@copass/langchain - npm Package Compare versions

Comparing version 0.5.9 to 0.5.10

dist/index.cjs

@@ -108,3 +108,32 @@ "use strict";
   );
-  return { discover, interpret, search };
+  const get_origin = (0, import_tools.tool)(
+    async ({
+      canonical_ids,
+      limit_per_canonical
+    }) => {
+      const response = await client.retrieval.getOrigin(sandbox_id, {
+        canonical_ids,
+        ...limit_per_canonical !== void 0 ? { limit_per_canonical } : {}
+      });
+      return {
+        sandbox_id: response.sandbox_id,
+        origins: response.origins.map((entry) => ({
+          canonical_id: entry.canonical_id,
+          files: entry.files.map((f) => ({
+            file_path: f.file_path,
+            extraction_count: f.extraction_count
+          }))
+        }))
+      };
+    },
+    {
+      name: "get_origin",
+      description: import_config.GET_ORIGIN_DESCRIPTION,
+      schema: import_zod.z.object({
+        canonical_ids: import_zod.z.array(import_zod.z.string()).min(1).max(100).describe(import_config.ORIGIN_CANONICAL_IDS_PARAM),
+        limit_per_canonical: import_zod.z.number().int().min(1).max(50).optional().describe(import_config.ORIGIN_LIMIT_PARAM)
+      })
+    }
+  );
+  return { discover, interpret, search, get_origin };
 }
@@ -111,0 +140,0 @@

dist/index.cjs.map

@@ -1,1 +0,1 @@
-{"version":3,"sources":["../src/index.ts","../src/agent.ts","../src/tools.ts","../src/callback.ts"],"sourcesContent":["export { createCopassAgent } from './agent.js';\nexport type { CreateCopassAgentOptions } from './agent.js';\nexport { copassTools } from './tools.js';\nexport type { CopassToolsOptions } from './tools.js';\nexport { CopassWindowCallback } from './callback.js';\nexport type { CopassWindowCallbackOptions } from './callback.js';\n","import { createReactAgent } from '@langchain/langgraph/prebuilt';\nimport type { BaseChatModel } from '@langchain/core/language_models/chat_models';\nimport type { StructuredToolInterface } from '@langchain/core/tools';\nimport type { Runnable, RunnableConfig } from '@langchain/core/runnables';\nimport type { BaseMessage } from '@langchain/core/messages';\nimport type { CopassClient, ContextWindow, SearchPreset } from '@copass/core';\nimport { copassTools } from './tools.js';\nimport { CopassWindowCallback } from './callback.js';\n\ntype ReactAgentExtraOptions = Omit<\n  Parameters<typeof createReactAgent>[0],\n  'llm' | 'tools'\n>;\n\n/**\n * Output type of {@link createCopassAgent}. A standard LangChain `Runnable`\n * producing `{ messages: BaseMessage[] }` on `.invoke()` / `.stream()`.\n *\n * The concrete inferred type (a LangGraph `CompiledStateGraph`) can't be\n * serialized into a portable declaration, so we expose the narrower\n * `Runnable` surface that's sufficient for every public method a caller\n * needs — `.invoke`, `.stream`, `.streamEvents`, `.batch`, `.pipe`,\n * `.withConfig`, etc. Escape hatch if you need the raw graph: cast the\n * result.\n */\nexport type CopassAgent = Runnable<\n  { messages: BaseMessage[] } | BaseMessage[] | string,\n  { messages: BaseMessage[] },\n  RunnableConfig\n>;\n\nexport interface CreateCopassAgentOptions extends ReactAgentExtraOptions {\n  /** An authenticated `@copass/core` client. */\n  client: CopassClient;\n  /** Sandbox all retrieval runs against. */\n  sandbox_id: string;\n  /**\n   * Context Window for this conversation thread. Turn history is mirrored\n   * into it automatically across every chat model invocation — retrieval\n   * becomes window-aware with no per-call wiring.\n   */\n  window: ContextWindow;\n  /** The chat model (e.g. `new ChatAnthropic(...)`, `new ChatOpenAI(...)`). */\n  llm: BaseChatModel;\n  /** Additional tools to mix in alongside `discover` / `interpret` / `search`. */\n  tools?: StructuredToolInterface[];\n  /** Optional project scoping for retrieval. */\n  project_id?: string;\n  /** Preset for `interpret` / `search`. Default: `\"copass/1.0\"`. */\n  preset?: SearchPreset;\n  /** Include LangChain `ToolMessage`s in the Context Window. Default: `false`. */\n  includeToolMessages?: boolean;\n}\n\n/**\n * Create a LangChain agent pre-wired with Copass retrieval tools and\n * window-aware memory.\n *\n * Returns a standard LangChain Runnable — call `.invoke()`, `.stream()`,\n * `.streamEvents()`, `.batch()` as you would with the output of\n * `createReactAgent`. The Copass Context Window is mirrored from the\n * conversation history automatically; you don't pass callbacks, trackers,\n * or lifecycle hooks.\n *\n * @example\n * ```ts\n * import { ChatAnthropic } from '@langchain/anthropic';\n * import { CopassClient } from '@copass/core';\n * import { createCopassAgent } from '@copass/langchain';\n *\n * const copass = new CopassClient({ auth: { type: 'bearer', token: process.env.COPASS_API_KEY! } });\n * const window = await copass.contextWindow.create({ sandbox_id });\n *\n * const agent = createCopassAgent({\n *   client: copass,\n *   sandbox_id,\n *   window,\n *   llm: new ChatAnthropic({ model: 'claude-opus-4-7' }),\n * });\n *\n * const result = await agent.invoke({\n *   messages: [{ role: 'user', content: 'why is checkout flaky?' }],\n * });\n * ```\n *\n * ### How the wiring works\n *\n * Under the hood this composes three primitives also exported from this\n * package — in case you want to build your own variant:\n *\n * - `copassTools({ client, sandbox_id, window, ... })` — the three retrieval\n *   tools, window-aware at the server call level.\n * - `createReactAgent({ llm, tools, ... })` — the standard LangGraph ReAct agent.\n * - `CopassWindowCallback({ window })` — bound via `Runnable.withConfig` so\n *   every chat model invocation auto-mirrors messages into the window.\n */\nexport function createCopassAgent(options: CreateCopassAgentOptions): CopassAgent {\n  const {\n    client,\n    sandbox_id,\n    window,\n    llm,\n    tools: extraTools = [],\n    project_id,\n    preset,\n    includeToolMessages,\n    ...reactAgentOptions\n  } = options;\n\n  const copass = copassTools({ client, sandbox_id, window, project_id, preset });\n  const callback = new CopassWindowCallback({ window, includeToolMessages });\n\n  const agent = createReactAgent({\n    llm,\n    tools: [copass.discover, copass.interpret, copass.search, ...extraTools],\n    ...reactAgentOptions,\n  });\n\n  // `withConfig` returns a RunnableBinding that propagates these callbacks\n  // to every child Runnable invocation — including the chat model calls\n  // that trigger `CopassWindowCallback.handleChatModelStart`. The cast\n  // narrows LangGraph's concrete graph type to the portable Runnable\n  // surface we want to expose.\n  return agent.withConfig({ callbacks: [callback] }) as unknown as CopassAgent;\n}\n","import { tool } from '@langchain/core/tools';\nimport { z } from 'zod';\nimport {\n  DISCOVER_DESCRIPTION,\n  DISCOVER_QUERY_PARAM,\n  INTERPRET_DESCRIPTION,\n  INTERPRET_ITEMS_PARAM,\n  INTERPRET_QUERY_PARAM,\n  SEARCH_DESCRIPTION,\n  SEARCH_QUERY_PARAM,\n} from '@copass/config';\nimport type { CopassClient, SearchPreset, WindowLike } from '@copass/core';\n\nexport interface CopassToolsOptions {\n  /** An authenticated `@copass/core` client. */\n  client: CopassClient;\n  /** Sandbox all retrieval runs against. */\n  sandbox_id: string;\n  /** Optional project scoping for retrieval calls. */\n  project_id?: string;\n  /**\n   * Optional conversation window — a {@link WindowLike} (typically a\n   * `ContextWindow` from `client.contextWindow.create()`). When provided,\n   * every retrieval call is automatically window-aware.\n   */\n  window?: WindowLike;\n  /**\n   * Preset for `discover`, `interpret`, and `search`. Defaults to\n   * `\"copass/copass_1.0\"`. Under `\"copass/copass_2.0\"` discover items\n   * carry `subgraph` (pre-rendered ASCII tree) and `matched_query_nodes`\n   * fields. Append `\":thinking\"` (e.g. `\"copass/copass_2.0:thinking\"`)\n   * to enable task decomposition before retrieval on `search`.\n   */\n  preset?: SearchPreset;\n}\n\n/**\n * Return Copass retrieval as a set of LangChain tool objects (built with\n * `tool()` from `@langchain/core/tools`).\n *\n * Agent-framework-neutral shape: the LLM picks `discover` (menu of\n * relevant items) or `search` (synthesized answer). `interpret` stays\n * registered for back-compat but is legacy — prefer `search` for drill-in.\n *\n * @example\n * ```ts\n * import { ChatAnthropic } from '@langchain/anthropic';\n * import { createReactAgent } from '@langchain/langgraph/prebuilt';\n * import { copassTools } from '@copass/langchain';\n *\n * const window = await copass.contextWindow.create({ sandbox_id });\n * const tools = copassTools({ client: copass, sandbox_id, window });\n *\n * const agent = createReactAgent({\n *   llm: new ChatAnthropic({ model: 'claude-opus-4-7' }),\n *   tools: [tools.discover, tools.interpret, tools.search],\n * });\n *\n * const result = await agent.invoke({\n *   messages: [{ role: 'user', content: 'why is checkout flaky?' }],\n * });\n * ```\n */\nexport function copassTools(options: CopassToolsOptions) {\n  const { client, sandbox_id, project_id, window, preset = 'copass/copass_1.0' } = options;\n\n  const discover = tool(\n    async ({ query }: { query: string }) => {\n      const response = await client.retrieval.discover(sandbox_id, {\n        query,\n        project_id,\n        window,\n        preset,\n      });\n      return {\n        header: response.header,\n        // Project the v2 fields (`subgraph` + `matched_query_nodes`)\n        // alongside the v1 fields. Populated only under\n        // `copass/copass_2.0` (or its `copass/2.0` alias); `null` under\n        // v1 — agents can ignore them when not present.\n        items: response.items.map((item) => ({\n          score: item.score,\n          summary: item.summary,\n          canonical_ids: item.canonical_ids,\n          subgraph: item.subgraph ?? null,\n          matched_query_nodes: item.matched_query_nodes ?? null,\n        })),\n        next_steps: response.next_steps,\n      };\n    },\n    {\n      name: 'discover',\n      description: DISCOVER_DESCRIPTION,\n      schema: z.object({\n        query: z.string().describe(DISCOVER_QUERY_PARAM),\n      }),\n    },\n  );\n\n  const interpret = tool(\n    async ({ query, items }: { query: string; items: string[][] }) => {\n      const response = await client.retrieval.interpret(sandbox_id, {\n        query,\n        items,\n        project_id,\n        window,\n        preset,\n      });\n      return { brief: response.brief };\n    },\n    {\n      name: 'interpret',\n      description: INTERPRET_DESCRIPTION,\n      schema: z.object({\n        query: z.string().describe(INTERPRET_QUERY_PARAM),\n        items: z.array(z.array(z.string())).min(1).describe(INTERPRET_ITEMS_PARAM),\n      }),\n    },\n  );\n\n  const search = tool(\n    async ({ query }: { query: string }) => {\n      const response = await client.retrieval.search(sandbox_id, {\n        query,\n        project_id,\n        window,\n        preset,\n      });\n      return { answer: response.answer };\n    },\n    {\n      name: 'search',\n      description: SEARCH_DESCRIPTION,\n      schema: z.object({\n        query: z.string().describe(SEARCH_QUERY_PARAM),\n      }),\n    },\n  );\n\n  return { discover, interpret, search };\n}\n","import { BaseCallbackHandler, type BaseCallbackHandlerInput } from '@langchain/core/callbacks/base';\nimport type { BaseMessage } from '@langchain/core/messages';\nimport type { ChatMessage, ChatRole, ContextWindow } from '@copass/core';\n\nexport interface CopassWindowCallbackOptions extends BaseCallbackHandlerInput {\n  /** The Context Window to mirror messages into. */\n  window: ContextWindow;\n  /**\n   * Include LangChain `ToolMessage`s (tool-call results) as turns. Default: false.\n   *\n   * Tool results tend to be noisy — the underlying graph already has the\n   * retrieved content indexed — so they're skipped by default. Enable only if\n   * your agent's tool results carry conceptual content you want retrieval to\n   * dedupe against.\n   */\n  includeToolMessages?: boolean;\n}\n\n/**\n * LangChain callback that auto-mirrors the chat model's conversation history\n * into a Copass {@link ContextWindow}.\n *\n * Hooks `handleChatModelStart`, which fires before every chat model invocation\n * with the full message history. We walk that history and call\n * {@link ContextWindow.addTurn} for any message we haven't seen — so retrieval\n * tools called inside the same agent step get a window that reflects the\n * actual conversation, not an empty buffer.\n *\n * @example\n * ```ts\n * import { CopassWindowCallback, copassTools } from '@copass/langchain';\n *\n * const tools = copassTools({ client, sandbox_id, window });\n * const agent = createReactAgent({ llm, tools: Object.values(tools) });\n *\n * await agent.invoke(\n *   { messages: [{ role: 'user', content: 'why is checkout flaky?' }] },\n *   { callbacks: [new CopassWindowCallback({ window })] },\n * );\n * ```\n */\nexport class CopassWindowCallback extends BaseCallbackHandler {\n  readonly name = 'copass-window';\n  private readonly window: ContextWindow;\n  private readonly includeToolMessages: boolean;\n  private readonly seen = new Set<string>();\n\n  constructor(options: CopassWindowCallbackOptions) {\n    super(options);\n    this.window = options.window;\n    this.includeToolMessages = options.includeToolMessages ?? false;\n    // Seed the dedup set with turns already in the window so we don't re-add.\n    for (const turn of options.window.getTurns()) {\n      this.seen.add(hashTurn(turn));\n    }\n  }\n\n  async handleChatModelStart(\n    _llm: unknown,\n    messages: BaseMessage[][],\n  ): Promise<void> {\n    // `messages` is `BaseMessage[][]` because chat models support batched calls.\n    // Flatten — in agent loops this is always a single conversation.\n    for (const msg of messages.flat()) {\n      const turn = toTurn(msg, this.includeToolMessages);\n      if (!turn) continue;\n\n      const key = hashTurn(turn);\n      if (this.seen.has(key)) continue;\n      this.seen.add(key);\n\n      // Fire-and-forget — window.addTurn pushes into the graph. 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+{"version":3,"sources":["../src/index.ts","../src/agent.ts","../src/tools.ts","../src/callback.ts"],"sourcesContent":["export { createCopassAgent } from './agent.js';\nexport type { CreateCopassAgentOptions } from './agent.js';\nexport { copassTools } from './tools.js';\nexport type { CopassToolsOptions } from './tools.js';\nexport { CopassWindowCallback } from './callback.js';\nexport type { CopassWindowCallbackOptions } from './callback.js';\n","import { createReactAgent } from '@langchain/langgraph/prebuilt';\nimport type { BaseChatModel } from '@langchain/core/language_models/chat_models';\nimport type { StructuredToolInterface } from '@langchain/core/tools';\nimport type { Runnable, RunnableConfig } from '@langchain/core/runnables';\nimport type { BaseMessage } from '@langchain/core/messages';\nimport type { CopassClient, ContextWindow, SearchPreset } from '@copass/core';\nimport { copassTools } from './tools.js';\nimport { CopassWindowCallback } from './callback.js';\n\ntype ReactAgentExtraOptions = Omit<\n  Parameters<typeof createReactAgent>[0],\n  'llm' | 'tools'\n>;\n\n/**\n * Output type of {@link createCopassAgent}. A standard LangChain `Runnable`\n * producing `{ messages: BaseMessage[] }` on `.invoke()` / `.stream()`.\n *\n * The concrete inferred type (a LangGraph `CompiledStateGraph`) can't be\n * serialized into a portable declaration, so we expose the narrower\n * `Runnable` surface that's sufficient for every public method a caller\n * needs — `.invoke`, `.stream`, `.streamEvents`, `.batch`, `.pipe`,\n * `.withConfig`, etc. Escape hatch if you need the raw graph: cast the\n * result.\n */\nexport type CopassAgent = Runnable<\n  { messages: BaseMessage[] } | BaseMessage[] | string,\n  { messages: BaseMessage[] },\n  RunnableConfig\n>;\n\nexport interface CreateCopassAgentOptions extends ReactAgentExtraOptions {\n  /** An authenticated `@copass/core` client. */\n  client: CopassClient;\n  /** Sandbox all retrieval runs against. */\n  sandbox_id: string;\n  /**\n   * Context Window for this conversation thread. Turn history is mirrored\n   * into it automatically across every chat model invocation — retrieval\n   * becomes window-aware with no per-call wiring.\n   */\n  window: ContextWindow;\n  /** The chat model (e.g. `new ChatAnthropic(...)`, `new ChatOpenAI(...)`). */\n  llm: BaseChatModel;\n  /** Additional tools to mix in alongside `discover` / `interpret` / `search`. */\n  tools?: StructuredToolInterface[];\n  /** Optional project scoping for retrieval. */\n  project_id?: string;\n  /** Preset for `interpret` / `search`. Default: `\"copass/1.0\"`. */\n  preset?: SearchPreset;\n  /** Include LangChain `ToolMessage`s in the Context Window. Default: `false`. */\n  includeToolMessages?: boolean;\n}\n\n/**\n * Create a LangChain agent pre-wired with Copass retrieval tools and\n * window-aware memory.\n *\n * Returns a standard LangChain Runnable — call `.invoke()`, `.stream()`,\n * `.streamEvents()`, `.batch()` as you would with the output of\n * `createReactAgent`. The Copass Context Window is mirrored from the\n * conversation history automatically; you don't pass callbacks, trackers,\n * or lifecycle hooks.\n *\n * @example\n * ```ts\n * import { ChatAnthropic } from '@langchain/anthropic';\n * import { CopassClient } from '@copass/core';\n * import { createCopassAgent } from '@copass/langchain';\n *\n * const copass = new CopassClient({ auth: { type: 'bearer', token: process.env.COPASS_API_KEY! } });\n * const window = await copass.contextWindow.create({ sandbox_id });\n *\n * const agent = createCopassAgent({\n *   client: copass,\n *   sandbox_id,\n *   window,\n *   llm: new ChatAnthropic({ model: 'claude-opus-4-7' }),\n * });\n *\n * const result = await agent.invoke({\n *   messages: [{ role: 'user', content: 'why is checkout flaky?' }],\n * });\n * ```\n *\n * ### How the wiring works\n *\n * Under the hood this composes three primitives also exported from this\n * package — in case you want to build your own variant:\n *\n * - `copassTools({ client, sandbox_id, window, ... })` — the three retrieval\n *   tools, window-aware at the server call level.\n * - `createReactAgent({ llm, tools, ... })` — the standard LangGraph ReAct agent.\n * - `CopassWindowCallback({ window })` — bound via `Runnable.withConfig` so\n *   every chat model invocation auto-mirrors messages into the window.\n */\nexport function createCopassAgent(options: CreateCopassAgentOptions): CopassAgent {\n  const {\n    client,\n    sandbox_id,\n    window,\n    llm,\n    tools: extraTools = [],\n    project_id,\n    preset,\n    includeToolMessages,\n    ...reactAgentOptions\n  } = options;\n\n  const copass = copassTools({ client, sandbox_id, window, project_id, preset });\n  const callback = new CopassWindowCallback({ window, includeToolMessages });\n\n  const agent = createReactAgent({\n    llm,\n    tools: [copass.discover, copass.interpret, copass.search, ...extraTools],\n    ...reactAgentOptions,\n  });\n\n  // `withConfig` returns a RunnableBinding that propagates these callbacks\n  // to every child Runnable invocation — including the chat model calls\n  // that trigger `CopassWindowCallback.handleChatModelStart`. The cast\n  // narrows LangGraph's concrete graph type to the portable Runnable\n  // surface we want to expose.\n  return agent.withConfig({ callbacks: [callback] }) as unknown as CopassAgent;\n}\n","import { tool } from '@langchain/core/tools';\nimport { z } from 'zod';\nimport {\n  DISCOVER_DESCRIPTION,\n  DISCOVER_QUERY_PARAM,\n  GET_ORIGIN_DESCRIPTION,\n  INTERPRET_DESCRIPTION,\n  INTERPRET_ITEMS_PARAM,\n  INTERPRET_QUERY_PARAM,\n  ORIGIN_CANONICAL_IDS_PARAM,\n  ORIGIN_LIMIT_PARAM,\n  SEARCH_DESCRIPTION,\n  SEARCH_QUERY_PARAM,\n} from '@copass/config';\nimport type {\n  CopassClient,\n  OriginEntry,\n  OriginFile,\n  SearchPreset,\n  WindowLike,\n} from '@copass/core';\n\nexport interface CopassToolsOptions {\n  /** An authenticated `@copass/core` client. */\n  client: CopassClient;\n  /** Sandbox all retrieval runs against. */\n  sandbox_id: string;\n  /** Optional project scoping for retrieval calls. */\n  project_id?: string;\n  /**\n   * Optional conversation window — a {@link WindowLike} (typically a\n   * `ContextWindow` from `client.contextWindow.create()`). When provided,\n   * every retrieval call is automatically window-aware.\n   */\n  window?: WindowLike;\n  /**\n   * Preset for `discover`, `interpret`, and `search`. Defaults to\n   * `\"copass/copass_1.0\"`. Under `\"copass/copass_2.0\"` discover items\n   * carry `subgraph` (pre-rendered ASCII tree) and `matched_query_nodes`\n   * fields. Append `\":thinking\"` (e.g. `\"copass/copass_2.0:thinking\"`)\n   * to enable task decomposition before retrieval on `search`.\n   */\n  preset?: SearchPreset;\n}\n\n/**\n * Return Copass retrieval as a set of LangChain tool objects (built with\n * `tool()` from `@langchain/core/tools`).\n *\n * Agent-framework-neutral shape: the LLM picks `discover` (menu of\n * relevant items) or `search` (synthesized answer). `interpret` stays\n * registered for back-compat but is legacy — prefer `search` for drill-in.\n *\n * @example\n * ```ts\n * import { ChatAnthropic } from '@langchain/anthropic';\n * import { createReactAgent } from '@langchain/langgraph/prebuilt';\n * import { copassTools } from '@copass/langchain';\n *\n * const window = await copass.contextWindow.create({ sandbox_id });\n * const tools = copassTools({ client: copass, sandbox_id, window });\n *\n * const agent = createReactAgent({\n *   llm: new ChatAnthropic({ model: 'claude-opus-4-7' }),\n *   tools: [tools.discover, tools.interpret, tools.search],\n * });\n *\n * const result = await agent.invoke({\n *   messages: [{ role: 'user', content: 'why is checkout flaky?' }],\n * });\n * ```\n */\nexport function copassTools(options: CopassToolsOptions) {\n  const { client, sandbox_id, project_id, window, preset = 'copass/copass_1.0' } = options;\n\n  const discover = tool(\n    async ({ query }: { query: string }) => {\n      const response = await client.retrieval.discover(sandbox_id, {\n        query,\n        project_id,\n        window,\n        preset,\n      });\n      return {\n        header: response.header,\n        // Project the v2 fields (`subgraph` + `matched_query_nodes`)\n        // alongside the v1 fields. Populated only under\n        // `copass/copass_2.0` (or its `copass/2.0` alias); `null` under\n        // v1 — agents can ignore them when not present.\n        items: response.items.map((item) => ({\n          score: item.score,\n          summary: item.summary,\n          canonical_ids: item.canonical_ids,\n          subgraph: item.subgraph ?? null,\n          matched_query_nodes: item.matched_query_nodes ?? null,\n        })),\n        next_steps: response.next_steps,\n      };\n    },\n    {\n      name: 'discover',\n      description: DISCOVER_DESCRIPTION,\n      schema: z.object({\n        query: z.string().describe(DISCOVER_QUERY_PARAM),\n      }),\n    },\n  );\n\n  const interpret = tool(\n    async ({ query, items }: { query: string; items: string[][] }) => {\n      const response = await client.retrieval.interpret(sandbox_id, {\n        query,\n        items,\n        project_id,\n        window,\n        preset,\n      });\n      return { brief: response.brief };\n    },\n    {\n      name: 'interpret',\n      description: INTERPRET_DESCRIPTION,\n      schema: z.object({\n        query: z.string().describe(INTERPRET_QUERY_PARAM),\n        items: z.array(z.array(z.string())).min(1).describe(INTERPRET_ITEMS_PARAM),\n      }),\n    },\n  );\n\n  const search = tool(\n    async ({ query }: { query: string }) => {\n      const response = await client.retrieval.search(sandbox_id, {\n        query,\n        project_id,\n        window,\n        preset,\n      });\n      return { answer: response.answer };\n    },\n    {\n      name: 'search',\n      description: SEARCH_DESCRIPTION,\n      schema: z.object({\n        query: z.string().describe(SEARCH_QUERY_PARAM),\n      }),\n    },\n  );\n\n  const get_origin = tool(\n    async ({\n      canonical_ids,\n      limit_per_canonical,\n    }: {\n      canonical_ids: string[];\n      limit_per_canonical?: number;\n    }) => {\n      const response = await client.retrieval.getOrigin(sandbox_id, {\n        canonical_ids,\n        ...(limit_per_canonical !== undefined ? { limit_per_canonical } : {}),\n      });\n      return {\n        sandbox_id: response.sandbox_id,\n        origins: response.origins.map((entry: OriginEntry) => ({\n          canonical_id: entry.canonical_id,\n          files: entry.files.map((f: OriginFile) => ({\n            file_path: f.file_path,\n            extraction_count: f.extraction_count,\n          })),\n        })),\n      };\n    },\n    {\n      name: 'get_origin',\n      description: GET_ORIGIN_DESCRIPTION,\n      schema: z.object({\n        canonical_ids: z\n          .array(z.string())\n          .min(1)\n          .max(100)\n          .describe(ORIGIN_CANONICAL_IDS_PARAM),\n        limit_per_canonical: z\n          .number()\n          .int()\n          .min(1)\n          .max(50)\n          .optional()\n          .describe(ORIGIN_LIMIT_PARAM),\n      }),\n    },\n  );\n\n  return { discover, interpret, search, get_origin };\n}\n","import { BaseCallbackHandler, type BaseCallbackHandlerInput } from '@langchain/core/callbacks/base';\nimport type { BaseMessage } from '@langchain/core/messages';\nimport type { ChatMessage, ChatRole, ContextWindow } from '@copass/core';\n\nexport interface CopassWindowCallbackOptions extends BaseCallbackHandlerInput {\n  /** The Context Window to mirror messages into. */\n  window: ContextWindow;\n  /**\n   * Include LangChain `ToolMessage`s (tool-call results) as turns. Default: false.\n   *\n   * Tool results tend to be noisy — the underlying graph already has the\n   * retrieved content indexed — so they're skipped by default. Enable only if\n   * your agent's tool results carry conceptual content you want retrieval to\n   * dedupe against.\n   */\n  includeToolMessages?: boolean;\n}\n\n/**\n * LangChain callback that auto-mirrors the chat model's conversation history\n * into a Copass {@link ContextWindow}.\n *\n * Hooks `handleChatModelStart`, which fires before every chat model invocation\n * with the full message history. We walk that history and call\n * {@link ContextWindow.addTurn} for any message we haven't seen — so retrieval\n * tools called inside the same agent step get a window that reflects the\n * actual conversation, not an empty buffer.\n *\n * @example\n * ```ts\n * import { CopassWindowCallback, copassTools } from '@copass/langchain';\n *\n * const tools = copassTools({ client, sandbox_id, window });\n * const agent = createReactAgent({ llm, tools: Object.values(tools) });\n *\n * await agent.invoke(\n *   { messages: [{ role: 'user', content: 'why is checkout flaky?' }] },\n *   { callbacks: [new CopassWindowCallback({ window })] },\n * );\n * ```\n */\nexport class CopassWindowCallback extends BaseCallbackHandler {\n  readonly name = 'copass-window';\n  private readonly window: ContextWindow;\n  private readonly includeToolMessages: boolean;\n  private readonly seen = new Set<string>();\n\n  constructor(options: CopassWindowCallbackOptions) {\n    super(options);\n    this.window = options.window;\n    this.includeToolMessages = options.includeToolMessages ?? false;\n    // Seed the dedup set with turns already in the window so we don't re-add.\n    for (const turn of options.window.getTurns()) {\n      this.seen.add(hashTurn(turn));\n    }\n  }\n\n  async handleChatModelStart(\n    _llm: unknown,\n    messages: BaseMessage[][],\n  ): Promise<void> {\n    // `messages` is `BaseMessage[][]` because chat models support batched calls.\n    // Flatten — in agent loops this is always a single conversation.\n    for (const msg of messages.flat()) {\n      const turn = toTurn(msg, this.includeToolMessages);\n      if (!turn) continue;\n\n      const key = hashTurn(turn);\n      if (this.seen.has(key)) continue;\n      this.seen.add(key);\n\n      // Fire-and-forget — window.addTurn pushes into the graph. We don't block\n      // the model call on ingestion latency; we swallow errors because missing\n      // a turn is recoverable (retrieval still sees already-added turns).\n      this.window.addTurn(turn).catch(() => {\n        /* intentionally empty */\n      });\n    }\n  }\n}\n\nfunction toTurn(msg: BaseMessage, includeToolMessages: boolean): ChatMessage | null {\n  const role = roleFromMessage(msg, includeToolMessages);\n  if (!role) return null;\n\n  const content = contentToString(msg.content);\n  if (!content.trim()) return null;\n\n  return { role, content };\n}\n\nfunction roleFromMessage(msg: BaseMessage, includeToolMessages: boolean): ChatRole | null {\n  const type = msg.getType();\n  switch (type) {\n    case 'human':\n      return 'user';\n    case 'ai':\n      return 'assistant';\n    case 'system':\n      return 'system';\n    case 'tool':\n      return includeToolMessages ? 'system' : null;\n    default:\n      return null;\n  }\n}\n\nfunction contentToString(content: unknown): string {\n  if (typeof content === 'string') return content;\n  if (!Array.isArray(content)) return String(content ?? '');\n  return content\n    .map((part) => {\n      if (typeof part === 'string') return part;\n      if (part && typeof part === 'object' && 'text' in (part as Record<string, unknown>)) {\n        return String((part as Record<string, unknown>).text ?? 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dist/index.d.cts

@@ -182,2 +182,21 @@ import { createReactAgent } from '@langchain/langgraph/prebuilt';
     }, unknown, "search">;
+    get_origin: _langchain_core_tools.DynamicStructuredTool<z.ZodObject<{
+        canonical_ids: z.ZodArray<z.ZodString>;
+        limit_per_canonical: z.ZodOptional<z.ZodNumber>;
+    }, z.core.$strip>, {
+        canonical_ids: string[];
+        limit_per_canonical?: number | undefined;
+    }, {
+        canonical_ids: string[];
+        limit_per_canonical?: number | undefined;
+    }, {
+        sandbox_id: string;
+        origins: {
+            canonical_id: string;
+            files: {
+                file_path: string;
+                extraction_count: number;
+            }[];
+        }[];
+    }, unknown, "get_origin">;
 };
@@ -184,0 +203,0 @@

dist/index.d.ts

@@ -182,2 +182,21 @@ import { createReactAgent } from '@langchain/langgraph/prebuilt';
     }, unknown, "search">;
+    get_origin: _langchain_core_tools.DynamicStructuredTool<z.ZodObject<{
+        canonical_ids: z.ZodArray<z.ZodString>;
+        limit_per_canonical: z.ZodOptional<z.ZodNumber>;
+    }, z.core.$strip>, {
+        canonical_ids: string[];
+        limit_per_canonical?: number | undefined;
+    }, {
+        canonical_ids: string[];
+        limit_per_canonical?: number | undefined;
+    }, {
+        sandbox_id: string;
+        origins: {
+            canonical_id: string;
+            files: {
+                file_path: string;
+                extraction_count: number;
+            }[];
+        }[];
+    }, unknown, "get_origin">;
 };
@@ -184,0 +203,0 @@

dist/index.js

@@ -10,5 +10,8 @@ // src/agent.ts
   DISCOVER_QUERY_PARAM,
+  GET_ORIGIN_DESCRIPTION,
   INTERPRET_DESCRIPTION,
   INTERPRET_ITEMS_PARAM,
   INTERPRET_QUERY_PARAM,
+  ORIGIN_CANONICAL_IDS_PARAM,
+  ORIGIN_LIMIT_PARAM,
   SEARCH_DESCRIPTION,
@@ -89,3 +92,32 @@ SEARCH_QUERY_PARAM
   );
-  return { discover, interpret, search };
+  const get_origin = tool(
+    async ({
+      canonical_ids,
+      limit_per_canonical
+    }) => {
+      const response = await client.retrieval.getOrigin(sandbox_id, {
+        canonical_ids,
+        ...limit_per_canonical !== void 0 ? { limit_per_canonical } : {}
+      });
+      return {
+        sandbox_id: response.sandbox_id,
+        origins: response.origins.map((entry) => ({
+          canonical_id: entry.canonical_id,
+          files: entry.files.map((f) => ({
+            file_path: f.file_path,
+            extraction_count: f.extraction_count
+          }))
+        }))
+      };
+    },
+    {
+      name: "get_origin",
+      description: GET_ORIGIN_DESCRIPTION,
+      schema: z.object({
+        canonical_ids: z.array(z.string()).min(1).max(100).describe(ORIGIN_CANONICAL_IDS_PARAM),
+        limit_per_canonical: z.number().int().min(1).max(50).optional().describe(ORIGIN_LIMIT_PARAM)
+      })
+    }
+  );
+  return { discover, interpret, search, get_origin };
 }
@@ -92,0 +124,0 @@

dist/index.js.map

@@ -1,1 +0,1 @@
-{"version":3,"sources":["../src/agent.ts","../src/tools.ts","../src/callback.ts"],"sourcesContent":["import { createReactAgent } from '@langchain/langgraph/prebuilt';\nimport type { BaseChatModel } from '@langchain/core/language_models/chat_models';\nimport type { StructuredToolInterface } from '@langchain/core/tools';\nimport type { Runnable, RunnableConfig } from '@langchain/core/runnables';\nimport type { BaseMessage } from '@langchain/core/messages';\nimport type { CopassClient, ContextWindow, SearchPreset } from '@copass/core';\nimport { copassTools } from './tools.js';\nimport { CopassWindowCallback } from './callback.js';\n\ntype ReactAgentExtraOptions = Omit<\n  Parameters<typeof createReactAgent>[0],\n  'llm' | 'tools'\n>;\n\n/**\n * Output type of {@link createCopassAgent}. A standard LangChain `Runnable`\n * producing `{ messages: BaseMessage[] }` on `.invoke()` / `.stream()`.\n *\n * The concrete inferred type (a LangGraph `CompiledStateGraph`) can't be\n * serialized into a portable declaration, so we expose the narrower\n * `Runnable` surface that's sufficient for every public method a caller\n * needs — `.invoke`, `.stream`, `.streamEvents`, `.batch`, `.pipe`,\n * `.withConfig`, etc. Escape hatch if you need the raw graph: cast the\n * result.\n */\nexport type CopassAgent = Runnable<\n  { messages: BaseMessage[] } | BaseMessage[] | string,\n  { messages: BaseMessage[] },\n  RunnableConfig\n>;\n\nexport interface CreateCopassAgentOptions extends ReactAgentExtraOptions {\n  /** An authenticated `@copass/core` client. */\n  client: CopassClient;\n  /** Sandbox all retrieval runs against. */\n  sandbox_id: string;\n  /**\n   * Context Window for this conversation thread. Turn history is mirrored\n   * into it automatically across every chat model invocation — retrieval\n   * becomes window-aware with no per-call wiring.\n   */\n  window: ContextWindow;\n  /** The chat model (e.g. `new ChatAnthropic(...)`, `new ChatOpenAI(...)`). */\n  llm: BaseChatModel;\n  /** Additional tools to mix in alongside `discover` / `interpret` / `search`. */\n  tools?: StructuredToolInterface[];\n  /** Optional project scoping for retrieval. */\n  project_id?: string;\n  /** Preset for `interpret` / `search`. Default: `\"copass/1.0\"`. */\n  preset?: SearchPreset;\n  /** Include LangChain `ToolMessage`s in the Context Window. Default: `false`. */\n  includeToolMessages?: boolean;\n}\n\n/**\n * Create a LangChain agent pre-wired with Copass retrieval tools and\n * window-aware memory.\n *\n * Returns a standard LangChain Runnable — call `.invoke()`, `.stream()`,\n * `.streamEvents()`, `.batch()` as you would with the output of\n * `createReactAgent`. The Copass Context Window is mirrored from the\n * conversation history automatically; you don't pass callbacks, trackers,\n * or lifecycle hooks.\n *\n * @example\n * ```ts\n * import { ChatAnthropic } from '@langchain/anthropic';\n * import { CopassClient } from '@copass/core';\n * import { createCopassAgent } from '@copass/langchain';\n *\n * const copass = new CopassClient({ auth: { type: 'bearer', token: process.env.COPASS_API_KEY! } });\n * const window = await copass.contextWindow.create({ sandbox_id });\n *\n * const agent = createCopassAgent({\n *   client: copass,\n *   sandbox_id,\n *   window,\n *   llm: new ChatAnthropic({ model: 'claude-opus-4-7' }),\n * });\n *\n * const result = await agent.invoke({\n *   messages: [{ role: 'user', content: 'why is checkout flaky?' }],\n * });\n * ```\n *\n * ### How the wiring works\n *\n * Under the hood this composes three primitives also exported from this\n * package — in case you want to build your own variant:\n *\n * - `copassTools({ client, sandbox_id, window, ... })` — the three retrieval\n *   tools, window-aware at the server call level.\n * - `createReactAgent({ llm, tools, ... })` — the standard LangGraph ReAct agent.\n * - `CopassWindowCallback({ window })` — bound via `Runnable.withConfig` so\n *   every chat model invocation auto-mirrors messages into the window.\n */\nexport function createCopassAgent(options: CreateCopassAgentOptions): CopassAgent {\n  const {\n    client,\n    sandbox_id,\n    window,\n    llm,\n    tools: extraTools = [],\n    project_id,\n    preset,\n    includeToolMessages,\n    ...reactAgentOptions\n  } = options;\n\n  const copass = copassTools({ client, sandbox_id, window, project_id, preset });\n  const callback = new CopassWindowCallback({ window, includeToolMessages });\n\n  const agent = createReactAgent({\n    llm,\n    tools: [copass.discover, copass.interpret, copass.search, ...extraTools],\n    ...reactAgentOptions,\n  });\n\n  // `withConfig` returns a RunnableBinding that propagates these callbacks\n  // to every child Runnable invocation — including the chat model calls\n  // that trigger `CopassWindowCallback.handleChatModelStart`. The cast\n  // narrows LangGraph's concrete graph type to the portable Runnable\n  // surface we want to expose.\n  return agent.withConfig({ callbacks: [callback] }) as unknown as CopassAgent;\n}\n","import { tool } from '@langchain/core/tools';\nimport { z } from 'zod';\nimport {\n  DISCOVER_DESCRIPTION,\n  DISCOVER_QUERY_PARAM,\n  INTERPRET_DESCRIPTION,\n  INTERPRET_ITEMS_PARAM,\n  INTERPRET_QUERY_PARAM,\n  SEARCH_DESCRIPTION,\n  SEARCH_QUERY_PARAM,\n} from '@copass/config';\nimport type { CopassClient, SearchPreset, WindowLike } from '@copass/core';\n\nexport interface CopassToolsOptions {\n  /** An authenticated `@copass/core` client. */\n  client: CopassClient;\n  /** Sandbox all retrieval runs against. */\n  sandbox_id: string;\n  /** Optional project scoping for retrieval calls. */\n  project_id?: string;\n  /**\n   * Optional conversation window — a {@link WindowLike} (typically a\n   * `ContextWindow` from `client.contextWindow.create()`). When provided,\n   * every retrieval call is automatically window-aware.\n   */\n  window?: WindowLike;\n  /**\n   * Preset for `discover`, `interpret`, and `search`. Defaults to\n   * `\"copass/copass_1.0\"`. Under `\"copass/copass_2.0\"` discover items\n   * carry `subgraph` (pre-rendered ASCII tree) and `matched_query_nodes`\n   * fields. Append `\":thinking\"` (e.g. `\"copass/copass_2.0:thinking\"`)\n   * to enable task decomposition before retrieval on `search`.\n   */\n  preset?: SearchPreset;\n}\n\n/**\n * Return Copass retrieval as a set of LangChain tool objects (built with\n * `tool()` from `@langchain/core/tools`).\n *\n * Agent-framework-neutral shape: the LLM picks `discover` (menu of\n * relevant items) or `search` (synthesized answer). `interpret` stays\n * registered for back-compat but is legacy — prefer `search` for drill-in.\n *\n * @example\n * ```ts\n * import { ChatAnthropic } from '@langchain/anthropic';\n * import { createReactAgent } from '@langchain/langgraph/prebuilt';\n * import { copassTools } from '@copass/langchain';\n *\n * const window = await copass.contextWindow.create({ sandbox_id });\n * const tools = copassTools({ client: copass, sandbox_id, window });\n *\n * const agent = createReactAgent({\n *   llm: new ChatAnthropic({ model: 'claude-opus-4-7' }),\n *   tools: [tools.discover, tools.interpret, tools.search],\n * });\n *\n * const result = await agent.invoke({\n *   messages: [{ role: 'user', content: 'why is checkout flaky?' }],\n * });\n * ```\n */\nexport function copassTools(options: CopassToolsOptions) {\n  const { client, sandbox_id, project_id, window, preset = 'copass/copass_1.0' } = options;\n\n  const discover = tool(\n    async ({ query }: { query: string }) => {\n      const response = await client.retrieval.discover(sandbox_id, {\n        query,\n        project_id,\n        window,\n        preset,\n      });\n      return {\n        header: response.header,\n        // Project the v2 fields (`subgraph` + `matched_query_nodes`)\n        // alongside the v1 fields. Populated only under\n        // `copass/copass_2.0` (or its `copass/2.0` alias); `null` under\n        // v1 — agents can ignore them when not present.\n        items: response.items.map((item) => ({\n          score: item.score,\n          summary: item.summary,\n          canonical_ids: item.canonical_ids,\n          subgraph: item.subgraph ?? null,\n          matched_query_nodes: item.matched_query_nodes ?? null,\n        })),\n        next_steps: response.next_steps,\n      };\n    },\n    {\n      name: 'discover',\n      description: DISCOVER_DESCRIPTION,\n      schema: z.object({\n        query: z.string().describe(DISCOVER_QUERY_PARAM),\n      }),\n    },\n  );\n\n  const interpret = tool(\n    async ({ query, items }: { query: string; items: string[][] }) => {\n      const response = await client.retrieval.interpret(sandbox_id, {\n        query,\n        items,\n        project_id,\n        window,\n        preset,\n      });\n      return { brief: response.brief };\n    },\n    {\n      name: 'interpret',\n      description: INTERPRET_DESCRIPTION,\n      schema: z.object({\n        query: z.string().describe(INTERPRET_QUERY_PARAM),\n        items: z.array(z.array(z.string())).min(1).describe(INTERPRET_ITEMS_PARAM),\n      }),\n    },\n  );\n\n  const search = tool(\n    async ({ query }: { query: string }) => {\n      const response = await client.retrieval.search(sandbox_id, {\n        query,\n        project_id,\n        window,\n        preset,\n      });\n      return { answer: response.answer };\n    },\n    {\n      name: 'search',\n      description: SEARCH_DESCRIPTION,\n      schema: z.object({\n        query: z.string().describe(SEARCH_QUERY_PARAM),\n      }),\n    },\n  );\n\n  return { discover, interpret, search };\n}\n","import { BaseCallbackHandler, type BaseCallbackHandlerInput } from '@langchain/core/callbacks/base';\nimport type { BaseMessage } from '@langchain/core/messages';\nimport type { ChatMessage, ChatRole, ContextWindow } from '@copass/core';\n\nexport interface CopassWindowCallbackOptions extends BaseCallbackHandlerInput {\n  /** The Context Window to mirror messages into. */\n  window: ContextWindow;\n  /**\n   * Include LangChain `ToolMessage`s (tool-call results) as turns. Default: false.\n   *\n   * Tool results tend to be noisy — the underlying graph already has the\n   * retrieved content indexed — so they're skipped by default. Enable only if\n   * your agent's tool results carry conceptual content you want retrieval to\n   * dedupe against.\n   */\n  includeToolMessages?: boolean;\n}\n\n/**\n * LangChain callback that auto-mirrors the chat model's conversation history\n * into a Copass {@link ContextWindow}.\n *\n * Hooks `handleChatModelStart`, which fires before every chat model invocation\n * with the full message history. We walk that history and call\n * {@link ContextWindow.addTurn} for any message we haven't seen — so retrieval\n * tools called inside the same agent step get a window that reflects the\n * actual conversation, not an empty buffer.\n *\n * @example\n * ```ts\n * import { CopassWindowCallback, copassTools } from '@copass/langchain';\n *\n * const tools = copassTools({ client, sandbox_id, window });\n * const agent = createReactAgent({ llm, tools: Object.values(tools) });\n *\n * await agent.invoke(\n *   { messages: [{ role: 'user', content: 'why is checkout flaky?' }] },\n *   { callbacks: [new CopassWindowCallback({ window })] },\n * );\n * ```\n */\nexport class CopassWindowCallback extends BaseCallbackHandler {\n  readonly name = 'copass-window';\n  private readonly window: ContextWindow;\n  private readonly includeToolMessages: boolean;\n  private readonly seen = new Set<string>();\n\n  constructor(options: CopassWindowCallbackOptions) {\n    super(options);\n    this.window = options.window;\n    this.includeToolMessages = options.includeToolMessages ?? false;\n    // Seed the dedup set with turns already in the window so we don't re-add.\n    for (const turn of options.window.getTurns()) {\n      this.seen.add(hashTurn(turn));\n    }\n  }\n\n  async handleChatModelStart(\n    _llm: unknown,\n    messages: BaseMessage[][],\n  ): Promise<void> {\n    // `messages` is `BaseMessage[][]` because chat models support batched calls.\n    // Flatten — in agent loops this is always a single conversation.\n    for (const msg of messages.flat()) {\n      const turn = toTurn(msg, this.includeToolMessages);\n      if (!turn) continue;\n\n      const key = hashTurn(turn);\n      if (this.seen.has(key)) continue;\n      this.seen.add(key);\n\n      // Fire-and-forget — window.addTurn pushes into the graph. 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A standard LangChain `Runnable`\n * producing `{ messages: BaseMessage[] }` on `.invoke()` / `.stream()`.\n *\n * The concrete inferred type (a LangGraph `CompiledStateGraph`) can't be\n * serialized into a portable declaration, so we expose the narrower\n * `Runnable` surface that's sufficient for every public method a caller\n * needs — `.invoke`, `.stream`, `.streamEvents`, `.batch`, `.pipe`,\n * `.withConfig`, etc. Escape hatch if you need the raw graph: cast the\n * result.\n */\nexport type CopassAgent = Runnable<\n  { messages: BaseMessage[] } | BaseMessage[] | string,\n  { messages: BaseMessage[] },\n  RunnableConfig\n>;\n\nexport interface CreateCopassAgentOptions extends ReactAgentExtraOptions {\n  /** An authenticated `@copass/core` client. */\n  client: CopassClient;\n  /** Sandbox all retrieval runs against. */\n  sandbox_id: string;\n  /**\n   * Context Window for this conversation thread. Turn history is mirrored\n   * into it automatically across every chat model invocation — retrieval\n   * becomes window-aware with no per-call wiring.\n   */\n  window: ContextWindow;\n  /** The chat model (e.g. `new ChatAnthropic(...)`, `new ChatOpenAI(...)`). */\n  llm: BaseChatModel;\n  /** Additional tools to mix in alongside `discover` / `interpret` / `search`. */\n  tools?: StructuredToolInterface[];\n  /** Optional project scoping for retrieval. */\n  project_id?: string;\n  /** Preset for `interpret` / `search`. Default: `\"copass/1.0\"`. */\n  preset?: SearchPreset;\n  /** Include LangChain `ToolMessage`s in the Context Window. Default: `false`. */\n  includeToolMessages?: boolean;\n}\n\n/**\n * Create a LangChain agent pre-wired with Copass retrieval tools and\n * window-aware memory.\n *\n * Returns a standard LangChain Runnable — call `.invoke()`, `.stream()`,\n * `.streamEvents()`, `.batch()` as you would with the output of\n * `createReactAgent`. The Copass Context Window is mirrored from the\n * conversation history automatically; you don't pass callbacks, trackers,\n * or lifecycle hooks.\n *\n * @example\n * ```ts\n * import { ChatAnthropic } from '@langchain/anthropic';\n * import { CopassClient } from '@copass/core';\n * import { createCopassAgent } from '@copass/langchain';\n *\n * const copass = new CopassClient({ auth: { type: 'bearer', token: process.env.COPASS_API_KEY! } });\n * const window = await copass.contextWindow.create({ sandbox_id });\n *\n * const agent = createCopassAgent({\n *   client: copass,\n *   sandbox_id,\n *   window,\n *   llm: new ChatAnthropic({ model: 'claude-opus-4-7' }),\n * });\n *\n * const result = await agent.invoke({\n *   messages: [{ role: 'user', content: 'why is checkout flaky?' }],\n * });\n * ```\n *\n * ### How the wiring works\n *\n * Under the hood this composes three primitives also exported from this\n * package — in case you want to build your own variant:\n *\n * - `copassTools({ client, sandbox_id, window, ... })` — the three retrieval\n *   tools, window-aware at the server call level.\n * - `createReactAgent({ llm, tools, ... })` — the standard LangGraph ReAct agent.\n * - `CopassWindowCallback({ window })` — bound via `Runnable.withConfig` so\n *   every chat model invocation auto-mirrors messages into the window.\n */\nexport function createCopassAgent(options: CreateCopassAgentOptions): CopassAgent {\n  const {\n    client,\n    sandbox_id,\n    window,\n    llm,\n    tools: extraTools = [],\n    project_id,\n    preset,\n    includeToolMessages,\n    ...reactAgentOptions\n  } = options;\n\n  const copass = copassTools({ client, sandbox_id, window, project_id, preset });\n  const callback = new CopassWindowCallback({ window, includeToolMessages });\n\n  const agent = createReactAgent({\n    llm,\n    tools: [copass.discover, copass.interpret, copass.search, ...extraTools],\n    ...reactAgentOptions,\n  });\n\n  // `withConfig` returns a RunnableBinding that propagates these callbacks\n  // to every child Runnable invocation — including the chat model calls\n  // that trigger `CopassWindowCallback.handleChatModelStart`. The cast\n  // narrows LangGraph's concrete graph type to the portable Runnable\n  // surface we want to expose.\n  return agent.withConfig({ callbacks: [callback] }) as unknown as CopassAgent;\n}\n","import { tool } from '@langchain/core/tools';\nimport { z } from 'zod';\nimport {\n  DISCOVER_DESCRIPTION,\n  DISCOVER_QUERY_PARAM,\n  GET_ORIGIN_DESCRIPTION,\n  INTERPRET_DESCRIPTION,\n  INTERPRET_ITEMS_PARAM,\n  INTERPRET_QUERY_PARAM,\n  ORIGIN_CANONICAL_IDS_PARAM,\n  ORIGIN_LIMIT_PARAM,\n  SEARCH_DESCRIPTION,\n  SEARCH_QUERY_PARAM,\n} from '@copass/config';\nimport type {\n  CopassClient,\n  OriginEntry,\n  OriginFile,\n  SearchPreset,\n  WindowLike,\n} from '@copass/core';\n\nexport interface CopassToolsOptions {\n  /** An authenticated `@copass/core` client. */\n  client: CopassClient;\n  /** Sandbox all retrieval runs against. */\n  sandbox_id: string;\n  /** Optional project scoping for retrieval calls. */\n  project_id?: string;\n  /**\n   * Optional conversation window — a {@link WindowLike} (typically a\n   * `ContextWindow` from `client.contextWindow.create()`). When provided,\n   * every retrieval call is automatically window-aware.\n   */\n  window?: WindowLike;\n  /**\n   * Preset for `discover`, `interpret`, and `search`. Defaults to\n   * `\"copass/copass_1.0\"`. Under `\"copass/copass_2.0\"` discover items\n   * carry `subgraph` (pre-rendered ASCII tree) and `matched_query_nodes`\n   * fields. Append `\":thinking\"` (e.g. `\"copass/copass_2.0:thinking\"`)\n   * to enable task decomposition before retrieval on `search`.\n   */\n  preset?: SearchPreset;\n}\n\n/**\n * Return Copass retrieval as a set of LangChain tool objects (built with\n * `tool()` from `@langchain/core/tools`).\n *\n * Agent-framework-neutral shape: the LLM picks `discover` (menu of\n * relevant items) or `search` (synthesized answer). `interpret` stays\n * registered for back-compat but is legacy — prefer `search` for drill-in.\n *\n * @example\n * ```ts\n * import { ChatAnthropic } from '@langchain/anthropic';\n * import { createReactAgent } from '@langchain/langgraph/prebuilt';\n * import { copassTools } from '@copass/langchain';\n *\n * const window = await copass.contextWindow.create({ sandbox_id });\n * const tools = copassTools({ client: copass, sandbox_id, window });\n *\n * const agent = createReactAgent({\n *   llm: new ChatAnthropic({ model: 'claude-opus-4-7' }),\n *   tools: [tools.discover, tools.interpret, tools.search],\n * });\n *\n * const result = await agent.invoke({\n *   messages: [{ role: 'user', content: 'why is checkout flaky?' }],\n * });\n * ```\n */\nexport function copassTools(options: CopassToolsOptions) {\n  const { client, sandbox_id, project_id, window, preset = 'copass/copass_1.0' } = options;\n\n  const discover = tool(\n    async ({ query }: { query: string }) => {\n      const response = await client.retrieval.discover(sandbox_id, {\n        query,\n        project_id,\n        window,\n        preset,\n      });\n      return {\n        header: response.header,\n        // Project the v2 fields (`subgraph` + `matched_query_nodes`)\n        // alongside the v1 fields. Populated only under\n        // `copass/copass_2.0` (or its `copass/2.0` alias); `null` under\n        // v1 — agents can ignore them when not present.\n        items: response.items.map((item) => ({\n          score: item.score,\n          summary: item.summary,\n          canonical_ids: item.canonical_ids,\n          subgraph: item.subgraph ?? null,\n          matched_query_nodes: item.matched_query_nodes ?? null,\n        })),\n        next_steps: response.next_steps,\n      };\n    },\n    {\n      name: 'discover',\n      description: DISCOVER_DESCRIPTION,\n      schema: z.object({\n        query: z.string().describe(DISCOVER_QUERY_PARAM),\n      }),\n    },\n  );\n\n  const interpret = tool(\n    async ({ query, items }: { query: string; items: string[][] }) => {\n      const response = await client.retrieval.interpret(sandbox_id, {\n        query,\n        items,\n        project_id,\n        window,\n        preset,\n      });\n      return { brief: response.brief };\n    },\n    {\n      name: 'interpret',\n      description: INTERPRET_DESCRIPTION,\n      schema: z.object({\n        query: z.string().describe(INTERPRET_QUERY_PARAM),\n        items: z.array(z.array(z.string())).min(1).describe(INTERPRET_ITEMS_PARAM),\n      }),\n    },\n  );\n\n  const search = tool(\n    async ({ query }: { query: string }) => {\n      const response = await client.retrieval.search(sandbox_id, {\n        query,\n        project_id,\n        window,\n        preset,\n      });\n      return { answer: response.answer };\n    },\n    {\n      name: 'search',\n      description: SEARCH_DESCRIPTION,\n      schema: z.object({\n        query: z.string().describe(SEARCH_QUERY_PARAM),\n      }),\n    },\n  );\n\n  const get_origin = tool(\n    async ({\n      canonical_ids,\n      limit_per_canonical,\n    }: {\n      canonical_ids: string[];\n      limit_per_canonical?: number;\n    }) => {\n      const response = await client.retrieval.getOrigin(sandbox_id, {\n        canonical_ids,\n        ...(limit_per_canonical !== undefined ? { limit_per_canonical } : {}),\n      });\n      return {\n        sandbox_id: response.sandbox_id,\n        origins: response.origins.map((entry: OriginEntry) => ({\n          canonical_id: entry.canonical_id,\n          files: entry.files.map((f: OriginFile) => ({\n            file_path: f.file_path,\n            extraction_count: f.extraction_count,\n          })),\n        })),\n      };\n    },\n    {\n      name: 'get_origin',\n      description: GET_ORIGIN_DESCRIPTION,\n      schema: z.object({\n        canonical_ids: z\n          .array(z.string())\n          .min(1)\n          .max(100)\n          .describe(ORIGIN_CANONICAL_IDS_PARAM),\n        limit_per_canonical: z\n          .number()\n          .int()\n          .min(1)\n          .max(50)\n          .optional()\n          .describe(ORIGIN_LIMIT_PARAM),\n      }),\n    },\n  );\n\n  return { discover, interpret, search, get_origin };\n}\n","import { BaseCallbackHandler, type BaseCallbackHandlerInput } from '@langchain/core/callbacks/base';\nimport type { BaseMessage } from '@langchain/core/messages';\nimport type { ChatMessage, ChatRole, ContextWindow } from '@copass/core';\n\nexport interface CopassWindowCallbackOptions extends BaseCallbackHandlerInput {\n  /** The Context Window to mirror messages into. */\n  window: ContextWindow;\n  /**\n   * Include LangChain `ToolMessage`s (tool-call results) as turns. Default: false.\n   *\n   * Tool results tend to be noisy — the underlying graph already has the\n   * retrieved content indexed — so they're skipped by default. Enable only if\n   * your agent's tool results carry conceptual content you want retrieval to\n   * dedupe against.\n   */\n  includeToolMessages?: boolean;\n}\n\n/**\n * LangChain callback that auto-mirrors the chat model's conversation history\n * into a Copass {@link ContextWindow}.\n *\n * Hooks `handleChatModelStart`, which fires before every chat model invocation\n * with the full message history. We walk that history and call\n * {@link ContextWindow.addTurn} for any message we haven't seen — so retrieval\n * tools called inside the same agent step get a window that reflects the\n * actual conversation, not an empty buffer.\n *\n * @example\n * ```ts\n * import { CopassWindowCallback, copassTools } from '@copass/langchain';\n *\n * const tools = copassTools({ client, sandbox_id, window });\n * const agent = createReactAgent({ llm, tools: Object.values(tools) });\n *\n * await agent.invoke(\n *   { messages: [{ role: 'user', content: 'why is checkout flaky?' }] },\n *   { callbacks: [new CopassWindowCallback({ window })] },\n * );\n * ```\n */\nexport class CopassWindowCallback extends BaseCallbackHandler {\n  readonly name = 'copass-window';\n  private readonly window: ContextWindow;\n  private readonly includeToolMessages: boolean;\n  private readonly seen = new Set<string>();\n\n  constructor(options: CopassWindowCallbackOptions) {\n    super(options);\n    this.window = options.window;\n    this.includeToolMessages = options.includeToolMessages ?? false;\n    // Seed the dedup set with turns already in the window so we don't re-add.\n    for (const turn of options.window.getTurns()) {\n      this.seen.add(hashTurn(turn));\n    }\n  }\n\n  async handleChatModelStart(\n    _llm: unknown,\n    messages: BaseMessage[][],\n  ): Promise<void> {\n    // `messages` is `BaseMessage[][]` because chat models support batched calls.\n    // Flatten — in agent loops this is always a single conversation.\n    for (const msg of messages.flat()) {\n      const turn = toTurn(msg, this.includeToolMessages);\n      if (!turn) continue;\n\n      const key = hashTurn(turn);\n      if (this.seen.has(key)) continue;\n      this.seen.add(key);\n\n      // Fire-and-forget — window.addTurn pushes into the graph. 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package.json

 {
   "name": "@copass/langchain",
-  "version": "0.5.9",
+  "version": "0.5.10",
   "description": "LangChain tool adapters for Copass — drop-in discover/interpret/search tools for LangChain agents",
@@ -58,3 +58,3 @@ "publishConfig": {
   "dependencies": {
-    "@copass/config": "^0.5.2"
+    "@copass/config": "^0.5.3"
   },
@@ -73,3 +73,3 @@ "peerDependencies": {
   "devDependencies": {
-    "@copass/core": "^0.8.1",
+    "@copass/core": "^0.11.1",
     "@langchain/core": "^1.1.0",
@@ -82,3 +82,3 @@ "@langchain/langgraph": "^1.2.0",
   },
-  "gitHead": "778b1411e89f721857fc9c3ca7c41b5903cd0056"
+  "gitHead": "647e1abcdbdc52dae62913df5cf86aeaf68dc2ff"
 }

README.md

 # @copass/langchain
 
-**Copass retrieval as LangChain tools.** The LLM picks `discover` (menu of relevant items) or `search` (synthesized answer) — you don't write the tool-calling loop. `interpret` is exposed for back-compat but legacy; prefer `search` for drill-in.
+**Copass retrieval as LangChain tools.** The LLM picks `discover` (menu of relevant items), `search` (synthesized answer), or `get_origin` (map canonical_ids to source files) — you don't write the tool-calling loop. `interpret` is exposed for back-compat but legacy; prefer `search` for drill-in.
 
@@ -74,5 +74,6 @@ ## Prerequisites
 | `search` | "Tell me about X" / "Answer this." — synthesized answer (canonical drill-in) |
+| `get_origin` | "Where does this live?" — maps canonical_ids from `discover` to source files. Cheap, no LLM. |
 | `interpret` | Legacy — brief pinned to canonical_ids. Prefer `search` for drill-in. |
 
-Add your own tools via the `tools` option — they'll be mixed in alongside the Copass three:
+Add your own tools via the `tools` option — they'll be mixed in alongside the Copass four:
 
@@ -79,0 +80,0 @@ ```ts

No alert changes

Improved metrics

Total package byte prevSize

80277

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Lines of code

680

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Number of lines in readme file

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Dependency changes

• Updated

  @copass/config@^0.5.3