a2acompress offers a compact and lossless solution for agent-to-agent data transfers, significantly reducing the token count in multi-agent workflows. With byte-exact round-trip compression and transparent costs for decoding, this project ensures efficiency and accuracy, making it ideal for advanced orchestration in AI applications.
a2acompress is a cutting-edge, lossless wire format designed specifically for agent-to-agent handoffs, resulting in an impressive reduction of 36.6% in real cl100k_base tokens during testing against ToolBench trajectories. This format accounts for every byte of decoding overhead, ensuring a byte-exact round trip without any loss of data.
In multi-agent workflows, such as those involving planners, builders, and reviewers, a significant amount of context is often wasted in re-sending structured information like JSON keys, quoted identifiers, and tool catalog references. The a2acompress codec addresses these inefficiencies by offering an exact, reversible compression method that is straightforward and cost-effective in terms of decoding.
a2acompress compacts data without losing critical information, maintaining the fidelity of each handoff.Utilization of the codec can be achieved seamlessly through a concise Python API:
from a2acompress.protocol import ProtocolOptions, encode_session, decode_session
options = ProtocolOptions(
positional_body=True, packed_header=True,
minimal_instructions=True, inline_backrefs=True, token_aligned_refs=True,
)
encoded = encode_session(records, options) # Encode your session records
restored = decode_session(encoded.payload, options)
assert restored == records # Confirm no data loss
The effectiveness of a2acompress is showcased in rigorous benchmarks, with the full cost defined as the sum of the compact payload, reconstruction instructions, and any dictionary used. The method adheres to stringent performance rules to ensure accuracy in reporting token savings and decoding standards.
This project presents a thorough performance assessment, enabling users to replicate results and evaluate the efficiency of their own datasets against predefined benchmarks. For additional insights and data availability, refer to the detailed optimization report and accompanying results.
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