Your AI tools change. Your project memory stays.
Snipara gives every AI coding agent shared project context, persistent memory and reusable knowledge across users, sessions and models.
From codebases to organizational knowledge, Snipara keeps AI context persistent and reusable.
Snipara does not run your model. It gives your model the project context it needs.
AI agents forget your project between sessions.
The failure is not only memory loss. It is fragmented context across tools, users, models and repeated project discovery.
Agents forget between sessions
A fresh run often starts by scanning the same files, rediscovering the same architecture and rebuilding the same prompt context.
Memory is trapped inside tools
Claude Code, Codex, Cursor and custom clients each keep their own view. Useful context rarely survives tool changes cleanly.
Context windows are not project memory
A larger window still needs selection, source authority, structure and continuity across users, sessions and models.
Teams repeat decisions
Architecture notes, conventions, API patterns and review rules are easy to miss when they are not retrieved at the right moment.
One shared memory layer for every AI coding agent.
Snipara creates persistent, shared, verifiable project memory that any compatible AI client can reuse. Your model reasons. Snipara supplies the project context.
Connect, index, retrieve, review.
The value is visible: ingestion, chunking, embeddings, source authority, retrieval and reviewed memory stay separate from the model that executes the work.
Connect your project
Link docs, repos, standards, decisions and useful historical context to a project-scoped memory layer.
Snipara indexes useful context
Files become searchable, source-linked context and reviewable memory candidates before they persist.
Your AI client asks for context
Claude Code, Codex, Cursor, Windsurf, VS Code, or an API client requests context through MCP or API.
Snipara returns the relevant memory
The active agent receives task-scoped project context, source-aware decisions, conventions and current architecture notes.
New decisions can be reviewed
Important outcomes can be approved, sourced, updated, or revoked so the next run starts from validated project state.
$ snipara context
Task: add SSO to the existing app.
Need: auth architecture, schema constraints, route conventions, reviewed decisions.
The same context infrastructure works for engineering and business workflows.
Snipara starts where AI coding agents feel the pain first. The underlying layer is broader: persistent, structured, reviewable context for AI-assisted work.
Persistent project memory for AI coding agents.
Persistent organizational memory for AI-assisted business workflows.
Memory belongs to the project, not the model.
Most AI systems attach memory to a single user, session, or model. Snipara keeps memory project-scoped and reviewable so authorized users and compatible AI clients can reuse the same validated state.
A bigger window is not a memory strategy.
The question is not how much text you can paste. The question is which source-aware project context should guide the current task.
More tokens, still no memory system
A large dump can include stale notes, irrelevant files, duplicated decisions and no clear authority for what the agent should trust.
The right context, with structure
The agent receives a compact, sourced context package selected for the task instead of a raw archive of everything nearby.
We measured the difference on Snipara itself.
The full indexed corpus was 601,207 tokens. In the latest OpenAI GPT-4.1 rerun, Snipara selected about 2,981 tokens per task instead of sending a 32K raw window, improved answer quality, and reduced claims unsupported by the supplied context. The interesting model result: with Snipara, GPT-4.1 and GPT-5.5 landed in the same quality band.
The primary published run is OpenAI gpt-4.1 on 12 project-context tasks. The claim-grounding audit counts unsupported factual claims against the exact context each answer path received; it is not a standalone answer-quality score.
GPT-4.1 run: 2,981 Snipara tokens/query versus a 32K raw-window baseline.
GPT-4.1 answer quality: 8.42/10 with Snipara versus 6.25/10 raw baseline.
GPT-4.1 grounding audit: 3/69 unsupported Snipara claims versus 11/52 for raw baseline.
The headline result is source selection with better measured quality, smaller prompts, and fewer unsupported claims in the GPT-4.1 run. Snipara's real job is turning project memory into context the agent can use.
Read benchmark notesContinuity for the way teams already use agents.
Resume a feature without rediscovering the repo
Load architecture, route patterns, schema decisions and active constraints before the next coding session starts.
Follow team conventions
Retrieve style rules, testing expectations, deployment notes and repo-specific review standards.
Recover architecture decisions
Bring back sourced decisions and their rationale instead of asking the model to infer from fragments.
Get project context instantly
Give a teammate the current project state without forcing them to read every doc and closed PR first.
Survive tool changes
Keep memory reusable when the team experiments with a different agent, model, coding client, or workflow.
Built as context infrastructure, not another AI assistant.
Snipara sits between project knowledge and AI clients. It prepares and retrieves context. It does not claim ownership of the model layer.
Model-agnostic
Snipara works with the model and coding client you already use.
Project-scoped memory
Context is scoped to the repo, workspace, or project instead of one user's session.
Reviewed memory
Important memory can move through review before it becomes reusable project state.
Source authority
Retrieved context can carry source references, freshness and authority signals.
Revocable entries
Outdated decisions and conventions can be removed instead of silently living forever.
MCP and API support
Use hosted MCP for AI clients or API access for product and workflow integrations.
Add persistent execution to persistent memory.
Snipara Sandbox adds stateful execution, resumable workflows and sandboxed task continuity for long-running AI systems.
Deep dives for the workflows people search for.
Start with the product, then open the setup and integration notes that match the agent workflow in front of you.
Claude Code MCP plugin setup
Install the optional Claude Code plugin after Hosted MCP is configured, then use slash commands and Snipara skills from the coding client.
Production-ready AI code with Snipara Sandbox
See how source-grounded context, Docker execution, and tests keep AI-generated code closer to production standards.
OpenClaw and Snipara for coordinated agents
OpenClaw handles agent execution; Snipara adds shared context, memory, and coordination support for real codebases.
Free includes full context, full memory and one swarm.
The free plan should be generous enough to build the habit. Upgrade to Pro when a solo developer needs more capacity, then Team for governance, scale and shared collaboration.
- Complete context
- Complete memory
- 1 included swarm
- 3 projects
- 1,000 context queries/month
- 500 reviewed hosted memories
- Bring your own AI client
A durable context layer for agentic projects.
Snipara is a project-scoped context infrastructure layer for teams using AI coding agents. It indexes repositories, docs, architecture decisions, conventions and approved knowledge, then retrieves the right context for the current task.
The same infrastructure can carry business context: company truth, client-specific facts, proposals, diagrams, approved templates and historical precedent with source authority.
The model stays in the customer's chosen tool. The memory belongs to the project, workspace, or organization, so it remains reusable when users, sessions, tools and models change.
Clear answers before you wire it into your workflow.
Is Snipara an AI model?+
No. Snipara does not replace your LLM. It gives your AI tools shared project context and memory.
Does Snipara depend on Claude, OpenAI, or Cursor?+
No. Snipara is model-agnostic and client-agnostic.
Who owns the memory?+
The project or workspace owns the memory, not a single user or model.
What happens if we change AI tools?+
Your project memory stays reusable.
What does Snipara process?+
Project docs, code context, business context, decisions, conventions and approved knowledge. Snipara chunks, embeds and retrieves relevant context.
Is memory automatically trusted?+
No. Important memory can be reviewed, sourced and revoked.
Why not just use a bigger context window?+
Bigger context windows still need selection, structure, source authority and continuity across sessions.
One memory layer for every AI workflow.
Engineering, organizational and business knowledge, shared across users, sessions and models.