Grounded Context for AI Agents
Snipara retrieves and compresses relevant documentation, decomposes complex queries with RELP, and delivers cited answers that reduce hallucinations. Verified learnings persist as agent memory.
The Problem
Feeding raw codebases to LLMs doesn't scale.
LLMs fail when fed raw repos
Dumping entire codebases leads to confusion, hallucination, and irrelevant answers.
Context windows wasted on noise
Most of what you send is irrelevant. Your token budget goes to files that don't matter.
Costs scale with tokens, not value
You pay for every token whether it helps or not. Larger codebases = larger bills.
Answers are hard to verify
No citations, no sources. When the LLM is wrong, you have no way to trace why.
Agents forget between sessions
Every conversation starts fresh. Past decisions, user preferences, learned patterns — all gone.
How Snipara Works
We don't run an LLM. We optimize and deliver the most relevant context to your LLM.
Tokens in your docs
Snipara optimizes
Relevant context
Query Pipeline
Anti-Hallucination by Design
- ✓Answers are grounded in retrieved documentation — not generated from thin air
- ✓Every claim is linked to a source section you can verify
- ✓If information isn't in your docs, the system says so instead of guessing
- ✓Agent memory only stores source-linked, verified outcomes
Project Indexing
Files, docs, GitHub repos — indexed and embedded automatically
Query Decomposition
RELP breaks complex queries into focused sub-queries
Hybrid Search
Semantic embeddings + keyword matching for precision
Context Compression
Deduplication and ranking to fit your token budget
Cited Outputs
Every answer linked to source sections you can verify
Measurable Results
Benchmarks run on internal and open-source codebases. Your results may vary.
See It In Action
Try the interactive demo — no signup required. Watch how a query transforms into optimized context.
Works With Your Stack
Native MCP support for Claude Code, Cursor, ChatGPT, and more. Or use the REST API.
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/YOUR_PROJECT_ID",
"headers": {
"X-API-Key": "YOUR_API_KEY"
}
}
}
}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.
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
- 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.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 →