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code intelligence for AI agents: a compiled typed symbol graph over MCP.","maintainers":[{"name":"josephmerrill","email":"joseph@papyruslabs.ai"}],"readme":"# Seshat — structural code intelligence for AI agents\n\n**Your agent reconstructs your codebase from likelihood. Seshat compiles it.**\n\nSeshat turns a repository into a typed symbol graph (every function, class, route, and\ntable, with its *real* dependency edges, data flow, and constraints) and serves it to your\nagent over MCP. So before your agent edits a function, it can ask what will actually break\ninstead of guessing.\n\nBacked by a compiled intermediate representation, not text search or embeddings: if Seshat\nsays a function has three callers, it has exactly three.\n\n## Why\n\nAI coding agents are fast but structurally blind. When an agent changes one function, it\noften cannot see everything that depends on it, so it silently breaks callers it never\nlooked at. A large share of AI-introduced regressions come from exactly this. Seshat gives\nthe agent the map.\n\n## Try it first (no install, no login)\n\nPaste any public repo at **https://seshat.papyruslabs.ai/try** and watch it trace what a\nchange would break.\n\n## Install\n\n```\nnpx -y @papyruslabsai/seshat-mcp setup <your-key>\n```\n\nGet a free key at **https://seshat.papyruslabs.ai** (first extraction is free). The setup\ncommand writes your MCP config and stores the key.\n\nOr configure manually (Claude Code, Cursor, or any MCP client):\n\n```json\n{\n  \"mcpServers\": {\n    \"seshat\": {\n      \"command\": \"npx\",\n      \"args\": [\"-y\", \"@papyruslabsai/seshat-mcp\"],\n      \"env\": { \"SESHAT_API_KEY\": \"your-key\" }\n    }\n  }\n}\n```\n\n## Tools\n\nPoint your agent at a repo with `sync_project`, then it investigates the way a senior\nengineer does: orient, trace, verify.\n\n**Orient**\n- `list_projects` — what is synced\n- `list_modules` — how the codebase is organized, by layer or module\n- `query_entities` — find functions, classes, and routes by name, layer, or module\n- `find_entry_points` — routes, exports, and the public API surface\n\n**Investigate a symbol**\n- `get_entity` — signature, callers, callees, data flow, side effects, and tables touched\n- `get_dependencies` — the real call chain, callers and callees\n- `get_blast_radius` — everything that breaks if you change it, transitively\n- `get_data_flow` — what a function reads, returns, and mutates\n- `get_optimal_context` — the minimal, ranked set of files to read before editing\n- `find_by_constraint` — every function that touches a given table (or carries a given trait)\n- `find_dead_code` — unreachable symbols, safe to delete\n\nEvery answer comes from the compiled graph and discloses the coverage behind it.\n\n> History and cross-cutting audit tools (hotspots, co-change clusters, lineage, topology,\n> semantic clones) are being hardened and will be added to this list as they land.\n\n## Privacy\n\nAnalysis runs in the Papyrus Labs cloud, by design: the compiled graph is the product's\nmoat, and keeping extraction server-side is how that stays protected. Public repos are\ncloned from GitHub; private repos require you to connect your GitHub account. Source is\nprocessed to build the graph and cached to serve queries. See the privacy policy at\nseshat.papyruslabs.ai.\n\n## Pricing\n\nFirst extraction is free. $0.03 per query after a free tier; a typical investigation is 5\nto 15 queries. Details at https://seshat.papyruslabs.ai.\n\nMIT licensed server. Named for the goddess who kept the records, built so your agent can\nread them.\n","readmeFilename":"README.md"}