Unblocked builds one graph over the sources a team already has. GitMir adds the business logic those sources describe — the rules, customer paths and features, and how they connect — current with every push.
Updated
Yes. Both connect to the same coding agents over MCP. Nothing stops a team running source retrieval and a business-logic model side by side.
Unblocked assembles context for a request; GitMir holds the business logic between requests, current with every push, so the next question and the next agent start from it.
Before a change merges: the areas, customer-path steps and records it reaches — including those outside the diff that a search does not find.
A drawing and answers your team can review — the same understanding your agents get, not a slice that disappears with the prompt.
Every push read and compared with the product, and each agent change checked with GitMir Vibe before it is committed.
How to connect
Add GitMir's MCP server beside Unblocked in the same agents: Unblocked brings what was said and written, GitMir brings what the product does and what a change reaches.
If your agents need how the product works rather than where something was discussed, GitMir can be the context layer on its own — in Benchmark 001 with fewer errors and on a third of the context per question. For conversations, tickets and documents as sources, keep a source graph beside it.
Add GitMir when the question becomes what the product does and what a change reaches — the rules, customer paths and status flows behind the code, kept current between questions.
Fifty questions in the words work is assigned in, the same model and settings for every system, answers graded blind.
Unblocked
A context layer for agentic development: repositories, conversations, tickets, documentation, analytics and production in one graph, traversed at once.
Describes itself as “the context layer for agentic software development”: one graph over every source a team already has — repositories, conversations, tickets, documentation, analytics and production — traversed at once instead of searched silo by silo. The graph is of the sources; what the product does and which rule holds which state is left for the model to infer from them on each request.
GitMir
Features, rules, customer paths and how they connect, built from your code and kept current with every push.
GitMir establishes what a product does once, from the repository, and serves each task only what it needs — with every statement traceable to the source it came from.
A graph of sources answers where something was said or written. It leaves “which rule holds this state, and what else does this change reach” to the model, on every request.
| Dimension | Unblocked | GitMir |
|---|---|---|
| Primary layer | AI Context | Meaning / Product |
| What it understands | Code, conversations, tickets, docs, analytics, production | What the product does and how it behaves |
| Relationships | Cross-source graph | Business + technical |
| AI agent context | Core | Established product knowledge |
| Change impact | Code-level | Product-logic level |
| Primary buyer | Developers / AI engineering | CTO / VP Eng / Product / AI |
Positioning per each vendor’s own public description. Nothing here says a tool is bad at what it is built for.
Yes. Both connect to the same coding agents over MCP. Nothing stops a team running source retrieval and a business-logic model side by side.
If your agents need how the product works rather than where something was discussed, GitMir can be the context layer on its own — in Benchmark 001 with fewer errors and on a third of the context per question. For conversations, tickets and documents as sources, keep a source graph beside it.
Add GitMir's MCP server beside Unblocked in the same agents: Unblocked brings what was said and written, GitMir brings what the product does and what a change reaches.
A graph of sources answers where something was said or written. It leaves “which rule holds this state, and what else does this change reach” to the model, on every request. GitMir builds the business logic of the product once — features, rules, customer paths and how they connect — keeps it current with every push, and gives the part a task touches to people and to AI.
Benchmark 001: one repository, fifty questions in the words work is actually assigned in, the same AI model and settings for every system, answers graded blind. GitMir's answers lost 8% of the score to errors and omissions against 22% for Unblocked, on 8.4K tokens of context per question against 27.4K. The full method and every number are published.
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