An agentic retrieval assistant that pulls from six distinct knowledge surfaces and picks the right tool itself. Talk to it through a CLI or a browser UI; drag a file in and it'll route it into the right backend automatically.
v0.3.0 is a preview release on PyPI. New code lives under
datamind/; legacy v0.1 (main.py/server.py/modules/) still works for comparison. End-to-end walkthrough:GETTING_STARTED.md· docs site.
pip install datamindOptional extras:
pip install 'datamind[mysql]' # MySQL dialect
pip install 'datamind[postgres]' # PostgreSQL dialect
pip install 'datamind[voyage]' # Voyage embeddings
pip install 'datamind[huggingface]' # Local BGE / e5 embeddings
pip install 'datamind[dev]' # pytest + build + twineConfigure a Claude-compatible gateway and start chatting:
export DATAMIND__LLM__API_BASE=https://your-gateway.example.com
export DATAMIND__LLM__API_KEY=sk-...
export DATAMIND__LLM__MODEL=claude-sonnet-4-6
datamind chat # CLI
python -m uvicorn datamind.server:app --port 8000 # browser UI on http://127.0.0.1:8000| Capability | Backend | Tools the agent gets |
|---|---|---|
| KB (RAG) | Chroma + BM25 with Reciprocal Rank Fusion | kb_search, kb_list_documents, kb_count, kb_reindex |
| Graph | NetworkX, JSON-persisted | graph_search_entities, graph_traverse, graph_neighbors, graph_upsert_triples |
| Database | SQLAlchemy (SQLite / MySQL / Postgres) | db_list_tables, db_describe_table, db_query_sql, db_query_nl |
| Skills | .claude/skills/<name>/SKILL.md + safe Python tools |
skill_search, skill_get, skill_list, calculator, unit_convert, get_current_time, analyze_text |
| Memory | SQLite with cosine recall + LLM fact extraction; scope-typed (global / profile / session) for multi-tenant isolation |
memory_save, memory_recall, memory_forget, memory_list_profiles |
| Ingest ✨ | Conversational data import — drop a file in via chat or the browser drag-drop zone | kb_add_file, kb_add_path, db_import_csv, graph_add_triples_from_text |
| Hooks ✨ v0.3 | Sandboxed tool dispatch — every call is intercepted; Allow / Deny / AskUser / Rewrite; tamper-evident audit log per profile |
PathAllowlistHook, DestructiveSqlHook, AuditLogHook (built-in; user hooks pluggable) |
27 tools total. All routed through one ToolRegistry; the agent decides what to call and in what order.
Just want to use it?
pip install datamind, setDATAMIND__LLM__API_KEY, rundatamind chat. The walkthrough below clones the repo so you also get the seed scripts and the enterprise-demo dataset.
git clone https://github.com/OpenDCAI/DataMind.git && cd DataMind
python -m venv .venv && source .venv/bin/activate
pip install -e .
cp .env.datamind.example .env.datamind
$EDITOR .env.datamind # set DATAMIND__LLM__API_KEY at minimum
# 1. Smoke-test the gateway (~2 s)
python -m datamind.scripts.hello_sdk
# 2. Seed a realistic enterprise dataset (17 docs / 64 graph nodes / 6 tables / 101 rows)
python -m datamind.scripts.seed_enterprise_demo
# 3. Watch the agent answer 8 cross-backend questions on its own
DATAMIND__DATA__PROFILE=enterprise_demo \
python -m datamind.scripts.hello_enterprise
# 4. Or just open the browser UI
DATAMIND__DATA__PROFILE=enterprise_demo \
python -m uvicorn datamind.server:app --port 8000
# → http://127.0.0.1:8000 — drag any .md / .csv / .txt into the dropzone, ask questions, watch tools fireMore detail in GETTING_STARTED.md.
Ask: "工程部 Shanghai 的员工工资加起来是多少？"
The agent figures out it needs SQL, tries db_query_nl, gets an empty result, recovers by inspecting the schema (db_list_tables → db_describe_table), discovers the column is Eng not Engineering, rewrites the SQL itself, and answers ¥26,000 — without any of that being hard-coded. Same agent picks graph_search_entities + graph_neighbors for relationship questions, kb_search + skill_get for SOP questions, memory_save for "remember this for me" requests.
Frontend stays the same regardless. The 27 tools, the streaming SSE protocol, the chat UI, and DataMind's own safety HookChain work identically across two interchangeable agent backends:
DATAMIND__AGENT__BACKEND=native # default — pure-Python anthropic SDK + self-written loop
DATAMIND__AGENT__BACKEND=sdk # claude-agent-sdk + claude-code-router (CCR)
# adds the SDK's Subagents / Compaction / Plan mode
DataMind's HookChain (path allow-list, destructive-SQL gate, tamper-evident audit) is enforced on both backends — at the dispatch chokepoint on native, inside each MCP tool wrapper on sdk. Both verified end-to-end against the same 8 enterprise-demo questions (numbers here).
The 4 ingest tools turn the agent into a read-and-write surface:
you → "把 /Users/foo/sales-q2.csv 导入成数据表 q2_sales"
agent → calls db_import_csv(path=..., table='q2_sales') ✓ 18 rows inserted
you → "Q2 sales pipeline 里 in-pipeline 单子总额是多少？哪个 sales rep 单子最多？"
agent → calls db_query_sql(...) ✓ answers from the freshly-imported table
Or drop the file into the browser dropzone and click 导入. Or say "把这段加进图谱：陈诚晋升 Tech Lead，向 Ann 汇报" → agent calls graph_add_triples_from_text, LLM extracts triples, graph upserts them. No restart, no reindex.
v0.1 was functional but coupled: a global AppState, hard-wired modules, vendor-locked to the claude CLI. v0.2 reshapes it around:
- Protocols + registries — every capability is a
Protocol; concrete classes register under a short name. New DB dialect / embedding provider / retriever strategy = one file. - Pluggable agent loop —
native(anthropic SDK) orsdk(claude-agent-sdk + CCR), one ENV switch. - Real SSE streaming through FastAPI — not v0.1's fake character-sliced streaming.
- Zero global state — every request owns its own
RequestContextwith a trace id. - Side-by-side with v0.1 — old code paths untouched, easy comparison.
See Architecture for full detail.
DataMind/
├── datamind/ # ── v0.2 (new code) ──────────────────
│ ├── agent/ # base.py + loop_native.py + loop_sdk.py
│ ├── capabilities/ # kb / graph / db / skills / memory /
│ │ # ingest / embedding
│ ├── core/ # Protocol, Registry, Config, Logging, Tools
│ ├── scripts/ # hello_*.py + seed_enterprise_demo.py
│ ├── cli.py # `python -m datamind ...`
│ ├── server.py # FastAPI + real SSE + /api/upload
│ └── tests/ # 95 passing tests (no network required)
│
├── .claude/skills/ # SDK-style knowledge skills (SKILL.md)
├── static/app.html # browser UI (drag-drop + tool cards + sidebar)
├── scripts/start_ccr.sh # one-line CCR launcher (for sdk backend)
├── demo-uploads/ # 6 sample files to drag-drop into the UI
│
├── modules/ core/ main.py server.py benchmark/ # ── v0.1 legacy ─
│
├── data/profiles/<profile>/ # per-profile raw inputs
├── storage/<profile>/ # per-profile indexes & DBs
├── pyproject.toml # v0.2 install + CLI entry
└── .env.datamind.example # nested env template
One environment variable switches data + storage directories in lockstep:
DATAMIND__DATA__PROFILE=customer_a python -m datamind chatMaps to data/profiles/customer_a/ and storage/customer_a/.
pytest datamind/tests/
# 95 passed in ~0.6s — no network requiredPlus live smoke + benchmark scripts:
hello_sdk, hello_kb, hello_db, hello_graph, hello_skills, hello_memory, hello_agent,
seed_enterprise_demo, hello_enterprise (8 cross-backend questions).
See DataMind-Doc for architecture, configuration reference, per-capability deep dives, and tutorials in English and Chinese.