The context layeryour agents are missing
A context engine for large codebases, with graph-based retrieval and an MCP endpoint. Plug it into Codex, Claude Code, or Cursor so they answer system-level questions instead of grep-ing your repo.
- ~25×
- lower model cost at frontier-agent quality
- 45%
- fewer tokens with semantic search
Based on our RepoQA benchmark: CodeAlive + Qwen3.6 deep vs Claude Opus 4.8 max / Codex GPT-5.5 high.
AI writes code faster than your team can review it
Industry surveys and developer research; see references on our blog.
They Write Code Fast
AI agents read a handful of open files and run top-K vector search. They almost never see how a change ripples across services and repos.
They Don't Understand Your System
Without your architecture and business rules in scope, agents reinvent code that already exists and break conventions you've spent years setting.
They Ship Tomorrow's Legacy Today
Every shortcut compounds. Six months in, you're maintaining code nobody reviewed properly the first time.
AI made writing code cheap. It didn't make understanding code any cheaper, and that's the part teams were already short on.
What your seniors know, now queryable by everyone
CodeAlive indexes how your code actually fits together, so any engineer or agent can ask and get an answer backed by the real system.
Full Codebase Context
- Relationships between classes, methods, and modules
- Service-to-service calls and shared schemas
- Dependency graphs spanning every repo
Multi-Repository Intelligence
- Same index across 10 repos or 1,000
- Cross-repo impact analysis without context-switching
- One place to ask, regardless of where the code lives
Shared Knowledge for Humans & AI
- Engineers and agents query the same index
- Every answer cites the files and lines it came from
- No invented APIs, no fabricated functions
One platform, four products
All four share the same context engine, so reviews, research, and your MCP-aware agents see the same model of your code.
Semantic search cuts tokens by 45%
Same RepoQA benchmark. Same Qwen3.6 deep model. With CodeAlive semantic search enabled, the agent used fewer tokens and scored higher.
Qwen3.6 deep max run, 20 RepoQA tasks
Same benchmark setup, semantic_search enabled
Based on Qwen3.6-35B deep max benchmark runs over 20 RepoQA tasks; totals count every captured benchmark token per run. Real results vary by repository and task mix.
What our customers are saying
The CodeAlive's "Deep" chat really creates nice results. From a developer's perspective - joining a new project and getting their first tasks (e.g., add a new DDD Entity in a complex project) - the AI produces an accurate how-to. I like this!!!
Hauke Feddersen
CTO @ grasbyte GmbH
During our work with CodeAlive, we've found that it's beneficial for our QA by helping with writing test cases and describing bugs. It also accelerates project onboarding. A very useful product!
Zhaksylyk Ualiyev
CTO @ Esqadra Technologies
We have a pretty complex codebase that tools like Cursor and DeepWiki couldn't quite figure out on their own. I decided to index our project with CodeAlive and was pleasantly surprised, as its responses demonstrated a deeper understanding of our project's logic.
Alexander Kolotov
Blockscout
Everything works great. Performance is around o3/Deep Research level on tasks that require understanding context and framework specifics (in some areas it dove quite deep, even finding things that o3 DeepResearch couldn't find).
Sergey Loginov
Engineer
A graph, not a pile of files
Who calls what, what implements what, and what covers it with tests. CodeAlive maps the relationships in your code, so a query about payments returns the call chain, not 31 file snippets.
How CodeAlive sees your code
What teams actually do with the context engine
Eight workflows we hear about most from teams using CodeAlive in production.
What each role does with it
Engineering isn't the only team that needs to understand the code.
Built for codebases you can't fit in a context window
Generic assistants only see what fits in their context window. RAG tools return file fragments. CodeAlive indexes the graph of how your code connects and queries that.
Full codebase understanding
- Generic AI AssistantsNo
- Simple RAG ToolsPartial
- CodeAliveYes
Multi-repo support
- Generic AI AssistantsNo
- Simple RAG ToolsNo
- CodeAliveYes
Knowledge graph + relationships
- Generic AI AssistantsNo
- Simple RAG ToolsNo
- CodeAliveYes
Hybrid retrieval (vector + lexical + graph)
- Generic AI AssistantsNo
- Simple RAG ToolsPartial
- CodeAliveYes
Tracks API calls between services
- Generic AI AssistantsNo
- Simple RAG ToolsNo
- CodeAliveYes
Grounded in your code
- Generic AI AssistantsNo
- Simple RAG ToolsPartial
- CodeAliveYes
Works for agents & humans
- Generic AI AssistantsNo
- Simple RAG ToolsNo
- CodeAliveYes
Answers support questions from code
- Generic AI AssistantsNo
- Simple RAG ToolsNo
- CodeAliveYes
Same frontier-agent quality, ~25× lower model cost
77.3%
quality score with CodeAlive + Qwen3.6 deep
45%
fewer tokens with CodeAlive's semantic search enabled
~25×
lower model cost than Claude Opus max at similar quality
Based on our RepoQA benchmark. Qwen3.6 deep scored 77.3% quality at an estimated €0.31 model cost; Claude Opus max scored 77.5% at $7.71 actual provider-reported model cost.
Works with
Plugs into your existing stack
GitHub, Cursor, Claude Code, your CI. Wire CodeAlive in once.
Git Providers
- GitHub
- GitLab
- Bitbucket
IDEs
- VS Code
- JetBrains (via MCP)
AI Agents
- Cursor
- Claude Code
- Codex
- Windsurf
- Continue
- Cline
CI/CD
- API integration for pipelines
LLM Providers
- OpenAI
- Anthropic
- Google Gemini
- Local LLMs (Llama, Qwen, GLM, DeepSeek)
Star our MCP Server on GitHub
100+ starsOpen-source MCP server. Works with Cursor, Claude Code, Continue, Cline, and anything else MCP-compatible.
Runs entirely inside your perimeter
Same engine, your hardware. Point it at your Git server, your LLM, your observability stack. Code never leaves your network.
Your code stays put
Docker Compose or Kubernetes / Helm. Your code never leaves your perimeter.
Bring Your Own LLM
Works with gpt-oss, GLM, Kimi, DeepSeek, Qwen, or anything OpenAI-compatible.
Isolated per-org by default
Per-org envelope encryption (AES-256-GCM) and sandboxed indexing: even with the master key, another org's data won't decrypt.
Start free, upgrade when you need more power.
Try CodeAlive without signing up
Browse our hand-picked, pre-indexed open source repositories.
Free
$0/ month
- 1 user
- 15 MB total repo size
- 1 workspace
- Public & private repos
- 100 chat requests / month
- 10 deep research / month
- MCP access
Get Started Free
For individual developers indexing a single project.
Pro
$29/ user / month
billed monthly
- Up to 10 users
- 200 MB total repo size
- 10 workspaces
- 2,000 chat requests / month
- 100 deep research / month
- MCP + API access
- Priority support
Start Pro Trial
For developers and small teams who need more repos, more requests, and API access.
Team
$49/ user / month
billed monthly
- Unlimited users
- 1 GB total repo size
- Unlimited workspaces
- 5,000 chat requests / month
- 300 deep research / month
- MCP + API access
- Team collaboration
- Admin controls
- Priority support
Start Team Trial
For growing engineering teams with complex codebases.
Enterprise
Custom
- On-premises deployment
- Local LLM support (Qwen 3, Llama 4, GLM, DeepSeek, gpt-oss)
- SSO / SAML
- Dedicated support
- Custom integrations
- SLA
Contact Sales
For organizations that need scale, security, and customization.