Analysis Capability

AnalysisCapability adds search, citations, and sandboxed Python computation over the document corpus. Use it for counts, aggregation, comparison, structural traversal, and section-scoped reading.

It is deferred by default, keeping its substantial instructions and tool schemas out of context until the model chooses to load it.

The default request limit is 30 model requests per question. Override it with create_capability(request_limit=...), or set request_limit=None to disable it. As with the RAG capability, create_capability(vision=...) overrides the image-attachment gate, defaulting to the configured analysis model's vision flag. At the limit, analysis_search and analysis_execute_code are removed while analysis_cite remains for two further requests that call an analysis tool, so the model can register citations before answering from gathered evidence. Requests spent on other capabilities do not count against that window. Other agent and capability tools remain available, and the budget resets for every agent run.

When qa.max_searches or analysis.max_executions runs out, the exhausted tool keeps failing rather than disappearing, and the instructions name it on every following request. Searching from inside analysis_execute_code does not count against qa.max_searches.

Tools

The sandbox exposes documents under /documents/{document_id}/ with metadata.json, content.txt, items.jsonl, chunks.jsonl (chunk ids with their metadata) and toc.json. In code, await search() results carry chunk_meta and await list_documents() rows carry metadata. The interpreter's limits and the per-call budgets are listed under MCP, Code.

Compose an agent

Register it on its own, not alongside RAGCapability: it already searches and cites, and the two together give the model duplicate tools and separate budgets. See Capabilities.

from pydantic_ai import Agent
from haiku.rag.capabilities.analysis import create_capability as analysis
from haiku.rag.capabilities.compaction import create_capability as compaction
from haiku.rag.capabilities.policy import create_capability as citation_policy

agent = Agent(
    "openai:gpt-5",
    capabilities=[
        analysis(db_path="my.lancedb"),
        compaction(),
        citation_policy(),
    ],
)

For the high-level convenience API:

from haiku.rag.client import HaikuRAG

async with HaikuRAG("my.lancedb") as client:
    result = await client.analyze("Which quarter had the highest revenue?")
    print(result.answer)

State

When dependencies expose a state dictionary, AnalysisState is stored under "analysis". It contains the document filter, code execution log, searches, citations, and the evidence record of what was retrieved and cited per question. Searches and executions are cleared when a new question starts, and a resumed question keeps them; the filter, citation index and evidence record persist.

This capability does not alter the message history either. Register the compaction capability to compact earlier questions.

The capability lazily opens both LanceDB and the sandbox only after it is loaded and a tool requires them. Resources close at the end of the agent run.