30 Days of PRO Features FREE!
Your AI keeps writing code that breaks?
That's because it doesn't know your codebase. Snipara teaches AI tools about your project so they write code that actually works.
See the Difference
Same prompt, same LLM. The only difference is what context it receives.
Left: LLM generates from training data alone. Right: LLM receives optimized context from Snipara, code tested with RLM-Runtime.
"Create a JWT authentication endpoint"
How It Works
Snipara indexes your docs and delivers only the relevant context to your AI tool.
Connect your project
Upload docs, connect GitHub, or drag & drop files. We index everything automatically.
Add to your AI tool
One line of config for Claude Code, Cursor, VS Code, or any MCP-compatible client.
Ask questions, get answers
Your AI now understands your project. Code suggestions actually fit your codebase.
Try it: Ask a question about your docs
We compress your entire codebase into just the relevant context
Why developers use Snipara
Code that actually works
AI suggestions match your existing patterns, imports, and coding style.
No more hallucinations
Every answer is grounded in your actual documentation, not made up.
Works with your tools
Claude Code, Cursor, VS Code, ChatGPT — one config line and you're set.
AI remembers your decisions
Past learnings persist across sessions. No more repeating yourself.
Works With Your Stack
Native MCP support for Claude Code, Cursor, ChatGPT, and more. Or use the Python SDK.
claude mcp add snipara \
--header "X-API-Key: YOUR_API_KEY" \
https://api.snipara.com/mcp/YOUR_PROJECT_ID{
"mcpServers": {
"snipara": {
"type": "http",
"url": "https://api.snipara.com/mcp/...",
"headers": {
"X-API-Key": "YOUR_API_KEY"
}
}
}
}npx snipara-openclaw-install
# Installs:
# - Multi-agent swarms
# - Context optimization
# - Safe Python execution
# - Persistent memorypip install snipara
from snipara import Snipara
async with Snipara() as s:
result = await s.query(
"How does auth work?"
)Built for Developers
Grounded context retrieval, RELP decomposition, and cited answers — with agent memory to accelerate future queries.
Use Your Own LLM
Claude, GPT, Gemini, or any AI. We deliver grounded context, you choose the brain. Zero vendor lock-in.
Grounded Responses
Every answer is anchored to retrieved documentation with source citations. If info is missing, the system says so instead of guessing.
Semantic + Hybrid Search
Beyond keyword matching. Embedding-based similarity finds conceptually relevant content from your indexed docs.
RELP Engine
Recursive decomposition breaks complex queries into sub-queries. Handle docs 100x larger than context windows.
Agent Memory
Persist verified outcomes (summaries, decisions, conventions) linked to source documents. Future queries reuse knowledge without re-exploring.
Group Memory
Share grounded learnings across agents and teammates. One agent's verified discovery becomes project knowledge for all.
Multi-Agent Coordination
Real-time state sync and task handoffs. Coordination is grounded — agents work from the same verified context.
GitHub Auto-Sync
Connect your repo once. Docs stay indexed and current automatically on every push.
Query Caching
Repeated queries hit cache instead of re-processing. Sub-millisecond responses for common patterns.
Best Practices Discovery
AI identifies coding patterns across your projects and suggests team standards. No manual curation needed.
Shared Context Collections
Discover and access team-wide coding standards, best practices, and prompt templates. One command lists all available collections.
Session Continuity
Context persists across sessions automatically. Pick up exactly where you left off, every time.
Cross-Project Search
Search across ALL your projects with a single query. Find implementations, patterns, and code across your entire organization.
Autonomous Agent Runtime
Run observe-think-act agent loops with sandboxed code execution. Sub-LLM orchestration, cost guardrails, and full trajectory logging.
WebAssembly Execution
Execute code safely in browser-compatible WebAssembly sandboxes. No Docker required, strongest isolation.
Python SDK
Unified Python client with .snipara.toml config. Async/sync API, CLI, file watcher, and auto-feedback loop.
Webhooks
Real-time notifications when documents are indexed, queries complete, or memory is updated. Integrate with any workflow.
Team Permissions
Role-based access control for projects and collections. Admin, editor, and viewer roles with granular permissions.
Agents That Learn Your Project
Agent memory stores verified outcomes from RELP-driven, grounded retrieval runs — not speculative reasoning.
Retrieval and grounding remain the source of truth. Memory reduces repeated exploration; it never replaces it.
Store Verified Decisions
Store verified decisions and summaries from grounded runs (e.g., "auth uses JWT refresh flow; errors inherit from AuthError"). Outcomes are linked to source docs and decay over time if not re-confirmed.
agent.memory.store("auth uses JWT", source="docs/auth.md")✓ Stored: DECISION, confidence=1.0agent.memory.recall("auth flow")→ "auth uses JWT" (0.94) [cited]Share Conventions Across Agents
Share verified conventions across your agent team and teammates — so every agent starts project-aware without re-tokenizing the entire codebase.
memory.share("use AuthError", src="errors.ts")memory.recall("error handling")→ "use AuthError" (Agent1) [cited]Simple, Transparent Pricing
Start free, scale as you grow
Most teams start with context optimization to ground their AI workflows. Add agent memory when building persistent, multi-session workflows.
Grounded context retrieval, RELP decomposition, and cited answers
Free
No credit card required
Includes 30-day PRO boost free
- 100 queries/mo
- 1 projects
- 1 team members
- Keyword search
- Token budgeting
- Session persistence
- GitHub sync
Pro
Most common for solo devs
- 5,000 queries/mo
- 5 projects
- 1 team members
- Everything in Free
- Semantic + Hybrid search
- RELP decomposition
- Summary storage
Team
Shared context across team
- 20,000 queries/mo
- Unlimited projects
- 10 team members
- Everything in Pro
- Advanced planning
- Team context sharing
- Priority support
Enterprise
For larger teams
- Unlimited queries/mo
- Unlimited projects
- Unlimited team members
- Everything in Team
- 99.9% SLA guarantee
- SSO/SAML support
- Dedicated support
- Custom integrations
Persist verified outcomes and coordinate multi-agent workflows
Starter
Solo devs experimenting
- 1,000 agent memories
- 7-day retention
- Semantic recall
- 5-min cache TTL
- 1 swarm (2 agents)
- Community support
Pro
Teams building workflows
- 5,000 agent memories
- 30-day retention
- Semantic cache (L2)
- 30-min cache TTL
- 5 swarms (5 agents each)
- Task queue
- Email support
Team
Production multi-agent
- 25,000 agent memories
- 90-day retention
- 2-hour cache TTL
- 20 swarms (15 agents)
- Real-time events
- 100 pre-warm queries
- Priority support
Enterprise
Large-scale infrastructure
- Unlimited memories
- Unlimited retention
- 24-hour cache TTL
- Unlimited swarms (50 agents)
- Unlimited pre-warming
- Dedicated support
- SLA guarantee
Prefer to Self-Host?
docker compose up and get a 30-day trial of all features. No license key required.Resell Snipara to Your Clients?
Grounded context for your AI workflows in under 60 seconds.
No credit card required. Cited answers, RELP decomposition, and agent memory via MCP.
Start Free →