous experiment entry button is more prominent, with refreshed blue styling and a yellow lightning icon for faster scanning
- The experiment detail sheet is wider on large screens, giving experiment plans, results, and progress logs more room
v0.6.0 β Agent Types & Research Copilot (2026-04-12)
- Agents now have a
typefield (OpenClaworClaude Code) with internal transport mapping β Web UI dispatch features only show realtime-capable agents - Claude Code Research Copilot: SessionStart presents projects with progress summaries and guides users through the research lifecycle (paper search β deep research β questions β experiments β analysis)
synapse_checkinreturns assigned experiments and project progress for intelligent workflow suggestions- Agent management UI gains a type selector and badge display
v0.5.1 β DeepXiv Integration (2026-04-10)
- Paper search now uses DeepXiv hybrid search (BM25 + vector) over arXiv, with arXiv API as fallback. Removed Semantic Scholar and OpenAlex.
- Agents can read full paper content via progressive reading tools:
synapse_read_paper_brief,synapse_read_paper_head,synapse_read_paper_section,synapse_read_paper_full - Deep research literature reviews are now based on actual paper content, not just abstracts
- DeepXiv token configurable from Settings > Integrations
v0.5.0 β Autonomous Loop & Related Works (2026-03-29)
- Autonomous experiment loop: agents propose β humans review β agents execute
- Related Works page with auto-search, manual arXiv URL addition, and deep research reports
- Experiment live status tracking (sent/ack/checking/queuing/running)
- Compute pool binding per project
Table of Contents
Vibe Research
Vibe Coding showed that people can describe intent and let AI handle execution. Vibe Research applies that same shift to the research lifecycle:
Humans set direction. Agents execute, report, propose, and iterate. Humans review, steer, and decide.
Stages of Agent Autonomy in Research
Synapse is built to move research teams through these stages deliberately.
- Streamline Stage 1 by making experiment execution, compute access, result capture, and reporting a default operational loop instead of a pile of manual handoffs.
- Make Stage 2 reliable by keeping context, papers, experiments, progress, and review in one system, so agents can act independently without drifting off-task.
- Make Stage 3 feasible by building the control plane for project-level delegation: structured context, observability, orchestration, permissions, and human steering when it matters most.
Features
Project Workspace
Synapse gives each research project a shared operational home for briefs, datasets, evaluation methods, research questions, experiments, reports, and rolling synthesis. Instead of bouncing across docs, scripts, spreadsheets, and chat threads, humans and agents work from the same source of truth.
Related Works and Deep Research
- Paste an arXiv URL to add a paper with metadata fetched automatically
- Assign a
pre_researchagent to search Semantic Scholar and build a project paper set - Generate literature review documents directly inside the project workspace
Research Question Canvas
- Organize research questions in a canvas-style hierarchy with parent-child structure
- Track question progress from exploration to experiment creation and completion
- Keep question context connected to the experiments and reports it produces
Experiment Execution Board
- Five-column experiment pipeline:
draftβpending_reviewβpending_startβin_progressβcompleted - Live status badges for agent execution:
sent,ack,checking_resources,queuing,running - Progress reporting through
synapse_report_experiment_progress - Autonomous loop support, so agents can propose the next experiments when queues are empty
Agent Management
- API-key based agent access to Synapse MCP tools
- User-scoped agent ownership, key management, and session observability
Five agent permission roles (composable):
| Permission | Responsibility |
|---|---|
| Pre-research | Literature search, related works discovery via Semantic Scholar |
| Research | Propose research questions, hypothesis formulation |
| Experiment | Execute experiments, allocate compute, report progress |
| Report | Generate experiment reports, literature reviews, synthesis documents |
| Admin | Create/delete projects, manage groups, review research questions |
Compute Orchestration
- Compute pools, node inventory, GPU reservations, and per-project pool binding
- Managed SSH access bundles for secure compute access from agent environments
- Keep agents aligned with available resources before, during, and between runs
Reports, Synthesis, and MCP Surface
- Agents write experiment reports in the context of the project instead of filling rigid templates
- Synapse maintains project-level synthesis documents as research evolves
- 70+ MCP tools cover project context, literature search, experiment execution, compute access, and collaboration
Getting Started
Quick Start (No Dependencies)
npm install -g @synapse-research/synapse
synapse
Open http://localhost:13000 and log in with admin@synapse.local / synapse.
This runs Synapse with an embedded PGlite database β no PostgreSQL or Redis required. Data is stored in ~/.synapse/data.
Options: synapse --port 3000 --data-dir /path/to/data --help
Docker
git clone https://github.com/Vincentwei1021/Synapse.git
cd Synapse
export DEFAULT_USER=admin@example.com
export DEFAULT_PASSWORD=changeme
docker compose up -d
Open http://localhost:3000 and log in.
Local Development
Prerequisites: Node.js 22+, pnpm 9+, PostgreSQL
cp .env.example .env
# Edit .env to configure DATABASE_URL
pnpm install
pnpm db:push
pnpm dev
open http://localhost:3000
The default .env.example includes a built-in login account:
| Field | Value |
|---|---|
dev@synapse.local | |
| Password | synapse123 |
You can change these by editing DEFAULT_USER and DEFAULT_PASSWORD in your .env file. When both variables are set, Synapse enables a simple email/password login without requiring OIDC configuration.
Connect AI Agents
Option 1: OpenClaw (Recommended)
openclaw plugins install @vincentwei1021/synapse-openclaw-plugin
Then configure in OpenClaw settings: set synapseUrl and apiKey.
Tip: If you encounter
Request timed out before a response was generated, increase the idle timeout in your OpenClaw config: setagents.defaults.llm.idleTimeoutSecondsto300.
Option 2: Claude Code Plugin
claude
/plugin marketplace add Vincentwei1021/Synapse
/plugin install synapse@synapse-plugins
Set environment variables:
export SYNAPSE_URL="http://localhost:3000"
export SYNAPSE_API_KEY="syn_your_api_key"
Option 3: Manual MCP Configuration
Create .mcp.json in your project root:
{
"mcpServers": {
"synapse": {
"type": "http",
"url": "http://localhost:3000/api/mcp",
"headers": {
"Authorization": "Bearer syn_your_api_key"
}
}
}
}
Progress
Implemented
- Research-project workspace with briefs, datasets, evaluation methods, experiments, documents, and rolling synthesis
- Research-question hierarchy and canvas-style question management
- Five-stage experiment board with live execution status and progress updates
- Agent-generated experiment reports and project-level synthesis documents
- Related works workflow with Semantic Scholar search, paper collection, and deep research reports
- Composable agent permissions:
pre_research,research,experiment,report,admin - User-scoped agent ownership, API keys, and agent session observability
- Compute pools, node inventory, GPU reservations, and project-level pool binding
- Managed node access bundles for secure agent access to compute
- Autonomous experiment proposal loop for keeping project momentum when queues empty out
- Comments, mentions, notifications, and real-time SSE updates
- 70+ MCP tools covering context retrieval, literature, experiments, compute, and collaboration
Planned
- Steer running agents during an
in_progressexperiment - Stream raw experiment logs back into the panel in real time
- Run experiments in parallel via isolated git trees / worktrees
- Strengthen evaluation loops with first-class baselines and accept/reject criteria
- Track reproducibility artifacts: code revision, config, outputs, and environment
Documentation
| Document | Description |
|---|---|
| CLAUDE.md | Development guide and coding conventions |
| Architecture | Technical architecture |
| MCP Tools | MCP tools reference |
| OpenClaw Plugin | Plugin design and hooks |
| Docker | Docker deployment guide |
License
AGPL-3.0 β see LICENSE.txt
