The full upstream README, mirrored here for reference. Install config, tool schemas, adoption signals, and an original overview live on the Briefd listing page.
Your agents, briefed. Not flooded.
A self-hosted context compiler for AI coding teams: git-backed knowledge, served to coding agents
as token-budgeted context bundles over MCP.

▶ Watch the 90-second explainer · How it works · Quickstart
Teams that build many projects in one domain keep the same knowledge in their heads and in
scattered CLAUDE.md / AGENTS.md files: terminology, business rules, architecture decisions,
conventions. Loading all of it into every session burns thousands of tokens on every turn, and
whatever doesn't fit gets left out.
briefd inverts the model: context on demand, not up front. Your knowledge lives as Markdown in a git repository. briefd indexes it and answers one question from your coding agent — "what do I need to know for this task?" — with a compiled, deduplicated bundle that never exceeds the token budget you set.
On the sample knowledge repo in this repository (44 documents, 47 realistic developer tasks),
compile_bundle spends 86% fewer tokens per task than pasting everything into CLAUDE.md
while still containing the section that answers the task 96% of the time (98% with the
English-only all-MiniLM-L6-v2 model, the figure the explainer video quotes). The realistic
middle ground — a hand-curated CLAUDE.md with just conventions and the glossary — costs
2.8× more than a bundle and has the answer less than half the time.
Reproduce it with make bench; the method is in internal/eval/bench.go.
That is what the tokenizer says. Inside real Claude Code sessions
(eval/session/, Sonnet, 10 tasks, same prompts) briefd cut the context
carried per turn by 35% and the cost per task by 40% with identical answers — at the price of
3–4 extra tool-call round trips per task. The saving grows with the size of your knowledge repo;
a static CLAUDE.md cannot.
Memory tools (agentmemory, Mem0, claude-mem) record what an agent observed in its sessions, automatically, per user. briefd serves what the team decided, written by people and reviewed like code. They answer different questions and run side by side.
| Agent memory | briefd | |
|---|---|---|
| Source of truth | A database the agent writes to | Markdown in a git repository |
| Who writes | The agent, automatically | People; agents open pull requests |
| Scope | One agent / one user | The whole team, every project in the domain |
| Review | None | Every change has a diff, a reviewer and a name |
| Staleness | Unknown | Dates on every section; drift against the code it governs |
| What it can't answer | Silently absent | Listed on the dashboard as a backlog |
| Footprint | Runtime + engine + several ports | One static binary, one SQLite file, one port (or stdio) |
| Tool surface | Dozens of tools, thousands of tokens per session | 7 tools, ~2.8k tokens per session |
Use a memory tool so your agent remembers what it tried last week. Use briefd so every agent on the team applies the same rules — and so someone notices when a rule falls behind the code.
propose_update opens a reviewable branch/PR; what
briefd serves changes only when a human merges.multilingual-e5-small, 100+ languages) computed by a pure-Go encoder, fused with
reciprocal rank fusion. No Postgres, no vector database, no ONNX runtime, no CGO.max_tokens and never exceeds it.Try it in 30 seconds — no repository needed:
Then open the dashboard, or point an agent at it: claude mcp add --transport http briefd http://127.0.0.1:7788/mcp.
The first run downloads the embedding model (about 470 MB); briefd demo --embeddings none skips it.
With your own knowledge:
Open http://localhost:7788/ for the dashboard, then ask Claude Code something the sample
corpus knows — "what's our retry policy for acquirer calls?" or "ters ibraz nedir?" — and
watch search_context / compile_bundle show up in the request log.
Any MCP client that speaks streamable HTTP works. For a project-level .mcp.json:
Let the agent install it. Hand your coding agent one instruction:
Retrieve and follow the instructions at: https://raw.githubusercontent.com/ismailperim/briefd/main/INSTALL_FOR_AGENTS.md
Teach the agent when to ask. Tools an agent does not call save nothing. The
skills/briefd skill tells Claude Code (and any agent that reads
SKILL.md) when to compile a bundle, how to treat a stale section and when to propose an
update; the same guidance as a CLAUDE.md paragraph is in deploy/local/CLAUDE.md.
Single-user, no server? briefd mcp speaks MCP over stdio — the same tools, the same
index, no port and no token. Claude Desktop, Cursor's stdio config and MCP directory
inspectors launch it directly:
Use serve when a team shares one instance (dashboard, metrics, webhook, REST); use mcp
when the agent runs on the machine that holds the checkout.
| Tool | What it does |
|---|---|
compile_bundle(task_description, max_tokens?, scopes?, paths?) | One deduplicated context block within the budget, ordered domain → conventions → project, with a source line per section and a bundle_id. Deterministic and cached. |
search_context(query, max_tokens?, scopes?, top_k?, paths?) | Ranked sections that fit the budget, for inspection. paths (code paths being edited) pull the documents whose refs cover them to the top. |
get_document(doc_path, scopes?) | One document in full. |
list_scopes() | Scopes with document/section counts. |
propose_update(doc_path, change_description, new_content) | Creates branch briefd/proposal-<id> (+ pull request when configured). Never touches the index. |
report_usage(bundle_id, useful_chunk_ids) | Optional feedback: which sections helped. An empty list marks the question as a knowledge gap. |
suggest_links(doc_path?, limit?) | Links the knowledge base is missing (a document names another without linking it) and broken links — for an agent tidying the knowledge base via propose_update. |
The same operations are available over REST (/api/search, POST /api/bundle, /api/docs/{path},
/api/scopes, POST /api/proposals, POST /api/usage, /api/gaps, /api/health, /api/stats)
behind the same bearer token.
briefd expects a git repository (or directory) of Markdown with three kinds of folders
(briefd init <dir> scaffolds it with example documents):
Documents are split on ##/### headings into sections of roughly 200–800 tokens with stable
ids, so a section can be quoted on its own. Optional front matter adds metadata:
testdata/knowledge/ is a complete example (a fictional payments
platform) and doubles as the evaluation corpus.
A knowledge repository can be an Obsidian vault. briefd reads [[wikilinks]] in the text and in front-matter properties such as related: (including
[[note|alias]] and [[note#heading]]) and relative Markdown links, resolves them the way
Obsidian does (by path, or by file name anywhere in the repository), and builds a link graph:
the dashboard draws it, get_document returns each document's links and backlinks so an agent
can follow them, and orphans (nothing links here) and broken links (the target does not exist)
are listed as maintenance signals next to gaps and coverage. Folder scopes still apply —
domain/, conventions/, projects/<name>/ — so keep the vault's top level in that shape.
briefd clones the repository, follows the branch with fetch + hard reset every sync.interval
(default 60 s), or immediately when your forge calls POST /webhook/git with a GitHub-style
HMAC signature. Only changed files are re-parsed and re-embedded.
Languages. The default embedding model, multilingual-e5-small, covers 100+ languages,
so a Turkish, German or Japanese knowledge repo — or English docs queried in another language —
works out of the box. English-only teams can set embeddings.model: all-MiniLM-L6-v2 (87 MB,
~2.5× faster indexing). briefd model pull pre-fetches a model for offline or image-build use;
--embeddings none gives BM25-only mode; Ollama and OpenAI-compatible services are alternative
providers.
Docker
The image is distroless and pure Go (~34 MB, linux/amd64 + arm64). Database, checkout and model
live in the briefd-data volume. Mount a directory and set BRIEFD_SOURCE=/knowledge to serve
local files instead.
Deployment guide: deploy/README.md covers Compose and systemd
setups, git forges (GitHub, GitLab, Azure DevOps, Bitbucket, SSH), installing the embedding model
offline, proxies and private CAs, exposure/security, upgrades and monitoring. For a laptop-only
setup see deploy/local/.
Configuration — briefd.yaml (see deploy/briefd.example.yaml)
or BRIEFD_* environment variables; flags override both. The ones you will actually touch:
| Setting | Env | Default | Notes |
|---|---|---|---|
source | BRIEFD_SOURCE | — | git URL or directory |
api_token | BRIEFD_API_TOKEN | (none) | empty = unauthenticated (only on trusted networks) |
listen | BRIEFD_LISTEN | :7788 | |
sync.interval | BRIEFD_SYNC_INTERVAL | 60s | 0 disables polling |
sync.webhook_secret | BRIEFD_SYNC_WEBHOOK_SECRET | — | enables POST /webhook/git |
git.token | BRIEFD_GIT_TOKEN | — | HTTPS remotes; git.ssh_key for SSH |
forge.type, forge.token | BRIEFD_FORGE_* | — | github or gitlab: opens a pull / merge request for each proposal |
embeddings.provider | BRIEFD_EMBEDDINGS_PROVIDER | local | ollama, openai, or none for BM25-only |
embeddings.model | BRIEFD_EMBEDDINGS_MODEL | multilingual-e5-small | or all-MiniLM-L6-v2 (English, faster) |
search.default_max_tokens | BRIEFD_DEFAULT_MAX_TOKENS | 2000 | |
query_log.retention_days | BRIEFD_QUERY_LOG_RETENTION_DAYS | 30 | feeds the knowledge-gap report; query_log.enabled: false turns it off |
code.repos | — | (none) | code repositories (URL or path) compared against documents' refs for drift |
briefd model pull pre-fetches the embedding model for offline or image-build use.
GET / is a dashboard embedded in the binary (no build step, no external assets):
#graph=<doc path> links straight to it).GET /metrics exposes the counters in Prometheus text format; GET /api/stats as JSON.
![]() | ![]() |
Knowledge gaps. Every search_context / compile_bundle call is logged with its retrieval
confidence (query_log, 30-day retention). The dashboard lists the questions of the last seven
days that the knowledge base did not answer — nothing matched, or the agent's report_usage said
no section helped — grouped by question and ranked by how often they were asked, plus the answered
questions whose top result barely stood out from the rest. That list is the backlog for whoever
maintains the repository; GET /api/gaps?days=7&limit=20 returns it as JSON.
Document age. Every section in a bundle carries the date its document last changed
(## path — heading (updated 2026-03-04), from git history, or the file mtime for a plain
directory), so an agent can weigh a rule by its age. The dashboard lists the documents that
changed longest ago — the ones to re-read first.
Behind the code. Give a document refs: ["services/payment/**"] in its front matter and
list the code repositories in code.repos; briefd follows their history (bare clones, never the
files) and counts the commits that touched a governed path after the document last changed.
The attribution line then reads (updated 2026-03-01; code changed since: 3 commits, last 2026-06-01), the dashboard lists the documents most behind, and briefd_documents_behind_code
is exported. The agent reading a stale rule is often the right one to fix it with
propose_update. Design in ADR-0007.
The same refs work the other way round: pass paths (the files the task touches) to
compile_bundle and the rules that govern them lead the bundle, and the dashboard's
Coverage panel lists the directories of each code repository that no document claims —
the knowledge base's blind spots (ADR-0008).
briefd does not index code itself; your agent's grep and LSP do that better.
Retrieval is measured, not assumed. make eval scores 47 English golden queries (keyword,
paraphrase, typo, mixed-language) over the sample corpus and 30 Turkish queries over a
Turkish corpus; CI fails if hybrid retrieval drops below eval/thresholds.yaml
or eval/thresholds-tr.yaml:
| Mode | English R@5 | English R@10 | English MRR | Turkish R@5 | Turkish R@10 | Turkish MRR |
|---|---|---|---|---|---|---|
| BM25 only | 0.681 | 0.755 | 0.591 | 0.733 | 0.767 | 0.602 |
| Vector only | 0.830 | 0.936 | 0.771 | 0.950 | 1.000 | 0.832 |
| Hybrid (default) | 0.830 | 0.926 | 0.746 | 0.933 | 1.000 | 0.847 |
On public BEIR datasets briefd's vector-only mode reproduces the published quality of both
embedding models and hybrid mode beats BM25 and vector-only on each — SciFact nDCG@10 0.714
vs 0.665 for the BEIR BM25 baseline; see eval/beir/ to reproduce.
Every change to chunking, embeddings or fusion ships with before/after numbers (ADR-0004 is an example).
v0.1 is feature-complete; expect rough edges before 1.0. Planned next:
report_usageRead docs/ARCHITECTURE.md for a guided tour with diagrams. The full
specification is in SPEC.md; decisions are recorded in docs/adr/.
Issues and pull requests are welcome — see CONTRIBUTING.md for the development setup, testing rules and conventions. Security issues: SECURITY.md.