Update haystack-ai requirement from >=3.0.0 to >=3.1.0

[dependabot]

Updates the requirements on haystack-ai to permit the latest version.

Release notes

Sourced from haystack-ai's releases.

v3.1.0

⭐️ Highlights

🪝CompactionHook and Built-in Compactors for Context Length Management

Experimental context compaction for Agent. Runs before LLM calls and shortens the conversation once it crosses a configured fraction of the context window. The built-in SlidingWindowCompactor first removes entire historical turns, then, if needed, individual steps of the current task, replacing what it removes with a short omission note. A second experimental compaction strategy, ToolResultPruningCompactor, instead of dropping whole turns, replaces older/large tool results with placeholders while keeping the most recent tool-calling steps (and their parallel results) intact.

from haystack.hooks.compaction import CompactionHook, SlidingWindowCompactor, ToolResultPruningCompactor
from haystack.components.generators.chat import OpenAIResponsesChatGenerator
hook = CompactionHook(
compactor=SlidingWindowCompactor(), # or ToolResultPruningCompactor(min_keep_steps=2, min_tokens=200),
context_window=400_000,
compact_at=0.7,
compact_to=0.4,
)
agent = Agent(
chat_generator=OpenAIResponsesChatGenerator(model="gpt-5.4-nano"),
tools=[web_search],
hooks={"before_llm": [hook]},
)

🪙 New Token Counters for Estimating Token Usage

New haystack.token_counters module for estimating message/tool-schema size before sending a request (e.g. to decide when to compact). ApproximateTokenCounter needs no dependencies; TiktokenCounter is a local counter, closer estimates for OpenAI models; OpenAITokenCounter calls OpenAI's counting API for exact, model-specific counts.

from haystack.token_counters import OpenAITokenCounter, ApproximateTokenCounter, TiktokenCounter
from haystack.dataclasses import ChatMessage
counter = OpenAITokenCounter("gpt-5-mini")
No dependencies: estimates from text length.
counter_app = ApproximateTokenCounter(chars_per_token=4.0).count(messages)
Closer for OpenAI models; needs: pip install tiktoken
counter_tiktoken = TiktokenCounter(encoding="o200k_base").count(messages)
count = counter.count([ChatMessage.from_user("Hello!")])

🛠️ AgentTool for Multi-Agent Systems

AgentTool wraps a Haystack Agent as a Tool so another Agent can delegate to it. Only the wrapped Agent's final reply is visible to the caller; its intermediate steps remain outside the main agent's context.

from haystack.components.agents import Agent
from haystack.components.generators.chat import OpenAIResponsesChatGenerator
from haystack.dataclasses import ChatMessage
from haystack.tools import AgentTool
researcher = Agent(
</tr></table>

... (truncated)

Commits

• 859a6eb bump version to 3.1.0
• 5de8d6c bump version to 3.1.0-rc3
• 46396f4 test: enable mypy typing checks for test/components/routers/ (#12354)
• e37cc94 bump version to 3.1.0-rc2
• 5bf0b09 test: reconcile env-var security test with callable traversal hardening (#12430)
• 28844a3 feat: add HAYSTACK_UNSAFE_DESERIALIZATION env var (#12397)
• 70f802c fix!: require unsafe loading for serialized Jinja filters (#12415)
• 93e4223 fix: validate modules during callable traversal (#12416)
• 4799c5a bump version to 3.1.0-rc1
• 1aa0ad9 ci: run type checking on Python 3.12 and unpin numpy (#12377)
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[dependabot]

Superseded by #17.