LearningCircuit/local-deep-research is a research assistant that uses language models and search engines to investigate questions and produce cited reports, while supporting local models, cloud models, and private documents. It is for people who want to conduct research with their own searchable knowledge base and keep their data local and encrypted. The catalogue lists three instructions for working with it.
Borrowing it
Nothing to install: this file belongs to LearningCircuit/local-deep-research. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/LearningCircuit/local-deep-research/main/AGENTS.mdgit clone --depth 1 https://github.com/LearningCircuit/local-deep-researchWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/instructions/learningcircuit/local-deep-research/agents-md)<a href="https://agentmods.dev/instructions/learningcircuit/local-deep-research/agents-md"><img src="https://agentmods.dev/badge/instructions/learningcircuit/local-deep-research/agents-md/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/instructions/learningcircuit/local-deep-research/agents-md"><img src="https://agentmods.dev/badge/instructions/learningcircuit/local-deep-research/agents-md.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.03818 | $0.03818 |
| Opus 5.5 | $0.01527 | $0.01527 |
| Sonnet 5.5 | $0.00764 | $0.00764 |
| Haiku 4.5 | $0.00382 | $0.00382 |
Grade A, and why
local-deep-research AGENTS.md scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 109 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AGENTS.md
Overview
Local Deep Research is a Python/PDM application with a FastAPI backend and a Vite frontend. Backend source is in src/local_deep_research/; web code is in src/local_deep_research/web/; tests are in tests/. src/local_deep_research/web/app.py:main() is the process entry point and launches Uvicorn; src/local_deep_research/web/fastapi_app.py constructs the FastAPI application, mounts Socket.IO, and manages startup/shutdown through lifespan().
Source-development prerequisites: Python ≥3.12, <3.15; Node.js 24.0.0+ (frontend build, JS tests and some pre-commit hooks), PDM, and SQLCipher; see docs/developing.md and docs/SQLCIPHER_INSTALL.md.
Architecture
Iterative research pipeline: a BaseSearchStrategy generates questions, runs parallel searches across BaseSearchEngine implementations, and synthesizes a report via report_generator.py. The web layer is FastAPI + Socket.IO (ASGI); each user has their own encrypted SQLCipher database (no shared application-data DB; a central ldr_auth.db stores only usernames). The main entry point is src/local_deep_research/search_system.py (AdvancedSearchSystem). New search engines require registration in ENGINE_REGISTRY (src/local_deep_research/web_search_engines/engine_registry.py) and the class allowlist in src/local_deep_research/security/module_whitelist.py; new strategies require wiring in create_strategy() (src/local_deep_research/search_system_factory.py) — only LLM providers are auto-discovered. See docs/architecture.md for the full thread lifecycle, QueuePool/FD budget, and cleanup model.
Where to look
| Task | Paths |
|---|---|
| Backend and server lifecycle | src/local_deep_research/web/app.py, src/local_deep_research/web/fastapi_app.py, src/local_deep_research/web/routers/, src/local_deep_research/web/services/ |
| Auth and sessions | src/local_deep_research/web/auth/, src/local_deep_research/database/encrypted_db.py |
| Settings | src/local_deep_research/settings/, src/local_deep_research/settings/env_definitions/, src/local_deep_research/web/routers/settings.py, src/local_deep_research/defaults/ |
| Security and safe HTTP | src/local_deep_research/security/ (safe_requests.py, ssrf_validator.py, egress/); policy in SECURITY.md |
| LLM and embedding providers | src/local_deep_research/llm/providers/, src/local_deep_research/config/llm_config.py, src/local_deep_research/embeddings/providers/ |
| Search engines | src/local_deep_research/web_search_engines/, src/local_deep_research/web_search_engines/engines/ |
| Research engine and strategies | src/local_deep_research/search_system.py, src/local_deep_research/search_system_factory.py, src/local_deep_research/advanced_search_system/strategies/ |
| Report generation | src/local_deep_research/report_generator.py |
| Database, models, migrations | src/local_deep_research/database/models/, src/local_deep_research/database/migrations/versions/, src/local_deep_research/database/thread_local_session.py |
| Vector store and RAG | src/local_deep_research/vector_stores/, src/local_deep_research/research_library/services/library_rag_service.py |
| Programmatic API and MCP | src/local_deep_research/api/, src/local_deep_research/mcp/ |
| News, benchmarks, notes | src/local_deep_research/news/, src/local_deep_research/benchmarks/, src/local_deep_research/research_library/notes/ |
| Frontend | src/local_deep_research/web/static/, src/local_deep_research/web/templates/, src/local_deep_research/web/themes/ |
| Tests and CI | tests/, .github/workflows/, .pre-commit-config.yaml, .pre-commit-hooks/, scripts/pre_commit/ |
| Contributor and release policy | CONTRIBUTING.md, changelog.d/README.md, docs/RELEASE_GUIDE.md |
| Architecture and decisions | docs/architecture.md, docs/decisions/ (accepted ADRs are binding) |
| Development setup and CI detail | docs/developing.md, docs/CI_CD_INFRASTRUCTURE.md |
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- yesterday Changed · +11 lines · -35 tokens per session 1feae245980a
- 7d ago First seen · 98 lines · 3,853 tokens per session scan A 991ca8ee884c
local-deep-research AGENTS.md is an instructions file published in the GitHub repository LearningCircuit/local-deep-research (9,506 stars, last pushed yesterday), licensed MIT. It adds 3,818 tokens to every session, about $0.0153 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-10-30.
local-deep-research AGENTS.md compared with instructions from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| local-deep-research AGENTS.mdLearningCircuit/local-deep-research | Instructions file | 9.5k | yesterday | MIT | A | 3,818 | Codex, OpenCode |
| vscode buildNext.instructions.mdmicrosoft/vscode | Instructions file | 195k | today | MIT | A | 9,719 | GitHub Copilot |
| next.js AGENTS.mdvercel/next.js | Instructions file | 143k | today | MIT | A | 7,461 | Codex, OpenCode |
| vscode oss-third-party-notices.instructions.mdmicrosoft/vscode | Instructions file | 195k | today | MIT | A | 5,516 | GitHub Copilot |
| langchain AGENTS.mdlangchain-ai/langchain | Instructions file | 149k | today | MIT | A | 4,680 | Codex, OpenCode |
| vscode copilot-instructions.mdmicrosoft/vscode | Instructions file | 195k | today | MIT | A | 3,093 | GitHub Copilot |
| deepseek-harness AGENTS.mddeepseek-ai/deepseek-harness | Instructions file | 274k | 2d ago | MIT | A | 4,313 | Codex, OpenCode |