Trust what you ship, at AI speed
SourceAnt is the knowledge layer for AI-heavy teams: it captures the decisions, constraints, and contracts behind your code and serves them to every engineer and every agent, so velocity never outruns understanding.
SourceAnt keeps a living map of how your codebase works and how it is changing, so your team understands the system as it grows and decides its direction, instead of discovering it after the fact.
Because the knowledge is structured, SourceAnt puts it to work: review that checks a diff against your actual decisions and blast radius, issue triage that dedupes and labels, and analytics on quality and spend.
Open core and self-hostable, usage-based pricing, bring-your-own-key. Your knowledge stays private to your workspace and travels across every tool over MCP. No per-seat tax, no lock-in.
Self-host free · No credit card to try the cloud
The daily reality
AI made code cheap to write and expensive to trust.
On a large, critical codebase, velocity now outruns anyone’s ability to hold the architecture in their head, judge a change against the real system, and ship it safely.
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The why lives in people’s heads, and they are outnumbered
Agents open more changes in a day than your seniors can trace back to the decisions behind them. The reasoning that keeps the system correct never made it out of a Slack thread or a closed PR.
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The system outgrows anyone’s grasp, and quietly drifts
It gets bigger and more AI touches it, until no one holds the whole shape anymore. It keeps changing in a direction nobody deliberately chose, and you only notice once it is hard to undo.
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Quality and reliability slip as you scale AI
The more you ship with agents, the more workarounds get reverted, conventions drift, and contracts break. Trust in the codebase erodes right when you are moving fastest.
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Your AI spend climbs and the context never sticks
Every session re-explains the same architecture, burning tokens and per-seat fees, and none of it is captured for the next engineer or the next agent.
Teams that capture the why compound understanding with every change. Teams that do not ship faster into a system they understand less each week.
One platform
One knowledge layer, many jobs on top
SourceAnt captures how your codebase works and serves it everywhere, so your tools and your team act on real understanding instead of skimming files.
Context that persists
Decisions, constraints, workarounds, conventions, and patterns captured once and governed by your team, so the why behind your code never has to be re-explained.
A graph, not guesswork
Your code, contracts, and decisions linked into a typed knowledge graph, so tools and people follow real relationships and see what a change actually touches.
Applications on top
Put the knowledge to work: review that checks a diff against real decisions and blast radius, issue triage that dedupes and auto-labels, and analytics on quality, coverage, and spend.
Lower token bills
Serve only the relevant slice of the graph instead of whole files or full history. Fewer tokens, sharper answers, and AI spend you can actually see and control.
The whole system
Capture the knowledge once. Put it to work everywhere.
One living map of how your system works: your team sees where it is heading and steers it, your agents build with the full context, and the jobs you rely on run on top.
Understand your own system, and keep your agents on it
SourceAnt captures the decisions, constraints, contracts, and conventions behind your code and structures them into a living map of how your system actually works, one that stays current as the code changes.
For your team
See how the system is built, how it is changing, and where it is heading, so you catch drift early and decide whether that is the direction you want before it is baked in.
For your agents
The relevant slice is served back over MCP the moment an agent acts, so it builds with the full why instead of guessing at it.
Context-aware review
Check each change against your real decisions and its blast radius, so the bugs that read fine and the workarounds it wants to delete get flagged before they merge.
Issue triage
Duplicate detection and intelligent auto-labeling on issues and PRs, so your backlog stays deduped and routed without hand-sorting.
Engineering analytics
See token usage and savings, review quality, context coverage, and triage stats, so you can watch reliability and AI spend as you scale.
Portable
Works with the tools you already use
SourceAnt speaks MCP, so your context and every application show up in whatever your team codes with. And it is not just for humans: agents query the knowledge headlessly, mid-task, without anyone re-pasting context. No lock-in, no new habits.
See it
See it in action
The knowledge graph, context served to your tools, and applications like review, all in one place.
How it works
A graph, not a pile of text
Capture the why, link it into a knowledge graph, and serve only the relevant slice to any AI tool the moment it acts.
1. Capture
Decisions, constraints, workarounds, conventions, and patterns are extracted from reviews, sessions, and discussions, captured automatically as proposals your team curates on its own schedule.
2. Structure
Everything is linked into a knowledge graph, code to decisions and decisions to each other, so retrieval is precise.
3. Serve via MCP
Any AI tool queries the relevant slice before it acts. No re-explaining, no context-blind suggestions.
Drag to rotate
In practice
Context arrives when your AI needs it
Before the assistant changes a line, SourceAnt hands it the decisions behind that code, so it does not quietly undo them.
You ask your AI: "Refactor the payment retry logic to use exponential backoff."
Max retry must stay at 3. Higher values caused duplicate charges in a past incident.
Jittered backoff did not work with the rate limiter. A fixed delay is intentional.
Idempotency keys are generated client-side. Changing this breaks the mobile app.
Illustrative example
Why SourceAnt
Why not comments, ADRs, a vector database, or another per-seat review bot?
Structured, not prose. Context is typed and linked in a graph, so tools retrieve the exact slice instead of grepping comments that have gone stale.
Governed, not hallucinated. Knowledge carries a status your team controls and your tools filter on: approval marks what the team vouches for, and it never queues capture behind a person. A vector store just guesses.
Portable and private. Served over MCP to any tool, open source you can self-host, with your own model keys. Your knowledge and your AI spend stay on your side, never in a vendor’s silo.
Priced for how you actually work. Usage-based and bring-your-own-key, one flat team price, no per-seat AI tax as you add engineers or agents.
Who it's for
For teams shipping fast with AI on code that cannot break
You run a large, critical codebase, you move at the pace of your agents, and you care as much about architecture and reliability as about velocity. You self-host, you want usage-based pricing and your own keys, and you need humans to keep understanding the system, not just the code.
The architect holding the why
You are the reason the system still hangs together. SourceAnt gets the decisions, constraints, and contracts out of your head and into a graph every tool and teammate can query, so the architecture survives the pace you are shipping at.
The platform lead who self-hosts
You run this on your own infrastructure, with your own keys, and you watch the AI bill. SourceAnt is open source, usage-priced, and private by default, and it cuts tokens by serving only the slice a task needs.
The engineer on the hook
You are accountable when an agent’s change breaks production. SourceAnt judges each change against the real system and hands your tools the reasoning behind the code, so speed does not cost you trust.
Keep understanding your codebase at AI speed
Self-host the open-source core for free, or start SourceAnt Cloud at $25 a month per team plus usage. Bring your own keys and keep your context private.