private beta · 194 founder seats left · onboarding in waves

The context layer
for AI-first companies.

One typed knowledge graph. One MCP server. Humans and agents share the same source of truth — markdown on disk, citation-grade retrieval, idempotent writes.

Markdown on disk. No proprietary format.MCP server exposed per workspacebuilt on Postgres · pgvector · Vercel AI SDK
bcontext.dev / mcpclaude desktoplive
12 MCP toolsexposed to every agent
128 msp50 retrieval latency
99.97 %uptime · last 90 days
0 vendorlock-in · markdown on disk
Three zones, one surface

Tree, editor, chat. The working unit of operational knowledge.

Tree on the left scopes context. Editor in the middle is markdown that round-trips cleanly. Chat on the right is grounded in the workspace and never out of date.

01
Tree
Folders, docs, tasks, decisions, meetings, bugs, ADRs. Everything is a typed node with parent/child plus typed edges.
Product
Tasks
P3-03 · Slash menu
P3-04 · Mentions
P3-07 · Idempotency
Decisions
ADR-001 · Markdown on disk
02
Editor
TipTap markdown. Slash menu inserts typed blocks. @mentions are real edges. Round-trips to clean .md with YAML frontmatter.
P3-03● in_progress
Slash menu in TipTap

Pressing / opens an inline command menu. Selecting an item inserts a structured block — never raw markdown.

Menu opens within 16ms of /
↵ open · ⌘K palette
03
Chat
RAG over the same graph. Citations resolve to rows. Agents reply with batched proposals you review like PRs.
t-p4-02 · 0.91t-p4-01 · 0.87
Yjs CRDT is blocked on [1] presence — Maite's note from the design review.
codex · proposal
3 ops · 1 task · 1 update · 1 edge
⌘↵ ask · ⌘/ chat
The agent loop

Agents read, reason, and propose — through the same surface you do.

No proprietary action language. No screen-scraping. Every agent goes through the MCP server or REST, with idempotency-key replay and a review queue between proposal and apply.

— step 01
Read context
Agents pull scoped retrievals through MCP. Results carry node ids — every fact has a row.
GET context.searchGET context.read_nodeGET activity.tail
— step 02
Reason grounded
The LLM stays in your scope. Citations resolve to rows in the same store. No vector blobs, no proprietary embeds.
CHAT claude-sonnet-4-6POST embed.run
— step 03
Propose batched
Writes land as one batched proposal. Idempotency-key makes retries safe. Humans review like a PR — apply, reject, or partial.
POST write.proposePOST write.applyPOST skill.run
12
MCP tools
context · write · activity · skills
Idempotency
200-key LRU, 10-min TTL
3 tier
Scopes
read · propose · apply
100 %
Audited
every write tags request-id
Built for retrieval

Not a Notion alternative. The substrate underneath one.

Other tools optimize for humans reading pages or moving cards. Bcontext optimizes for agents retrieving rows. The unit of value is a successful retrieval, not a DAU.

Wiki tools
Issue trackers
bcontext
Unit of value
pages read by humans
cards moved by humans
nodes retrieved by agents
On-disk format
proprietary blocks
vendor DB rows
markdown + YAML
Agent surface
none / scrape
REST, no batching
MCP + batched ops
Retrieval
full-text only
filter by status
pgvector + bm25
Export anytime
with friction
JSON if you're lucky
always (markdown)
See per-tool comparisons Notion · Linear · Mem · Glean · Reflect · Recall
For who

Three teams find this immediately useful.

AI-native startups
8–60 engineers already running 3+ production agents. Onboarding docs go stale weekly; decisions live in Slack. You need one place agents and humans both write to.
typical: 4 agents, 1.2k nodes/wk
Solo founders with agents
You orchestrate three agents (Codex, a research bot, a marketing one). You want them sharing context without copy-pasting between five tools.
typical: 1 user, 3 agents, MCP-first
Platform/ops teams
You own onboarding docs, runbooks, ADRs, incident postmortems. You want grep over the truth, not a 14-tab archaeology dig every time someone joins.
typical: 2.4k nodes, 240 retrievals/wk
From the field

Antikythera Labs · case study

Migrated · 38 engineers · 4 production agents · Feb 2026
−84%
decision archaeology time
1 d → 1 afternoon
eng onboarding
12 → 1
tools agents had to read from
“We stopped writing the same context into three places. Our agents read from the same graph we do; their writes go through a review queue. Onboarding dropped from a week to an afternoon.
MS
María Santos
head of platform · Antikythera Labs
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One workspace. No proprietary format. Yours to extend.

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