The context layer for agents that ship.
Give every agent the verified context it needs from the tools your team already uses. Grounded, inspectable, and ready for your stack.
Dashboard
Welcome, Nikhil Rajpurohit
Four steps to a workspace that hands your agents verified context.
4 sources connected · documents, slack, notion, github
- 1Connect memory and sourcesMemory, Slack, Notion, and GitHub are live today.
- 2Add workspace knowledgeUpload the documents your agents should reason over.
- 3Run a questionSee which context gets selected, and what it costs.
- 4Create an API keyCall getContext() from the agents you already run.
Building agents already? Integrate in code
Connect the tools and agents where your team already works.
More integrations are being added continuously.
- Slack
- Notion
- GitHub
- Gmail
- Linear
- Memory
- Jira
- Claude Code
- Codex
Your data is connected. Your agent still needs context.
Search gives you raw results. Chat history gives you everything. nmemo selects the useful evidence across your connected sources, fits it to the budget, and gives your agent one grounded package.
connected sources
Where is the answer?
Documents, memory, Slack, Notion, GitHub, and APIs all hold a partial view of the answer.
nmemo getContext()
What should the model actually see?
One ranked context package with citations, source status, token use, and the diagnostics behind every selection.
- Task
- OAuth middleware validation
- Status
- In progress · 2 days idle
- Last attempt
- Added session validation
- Files touched
- auth.ts · middleware.ts
- Tests
- 2 failing
- Decision
- Better Auth handles session lookup
- Next step
- Repair invalid-session fixture
- Evidence
- commit abc123 · test output
A context package, not another black box.
Your app gets the final prompt together with the selected evidence, citations, source health, token usage, and diagnostics to inspect or display however it needs.
memory scoped per repo · no cross-recall
Connect the sources your product already uses.
Start with documents and memory, then add Slack, Notion, GitHub, and the systems that hold the knowledge your agent needs.
carries 5,412 tokens of context · 18 memories · receipts intact
Keep your model and framework.
Use the Context API or SDK with the agent stack you already have. nmemo assembles the context; your application stays in control of generation.
The practical bits, answered.
What nmemo owns, what stays in your stack, and what arrives with every context package.
A multi-source context engine for AI agents. It retrieves relevant evidence, selects what fits the task and token budget, and returns one inspectable context package.
A ranked prompt package with citations, source status, token usage, retrieval diagnostics, and the evidence behind every selection.
Yes. You can inspect which sources responded, what was ranked or discarded, the scores behind each result, and how the token budget was used.
The launch surface supports documents, memory, Slack, Notion, GitHub, and Qdrant retrieval. The shared retriever contract keeps the source layer extensible.
No. Keep your current model and framework. Use the Context API or SDK inside the agent stack you already run.
A vector database retrieves stored data. nmemo coordinates sources, ranks evidence, controls the prompt budget, and shows exactly what enters the model.
Yes. The workspace SDK exposes getContext() and getContextFast(), alongside the Context API for direct HTTP integration.