braindb-agent

name	braindb-agent
description	Persistent memory across sessions via the BrainDB agent. Use at conversation start and whenever you need to recall what you know about the user or save new information to long-term memory.
allowed-tools	Bash Read

BrainDB Memory Agent

BrainDB has its own internal agent (LiteLLM with pluggable provider via LLM_PROFILE; defaults to deepinfra/google/gemma-4-31B-it) that handles all memory operations. You don't call individual endpoints — you ask the agent in plain English via one endpoint: POST http://localhost:8000/api/v1/agent/query.

Health check:

!curl -sf http://localhost:8000/health > /dev/null 2>&1 && echo "OK" || echo "BRAINDB_DOWN"

If the output contains BRAINDB_DOWN, the memory database is not running. Do this:

1. Ask the user: "BrainDB isn't running. Do you want me to start it for you?"
2. Find the braindb repo — look for a directory that has ALL of these:
   • docker-compose.yml at the root
   • braindb/main.py
   • pyproject.toml with name = "braindb" Search in: current dir, parent dirs (up to 3 levels), common locations like ~/source/repos/**/braindb/.
3. Start it: cd <braindb-path> && docker compose up -d
4. Cache the path: echo "<braindb-path>" > ~/.claude/skills/braindb-agent/.repo_path
5. Wait for it: poll curl -sf http://localhost:8000/health for up to 30 seconds.
6. If healthy, proceed. If the user declines or start fails, proceed WITHOUT memory.

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TOOL PRIORITY (read this first)

The agent has a clear order of tools it should reach for. When you phrase a request, lean into the sophisticated tools — don't ask it to "run SQL" for anything to do with recall or understanding.

1. Query-driven recall — "what do we know about X?" → the agent calls /memory/context (keyword-mediated fuzzy + embedding + graph + ranking, with diversity quotas). The default for ALL discovery and understanding.
2. Entity-driven neighbourhood — the agent's view_tree returns a nested JSON tree (root keyed by entity_type, children arrays per node, 1-N hops out, keyword + retired-wiki noise filtered by default, _truncated marker if more remain). Especially useful when an entity ID is already in hand — often sharper than another query about the same entity. On hub entities pass max_depth=3 for narrative chains.
3. Multi-step investigation — "investigate / disambiguate / resolve X" → the agent delegates to a subagent. Keeps the main context clean.
4. Direct lookups — view_entity_relations, get_entity, list_entities for narrow questions.
5. search_sql ⚠ exception only — for explicit aggregates (counts, GROUP BY, activity-log joins). Never for finding / understanding / "what's related to" — those are jobs for the tools above.

If you're tempted to phrase a request as "run a SQL query that finds…" for finding or understanding something, stop — that's the recall or tree path's job. Ask in plain English.

Wikis are first-class memory entities curated by an internal maintainer + writer pipeline. The agent surfaces them through recall automatically when relevant — you don't have to ask for them explicitly, and you don't have to trigger anything to make new ones. Saving facts with the right keywords is enough; the scheduler runs maintain → write on its 60s tick and the wikis materialise on their own.

Internally the agent now researches from short previews and reads a full body only by id (paging large ones, or delegating big documents to a subagent), so its context stays clean — just ask in natural language ("read and summarise datasource X"); it handles the chunking itself.

RECALL — at conversation start, and whenever you need context

Ask the agent in natural language. It handles keyword formulation, multi-query search, graph traversal, and summarization.

Encoding note: When constructing the curl JSON body, use ASCII characters only — plain hyphens (-), straight quotes (", '), no em-dashes (—) or smart quotes. On Windows shells these get mangled in the JSON body and the server returns 400 Bad Request.

curl -s -X POST http://localhost:8000/api/v1/agent/query \
  -H "Content-Type: application/json" \
  -d '{"query":"Tell me who the user is - role, expertise, preferences, recent projects."}'

For topic-specific recall:

curl -s -X POST http://localhost:8000/api/v1/agent/query \
  -H "Content-Type: application/json" \
  -d '{"query":"What do you know about the user React frontend experience?"}'

Read the answer field from the response. Use the summary to inform your response. Never paste raw JSON into the conversation.

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SAVE — after learning something new

Describe what you learned in natural language. The agent decides the entity type, picks keywords, generates embeddings, creates relations.

curl -s -X POST http://localhost:8000/api/v1/agent/query \
  -H "Content-Type: application/json" \
  -d '{"query":"Save: the user just told me they prefer simple code over abstractions. Source: user-stated. Connect to existing preference entities."}'

Proactive save — but ASK the user first

The pattern is RECALL → ASK → SAVE:

1. When the user shares something that might be worth remembering (a name, role, project, preference, decision, your own inference about them), RECALL first via the agent to check if it's already known.

2. If it's net-new, ASK the user:

   "I haven't seen this before — should I save it to BrainDB? I'd file it as a [fact / thought / rule] tagged with [keywords]."

3. Only on a 'yes', issue the save request to the agent.

Don't pre-save without confirmation. The user has the final say on what becomes long-term memory. User-confirmed memory is higher-signal and lets the user catch judgement-call mistakes early.

Exception: when the user explicitly framed it as a rule ("from now on, always X"; "never do Y"), save it without an extra confirmation — they already said it — but surface the action: "Saving that as a rule."

What's worth flagging to the user

• Identity / role / company (one-time setup info)
• Strong preferences or working-style rules
• Project / topic context the user just disclosed
• Decisions the user explicitly made
• Useful URLs or references the user shared
• Your own inferences about the user (tag as thought, source=agent-inference) — ASK before persisting these too; an inference is still memory.

The goal is to capture what the user gives you in conversation that isn't already in BrainDB — not to scrape every utterance. Information already in recall doesn't need saving again; ephemeral task details ("currently debugging X") don't need saving at all.

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Example queries

Recall (no confirmation needed — these are reads)

Situation	Query to send to the agent
Start of conversation	"Tell me who the user is - role, expertise, preferences, recent projects."
User mentions a topic	"What do you know about the user ML experience and AI projects?"
User asks about past work	"What has the user shipped recently? Check facts with source=user-stated from the last month."
Need to find duplicates	"Find near-duplicate entities in memory."
Explore the graph	"What are the densest topics in memory? Which entities have the most connections?"

Save (RECALL → ASK → SAVE — only send the agent query after the user confirms)

Situation	What Claude says to the user first	What Claude sends to the agent (on a 'yes')
User mentions something net-new	"I noticed you just said you're working on the IR pipeline multilingual extraction — that looks worth saving. Should I?"	"Save: user is working on the IR pipeline multilingual extraction. Connect to existing IR entities."
User shares a preference	"Should I save that as a long-term preference?"	"Save as fact: user prefers simple code over abstractions. Source: user-stated. Keywords: user-preference, code-style."
User explicitly states a rule	(no confirmation — they framed it as a rule) "Saving that as a rule."	"Save as rule: always prefer simple code over abstractions. Source: user-stated. Category: behavior."
You drew an inference about the user	"I'm getting the sense you're senior in ML — should I save that as a thought?"	"Save as thought: user appears senior in ML based on the depth of their question. Source: agent-inference. Certainty: 0.6."

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Delegation — ask the agent to spawn a subagent for focused work

To keep the agent's main context clean and your conversation uncluttered, you can explicitly tell it to delegate heavy tasks. Just include "use a subagent" or "delegate this" in the query — the agent has a delegate_to_subagent tool that runs a fresh agent instance in its own context and returns only a summary.

When to delegate

• Deep searches or graph exploration
• Duplicate detection, orphan cleanup, bulk relation work
• Any task that would produce a lot of intermediate tool output

Examples

# Find duplicates without seeing intermediate results
curl -s -X POST http://localhost:8000/api/v1/agent/query \
  -H "Content-Type: application/json" \
  -d '{"query":"Delegate to a subagent: find near-duplicate facts and return top 10 pairs with their IDs."}'

# Deep search on a topic
curl -s -X POST http://localhost:8000/api/v1/agent/query \
  -H "Content-Type: application/json" \
  -d '{"query":"Use a subagent: explore every entity tagged with investor-relations and return a clean summary of what is there."}'

# Create relations in bulk
curl -s -X POST http://localhost:8000/api/v1/agent/query \
  -H "Content-Type: application/json" \
  -d '{"query":"Spawn a subagent: find orphaned user-profile facts and create tagged_with links to relevant keyword entities."}'

Delegation is 1 level deep — subagents cannot spawn more subagents.

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File ingestion — automatic, no agent call needed

If the user wants a local file (article, transcript, note, document) ingested into BrainDB, don't ask the agent to do it. Instead, copy the file into the repo's data/sources/ directory and the system handles the rest:

1. The braindb_watcher sidecar polls data/sources/ every ~7 seconds.
2. New files are auto-ingested as datasource entities (content + hash + word count).
3. The watcher then runs an agent-driven extraction pass that creates one or more fact entities derived from the document and links them back via derived_from relations.
4. On success the file is moved to data/sources/ingested/; on failure to data/sources/failed/ with a sidecar .error.txt.

What this means for you (Claude) and the user:

• Tell the user: "Just drop the file into data/sources/ on the BrainDB repo. The watcher will pick it up within a few seconds and you'll see the facts appear in recall a minute or two later."
• Do not issue an /agent/query like "Save this file..." with the file contents pasted into the prompt — that bloats the LLM context and bypasses the proper extraction pipeline. The watcher path produces structured facts + derived_from relations + keyword auto-tagging; pasting bypasses all of it.
• Watch progress if you want to confirm completion:

docker logs braindb_watcher -f

You'll see ingested NEW: <filename> -> <id> words=N then later extraction complete for <id>: N facts total. After that the new facts surface naturally in /agent/query recall — no extra steps.

Edge cases:

• Very large files are chunked automatically; extraction takes proportionally longer (typically 60-180 seconds per chunk on local Qwen, faster on deepinfra).
• If a file ends up in data/sources/failed/, read the sidecar .error.txt next to it to see what went wrong.
• The watcher dedupes by file content hash, so re-dropping the same file won't re-extract.

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Verbose mode — watch the agent work in real time

Set AGENT_VERBOSE=true in the server's .env (default is false). When enabled, every tool call the agent makes is logged to stdout with args and result preview. Watch it live:

docker logs braindb_api -f

The HTTP response itself is unchanged (just {"answer": "..."}). Logs go to the server stdout only — clean separation between the response payload and operational logging.

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Error handling

• If the agent call fails (connection refused, 500, timeout): proceed WITHOUT memory. Don't retry, don't block the conversation.
• If the answer mentions an ERROR: the agent tried but some tool failed. Carry on — use whatever partial information came back.
• Agent calls can take up to 10 minutes if the LLM provider is slow. Add --max-time 600 to long curl calls.